Category: Manager

Being a good manager is more than just common sense. Managers over time have developed the need for additional skills to be successful in the workforce. While managers are very important, they are not just concerned with themselves, but also with everyone around them and the mindset they put forth. Although a manager’s achievement is commonly based on physical skills, talents, leadership skills, communication abilities, and organization in general, the ability to delegate and the ability to be anonymous are key factors that are essential to creating and making a good manager. Each of these factors doesn’t only deal with managers themselves, but also the people they work with and their overall qualities as an individual.

Having leadership skills is crucial and impeccable for management. Above all, managers need to be supportive, they set examples for all employees. As Pearce states, “Leadership styles are trusting, empowering and enabling others” (Pearce, 2019.) These traits are subfields of a manager who can express their leadership skills in a professional manner and that can represent their skills. Leadership itself relates managers to employees. By setting examples, managers can show characteristics of autonomy and self-direction. While autonomy on its own can help create a perfect and good manager. “Systems create an organizational climate wherein caring about employees’ concerns, fostering employee engagement, involvement and retention at workplace become normal but extremely powerful rituals played by the persons in the leadership roles” (Singh, 2018).

Communication remains a practice that benefits any environment and the people in it. A manager’s roles are to help and identify problems that arise within the workplace. Therefore, communication enforces reinsure-meant to employees that they are excelling in their tasks at hand. Indeed, being able to apply openly and feel that they’ve been heard encourages them, this is supported by Cyphert, Dodge, and Duclos’s article (2016): “Communication managerial applications require an ability to encourage and develop others”. The authors here are implying the importance of communication and the context it should be used in. While a respectable manager will have no problem applying their thoughts in a conversation, others can feel overwhelmed, which can be expressed negatively.

Employees are a company’s greatest asset. Assisting employees in being organized and having an organized mindset will help run a company smoothly and efficiently. The idea of organization is not just being clean and being able to know where everything and items are, organization is a way of practice and development, to be arranged in an orderly and logical way that benefits a company. Managers can create a climate and can lead by example by creating objectives and purpose described here: “An organization of people is intended to serve some human purpose and operates under the internal environmental influences of individual and group values and the needs of people within the organization” (Barber & Taylor, 1990). Consequently, having a specific organization of individuals can also create a theme for external controls that managers appreciate and recognize. Using resources that are currently and already present in a company can improve the organization within a company and its team members. Additionally, as something so raw and present in everyday life like an organization, it creates significant changes and improvements in the overall interactions between employees and managers. This is though in the certainty that managers are able to delegate their priorities and manage reality while completing their tasks.

The ability to delegate is the ability to assign responsibilities and encourage others with a successful work ethic. A good manager can encourage employees to make the right decisions by giving them freedom. Additionally, delegation in itself is not depending on others, it’s your ability to use others’ knowledge but to allow yourself to instruct what the next step might be and how to achieve it properly. Although many managers succeed in the ability others get baffled on how to help others, explained here, “many practice owners and managers don’t delegate effectively. You can’t expect someone to do a job if you haven’t outlined what you want him or her to do” (Opperman, 2006). Though this may seem easy there are factors, involving resources and their scarcity that increase the chance of an overall win. While the real reason to increase and improve the workload is to properly delegate employees, Johnston explains the reason why delegation is required, “appropriate delegation of decision-making results in an increase in productivity and efficiency” (Johnston, 2000). As a result, managers and employees benefit in such a way that implicates the need for the ability to delegate to be a key factor in constructing a good manager.

In conclusion, there are many factors that make a good manager, the most important of which have been described in this essay.

References

1. Barber, W. E., & Taylor, J. N. (1990). The Importance of Goals, Objectives, and Values in the Fisheries Management Process and Organization: A Review. North American Journal of Fisheries Management, 10(4), 365–373.
2. Cyphert, D., Dodge, E. N., & (Wilson), L. K. D. (2016). Developing Communication Management Skills. Business and Professional Communication Quarterly, 79(4), 416–441.
3. Johnston, M. A. (2000). Delegation and Organizational Structure in Small Businesses. Group & Organization Management, 25(1), 4–21.
4. Pearce, L. (2019). Leadership Needs to Come from All Levels to Make a Great Workplace. Nursing Standard, 34(6), 55–57.
5. Singh, S. K. (2018). Sustainable People, Process and Organization Management in Emerging Markets. Benchmarking: An International Journal, 25(3), 774–776.

Financial management can either help solve problems or it can create problems. From the moment a new business begins, it faces risks that must be managed and guided through the use of finance. Financial managers have the ultimate task of maximizing a firm’s value for its owners. This can be successfully fulfilled through financial managers that keep in mind both short-term and long-term consequences of each decision made and action taken. How do financial managers add value daily? They forecast, budget, raise funds, spend funds, and set financial goals that are in line with the business owners. Because financial managers have the strongest grasp on a company’s finances, they have the greater purpose of monitoring and maintaining the company’s financial health, by making legal and ethical financial decisions that will add value.

