Important issue relating to family businesses is succession means determining who will take over leadership and/or ownership of the company when the current generation retires or dies Succession planning can be explained as an effort to have a plan for the right number and quality of key position employees, including managers to cover retirements, promotions, serious illness, death or any new job, which may be created in the future of the organization's plans. In fact, succession planning is a vital structure that takes into account the organization's resources for the maintenance and development of high potential employees. Succession planning - the preparation to replace one leader with another - is one of the most difficult challenges associations face in this era of organizational management. Few events in the life of an association are as critical, visible, or stressful as when the leader leaves.
1.1 Family Business
A family business is a business in which one or more members of one or more families have a significant ownership interest and significant commitments toward the business' overall well-being. In some countries, many of the largest publicly listed firms are family-owned. A firm is said to be family-owned if a person is the controlling shareholder; that is, a person (rather than a state, corporation, management trust, or mutual fund) can garner enough shares to assure at least 20% of the voting rights and the highest percentage of voting rights in comparison to other shareholders.
Family owned businesses account for over 30% of companies. Today family owned businesses are recognized as important and dynamic participants in the world economy. According to the U.S. Bureau of the Census, about 90 percent of American businesses are family-owned or controlled. Ranging in size from two-person partnerships to Fortune 500 firms, these businesses account for half of the nation's employment.
1.2 Succession Planning
Succession planning involves deciding who will lead the company in the next generation. Unfortunately, less than one-third of family-owned businesses survive the transition from the first generation of ownership to the second, and only 13 percent of family businesses remain in the family over 60 years. Effective tax planning is also another factor to be considered in the succession planning process.
1.3 Process of Succession Planning
Family businesses are advised to follow a five-stage process in planning for succession:
' Finance preparation
' In the initiation phase, possible successors are introduced to the business and guided through a variety of work experiences of increasing responsibility.
' In the selection phase, a successor is chosen and a schedule is developed for the transition. Analysts almost unanimously recommend that the successor be a single individual and not a group of siblings or cousins. To some degree, by selecting a group, the existing leadership is merely postponing the decision or leaving it to the next generation to sort out.
' During the education phase, the business owner gradually hands over the reigns to the successor, one task at a time, so that he or she may learn the requirements of the position.
' Finance preparation involves making arrangements so that the departing management team can withdraw funds enough to retire. The more time is used in preparing for the financial implications of this transition the more likely a business will be able to avoid being burdened in the process.
' In the transition phase, the business changes hands'the business owner removes himself or herself from the daily operations of the firm. This final stage can be the most difficult, as many entrepreneurs experience great difficulty in letting go of the family business. It helps when the business owner establishes outside interests, creates a sound financial base for retirement, and gains confidence in the abilities of the successor.
' Most family-owned business owners put off their succession planning because they don't want to think about their retirement, disability or death
' However, business succession planning should be a priority in every family owned business
' A family owned business owner's decision to eventually retire is not as simple as no longer going to the office
' Key questions need to be answered before the family owned business owner can 'leave' the business: i) will he or she have enough money at retirement; ii) who is going to own and manage the business; iii) how will ownership and management be transferred to new owners; and iv) should the business be carried on or sold to a third party.
' A proper business succession seeks to alleviate or lessen the above issues by setting up a smooth transition between the family owner business owner and the future owners of the business.
Chapter 2 : The Myths of Succession Planning
Myth One: There's Plenty of Time
Reluctance to accept the realities of time can have disastrous consequences, especially since timing issues are often beyond an owner's control. Just as the aging CEOs of major corporations are pressed by shareholders who demand a succession plan to protect their investments, so too a small business owner should implement a succession plan to protect the interests of all stakeholders.
Myth Two: It's Easier to Just Sell It
Buyers are not necessarily prepared to wait until the time is right in a business's life cycle before making an offer. The same issues of timing influence the way businesses will be valued. Most business owners have an idea of the worth of their business based on revenues, fixed assets, profitability and a variety of balance sheet items.
Myth Three: Giving Up Ownership Means Losing Control and Income
Too many business owners see a succession plan as 'all or nothing''that changing ownership necessarily means giving up control and reducing income. However, it's entirely possible to create a succession plan that transfers business ownership to an eventual successor without losing control or income or'as is vitally important to many business owners'the continued opportunity to build a legacy.
Myth Four: Equal Is Synonymous With 'Fair'
If the business owner transfers a larger share of ownership to the primary successor, the siblings may not think that's fair. Knowing that someone is going to be disappointed makes it critically important for an advisor to establish what the owner really wants.It's also just as important for the owner to realize that, whatever the plan, there may be difficult and uncomfortable issues related to potential divorces and other matters that must be accounted for, if not predicted, in a succession plan.While many things can be guaranteed in a successful succession plan, happiness amongst all family members isn't one of them.
Setting goals and objectives for an effective family business succession plan
When implementing a succession plan without the necessary goals and objectives it can hold dire consequences for the business, and may very well lead to disagreements, low staff morale and a decline in productivity and profitability. A succession plan with effective goals and objectives that are aligned to the business strategy and thus the business objectives and business goals, will focus all activities on achieving well-defined targets
Why is it important to define goals when setting a family business succession plan?
Before setting out on the preparation of a succession plan, the family business owner should have a clear understanding of his or her role in the future of the company. Stakeholders take great comfort from knowing the direction the business is heading and understanding that eventualities have been catered for during the formulation of the succession plan. Starting the process of defining goals and objectives for a family business succession plan helps to push key issues to the forefront.
Establishing goals and objectives for a family business succession plan
Although the establishment of succession planning goals holds intrinsic challenges, family businesses should implement this process to ensure that all parties are knowledgeable and aware of the future of the business. The business should engage with the wider group of stakeholders, including family members, employees, suppliers and customers. Engagement and involvement of stakeholders in the development of the future of the company will ultimately result in their approval of the succession plan's goals and objectives and therefore foster greater business stability.
Aligning the goals and objectives of the succession strategy to the family business' overall strategic business plan
A succession plan can only successful if it is aligned to the strategic business plan of the business, linking the succession goals and objectives with the overall strategic business plan. The development of an overview of key competencies for each position in the business is the roadmap or template for the succession plan. Of vital importance is the development of specific benchmarks for each position in the business. The benchmark should be developed by skilled and qualified personnel that understands the overall strategic goals and objectives of the business, and can align the expectations and demands of the position with the business strategy and potential candidates, with the assistance of a human resources specialist. These detailed requirements can help ensure seamless changeovers of people into new positions, when required.
Finding Your Successor
1. Establish a time frame. Your retirement might be five years or 25 years away, but you should have a good idea of when you're going to move on. Even if that answer is never you should still have someone in place who can run the business if you're incapacitated. Be sure you have sufficient disability insurance to protect your income.
2. Focus on the skills not relationships. Don't let nepotism or friendship dictate the de facto successor. Instead, think about where you want the business to be in the years after you hand over the leadership. Make a list of the skills that your more mature business will require to continue its growth path.
3. Find the right fit. Once you have a clear vision of what the new leader's job description will be, then it's time to start looking for the right fit. It can be tough to be objective about recruiting the best fit from within or looking for someone outside of the organization. In cases where a successor is not obvious, it's a good idea to get an outside opinion from your existing advisors or an independent management consultant.
4. Start the challenge. Once your successor is in place, start giving him or her challenges that stretch or develop skills that will be necessary to lead the company,
Chapter 3 : Review of Literature
1. Alcorn (1982) explains further that the term "succession" refers specifically to changes in the top leadership, although changes taking place at lower levels may exhibit some of the same symptoms as top-level succession. She suggests that the focus needs to be at the highest level of the organization because "the real problem comes when the boss position itself is the place where change is in process"
2. Lansberg (1988) indicates how the "succession conspiracy" impacts stakeholders throughout the family business system, including family members, management, suppliers, and customers.
3. Handler and Kram (1988) also take a multilevel approach by showing that resistance factors to succession planning exist on the part of the founder, as well as the interpersonal, group, organizational, and environmental levels.
4. From 1990's most organizations figured out the necessity of succession planning and implementation of effective succession planning that fit their own organizations. At the first of this decade, Zajac in 1990 researched on CEO selection, succession, compensation and firm performance. He concluded that firms in which CEOs had a specific successor in mind tended to be significantly more profitable than firms in which CEOs did not have a specific successor in mind.
5. Eastman(1995) proposed the following list of commonly effective practices. 'A succession management program should: Receive visible support from the CEO and top management; Be owned by line management and be supported by staff; Be simple and tailored to unique organizational needs; Be flexible and linked with the strategic business plan; Evolve from a thorough human resources review process; Be based upon well-developed competencies and objective of candidates; Incorporate employee input; Be part of the broader management development effort; Include plans for development job assignments; Be integrated with other human resource systems; and Emphasize accountability and follow-up'
6. Davis and Harveston (1998) used Christensen's elements (1953) as variables and developed a model which explored the relationships between individual-level characteristics, group-level variables, organizational-level characteristics, and resources on the succession planning process. The individual level of Davis and Harveston's model contained demographic characteristics of the managers that controlled predictive validity regarding critical organizational processes. The group level looked at the family member's involvement and influence over the organizational processes. The organizational level of the model analyzed the organizational attributes such as size and formality, which could influence the organization's succession planning processes. Resources or capital was the final level of the model. Capital providers might demand succession plans before extending credit or lending money to the organization. The outcome of Davis and Harveston's model showed succession was not an event but a process which was influenced by many variables within and external to the organization.
7. Another popular theory in this age was 'Agency Theory'. This theory explains organizational behavior and succession planning. 'Agency theory dealt with the cooperative relationship which developed when one individual in an economic exchange (the principal) granted authority to another (the agent) to act on his or her behalf, and the welfare of the principal became affected by the decisions of the agent' .The main concern of this theory was that the welfare of the principal might not be maximized because the principal, and the agent tended to have different aims as well as contradictory predispositions toward risk
8. Baruch and Peiperl (2000) in their survey of 194 UK-based companies explained that succession management is an important characteristic of effective career management. Baruch and Peiperl categorized Succession management as 'active planning' along with performance reviews, counseling by managers and human resource. In their analysis, they observed that these active planning processes 'were strongly associated with dynamic, open and proactive climates' and were especially associated with organizations that relied heavily on internal labor markets.
9. In 2001, Huang studied about succession management systems and human resource outcomes. The purpose of the study was to find out whether local firms with a more sophisticated succession plan achieved more favorable human resource outcomes than those with fewer sophisticated plans. He found, in conclusion, that there was no significant difference in human resource outcomes between companies who adopted succession planning and those who did not. However, in detailed results, he argued that there was an important relationship between the level of sophistication with which succession plans were carried out and human resource outcomes. In addition, he fined that line-manager involvement, non-political succession criteria, and the credibility of succession planning staff, review and feedbacks, effective information systems, were some characteristics, which affected the performance of human resources.
10. Lynn (2001) focused on four elements as the key elements of the succession event. These elements are as follows: succession contingencies (industry issues, organizational characteristics, and selector variables, including incumbent and board power); Succession Antecedents (initiating forces for CEO departure, CEO roles, and candidate issues); the succession event (process, candidate, and choice issues); Succession consequences (organizational effectiveness, stakeholder issues, and evaluation outcomes)
11. Leadership Pipeline is a concept in a model created by Mahler, who contributed a lot to human resource planning and succession planning. The focus of Leadership Pipeline Model is on leadership development rather than succession planning and management. His approach laid the basis for the modern succession planning approach. The pipeline is not a straight cylinder but rather one that is bent in six places. Each passage shows a change in organizational situation''a different level and complexity of leadership''where a major turn has to be made. The main concerns of these turns are changes in job requirements, demanding new skills, time applications, and work value.