First, financial managers must solve problems. Financial managers can solve problems within a company by identifying problems, creating plans, implementing plans, then evaluating the consequences. These unknown consequences are referred to as risk, which can be defined as the potential for unexpected events that occur. Risk can never be entirely avoided because all companies are directly or indirectly affected, therefore, financial managers must try to find ways to reduce risk as part of the problem-solving process. Companies can reduce risk to a certain degree by reducing the volatility of fixed costs, obtaining insurance policies to protect against risk, and diversifying portfolios. Financial risk happens when companies borrow money and incur interest charges, which ultimately affects net income. Higher levels of financial risk can mean that there is a greater possibility of not being able to meet present and future financial obligations. It is up to financial managers to solve the problem of paying all obligations, both legally and ethically, through the proper management of risk.

Secondly, financial managers must define and maintain a financial strategy that involves a proper inflow of capital to continue the addition of value over the long term. This can be done through budgeting for capital projects. Because capital budgeting also involves risk, financial managers must determine how certain projects affect assets and how to compensate for degrees of risk. Financial managers must carefully allocate resources by maintaining proper estimations, timing, and predictability. Return on invested capital is just one way that financial managers measure a company’s efficiency to ensure that they are adding value.

Thirdly, financial managers must make choices about sources and allocations of funds. In order to grow and continue adding value to a company, financial managers must always decide what to do if the company is unable to meet financial obligations. Because risk is involved, managers must carefully consider financing costs, potential returns, investments, short-term versus long-term, and the issuing of shares or not. Not only does the finance manager have to plan, procure and utilize funds, but exercising control over those funds is part of their job. This is done through ratio analysis, fund analysis, financial forecasting, and cost and budget controls. All of these decisions can add or take away value from a company, so financial managers have a greater responsibility to ensure that legal and ethical choices about funds are being made.

Fourthly, finance managers have to make decisions with regard to cash management. Cash is required for paying wages and salaries, paying bills, paying creditors, meeting current liabilities, and maintaining enough stock within a company. Financial managers must determine present and future cash needs through the development of a cash budget. A cash budget is a detailed plan that shows where cash is coming and going for a specified period. Having a maximum cash balance enables companies to invest and earn even more cash so they can add value. Financial managers have an especially difficult job in the area of gaining cash since they have to be careful not to be so greedy that they forget to maintain legal and ethical standards.

Finally, financial managers have the greatest responsibility of all to ensure that all decisions and actions are performed in the utmost legal and ethical manner. All public corporations are required to follow strict guidelines when preparing taxes and financial statements. These guidelines were put into place to regulate financial and accounting practices and to ensure transparency from one organization to another.

What are some of the ethical issues involved in financial management? Ethics has become such a large part of the corporate world that most companies now have a code of ethics by which they operate. Ethics is the moral principles that guide a company’s behaviors and actions. They are values, or worth, the very thing that financial managers should be adding to companies. Value is added by being creative and proactive to bring new ideas to a company. So why does there appear to be creative resistance in corporate America when financial managers bring about change? Goetzmann, author of ‘Money Changes Everything: How Finance Made Civilization Possible’, explains that finance is historical and that financial innovations have been the driving force behind all civilizations. Goetzmann explains that finance is at the heart of almost everything, and has the power to change values, ethics, social class, goals, and self-esteem. Change is a concept that is not always accepted, but often needed. In Goetzmann’s book, he explains that in order to understand finance, one must look at history to study the building blocks of civilization. Goetzmann suggests that finance is an important technological tool that helped and continues to help, society advances through constant changes. The more society expands, the more finance is needed to keep expanding. He explains that finance is a type of ‘cycle’ that continually repeats itself through civilization. He summarizes the four key elements of finance as follows: reallocating value through time, reallocating risk, reallocating capital, and expanding those reallocations. Unfortunately, negative stereotypes of finance have emerged through civilization from corruption and failed choices caused by poor financial practices. Goetzmann suggests that studying finance is important to avoid repeating past mistakes in present and future civilizations. One historical finance idea worth analyzing is the invention of derivatives.

The video ‘Money, Power, and Wall Street’ sheds light on the origination, growth, and failures of credit derivatives, a powerful financial tool created in the early 1990s. In a quest to reduce risk, employees at JPMorgan created a new concept to swap risk by getting rid of risks not wanted and trading it for risk that was preferred. Though no longer new, credit derivatives are still a source of debate among financial managers today. Determining whether derivatives are legal or ethical requires a closer look at the results, and examining statements made by employees that worked directly with derivatives in their beginning phase.