12. Based on leadership pipeline model, Charan et al. (2001) suggested an alternative definition for succession planning from the Leadership Pipeline Model. In their definition, 'succession planning is perpetuating the enterprise by filling the pipeline with high-performing people to assure that every leadership level has an abundance of these performers to draw from, both now and in the future'
13. The Seven-Pointed Star Model is a famous model developed by Rothwell (2001, 2005) to achieve the systematic succession planning and management. Seven steps in this model are: Make the commitment, assess the present work/people requirements, appraise individual performance, assess the future work/people requirements, assess individual potential, close the development gap, and evaluate the succession planning and management program.
14. Byham et al. (2002) suggested a different approach to grooming executive talents. Rather than targeting one or two hand-picked people for each executive position, an Accelerate Pool emphasizes the development of a group of high-potential candidates for executive positions in general. The model highlights the accelerate development of pool members during stretch jobs and task-force assignments that offer the best learning, including mentoring, coaching, training, and special developmental activities such as university executive programs and in-company action learning sessions, and highest visibility opportunities. Byham explained the process of the Acceleration Pool in five phases. The five phases of the Acceleration Pool are: Identifying high potentials; diagnosing development opportunities; prescribing solutions to development opportunities; ensuring that development takes place/ documenting development; reviewing the progress and new assignments
15. In 2003, Kim in his study concluded that 'succession management practices should emphasize employee self-improvement by promoting cross-functional and cross-sector job assignments, executive coaching and mentoring'. In addition in this year, Conger and Fulmer illustrated five rules for succession management. At first, Succession management must be a flexible system oriented toward developmental activities. This is the fundamental rule that other rules are based on. The focus of second rule is on linchpin positions, jobs that are essential to the long-term health of the organization. Making succession management transparent, not a mystery, defines as a third rule. Rule four is the usual measurement of progress, moving away from the alternative mind-set of succession planning. Keep it flexible is the final rule.
16. Hunte-cox (2004) examined the relationship between executive succession planning and the collective learning capacity. The result of this quantitative research showed 'organization that scored high in executive succession planning scored high in organizational learning. Specifically, executive succession plans have significant and positive correlation with the organizational learning capacity
14. Dingman (2006) did a case study on the servant leadership role in the succession planning process. A positive relationship between servant leadership principles and the succession process was shown through the examination of the succession process in a servant-led organization
17. Krauss (2007), studied in succession planning and talent management, to having recommendations to reduce workforce attrition and prepare for an aging population. As a result of this study, the recommendations to his targeting organization were implementing a structured succession plan which distinguished the importance of establishing ownership of the succession plan and aligning the internal culture with external branding. These recommendations were included identifying, assessing and developing high potential in the organization. 'Once high potentials are selected, their development plan needs to include a job rotation program, a formal mentoring/coaching program, utilizing 360 feedback tools and receiving the appropriate reward for performance'. In addition, Fancher (2007) pointed out that an organizer has powerful influence on organizational .
18. Succession is so central to the firm's existence that Ward (1987) chooses to define family firms in terms of the potential for succession: "we define a family business as one that will be passed on for the family's next generation to manage and control
19. (Christensen, 1953; Dyer, 1986; Handler, 1989; Lansberg, 1988 )Theorists also agree that the continuity of businesses from one generation to the next depends highly on succession planning .(Christensen, 1953; Handler, 1989) Despite the importance of planning, research has found that succession planning is often not done by family firms
20. (Beckhard and Dyer, 1983 and 1983) The statistics confirm that succession is typically a problematic issue. Only 30 percent of the family firms survive the transition to second generation and only 10 percent make it to the third generation. The average life span of the founder of the firm is twenty-four years, which is also the average life tenure of the founder in the firm.
21. Researchers tend to agree that succession is more a process than an event (Churchill and Hatten, 1987; Farquhar, 1989; Friedman, 1987; Gilmore and McCann, 1983; Gordon and Rosen, 1981; Handler, 1990)
22. Succession is not simply a single step of handing the baton; it is a multi-staged process that exists over time, beginning before heirs even enter the business. Furthermore, the effectiveness of succession is not limited to whether a president has been designated; the ongoing health of the firm, quality of life, and family dynamics are critical to the success of the succession process. (Longenecker and Schoen, 1975, 1978; Vancil, 1987).
23. Family business succession has been defined as "the passing of the leadership baton from the founder-owner to a successor who will either be a family member or a non-family member; that is, a 'professional manager'" (Beckhard and Burke, 1983). Others have divided the "leadership baton" into two axes'that of ownership and management (Barry, 1975).
24. In 2009, Cheryl recommended some strategies for implementing deliberate and systemic succession plans in the academic environment via a research in higher education institutes. These strategies include: 'securing executive champions; aligning the succession plan to institutional culture, mission, vision and goals; taking an approach not unlike strategic planning; carefully constructing communication plans to embrace talent development without inferring entitlement; and a continuous evaluation of both the people and processes involved in succession planning.
Chapter 4. :Objective
1. To study how Succession planning is important for family business .
2. To study Global family business
4.1 NEED AND SCOPE OF STUDY
Succession planning gives your colleagues a voice. If you're running a family business, the process of succession planning will give family members an opportunity to express their needs and concerns. Giving them that voice will also help create a sense of responsibility throughout the organization, which is critical for successful succession planning.
4.2 RESEARCH METHODOLOGY
Research methodology is a way to systematically solve the research problem. It may be understood as a science of studying how research is done scientifically. It may be understood as science of studying how research is done systematically. In fact, research is an art of the scientific investigation.
The procedure adopted for conducting the research requires a lot of attention as it has direct bearing on accuracy, reliability and adequacy of results obtained. Research methodology is a way to systematically study and solve the research problems.
'Research Methodology comprise of defining & redefining problems, collecting, organizing &evaluating data, making deductions &researching to conclusions.'
a) Secondary data :-
o The secondary data are those records which have already been collected by institute and which have already been processed. Secondary data are information which has previously been collected by respective section/unit/departments of companies to satisfy its own need but it is being used by the management under references for an entirely optimistic reasons.
Sources for Secondary data are:-
' Data available on internet through various websites
Chapter 5 : Analysis
1. Bakrie & Brothers
' FAMILY: Bakrie
' SECTOR: Commodities, telecommunications and property
' COUNTRY: Indonesia
Bakrie & Brothers started out as a general merchants in 1942 before branching out into steel pipe manufacturing. It is now one of the largest commodity trading conglomerates in Indonesia with interests in coal, infrastructure, agribusiness, oil and gas, telecommunications, property and metal.
Today it is co-chaired by second-gen brothers Nirwan and Indra Bakrie, sons of the firm's founder Achmad. Scandals surrounding the financing and management of Bumi, Bakrie & Brothers' coal subsidiary in partnership with the Rothschild family, has rocked the company in recent years, but its finances have since stabilised, and it reported revenues of IDR15.5 trillion ('1 billion) in 2012.
2. Coopers Brewery
' FAMILY: Cooper
' SECTOR: Brewing
' COUNTRY: Australia
Coopers Brewery is the last major family-owned brewery in Australia, and the incumbent generation has a legacy to maintain. Since its foundation by Thomas Cooper in 1862, the business has stuck to what it knows ' brewing beer.
Although it hasn't diversified, Coopers has continued to grow, with revenues increasing 7.6% from 2011 to 2012 to reach a record AUD$186.3 million ('130.3 million). Fifth-gen Tim Cooper currently heads the business, and many sixth-gens fill senior roles. At the beginning of the year Coopers undertook an AUD$20 million expansion project at its Adelaide headquarters to allow it to more than double its production capacity from 71 million to 134 million litres annually.
3. Daphne International
' FAMILIES: Chang and Chen
' SECTOR: Footwear, apparel and accessories
' COUNTRY: Hong Kong
Founded in 1987, footwear manufacturer and distributer Daphne International revealed first half profits of HKD$5.2 billion ('505 million) for 2013. Daphne is its core label, but it also owns several others and distribution rights for a number of international footwear brands. The company has close to 6,000 directly managed stores and almost 900 franchises. Not one, but three families have a significant interest in the company with the largest stake (24%) held by the Chang family. The family of chairman and chief executive, Eddie Chen, also have a 9% stake. Another Chen family controls a further 10% and is represented on the board by cousins Tommy Chen and Ying-Chieh Chen.
4. DMCI Holdings
' FAMILY: Consunji
' SECTOR: Construction, real estate and commodities
' COUNTRY: The Philippines
DCMI Holdings founder David Consunji has been nicked named the 'father of construction' in the Philippines, as his company has done more than most to transform the country's skylines. Starting out in 1954, armed only with a truck and a cement mixer, his small building business grew into a vast conglomerate with subsidiaries in mining, real estate, power, water and, of course, construction. The 91-year-old Consunji still serves as chairman, while his son Isidro is chief executive and several of his eight children hold senior positions in the company. It reported revenues of PHP51.7 billion ('886.3 million) in 2012, up from PHP47.8 billion in 2011.
5. Genting Group
' FAMILY: Lim
' SECTOR: Resorts, gambling, plantations, power generation, oil and gas
' COUNTRY: Malaysia
The Genting Group story begins with a mountain resort opened in Malaysia in 1971. The company wanted to offer locals refuge from the summer heat, and the company has since expanded from leisure and hospitality to casinos, power generation and oil and gas exploration.
Led by Lim KokThay, the second son of founder Lim Goh Tong, the company saw its revenues more than double between 2009 and 2011 to MYR19,559 million ('4.6 billion). Its interests in hospitality and leisure are still very strong, and the company's portfolio now includes 4,500 hectares of prime resort land, including its first flagship resort
6 Hero MotoCorp
' FAMILY: Munjal
' SECTOR: Motorcycle and scooter manufacturer
' COUNTRY: India
Hero MotoCorp is already the largest two-wheeler manufacturing company in the world, a position it has held since 2001, but that has not stopped it developing bold expansion plans. Earlier this year it celebrated the production of its 50 millionth vehicle and hopes to double that by 2020. In that same year it also plans to be operational in 50 countries.
Founded in 1984, the company is part of Hero Group ' established by the four Munjal brothers in the 1950s, a few years after the partition of India. Its two-wheelers proved popular in India due to their low price and fuel economy and their popularity continues today ' for the 2012 financial year its total income was INR23,944 crore ('2.9 billion), up 21.6% on the previous year.
7. Murugappa Group
' FAMILY: Vellayan
' SECTOR: Cycles, insurance, fertilisers, sugar, automotive parts
' COUNTRY: India
Founded in 1900, Murugappa Group is one of India's oldest family businesses, with fourth-gen ArunachalamVellayan currently at the helm. It started in Burma as a money lending operation, but during the Japanese invasion in World War II, the family was forced to flee to India. Since then it has diversified into fertilisers, sugar, cement, and real estate, engineering and insurance.
Between 2009, when Vellayan took over from his uncle, and 2011 the company saw its revenues grow 64%. Among the company's top performing businesses are Coromandel International, one of the largest producers of fertilisers in the country, and TI Cycles ' the second-largest maker of bicycles in India. In 2013 it saw turnover of INR22466 crore ('2.7 billion).
8. New World Development
' FAMILY: Cheng
' SECTOR: Property, infrastructure and telecommunications
' COUNTRY: Hong Kong
New World Development, based in Hong Kong, saw its revenues increase 28% year-on-year in 2012, to HK$24.5 billion ('2.3 billion). Founded in 1970, the property and infrastructure company's latest large-scale developments include Hong Kong residential properties The Signature and The River park.
At the helm is chairman Henry Cheng who succeeded his father and company founder, Cheng Yu-tung, last year following 13 years as managing director. Cheng takes a philosophical approach to business, referencing German phenomenologist Edmund Husserl in his statement to shareholders this year.
Other appointments in last year's succession shake up included third-gen family member Adrian Cheng, who became joint general manager, and his sister Sonia, who was appointed to the post of executive director.
9. Taihan Electric Wire
' FAMILY: Sull
' SECTOR: Telecommunications, wiring and cables
' COUNTRY: South Korea
Taihan Electric Wire built its fortunes manufacturing different types of electric wire and cable for the various industries that fuel South Korea's economy including power, telecommunications and heating.
Controlled by the Sull family, descendants of Taihan's founder Sull Won-Ryang, the businesses saw revenues of KRW2 trillion ('1.4 billion) in 2012. It was founded in 1955 in the aftermath of the Korean War, coinciding with Korea's economic boom. Revenues have dipped in recent years due to stagnation in the South Korean economy, but in the future Taihan is looking to drive growth through the development of its fibre optic and communications-related products.
10. Tat Hong Holdings
' FAMILY: Ng
' SECTOR: Crawler and mobile crane rental
' COUNTRY: Singapore
Tat Hong Holdings, controlled by the Ng family, saw astonishing growth of 67% for the financial year ended 31 March 2013, reporting revenues of SGD$836.9 million ('495.7 million). The company feels there is reason for further optimism, with chief executive Roland Ng saying increased urbanisation in China and a huge pipeline of infrastructure and power generation projects would support growth momentum. The company, established in the 1970s, is already the number one crane owning company in the Asia Pacific region and the number one crawler crane company in the world. It serves industries such as infrastructure, oil and gas, resources, construction and engineering sectors across Asia, Australia and China.
11. Bertram Chemical (1982) Co.
A Thai family business manufacturer and distributor of homeopathic, non-prescription herbal products. Their core product Siang Pure Oil is based on a traditional recipe passed on over generations among practitioners of traditional Chinese medicine. The three sisters Supeeya Saenghirun, Titima Eiampikul, and Suwanna Akrapongpisak ' the second generation of the Eiampikul family to run Bertram Chemical (1982) Co., Ltd. Bertram Chemical has experienced an extraordinary success since the three sisters took over business management from their father. The history of Bertram Chemical goes back to 1963, when Boonchua Eiampikul ' the father of Supeeya Saenghirun, Titima Eiampikul, and Suwanna Akrapongpisak ' set up his first company. Chakrintr Limited Partnership with a registered capital of Bt 1 million at the Pak Klong Talad area, Bangkok's main wholesale market. Bertram Chemical has grown from a small one product company to a medium-sized enterprise with three product lines produced in factories in Thailand and China equipped with high-quality machinery. Nowadays, the company employs more than 140 workers, keeps an own R&D bureau and realizes more than 50% of its 2007 sales of Bt 330 million from export. Company's management is still in the hand of Boomchua's family. Supeeya Saenghirun, the eldest daughter who graduated in pharmaceuticals, oversees production, Titima Eiampikul ' graduated in accountancy ' is in charge of finance and personnel, and Suwanna Akrapongpisak who graduated in marketing is the company's marketing director.
12.Malay Small Businesses in Selangor
This is about business growth among Malay small family the members participate in family discussions as a precursor to final decisions being made by the father or family elders; a decision-making style well known as pseudo-participative. Results suggest that business and family related decisions about business growth consist of four component areas; finance, technology, employment and support. Business (finance and technology) and family (employment andsupport) issues need to be considered and appraised by owners of MSFBs as antecedents of growth; i.e., if thebusiness is to move from the a position of survival to one of growth business (MSFBs) in Selangor, Malaysia are explored. Focussing on business and family dynamics strategies for developing the MSFB sector. Ninety-eight percent of the small businesses under the Majlis Amanah Rakyat (MARA) are Malay small family
businesses. MARA is the government agency that provides business loans, shop facilities, training and business advice to Malays. Besides MARA, there are several other government agencies that provide facilities and small loans to Malays; such as the Department of Agriculture (DOA) and Amanah Ikhtiar Malaysia (AIM). Thedefinition for small businesses in Malaysia is based on two categories used within the industry sector. The first category includes manufacturing, manufacturing related services and agro-based industries that have a sales turnover of less than MYR10 million or less than 50 full-time employees. The second category comprises services, primary agriculture, and information and communication technology (ICT) businesses that have a sales turnover of less than 1 million or less than 20 full-time employees. the business was carried by two brothers and later by there daughters.
14. CJ MCLAINE'S DELI & BAKERY, LLC
CJ Mclaine's Deli and Bakery, LLC, a family owned small business venture, operated in the
town of Evergreen, Colorado. The Italian style deli and bakery focused primarily on its lunch business but also offered a limited breakfast menu, a dinner takeout menu, and general catering services. The business was launched in 2004 and gained significant momentum in its first year of operation. This initial success encouraged the family owners. During its second year, however, changes in the competitive environment and an economic recession created new obstacles that affected the business sales and bottom line. Somewhat discouraged, the owners recognized that strategic alternatives had to be considered if the business was to survive. The case demonstrates the difficulty of starting a business and sustaining its growth and momentum. It highlights the vulnerability of the small business to changes in the macro environment, especially changes in economic factors, and changes related to industry and competitive forces. Josephine settled into her seat and the idea of starting the business with her daughter Dina and daughter-in-law Angela In the last several months, both women had explored possible career moves. Angela had recently left her job as manager of a very successful bed and breakfast because she needed a job that would let her meet her responsibilities to her children and her husband's business. Dina was in a dead end job and was looking for a change. In the course of the conversation, the idea of the three women buying a bed and breakfast emerged. In an animated exchange it was proposed that Josephine could supply the funding, Angela could manage operations and Dina would help with operations and use her extensive restaurant experience to run a catering business from the kitchen of the inn.
After the analysis of 14 family business in global perspective we found that all the 14 family business do the succession planning though we can't say whether the business would be successful or not. In the end, we can conclude that succession planning is important for a family business in India as well as in Global family business. In Indian family business they hesitate in disclosing the succession planning of their business. But in Global family business they share and do their succession planning.
1. It's not urgent
For a healthy, relatively energetic 65-year-old with employee children ages 40, 38, and 36, there are many many pressing events demanding his attention every day. Succession planning is high on his list of IMPORTANCE, but it rarely makes it to the top of his list of URGENT things. It was a goal to undertake succession planning in 2011, but it didn't happen. The goal was pushed back to 2012; same story. In 2013, things have just been too busy, and there was that accident on the job site, and an unexpected lawsuit came up which required plenty of time and money. The patriarch says, 'We can do succession planning in 2014.' And so on, and so on. The fact is that the big picture, multiyear process of succession planning actually DOES lend itself to being pushed back a week, a month, a year. Business succession planning is rarely urgent ' except when it is, and then it's often too late!
2. The focus on tax avoidance and "Drop Dead Plans" creates a false sense of security
The fact that family businesses have spent hours and hours and thousands upon thousands of dollars to perfect their tax avoidance and Drop Dead Plans is a barrier to good family and management succession planning because it creates a false sense of security that everything is well in hand. That's simply not true, and estate planning, while an important component of business succession planning, is only a piece of the larger puzzle
3. Family member and/or employee push back
Families sometimes undertake ownership or management succession planning, think long and hard about pros and cons of potential outcomes, and then enter into discussion with family members or key nonfamily employees about the plan only to find that the employees give it a lukewarm or negative response and family members don't think the plans presented by the senior generation are all they're cracked up to be.
4. It's always safer not to change
5. Family businesses don't know how to undertake succession planning
Family businesses don't have a track record to fall back on in terms of how to get started, what to do, what problems might arise, how to resolve thorny issues, family business best practices, etc.
6. Lack of courage among the next generation family business leaders
7. Family businesses see succession planning as an event - not a process
The secret to succession planning, if there is one, is to allow enough time for the plans to work. There will be fits and starts, joys and disappointments, successes and failures, and home runs and strikeouts throughout the succession process. But the main thing is to recognize that it is a process with easily identifiable steps and milestones.
8. It costs too much
There's a vast difference between an EXPENSE, a cost, and an INVESTMENT which will pay a rate of return. All too often, family business leaders fail to make this distinction, they fail to recognize the opportunity costs of NOT engaging in a robust succession planning process, and the lack of planning doesn't really impede them in the short run.
Sucession planning hepls in the diversification and expansion of the business. It helps to recognize the Core competencies of the organisation so that Personal interest relatd to business/industry are maintained.The differences within the organisation should be sorted out with the appropriate solutions that leads to the openess and removes the contingencies within the organisation.
' CJ McLain's Deli & Bakery ;LLC:- a Small Family Business case study
' Bertram Chemical (1982) Co. case study
' Decision-making Related to Business Growth:Malay Small Businesses in Selangor case study
A Project Report
Relationship between Volume of Trade and Return in Stocks
For the partial fulfillment of the award of
Master of Business Administration
LOVELY PROFESSIONAL UNIVERSITY, PUNJAB
We, hereby declare that the work presented herein is genuine work done originally by us and has not been published or submitted elsewhere for the requirement of a degree programmed. Any literature, data or works done by others and cited within this dissertation has been given due acknowledgement and listed in the reference section.
It is a great pleasure for us to put on records my appreciation and gratitude towards Mr.Amitesh Kapoor for his encouragement towards preparation of this report. We would like to express us deep sense of gratitude for giving me valuable suggestion and encouragement. We offer heartfelt thanks to Parents, Brother for their moral support and inspiration.
Table of Content
Particular Page number
What we Plan to do
Number of Trades
Volume of trade
Review of Literature
2.1 Sentiment Indicator
2.2 Trading Volume
2.3 Number of Trades
3.1 Need of the study
3.2 Scope of the Study
3.3 Objective of the study
4.1 Research Methodology
4.1.1 Data Collection
4.1.2 Research Tool
4.1.3 Research Design
4.2 Data Analysis
List of Figures
Figure number Particular Page number
1.1 Minimum & maximum correlation of companies (number of volume) 24-24
1.2 Minimum & maximum correlation of companies (number of trade) 24-24
1.3 Depict the top 10 companies by the market capitalization (number of volume) 24-25
1.4 Depict the bottom 10 companies by the market capitalization (number of volume) 25-26
1.5 Depict the mid 10 companies by the market capitalization (number of volume) 26-26
1.6 Depict the top 10 companies by the market capitalization (number of trade) 26-27
1.7 Depict the bottom 10 companies by the market capitalization (number of trade) 27-28
1.8 Depict the mid 10 companies by the market capitalization (number of trade) 28-28
1.9 Highest & lowest correlation of top 10 companies (number Of volume) 28-29
1.10 Highest & lowest correlation of bottom 10 companies (number Of volume) 29-29
1.11 Highest & lowest correlation of mid 10 companies (number Of volume) 29-29
1.12 Highest & lowest correlation of top 10 companies (number Of trade) 30-30
1.13 Highest and lowest correlation value of bottom 10 companies ( five time period in Trade) 30-30
1.14 Highest and lowest correlation value of mid 10 companies (five time period in Trade) 30-31
1.15 Highest correlation data of Top 10 companies with highest return (trade)
1.16 Highest correlation data of Bottom10 companies with highest return (trade)
1.17 Highest correlation data of Mid 10 companies with highest return (Trade)
1.18 Highest correlation data of Top 10 companies with highest return (Volume)
1.19 Highest correlation data of Bottom10 companies with highest return (Volume)
1.20 Highest correlation data of mid 10 companies with highest return (Volume)
1.21 Lowest correlation data of Top 10 companies with Lowest return (Trade)
1.22 Lowest correlation data of Bottom10 companies with Lowest return (Trade)
1.23 Lowest correlation data of mid 10 companies with Lowest return (Trade)
1.24 Lowest correlation data of Top 10 companies with Lowest return (Volume)
1.25 Lowest correlation data of Bottom 10 companies with Lowest return (Volume)
1.26 Lowest correlation data of mid 10 companies with Lowest return (Volume) 37-38
In this project we have defined the time varying contemporaneous correlation of number of trade, number of volume with the daily return or price movement. We have taken a sample of 400 companies. The study finds that contemporary trading volume change is positively related with the stock returns. The correlation in different time periods is 0.1. However, the correlation between past period (Feb, 2008-Feb, 2009, Dec, 2010-Dec, 2013) trading volume change and current period stock returns are negative. This data explained that stocks with low trading volume change outperform the stocks with high trading volume change in the subsequent period. We analyze that on the correlation of Top 10, Mid 10& Bottom 10 companies with higher correlation (positive). On the other hand we analyzed that the companies with higher correlation were not having that much high return and companies with low correlation were also with negative return. Our major finding are following: first, in maximum and minimum correlation between two variables are similar. Second, there exist a positive and negative relationship between the number of trade with the return and volume of trading with return in different time periods of Top, bottom and mid companies. Third, cross relation between the volume of trading and number of trade with their return is similar because in both average return and lowest correlation is negative. Fourth, The study revel that stock return are negatively related to the contemporary change in number of trades. Investor's misspecifications about future earning or Illiquidity of low volume stock can be the reason for the negative relationship between number of trades and stock return .
Keywords: contemporaneous correlation, multidimensional approach, sentiment indicators, number of trades, volume of trade
In this report we have introduced the Time Varying contemporaneous correlation in different time periods. We have introduced the 400 companies which are taken in five different time periods. Data is based on daily basis. Contemporaneous defines the more recent, more near to future. In this report we have checked the consistency of correlation on a large data set. We can relate this concept with the behavioral finance. Theoretically sentiment is usually introduced into asset pricing through the effect on risk or return probability. Although the role of sentiment is framing more and more attention from practitioners, academics and investors most of the research is done at the market level due to limited accessibility of security level sentiment indicators.
Graphical or numerical indicators deliberate to show how groups feel about the market, business environment, market volatility, political factor, legal issues or other factor. A sentiment indicator seeks to tell how various factors, such as unemployment, inflation, macroeconomic conditions or politics influence future behavior.
Sentiment indicators can be used by investors to see how cheerful or distrustful people are to current market conditions. For example, a consumer sentiment index that shows pessimism may make companies less likely to stock up on inventory, because they may fear that consumers will not spend. Sentiment indicators are also used to estimate the market's mood. When these indicators push too far in either direction, we must watch for reversals.
There are many different sentiment indicators in the market. Here we are defining 4 different ways to getting market sentiments for long term and short term:
COT Reports (Long Term)
Currency Index (Long Term)
Market Reaction on News Release (Intraday Trading)
SWFX Sentiment Index (Intraday Trading)
In every market, there are only three types of sentiments:
Market sentiment refers to the psychology or emotions of market participants. Like LAMA is a researcher who has introduced the investor sentiment with the indirect indicators, which has defined strong negative relationship between investor sentiment and future returns of the very tangible stocks. Another is Yuk Ying Chang examine the impact of local and global sentiment, gauged by consumer confidence indicators, on stock market returns. Many researchers show different sentiment in different situation & tell about how it will help to measure the asset pricing and how it will effect on investors return or risk.
1.2 What we plan to do
Most of the people think correlation is a simple thing but it's not a simple thing. We can say that vertically correlation is simple thing but horizontally it is not simple because we are doing in a large data set. That's why we have chosen contemporaneous correlation. We have defined this correlation with the number of trades, volume of trading with the daily return or price movement. We have verified the relationship with in multidimensional data interpret work. In this we are making 26 tables which have different meaning in number of trades, volume of trading & daily return in different time period. In this we have found out the minimum or maximum value of trade and volume & used a VLOOKUP option which tells information from a database/list based on a supplied instance of the unique identifier. In this report we have created 3 sets one is Top 10, Mid 10, Bottom 10 companies. If correlation is strong in top 10 companies we can invest or trading on those companies. Actually these sets give you idea about the trading volume correlation, where you can take a benefit. For the measuring the asset pricing, future effect the market & how it will impact on investors return. If my transactions are low what is it mean in the market, may be it means increasing liquidity. How it will help for academies, practitioner, investors, mutual fund investor, buyer, seller companies. We have to make 2 tables in number of trade and number of return which tells about how many companies will come up with the minimum or maximum correlation on a large data set. Other 24 tables are depicting every company's highest & lowest correlation in a five different time period with number of trades, volume of trading, market turnover & average return.
1.3Number of trades
It includes the number of transactions. How much time a company is doing trading in a day? How much time he is buying or selling the shares in a day. How it will increase in the price of the share & with those transaction how much he will earn a profit in a day, how it will help for practitioner or investors. Most stocks are traded on exchanges, which are placed where buyers and sellers meet and decide on a price. Like Gregory et al introduced the S&P 500 index futures affects the S&P 500 index volatility. This is estimated by E-GARCH model. It is shown that the bad news increases the volatility means more fluctuating in pricing, more risky, if the price goes down that means it will make more effect on investors return. Then the good news is the degree of asymmetry is much higher for the futures market. The correlation between the S&P 500 index future and S&P500 index declines during the October 1987 crash. In this report we have introduced the Top 10 Number of Trade, Bottom 10 Trade and Mid Trade within the different time periods. In these trades we have found the difference between maximum & minimum correlation in different time periods. The highest & lowest correlation between five different time periods with the market turnover has been defined.
1.4 Volume of trading
In capital market number of shares or contracts traded in a security or an entire market during a given period of time. It is just the amount of shares that trade hands from sellers to buyers as a measure of activity. If a buyer of a stock purchases 100 shares from a seller, then the volume for that period increases by 100 shares based on that transaction. Volume is a key indicator in technical analysis as it is used to measure the worth of a market move. If the markets have made strong price move either up or down the perceived strength of that move depends on the volume for that period. The higher the volume during that price move the more significant the move. Volume measures the commitment behind stock price movement. It lets you know how many people are involved in that move.
If a stock moves on low volume then that means that relatively few people are participating in this movement. And if a stock moves on high volume then many traders or investors are involved in that movement and it will be easier to find someone to buy from or sell to.
Volume tells us the emotional excitement (or lack thereof) in a stock.
Fama points out that a market is weak-form efficient if all the information contains in past stock prices fully reflect in current prices (Fama, 1970, 1991). This implies that past security prices cannot be used to predict the future price changes; technical analysis tools have no value. In contrast, technical analysts consider that information contained in past security prices is not fully incorporated in current security prices, and hence, they believe that by observing the past security prices, information can be obtained on future security prices. Therefore, it is an interesting topic in finance to ascertain whether a market is weak-form efficient. Technical analysts strongly believe that 'It takes volume to make price move'. Primary cause of illiquidity in financial markets is the undesirable selection which arises from the being there of privately informed traders. Even some correlation tell biases of noise traders affect the trading and prices of securities that are subject to speculation, but do not affect prices of securities. Speculative demand for equity options is positively related to investor sentiment, while hedging demand is invariant to sentiment. Consistent with a demand based view of option pricing, we find that sentiment is related to time-series variation in the slope of the implied volatility smile of stock options, but has little impact on the prices of index options.
Review of literature
2.1 Sentiment indicators:
In this project we have define time varying conditional correlation between number of trades and number of volume with return or price movement. Here many researchers show the different relationship in different situation. Many researchers' talks about Sentiment Indicators which is affect on investors return and volatility. Many researchers determine the volume price relationship have been done based on developed market. Schmidt, T.,and Vosen, S., (2009) introduced a new indicator for private consumption based on search query time series provided by Google trends. The indicator is based on factors extracted from consumption-related search categories. Almost all experiments conducted the Google indicators' in-sample and out-of-sample predictive power proved to be better than that of the conventional survey-based indicators. Kumar, A., and Lee, C., (2006) Examines the 1.85 million retail investor transactions, these trades are systematically correlated'that is, individuals buy (or sell) stocks, measure changes in investor sentiment based on the direction in concert. (Bandivadekar, S. & Ghosh, S. (2003)) explore the impact of introduction of index futures on spot market volatility on both S&P CNX Nifty and BSE Sensex using ARCH/GARCH technique during the daily basis. The empirical analysis points towards a decline in spot market volatility after the introduction of index futures due to increased impact of recent news and reduced effect of uncertainty originating from the old news. Surrogate indices like BSE 200 and Nifty Junior are introduced the 'futures effect' plays a definite role in the reduction of volatility in the case of S&P CNX Nifty, in the case of BSE Sensex, where derivative turnover is considerably low, its role seems to be ambiguous. (Coval, D.J.,2005) Introduce a dataset provided by a large discount brokerage firm on the trades placed by 115,856 accounts in different companies. The variability of the component of a trade's return that is unrelated to skill will account for an increasing fraction of total return variability as the holding period grows. Skillful individual investors exploit market inefficiencies to earn abnormal profits. ( Myers, J., 1999) comparing the performance of alternative estimates of intrinsic value for the Dow 30 stocks. In this they were forecasting horizons and terminal values .The time- series relation between price & intrinsic value as a co integrated system, so that price & value are long term convergent. Lamia.S(2013) The investor sentiment is a concept key in behavioral finance, it has attracted the interest of many researchers during the last decade. The presents study develops a new measure of the investor sentiment which includes indirect indicators. Using a VAR model, they were record strong negative relationship between investor sentiment and future returns of the very tangible stocks. Lowry M and Schwert G W (2002) IPO volume and average initial returns are highly auto correlated. They find level of average initial returns at the time of filing contains no information about that company's eventual under pricing. Both the cycles in initial returns and the lead-lag relation between initial returns and IPO volume are predominantly driven by information learned during the registration period. Statistical tests show only weak evidence of a negative relation between IPO volume and future initial returns. Yuk Ying Chang(2012) examine the impact of local and global sentiment, gauged by consumer confidence indicators, on stock market returns. Using domestic consumer confidence indicators to proxy investor sentiment and data from 23 different equity markets, More accessible markets have stronger global sentiment effects, suggesting that capital flows help the spread of sentiment across borders. For locally sourced sentiment, the core finding is of a stronger sentiment effect in a poorer environment. On the other hand, for globally sourced sentiment the opposite result is strongly evidenced, namely, that a stronger sentiment effect tends to be linked to a stronger environment. Broadly speaking, a better domestic legal and information environment is associated with weaker local and stronger global sentiment effects. Ying. C. C. (1966) examined the view that they are joint products of a single market mechanism. Findings tend to support the notion that any model of the stock market which separates prices from volumes or vice versa will inevitably yield incomplete if not erroneous results. Brown and Cliff (2004) also use sentiment measures including the ratio of the number of advancing issues to declining issues, the ARMS index, the ratio of new highs to new lows, the change in margin borrowing and short interest, the ratio of short sales to total sales. Baker and Stein (2004) introduced two types of investor, rational & overconfident. Both are having non-negative weights on the pricing function. There are also short-sales constraints on the market and insiders who trade on their private information. When investor sentiment increases, the overconfident investors under react further to the information contained in the insiders' trades and gain a greater weight on the pricing function. The rational investors cannot counteract the overconfident investors' transactions. To the extreme, when investor sentiment becomes very high, the overconfident investors dominate the market. The market is characterized with high liquidity and high trading volume. Since high sentiment leads to lower expected returns, this model provides an alternative explanation for the negative time-series relations between liquidity measures and expected returns. Campbell and Shiller (1988b) measure the role of earnings or dividend fundamentals that explain returns rather than valuation levels as a function of sentiment. These studies typically use a linearization of the valuation metric or avoid the step of capitalizing dividend. Lemmon, M.L., and Ni, S.X,( 2008) Introduce the speculative demand for equity options is positively related to investor sentiment, while hedging demand is invariant to sentiment. sentiment is related to time-series variation in the slope of the implied volatility smile of stock options, but has little impact on the prices of index options with higher concentration of speculative trading. Yuk Ying Chang(2012) introduced the effect of a good environment on facilitating arbitrage activity is stronger than its effect on attracting local behavioral investors but weaker than its effect in attracting foreign behavioral investors. Boyer, Kumagai and Yuan (2006) provide supportive evidence. Foreign investors are more (less) susceptible to global (local) sentiment. Moreover, more readily accessible markets allow foreign arbitrageurs to carry out arbitrage. in the domestic market, which also explains why we expect a weaker local sentiment effect in markets with higher foreign investors' accessibility. (Welch, 2000) might further strengthen sentiment effects on asset pricing. It suggests that stocks with smaller analyst coverage tend to be 'lottery-type' stocks (on which investors have a higher propensity to gamble) and more preferred by individual investors (Kumar, 2009). Consistent with this latter view, stocks with smaller analyst coverage are prone to sentiment influences. All plausible considerations, whether higher analyst coverage is associated with stronger sentiment effects
2.2 Trading volume
Trading volume can contain information on how security prices evolve over time. For instance, Brennan, Chordia, and Subrahmanyam (1998), Datar, Naik, and Radcliffe (1998), and argue that trading volume reflects liquidity. They find that stocks with lower trading volume earn higher expected returns as a liquidity premium. Atkins, A. & E. Dyl, (1997) introduced relationship between trading volume, information, and firm and market characteristics by examining the cross- sectional determinants of trading volume for individual firms in two distinct markets. They were use the median weekly turnover over the entire five- year period. Chordia, Subrahmanyam, and Anshuman (2001) employ both dollar trading volume and share turnover as their proxies of liquidity, and use the coefficient of variation as the variability measure to control for the level effect. Surprisingly, they find that the variability of liquidity is negatively related to expected returns for NYSE and AMEX securities in the period from 1966 to 1995. The same relation holds for NASDAQ securities in the period from 1984 to 1995. Since this result is unexpected, Chordia, Subrahmanyam, and Anshuman (2001) examine several possible explanations including an alternative GARCH(1,1) specification of conditional volatility, additional macroeconomic
variables, nonlinearity in the relation between expected returns and trading volume. Melvin and Yin (2000) investigated the relationship between the arrival of new public information, the quoting frequency and the volatility of dollar/yen and dollar/mark exchange rates. They analyzed intra-day data taken from Reuters screens on indicative quotes and news headlines related to the United States, Germany or Japan. They found that the amount of information arriving on a particular hour of a particular day of the week is positively related to the amount of quoting activity and exchange rate volatility. Visibility hypothesis is first tested by GKM and subsequently by Huang and Heian (2010) GKM examine the relationship between current trading volume with future returns for NYSE from 1963 to 1996 for both daily and weekly data. Number of shares traded is used as the measure of trading volume. Portfolios are formed in accordance with the Jegadeesh and Titman (1993). Formed high, medium and low volume portfolios based on daily and weekly data and without rebalancing. Findings said that portfolios with high trading volume. Lee and Swaminathan (2000), argue that trading volume does not measure liquidity. They find that momentum profits depend on the past level of trading volume. It's played a role in reconciling intermediate-term momentum with long-term reversal on stock returns . Chandrapala Pathirawasam Examines the relationship between trading volume and stock returns. This study follows the conventional methodology. It was found that past trading volume change is negatively related to stock returns. Jonathan M. KarpoffSource(Mar., 1987) Examine the relation between price changes and trading volume in financial markets, and makes four contributions. First, two empirical relations are established: volume is positively related to the magnitude of the price change and, in equity markets, to the price change per se. Second, previous theoretical research on the price-volume relation is summarized and critiqued, and major insights are emphasized. Third, a simple model of the price-volume relation is proposed that is consistent with several seemingly unrelated or contradictory observations. And fourth, several directions for future research are identified. Gabriele Galati (2000) introduced relationship between trading volumes, volatility and bid-ask spreads in foreign exchange markets. It uses a daily data on trading volumes for the dollar exchange rates of seven currencies from emerging market countries. In most cases unexpected trading volumes and volatility are positively correlated, suggesting that both are driven by the arrival of public information, as predicted by the mixture of distributions hypothesis. Correlation between trading volumes and volatility is positive during 'normal' periods but turns negative when volatility increases sharply. (Jegadeesh and Titman, 1993) to form volume based trading strategies. Trading volume is positively related with stock returns in the contemporary period and the relationship is negative when the past trading volume is related with stock returns.
2.3 Number of trades
Other than the traditional view, Huang and Heian examine the risk adjusted high value premium based on all firms listed on NYSE and AMEX. They use the conventional method widely used by momentum literature (Jegadeesh and Titman 1993) to test the strategy. They find statistically significant abnormal returns for high volume minus low volume portfolio for holding periods 1-4 weeks. However, they further to find that as the holding period increase beyond 8 weeks, abnormal returns decrease significantly. P. Hartmann(1999) estimation of the determinants of dollar/yen bid-ask spreads is undertaken. In particular, a long time-series of daily spot foreign exchange trading volumes is used for the first time. In line with standard spread models and volume theories, it can be shown that unpredictable foreign exchange turnover increases spreads, while predictable turnover decreases them. Both effects are strongly significant when employing spot turnover instead of proxies like forward turnover as in previous studies. Amihud (2002) defines this measure on an annual basis. Amihud (2002) examines both the cross-sectional relation between illiquidity and expected returns for individual stocks and the time-series relation between market illiquidity and expected market returns. He finds this measure to be negatively related to firm size and positively related to both the fixed and the variable proportional transaction costs estimated by Brennan and Subrahmanyam (1996), consistent with the role of this measure to be a proxy of illiquidity. Further, a stock's illiquidity defined over the previous year has a positive and significant effect on the stock's monthly returns in the current year, after controlling for the stock's beta, previous returns, size, daily return standard deviation, and dividend yield. For the market as a whole, expected market illiquidity has a positive and significant effect on the annual (equally-weighted) market risk premium and unexpected illiquidity shock has a negative and significant effect. Results for the whole market also hold when the market risk premium and the market illiquidity are defined on a monthly basis. In sum, these findings suggest that investors prefer liquidity and that market liquidity is one of the factors to determine market risk premium. Acharya and Pedersen (2003) examine the interactions among security returns, security 29 lliquidity, market returns, and market illiquidity. Security's illiquidity to market returns has the largest effect on the securities expected returns. (Karel Hrazdil, (2009) examine the effect of demand on stock prices by analyzing the transition of the S&P 500 index from market capitalization to free float weighting, which occurred in 2005. decrease in demand produced a permanent stock price decline, which was accompanied by significant abnormal trading volume. The results provide free-float adjustment, 98 stocks in the S&P 500 index had their weights decreased. Epps, T. W., Epps, M. L. (1976) introduced stochastic dependence between transaction volume and the change in the logarithm of security price from one transaction to the next. The change in the logarithm of price can therefore be viewed as following a mixture of distributions, with transaction volume as the mixing variable. Findings are consistent with the hypothesis that stock price changes over fixed intervals of time follow mixtures of finite-variance distributions.
(Robert W. Faff and Michael D. McKenzie) examine the stock index futures trading on the daily returns seasonality of the underlying index for seven national markets. This daily seasonality testing is performed with respect to (a) mean returns; (b) return autocorrelations; and (c) return volatilities using a modified GARCH model lead to reduced seasonality of mean returns. This is particularly the case with regard to the general weakening of the Monday effect in mean returns for the US; Germany; and Switzerland, and to a lesser extent for the UK. Gregory et al (1996) examined how volatility of S&P 500 index futures affects the S&P 500 index volatility. The study also examines the effect of good and bad news on the spot market volatility. Volatility is estimated by E-GARCH model. It is shown that the bad news increases the volatility than the good news and the degree of asymmetry is much higher for the futures market. The correlation between the S&P 500 index future and S&P500 index declines during the October 1987 crash. Campbell and Shiller (1988a ) examine the deal effectively with two problems in rational expectations present value model: non stationary of time series and incomplete data on information of market participants. With US Data they were find relatively encouraging results for rational expectation theory. (Brad M. Barber and Odean, T.(2001)) Introduce the online investors are also affect the market & investor's return. It has lowered both the fixed and marginal costs of producing financial services, thus enabling newer, smaller companies to challenge established providers of these services. E-commerce firms are transforming the way traditional services. Kawaller et al. (1987), found that futures price movement consistently led the spot index movement by 20-45 minutes. Similarly, Stoll and Wheley (1990) who examined the causal relationship between intra-day returns of stock index and stock index futures contracts found that the S&P500 and MM index futures returns tend to lead stock market returns by about five minutes on an average. Osbome. M. F. M. (1959) shows that common-stock prices, and the value of money can be regarded as an ensemble of decisions in statistical equilibrium, with properties quite analogous to an ensemble of particles in statistical mechanics. Shamsuddin.S(2012) explore causality relationship between cash index and futures index in case of Malaysia, trading on stock index futures contracts is relatively new to financial investors. The index has generally been accepted as the local stock market barometer. Granger causality tests found that the direction of causality relationship is unidirectional that running from cash market to futures market. Tauchen and Pitts (1983) show that volume and volatility can co-move for two reasons. First, as the number of traders grows, market prices become less volatile. Second, given the number of traders, an increase in volume reflects greater disagreement among traders and hence leads to higher volatility. This link is stronger when new information arrives at a faster rate. B.D. McCullough (1995) Spectral analysis analyze market data & conclude that the spectrum of rice changes is 'white noise' or very nearly so. It revivals' marked differences between the interaction of price change & volume & contradicts 'stylized fact' from time domain analysis of the price volume relation. Dow Jones Introduced the Time Varying contemporaneous correlation between S&P 500 index & Reven Pack sentiment index which is calculated based on the 90 day moving the average of the difference between the counts of positive or negative news. Sah and Omkarnath (2005) examined the nature and extent of relation between NSE-50 Futures and volatility of S&P CNX Nifty. They used Granger causality test to study relationship between volatility and futures market activity. Their empirical study suggested that futures market activity destabilized the underlying market. Teppo et al (1995) study the two-way causality between the Finnish stock index futures and the stock index for a period of one year from 1989 - 1990. Granger Causality tests are applied on the daily returns due to non-availability of intra-day data. The results indicate that the futures market provides predictive information for both frequent and infrequently traded stocks while the reverse causality is found to be weak.
3.1 Need of the study
This project basically helps the practitioner, investor, institutions, buyer seller, companies and mutual fund investor. Those who want to see correlation. It hold true volume of trading and number of trades. Because number of people don't understand the volume of trade and return . Through this project we want to add some value. Number of trades and volume of trading give the information to people. Need of this project is because practitioner like simple things that why we choose this project to help them by providing the information related to the number of trades and volume of trading. Practitioners don't like to see in depth but they like simple way or we can say flat way to understand. Under this we use the Time Varying contemporaneous correlation because it tells us the true value and it is very simple to understand. Trading volume is important factor for investors and practitioners because it helps to raise additional financial capital for expansion by selling shares of ownership of the company in a public market.
In a stock market volume of trading is a basic need for Market Participants. It includes individual retail investors, institutional investors such as mutual funds, banks, insurance companies and hedge funds, and also publicly traded corporations trading in their own shares. Some studies have suggested that institutional investors and corporations trading in their own shares generally receive higher risk-adjusted returns than retail investors. In worldwide, buyers and sellers were individual investors, such as wealthy businessmen, usually with long family histories to particular corporations. Over time, markets have become more "institutionalized"; buyers and sellers are largely institutions (e.g., pension funds, insurance companies, mutual funds, index funds, exchange-traded funds, hedge funds, investor groups, banks and various other financial institutions).
Number of trades is also important for Buyer & Seller. Because they also play an important role in a capital market in following manners:
Disposition effect: Sellers are more likely to initiate trades for past winners (stocks with positive past returns) than for past losers (stocks with negative past returns).
Tax-induced trading: Sellers are more likely to initiate trades for losers than for winners.
Seasonal tax-induced trading: Sellers are more likely initiate trades for losers than for winners in December but more likely to initiate trades for winners in January.
Momentum trading: Buyers are more likely to initiate trades for winners and sellers are more likely to initiate trades for losers.
Contrarian trading: Buyers are more likely to initiate trades for losers and sellers are more likely to initiate trades for winners.
Flight-to-quality: Sellers are more likely to initiate trades when market risk increases and buyers are more likely to initiate trades when market risk decreases.
3.2 Scope of the study
Our scope of study is a very large multi dynamically across all companies in BSE listed across various time periods because we want the applicability of various companies in various time periods. Basically this report investigates the relationship between stock market trading volume and the Time Varying contemporaneous correlation of daily stock returns with the market turnover. The paper explains this occurrence using a model in which risk-averse "market makers" accommodate buying or selling pressure from "liquidity" or "non informational" traders. Changing expected stock returns reward market makers for playing this role. The model implies that a stock price turn down on a high-volume day is more likely than a stock price turn down on a low-volume day to be associated with an increase in the expected stock return.
3.3 Objective of the study
To investigate whether number of trade suitable proxy for sentiment indicators.
To find out the time varying Time Varying contemporaneous correlation of number of trade and volume with the price movement.
To verifying the relationship with in multi dynamic frame work.
4.1 Research Methodology
Quantitative analysis, Observation of the data
We have taken a secondary data from the BSE Website. In BSE Website we have downloaded a Bhav Copy after that we sorted the shares of the copy in descending order. After that we have taken historical data of 1200 companies on a daily basis. That resulted in a larger data set so we reduced it and taken the historical data of 400 companies. Our sample data is based on daily basis during the Jan, 2007 to Dec, 2013. Then we divided it in different five time periods, which are Jan. 2007- Jan. 2008; Feb. 2008-Feb. 2009; Feb. 2009-Dec. 2010; Dec. 2010-Dec. 2011 and Dec. 2011-Dec. 2013. Then we have calculated the return on daily basis and calculated a correlation of Return Vs Volume & Return Vs Trade on daily basis in different time periods. After that we have calculated the Covariance of Return Vs Volume & Return Vs Trade on daily basis in different time period. And then we have calculated a standard deviation & Average return. Then we are created 26 tables on the basis of the data. Further we have divided the companies in three sets which is Top 10, Mid 10, Bottom 10, in number of trade & number of volume with correlation analysis.
4.1.2 Research Tool
Time varying contemporaneous correlation
P_1 Which is stands for closing price of previous day
P_0 Which is stands for closing price of spot day
R_t Which is stands for daily return
p_TR=corrl(T,R)=cov(T,R)/(??_R ??_T )
??_R = Standard Deviation of Daily Return
??_T= Standard Deviation of trade
cov(T,R) Which is define the covariance of Trade Vs Daily return
p_TR Define the coefficient of correlation of Trade Vs Daily Return
p_VR=corrl(V,R)=cov(V,R)/(??_R ??_V )
??_V= Standard Deviation of Volume
cov(V,R) Which is define the covariance of Volume Vs Daily return
p_VR Define the coefficient of correlation of Volume Vs Daily Return
4.1.3 Research Design
We have defined the time varying contemporaneous correlation. Our research design is descriptive. Because it describes market characteristics or functions, marked by the prior formulation of specific hypotheses, Preplanned and structured design
4.2 Data analysis
Fig 1.1 Extreme Correlation of companies in different time periods
Jan.2007- Jan.2008 Feb. 2008-Feb.2009 Feb.2009-Dec.2010 Dec. 2010- Dec.2011 Dec. 2011- Dec2013
Min 67 186 33 102 132
Max 194 30 124 71 101
In this we have 5 time period with maximum and minimum correlation under the volume. Number of companies came under the different time. Figure gives indication that the correlation between volume and return among various companies across various industrial sectors is different for these companies. There is no particular time period where all the companies or most of the companies are showing same extreme of a correlation in any time period.
Fig. 1.2 Extreme Correlation of companies in terms of number of trades
Jan.2007-Jan.2008 Feb2008-Feb.2009 Feb.2009-Dec.2010 Dec.2010-Dec.2011 Dec.2011-Dec.2013
Min 3 187 34 101 133
Max 194 31 124 72 100
Figure 1.2 explained time period with maximum and minimum correlation under the Number of trades. Number of companies came under the different time. That the correlation between number of trades and return. If we see the figure 1.1 which defines the correlation under the volume and returns that makes relation with the figure 1.2. Maximum correlation in Jan.2007-2008 in number of trades and volume are the same & in minimum correlation in Feb.2008-2009 no. of trades is 187 and in volume is 186.there are minor differences between number of trades and volume of trading. When we are correlating with in different time periods it results show almost the same transaction, no more affect on investor's return and future indexes.
Figure 1.3 Contemporaneous Correlation of Top 10 companies in a trading volume
Name of the companies Market turnover Jan.2007 -Jan.2008 Feb2008-Feb.2009 Feb. 2009 - Dec. 2010 Dec.2010- Dec.2011 Dec.2011-Dec.2013 Difference of maximum &minimum correlation
TATA Coffee 916217514 0.235 0.117 0.487 0.516 0.212 0.399
Gitanjali gems ltd 667281120 0.354 0.077 0.409 0.390 0.388 0.332
Unitdspr 625808159 0.143 -0.002 0.019 -0.019 0.319 0.338
State bank of India 573821981 0.243 -0.019 0.124 -0.331 -0.041 0.574
reliance 433264781 -0.160 -0.085 -0.058 -0.100 0.082 0.242
Jet Airways 423086938 0.222 0.055 0.511 0.283 0.108 0.456
SPICEJET LTD 386891053 0.346 0.327 0.343 0.052 0.447 0.395
Reliance capital 321090772 0.230 0.033 0.208 0.053 0.129 0.197
Tata motors 315597304 -0.030 0.028 0.054 0.008 0.134 0.164
Strids 302102887 0.164 0.166 0.107 0.352 -0.023 0.375
In figure 1.3 we have taken a top 10 companies from 400 companies in a five different time periods. This figure explore that the in a five different time period correlation is not a same .in different time period correlation is also a different there is minimum and maximum correlation is almost 0.3 to o.5 which is a hit the sign of positive .we can see that in a time of crisis these companies are performing very well with a high market earning. There are very few companies which are showing the negative correlation in particular time period. All companies are performing very well which is a symbol of that these top 10 companies are very important role play for economic growth.
Figure 1.4 Contemporaneous Correlation of Bottom 10 companies in a trading volume
Name of the companies Market
Turnover Jan.2007 -Jan.2008 Feb.2008 'Feb. 2009 Feb.2009-Dec. 2010 Dec.2010-Dec.2011 Dec.2011 -Dec. 2013 Difference of maximum &minimum correlation
Alps industries ltd 246116 0.357 0.050 0.390 -0.121 -0.062 0.510
Chandni textile engineering industry 240819 -0.028 -0.009 0.169 -0.060 0.031 0.229
Green Fire Agricultural commodities 204395 0.124 -0.066 0.318 0.314 -0.017 0.384
Hiran 203592 0.355 0.204 0.182 0.239 -0.163 0.518
ASHCONIUL 104786 0.165 0.106 0.016 -0.154 -0.059 0.319
Aadhaar venture India ltd 104558 0.118 0.018 0.062 0.407 -0.010 0.417
Bampsl securities ltd 95192 0.226 0.188 0.090 -0.001 -0.062 0.288
Kohinoor broadcast 77188 0.109 0.300 -0.183 -0.122 -0.113 0.483
GV films ltd 60928 0.416 0.191 0.318 -0.032 -0.067 0.484
Sanraa 40077 0.255 0.038 0.017 0.107 -0.099 0.354
Figure 1.4 explained the time varying contemporaneous correlation of bottom 10 companies with the market capitalization. In this figure their correlation value is lie between.02 to 0.5. That is not made a large difference with the figure 1.3. Top 10 companies and bottom 10companies correlation are not a same but near to matched together. Bottom 10 companies are also a positive correlation with the market capital. Every company has volume of trading is different in particular time period with the different market capitalization. But the correlation is almost the same with the top 10 companies. All were matched with each other. That is said top 10 and bottom 10 companies are playing in a bearish and bullish market.
Fig. 1.5 Contemporaneous Correlation of Mid 10 companies in a trading volume
Name of the companies Market Turnover Jan.2007 -Jan.2008 Feb.2008-Feb.2009 Feb.2009-Dec.2010 Dec.2010-Dec.2011 Dec.2011-Dec.2013 Difference of maximum &minimum correlation
DCB bank 26876573 0.454 0.354 0.401 0.343 0.237 0.217
AMBJA cement ltd 25992784 0.122 0.014 0.106 0.106 -0.018 0.140
Rander 24509610 0.081 -0.140 0.111 0.109 -0.073 0.251
Vakrangee 24268652 0.278 0.128 0.330 -0.025 -0.070 0.400
Rs software ltd 24230445 0.428 0.267 0.225 0.057 0.360 0.371
GIC housing 23250033 0.211 0.026 0.337 0.345 0.310 0.319
Gangotri Iorn 23215699 0.153 0.073 0.177 0.228 0.152 0.154
CompuCom software ltd 23079307 0.068 0.028 0.154 0.160 0.150 0.132
UB holding 22916059 0.049 0.183 0.341 0.201 0.258 0.292
Escorts Ltd 21697037 0.366 0.385 0.403 0.239 0.349 0.164
The table depicts the Mid10 companies by the market capitalization. In this calculate the correlation from January 2007 to December 2013, then find out the difference between maximum and minimum correlation from the 2007 to 2013. These mid 10 companies have come under the volume. From above figure we find that the difference of correlation in five time period is not that much varying.
Figure 1.6: Top 10 Companies with difference of maximum and minimum correlation (Trade)
Name of companies Jan2007-2008 Feb2008-2009 Feb2009-Dec2010 Dec2010-2011 Dec2011-2013 Difference
TATA Coffee 0.380 0.146 0.434 0.504 0.233 0.358
Gitanjali Gems Ltd 0.374 0.188 0.315 0.314 0.459 0.270
Unitdspr 0.374 -0.002 0.127 0.143 0.387 0.389
State Bank Of India 0.216 0.020 0.108 0.110 -0.068 0.284
Reliance -0.107 -0.046 -0.034 -0.031 0.106 0.214
Jet Airways 0.257 0.080 0.491 0.492 0.362 0.412
SPICEJET LTD 0.283 0.341 0.363 0.361 0.409 0.126
Relcapital 0.208 0.040 0.193 0.195 0.149 0.169
Tata Motors 0.003 -0.062 0.292 0.292 0.094 0.354
Strids 0.228 0.283 0.318 0.316 -0.052 0.370
Table depicts the top10 companies by the market turnover. In this calculate the correlation from Jan .2007 to Dec 2013. After that we have find out the difference between maximum and minimum correlation from the 2007 to 2013. These top 10 companies come under the no. of trade. As we can see the difference of correlation is 0.2 to 0.3. Which is shows the high variation in the number of trade in five time period.
Figure 1.7: Bottom 10 Companies with difference of maximum and minimum correlation (Trade)
Name Of Companies Jan2007-2008 Feb2008-2009 Feb2009-Dec2010 Dec2010-2011 Dec2011-2013 Difference
Chandni Textile Engieering Ind. -0.024 -0.004 0.129 0.128 -0.077 0.206
Green Fire Agri Commodities 0.159 -0.015 0.321 0.325 0.140 0.340
Hiran 0.355 0.128 0.113 0.114 -0.128 0.484
Ashconiul 0.116 0.046 0.118 0.118 -0.010 0.128
Aadhaar Venture India Ltd 0.130 -0.004 0.084 0.087 0.013 0.133
Bampsl Securties Ltd 0.029 0.117 0.160 0.159 -0.038 0.198
Kohinoor Broadcast 0.109 0.168 -0.096 -0.078 -0.067 0.264
Gv Films Ltd 0.267 0.138 0.268 0.267 -0.115 0.383
Sanraa 0.149 0.171 0.106 0.105 0.035 0.136
Sarang Chemical Ltd 0.235 0.017 0.158 0.117 0.051 0.218
Table depicts the bottom10 co's by the market turnover. In this calculate the correlation from Jan 2007 to Dec 2013, then find out the difference between maximum and minimum correlation from the 2007 to 2013. This bottom 10 co's come under the no. of trades. As we can see the table difference is in the range of 0.1 is minimum and maximum is 0.4 which is showing that no. Of trade is very dynamic because of some market condition.
Figure 1.8: Mid 10 Companies with difference of maximum and minimum correlation (Trade)
Name of the companies Jan2007-2008 Feb2008-2009 Feb2009-Dec2010 Dec2010-2011 Dec2011-2013 Difference
Dcb Bank 0.449 0.313 0.354 0.352 0.219 0.230
Ambja Cement Ltd -0.065 0.123 0.309 0.309 0.054 0.374
Rander 0.094 -0.090 0.120 0.123 0.018 0.213
Vakrangee 0.233 0.195 0.224 0.223 -0.111 0.343
Rssoftware Ltd 0.394 0.273 0.231 0.230 0.370 0.164
Gic Housing 0.335 0.111 0.339 0.338 0.319 0.228
Gangotri Iorn 0.110369 0.112 0.150 0.150 0.089 0.061
Compucom Software Ltd 0.123 0.033 0.209 0.209 0.118 0.176
Ub Holding 0.266 0.248 0.265 0.264 0.244 0.022
Escorts Ltd 0.365 0.360 0.392 0.392 0.383 0.032
The table depicts the mid 10 companies by the market capitalization. In this calculate the correlation from Jan 2007 to Dec 2013. There after found the difference between maximum and minimum correlation from the 2007 to 2013. These mid 10 companies come under the number of trades. From the figure we can analyze the correlation difference is similar to the volume.
Figure 1.9: Highest and lowest correlation value of top 10 companies (five time period in volume)
Time Highest Lowest
Jan2007-Jan2008 0.354 -0.160
Feb2008-2009 0.327 -0.085
Feb2009-Dec2010 0.511 -0.058
Dec2010-2011 0.516 -0.331
Dec2011-2013 0.447 -0.041
Table indicate the highest and lowest correlation of the top 10 companies which came under the volume with time period from 2007 to 2013. Here we can see the highest value of correlation is 0.3 to 0.5. But we can see the lowest correlation which is depict the negative deviation.
Figure 1.10: Highest and lowest correlation value of bottom 10 companies (five time period in volume)
Time Highest Lowest
Jan2007-Jan2008 0.41641288 -0.02817
Feb08-09 0.29978178 -0.06594
Feb2009-Dec2010 0.38960486 -0.18274
Dec10-11 0.40713225 -0.15426
Dec11-13 0.03129822 -0.16279
Figure indicate the highest and lowest correlation of the bottom 10 companies which came under the volume with time period from 2007 to 2013. Here we can see the correlation in highest value the difference between is 0.1. But we can see the lowest correlation which is depict the negative deviation.
Figure 1.11: Highest and lowest correlation value of Mid 10 companies ( five time period in volume)
Time Highest Lowest
Jan2007-Jan2008 0.453928718 0.048614511
Feb2008-2009 0.385449848 -0.13996909
Feb2009-Dec2010 0.402859541 0.106164618
Dec2010-2011 0.345015598 -0.02525815
Dec2011-2013 0.359871818 -0.0731169
Table indicates the highest and lowest correlation of the mid 10 companies which came under the volume of trading with time period from 2007 to 2013. Here we can see the correlation in highest value is 0.4 which is slightly positive. But we can see the lowest correlation which is depict the negative deviation as well as the positive correlation with small range of deviation.
Figure 1.12: Highest and lowest correlation value of Top10 companies ( five time period in Trade)
Time Highest Lowest
Jan2007-Jan2008 0.380282 -0.10715
Feb08-09 0.340902 -0.06247
Feb2009-Dec2010 0.491485 -0.0343
Dec10-11 0.503506 -0.03122
Dec11-13 0.458734 -0.06844
Table indicate the highest and lowest correlation of the top 10 companies which came under the number Of trade with time period from 2007 to 2013. Here we can see the highest value of correlation 0.2 - 0.4 which is slightly positive. But we can see the lowest correlation is range between-0.2 - 0 which is very weak and negative.
Figure 1.13: Highest and lowest correlation value of bottom 10 companies ( five time period in Trade)
Time Highest Lowest
Jan2007-Jan2008 0.355145 -0.02392
Feb2008-2009 0.171293 -0.01492
Feb2009-Dec2010 0.32069 -0.09581
Dec2010-2011 0.325087 -0.07798
Dec2011-2013 0.14023 -0.12838
Table indicate the highest and lowest correlation of the bottom 10 companies which came under the number Of trade with time period from 2007 to 2013. Here we can see the highest value of correlation 0 - 0.3 which is the very weak and positive. But we can see the lowest correlation -0.2 - 0 it means very weak and negative.
Figure 1.14: Highest and lowest correlation value of mid 10 companies (five time period in Trade)
Time Highest Lowest
Jan2007-Jan2008 0.449041 -0.06477
Feb2008-2009 0.360091 -0.08976
Feb2009-Dec2010 0.392034 0.119873
Dec2010-2011 0.392103 0.12315
Dec2011-2013 0.383409 -0.11074
Table indicates the highest and lowest correlation of the mid 10 companies which came under the number of trades with time period from 2007 to 2013. As we can see the correlation in highest value is range between the 0.2 - 0.4 it means slightly positive. But we can see the lowest correlation value is -0.2 - 0 it means very weak and negative.
Figure 1.15: Highest correlation data of Top 10 companies with highest return (trade)
Name Of Companies Highest Correlation Time Return
Tata Coffee 0.504 Dec2010-2011 0.31%
Gitanjali Gems Ltd 0.459 Dec2011-2013 0.45%
Unitdspr 0.387 Dec2011-2013 0.33%
State Bank Of India 0.216 Jan2007-Jan2008 0.41%
Reliance 0.106 Dec2011-2013 0.31%
Jet Airways 0.492 Dec2010-2011 0.44%
SPICEJET LTD 0.409 Feb2009-Dec2010 0.46%
Relcapital 0.208 Jan2007-Jan2008 0.60%
Tata Motors 0.292 Feb2009-Dec2010 0.53%
Strids 0.318 Feb2009-Dec2010 0.53%
This table shows highest correlation of top 10 co's with number of trade. Under this table shows the in which time top 10 companies had the highest correlation with their return here we can see that Tata coffee is highest correlation but return is not good as compare to other companies is means if company has highest correlation it doesn't mean that company will give you higher return.
Figure 1.16: Highest correlation data of Bottom10 companies with highest return (trade)
Name Of Co. Highest Correlation Time Return
Chandni Textile Engieering Ind. 0.129 Feb2009-Dec2010 4.29%
Green Fire Agri Commodities 0.325 Dec2010-2011 -0.05%
Hiran 0.355 Jan2007-Jan2008 0.02%
Ashconiul 0.118 Dec2010-2011 2.02%
Aadhaar Venture India Ltd 0.130 Jan2007-Jan2008 0.73%
Bampsl Securties Ltd 0.160 Feb2009-Dec2010 0.73%
Kohinoor Broadcast 0.168 Feb2008-2009 0.53%
Gv Films Ltd 0.268 Feb2009-Dec2010 0.01%
Sanraa 0.171 Feb2008-2009 0.1%
Sarang Chemical Ltd 0.235 Jan2007-Jan2008 0.2%
This table shows highest correlation of bottom 10 companies with number Of trade. Under this table shows the in which time bottom10 companies had the highest correlation. Here we can see that Hiran is highest correlation. As we see that the return is range between the 0.2% to 4.29 it means return is limited.
Figure 1.17: Highest correlation data of Mid 10 companies with highest return (Trade)
Name Of Companies Highest Correlation Time Return
Dcb Bank 0.449 Jan2007-Jan2008 0.17%
Ambja Cement Ltd 0.309 Feb2009-Dec2010 0.20%
Rander 0.123 Dec2010-2011 0.10%
Vakrangee 0.233 Jan2007-Jan2008 0.64%
Rssoftware Ltd 0.394 Jan2007-Jan2008 0.28%
Gic Housing 0.339 Feb2009-Dec2010 0.26%
Gangotri Iorn 0.150 Feb2009-Dec2010 0.28%
Compucom Software Ltd 0.209 Feb2009-Dec2010 0.32%
Ub Holding 0.266 Jan2007-Jan2008 0.53%
Escorts Ltd 0.392 Feb2009-Dec2010 -0.05%
This table shows highest correlation of mid 10 companies with number of trade. Under this table shows the in which time period mid 10 companies have the highest correlation. Last column shows the average return of the bottom 10 companies with the range between -0.05% to 0.53%and correlation lie in between range 0.2 to 0.4 it means slightly positive.
Figure 1.18: Highest correlation data of Top 10 companies with highest return (Volume)
Name Of Companies Highest Correlation Time Return
Tata Coffee 0.516 Jan2007-Jan2008 0.31%
Gitanjali Gems Ltd 0.409 Feb2009-Dec2010 0.45%
Unitdspr 0.319 Dec2011-2013 0.33%
State Bank Of India 0.243 Jan2007-Jan2008 0.55%
Reliance 0.082 Dec2011-2013 0.41%
Jet Airways 0.511 Feb2009-Dec2010 0.31%
Spice jet Ltd 0.447 Dec2011-2013 0.44%
Reliance capital 0.230 Jan2007-Jan2008 0.46%
Tata Motors 0.134 Dec2011-2013 0.60%
Strids 0.352 Dec2010-2011 0.53%
This table shows highest correlation of top 10 co's with volume. Under this table shows the in which time top 10 co's had the highest correlation. Last column shows the average return of the top 10 companies is range varying the 0.31% to 0.60% and correlation is range between the 0.4 - 0.7 it means fairly positive.
Figure 1.19: Highest correlation data of Bottom10 companies with highest return (Volume)
Name Of Companies Highest Correlation Time Return
Alps Industries Ltd 0.390 Feb2009-Dec2010 4.29%
Chandni Textile Engieering Ind. 0.169 Feb2009-Dec2010 -0.05%
Green Fire Agri Commodities 0.318 Feb2009-Dec2010 0.02%
Hiran 0.355 Jan2007-Jan2008 2.02%
Ashconiul 0.165 Jan2007-Jan2008 0.73%
Aadhaar Venture India Ltd 0.407 Dec2010-Dec2011 0.73%
Bampsl Securties Ltd 0.226 Jan2007-Jan2008 0.53%
Kohinoor Broadcast 0.300 Feb2008-Feb2009 0.01%
Gv Films Ltd 0.416 Jan2007-Jan2008 0.1%
Sanraa 0.255 Jan2007-Jan2008 0.2%
Table shows highest correlation of bottom 10 companies with volume. Under this table shows the in which time bottom 10 companies had the highest correlation .last column shows the average return of the top 10 companies here we can see the correlation is ranging between 0 ' 0.2 it means very weak and positive. On the other hand we can see the return doesn't effect on the correlation.
Figure 1.20: Highest correlation data of mid 10 companies with highest return (Volume)
Name Of Companies Highest Correlation Time Return
Dcb Bank 0.454 Jan2007-Jan2008 0.17%
Ambja Cement Ltd 0.122 Jan2007-Jan2008 0.20%
Rander 0.081 Jan2007-Jan2008 0.10%
Vakrangee 0.330 Feb2009-Dec2010 0.64%
Rssoftware Ltd 0.428 Jan2007-Jan2008 0.28%
Gic Housing 0.345 Dec2010-Dec2011 0.28%
Gangotri Iorn 0.228 Dec2010-Dec2011 0.28%
Compucom Software Ltd 0.160 Dec2010-Dec2011 0.32%
Ub Holding 0.341 Feb2009-Dec2010 0.53%
Escorts Ltd 0.403 Feb2009-Dec2010 -0.05%
Table shows highest correlation of mid 10 companies with volume of trading. Under this table shows the in which time mid 10 companies had the highest correlation .last column shows the average return of the mid 10 companies. As we can see correlation range is 0.1 to 0.4 slightly positive. And return is also not that much good.
Figure 1.21: Lowest correlation data of Top 10 companies with Lowest return (Trade)
Name Of Companies Lowest Correlation Time Return
Tata Coffee 0.146 Feb2008-Feb2009 -0.76%
Gitanjali Gems Ltd 0.188 Feb2008-2009 -0.69%
Unitdspr -0.002 Feb2008-2009 0.00%
State Bank Of India -0.068 Dec2011-2013 0.41%
Reliance -0.107 Jan2007-Jan2008 0.31%
Jet Airways 0.080 Feb2008-2009 -0.52%
Spice jet Ltd 0.283 Jan2007-Jan2008 -0.50%
Reliance capital 0.040 Feb2008-2009 -0.50%
Tata Motors -0.062 Feb2008-2009 -0.51%
Strids -0.052 Dec2011-2013 -0.51%
Fig. 1.21 shows lowest correlation of top 10 companies with number of trade. Under this table shows the in which time top 10 companies had the lowest correlation. Last column shows the average return of the top 10 company's lowest correlation is range between the -0.2 - 0 it shows the very weak and negative. On the other hand lower return with lowest correlation is going negative.
Figure 1.22: Lowest correlation data of Bottom10 companies with Lowest return (Trade)
Name Of Companies Lowest Correlation Time Return
Chandni Textile Engieering Ind. -0.077 Dec2011-2013 -0.66%
Green Fire Agri Commodities -0.015 Feb2008-2009 -0.83%
Hiran -0.128 Dec2011-2013 0.02%
Ashconiul -0.010 Dec2011-2013 -0.75%
Aadhaar Venture India Ltd -0.004 Feb2008-2009 -1.66%
Bampsl Securties Ltd -0.038 Dec2011-2013 -1.66%
Kohinoor Broadcast -0.096 Feb2009-Dec2010 -0.45%
GV Films Ltd -0.115 Dec2011-2013 -0.66%
Sanraa 0.035 Dec2011-2013 -0.66%
Sarang Chemical Ltd 0.017 Feb2008-2009 -0.66%
Fig. 1.22 shows lowest correlation of bottom 10 companies with number of trade. Under this table shows the in which time bottom 10 companies had the lowest correlation. Last column shows the average return of the bottom 10 companies. Figure 1.22 which is also showing a negative correlation with negative return in different time period.
Figure 1.23: Lowest correlation data of mid 10 companies with Lowest return (Trade)
Name Of Co. Lowest Correlation Time Return
Dcb Bank 0.219 Dec2011-2013 0.17%
Ambja Cement Ltd -0.065 Jan2007-Jan2008 0.17%
Rander -0.090 Feb2008-2009 0.17%
Vakrangee -0.111 Dec2011-2013 -0.72%
Rssoftware Ltd 0.230 Dec2010-2011 -1.00%
Gic Housing 0.111 Feb2008-2009 0.28%
Gangotri Iorn 0.089 Dec2011-2013 0.28%
Compucom Software Ltd 0.033 Feb2008-2009 -0.31%
Ub Holding 0.244 Dec2011-2013 -0.87%
Escorts Ltd 0.360 Feb2008-2009 -0.05%
Above table shows lowest correlation of mid 10 companies with number of trades. This table shows the in which time period mid 10 companies had the lowest correlation. Last column shows the average return of the mid 10 companies.
Figure 1.24: Lowest correlation data of Top 10 companies with Lowest return (Volume)
Name Of Companies Lowest Correlation Time Return
Tata Coffee 0.117 Feb2008-2009 -0.76%
Gitanjali Gems Ltd 0.077 Feb2008-2009 -0.69%
Unitdspr -0.019 Dec2010-2011 0.00%
State Bank Of India -0.331 Dec2010-2011 0.41%
Reliance -0.160 Jan2007-Jan2008 0.31%
Jet Airways 0.055 Feb2008-2009 -0.52%
Spicejet Ltd 0.052 Dec2010-2011 -0.50%
Relcapital 0.033 Feb2008-2009 -0.50%
Tata Motors -0.030 Jan2007-Jan2008 -0.51%
Strids -0.023 Dec2011-2013 -0.51%
Fig. 1.24 above table shows lowest correlation of top 10 companies with volume of trading. Under this table shows the in which time top 10 co's had the lowest correlation. Last column shows the average return of the top 10 companies.
Figure 1.25: Lowest correlation data of Bottom 10 companies with Lowest return (Volume)
Name Of Companies Lowest Correlation Time Return
Alps Industries Ltd -0.121 Dec2010-2011 -0.66%
Chandni Textile Engieering Ind. -0.060 Dec2010-2011 -0.83%
Green Fire Agri Commodities -0.066 Feb2008-2009 0.02%
Hiran -0.163 Dec2011-2013 -0.75%
Ashconiul -0.154 Dec2010-2011 -1.66%
Aadhaar Venture India Ltd -0.010 Dec2011-2013 -1.66%
Bampsl Securties Ltd -0.062 Dec2010-2011 -0.45%
Kohinoor Broadcast -0.183 Feb2009-Dec2010 -0.66%
GV Films Ltd -0.067 Dec2011-2013 -0.66%
Sanraa -0.099 Dec2011-2013 -0.66%
Fig. 1.25 shows lowest correlation of bottom 10 companies with volume. Under this table shows the in which time bottom 10 co's had the lowest correlation. Last column shows the average return of the bottom 10 companies.
Figure 1.26: Lowest correlation data of mid 10 companies with Lowest return (Volume)
Name Of Companies Lowest Correlation Time Return
Dcb Bank 0.237 Dec2011-2013 0.17%
Ambja Cement Ltd -0.018 Dec2011-2013 0.17%
Rander -0.140 Feb2008-2009 0.17%
Vakrangee -0.070 Dec2011-2013 -0.72%
Rssoftware Ltd 0.057 Dec2010-2011 -1.00%
Gic Housing 0.026 Feb2008-2009 0.28%
Gangotri Iorn 0.073 Feb2008-2009 0.28%
Compucom Software Ltd 0.028 Feb2008-2009 -0.31%
Ub Holding 0.049 Jan2007-2008 -0.87%
Escorts Ltd 0.239 Dec2010-2011 -0.05%
Fig. 1.26 shows lowest correlation of mid 10 companies with volume. Under this table shows the in which time mid 10 co's had the lowest correlation. Last column shows the average return of the mid 10 companies.
On the traditional view, some researcher examines the risk adjusted high value premium based on all firms listed on NYSE and AMEX. They use the conventional method widely used by momentum literature to test the strategy. They find statistically significant abnormal returns for high volume minus low volume portfolio for holding periods.
When investor sentiment increases, the overconfident investors under react further to the information contained in the insiders' trades and gain a greater weight on the pricing function. The rational investors cannot counteract the overconfident investors' transactions. To the extreme, when investor sentiment becomes very high, the overconfident investors dominate the market. The market is characterized with high liquidity and high trading volume. Since high sentiment leads to lower expected returns, this model provides an alternative explanation for the negative time-series relations between liquidity measures and expected returns.
More accessible markets have stronger global sentiment effects, suggesting that capital flows help the spread of sentiment across borders. For locally sourced sentiment, the core finding is of a stronger sentiment effect in a poorer environment. On the other hand, for globally sourced sentiment the opposite result is strongly evidenced, namely, that a stronger sentiment effect tends to be linked to a stronger environment. Broadly speaking, a better domestic legal and information environment is associated with weaker local and stronger global sentiment effects.
There is substantial interest in how trading volume is related to price movements in the stock return. Clearly, positive trading volume is needed to generate observed market prices. A naive view of the market is that the greater the volume of trading, the greater the price movement.
Some literature claims that the relationship between volume of trading and return movements depends on whether the market is in a bull or bear run. In a bull market, a relatively higher level of volume of trading is associated with a given stock return change in comparison to a bear market. However, these claims are anecdotal and unsubstantiated. The study also examined the stochastic dependence between transaction volume and the change in the logarithm of security price from one transaction to the next. The change in the logarithm of price can therefore be viewed as mixture of distributions, with transaction volume as the mixing variable.
We found the return, correlation, covariance, Standard deviation and Average return of top 400 companies in BSE INDEX. Then we created the 26 table companies data. We analyze that some of companies (data) correlation found that it was in negative in Feb.2008-Feb 2009(crises) time. Here Stock's illiquidity defined over the 2008-2009 time period has a positive and significant effect on the stock's after 2009. Number of trades and volume of trading with daily return is leads to the future price.
We analyze that on the correlation of Top 10, mid 10& Bottom 10 companies with higher correlation (positive). On the other hand we analyzed that the companies with higher correlation were not having that much high return and companies with low correlation were also with negative return.
Top 10, Mid 10 and bottom 10 companies as a sample in order to represent the overall scenario of market. So that investor can invest by analyzing this data. We are trying to make it easy for the practitioner, investor, institutions, buyer seller, companies and mutual fund investor to invest so that they could get more benefits.
This study explained the trading volume effect on stock returns at BSE from 2007-2013. The study adds some important findings for the existing literature as trading volume effect is proven on extensively in developed markets while little evidence on developing markets with the different indicators. This study examines the relationship between trading volume change and stock return in five stages. First contemporary relationship between number of trade and stock returns are examined and next, the relationship between number of volume and stock returns are examined.
Volatility is also reflected the market with the number of trades and return. Volatility can co-move for two reasons. First, as the number of traders grows, market prices become less volatile. Second, given the number of traders, an increase in volume reflects greater disagreement among traders and hence leads to higher volatility. This link is stronger when new information arrives at a faster rate.
Sub sample analyses show evidence of strong spillover effects after the 2007-2008 market crash and importance of trading volume as an information variable after the introduction of options in the Indian market. According to the analysis we found that the top 10, bottom 10, mid 10 companies correlation of number of trades with the daily return & volume of trading with the return is positively affected the market after the 2007-2008. The empirical shows that volatility is positively or negatively related to the number of trades & stock return with market capitalization that much of the frequency in the extremes of the differences of correlation changes can be accounted for by the level of trading volume. Similarity between number of trades with daily return and volume of trading with daily return
Minimum & maximum correlation: figure 1.1 and figure 1.2 define the contemporary correlation between volume of trading with return and number of trades with return among various companies across various industrial sectors is different. There is no particular time period where all the companies or most of the companies are showing same extreme of a correlation in any time period. In December 2011-2013 minimum contemporary correlation is 133 in figure 1.1 and in figure 1.2 is 132. And maximum correlation is 100 in figure 1.1 and in figure 1.2 is 101. This is almost similar with each other. That means number of trades with the daily return and volume of trading with the daily return in a different time period are showing the same difference.
Differences of maximum and minimum correlation: volume of trading is far than better with the daily return rather than the number of trades with return. If we consider the differences of two variables there is the difference is minor that is 0.1.which is not reflect the more fluctuation in the market. Top 10 companies in number of trades with the stock return is reflecting the correlation is 0.1 to 0.4 and in volume of trading with the stock return correlation is 0.1 to 0.5. In bottom 10 companies in number of trades with the stock return is reflecting the correlation is 0.1 to 0.4 and in volume of trading with the stock return correlation is 0.2 to 0.5. Mid 10 companies in number of trades with the stock return is reflecting the correlation is 0.0 to 0.3 and in volume of trading with the stock return correlation is 0.1 to 0.4 with the market capitalization.
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