One woman interviewed for the video was Kathy, a Yale-educated woman who felt the American goal was just to get rich. After working on Wall Street for several years, Kathy explained that although her company was getting rich, she could no longer continue working with derivatives because it felt immoral. Alexis, another woman interviewed, explained that she had no problem working with derivatives in their conception because what was happening in the future had no effect on the present value of the enormous amount of money she was obtaining. As one textbook explains, a dollar in hand today is worth more than a promise of a dollar tomorrow. Due to the lack of regulation and ethical decisions, these leveraged bets, or derivatives, eventually began to get out of hand. What was the driving force behind the success of this new idea? Money, power, and greed appeared to be the driving force. If proprietary bankers were doing their job of making legal and ethical financial decisions, why did sellers and traders of derivatives flee the country? They fled to avoid regulation which could possibly expose unethical practices. As Desiree Fixler stated in the video, one cannot sell debt created from debt, because it eventually crashes. That is what eventually happened to the U.S. economy in 2008. Fixler concluded that most of the derivatives were made up and sold for huge profits to unsuspecting victims that were being taken advantage of, simply because the incentive to cheat was so high. The economic crisis of 2008 began with defaults on mortgages that were based on speculation, not ethical decisions. Most parties involved with historic corruption ignored ethics and the time-bound value of finance where large risks and gains were involved. Poor financial decisions and a disregard for ethics led to unfortunate consequences with regard to the creation of derivatives.

Risk has been around for a very long time, and companies will almost always demand a higher rate of return to compensate for assuming risk. The riskier the project, the higher the return demanded. When investors adjust rates of return to compensate for risk, the question becomes, how much is too much? Since risk is difficult to measure, financial managers are needed to ethically determine rates of return, to add value and still retain a moral conscience. In the historic case of derivatives, most of those interviewed in the video felt that in the beginning derivatives were a great thing, but eventually greed caused them to grow into something destructive. While negotiating is a powerful tool that can make financial management easier, conflict and difficulties within those negotiations can cause breakdowns that destroy. Managing risk is a very important responsibility of financial managers to ensure that value is added to a company.

Financial management ultimately comes down to an ethical responsibility for every company. Financial managers face the important task of implementing accuracy, transparency, timeliness, and integrity in all their actions. As seen in the video ‘Money, Power, and Wall Street’ and throughout history, not all financial innovations and decisions are good ones. Was the creation of derivatives good? That is still worth debating. Derivatives, like any other aspect of finance, are powerful. Powerful tools are not necessarily bad, they just need to be monitored and controlled to make them as effective and ethical as possible, so the outcome yields value. A better question that financial managers might ask themselves each day is: are these decisions just good, or do they have good results? As financial managers, the responsibility and reward of adding value to a company come from making decisions that lead to good results. If the results are not good, a re-evaluation of the situation, more problem-solving, strategizing, and allocation assessing are necessary to ensure that the ultimate goal of adding value to the company occurs. Because financial managers have the strongest grasp on a company’s finances, they have the greater purpose of monitoring and maintaining the company’s financial health by making legal and ethical financial decisions that will add value.

A manager may be one that administers to associate organization by allocating resources amongst subordinate levels. The term ‘effective’ suggests that one is self-made in manufacturing desired or meant results. Effective managers communicate job expectations through appraisals, develop personal growth opportunities, and build a legal work surrounding for workers. Organizations want effective managers to realize their goals because the modern geographical point is various, comprehensive, and evolving. The question arises, how to be an effective manager?

Firstly, as leaders, managers need strong people skills, allowing them to act as coaches. A skill like good communication is good as the ability to get along with others, persuade others, get others to listen to your ideas, and the clarity of what you say. Managers should know themselves well enough to detect and work around their blind spots as they seek to build teams. Another thing about strong people skills is trust. Being a manager is all about trust. You must trust your team members have the business’s best interest at heart. You must trust that they will work together to complete any task that comes their way. And finally, you must trust that all of this will happen without your constant supervision. This can be seen in successful managers. As such, I strongly think that managers need to develop strong people skills in order to be effective.

Secondly, “self-management should be the manager’s number-one priority”, says consultant and executive management coach Lisa Baker. “It is important for a manager to understand how his capability for self-management impacts either positively or negatively on his abilities to manage his role, function and build relationships”. Without effective self-management, you will find it difficult to assess and manage your own workload well enough to effectively delegate the right work to your team, which could lead them to feel micromanaged or that you’re not empowering or developing them. Without effective self-management, you could lose credibility and your team’s respect as it will be difficult for them to take seriously any instruction, direction, leadership, or (heaven forbid) correction from someone who can’t apply the same principles to his or her own initiatives or work habits. Managers must know themselves, their values, strengths, weaknesses, and personal desires. Without an honest assessment of these things, it will be difficult to give real purpose and meaning to your work. So, to be an effective manager, it is important to learn to manage stress and conflict in order to achieve the emotional stability necessary to function well in all areas of life, not just on the job.

And finally, communication. Managers need to spend more time talking to others informally if they really wish to find out how others really feel, for example, about the project, the project manager, or other team members. To just talk and communicate well is not sufficient. Active listening is part of effective communication. Managers need to make more time to listen carefully to what others have to say. They need to use techniques such as paraphrasing to play back the message received to ensure the message sent equals the message received more effectively by becoming more people competent and by displaying appropriate behaviors that go with these. Managers of the future need to understand their company culture better and apply this knowledge through good people project management to achieve better results. Effective managers need to bring people together who do not want to work together. They need to become skilled negotiators, hard on issues, and soft on people. They need to show their own behavior in a caliber and quality that others can respect. Managers need to have better training in skills in working with people so they understand the people side better and communicate to others more effectively what project management is all about. They need to understand much better how people are made up, for example, how they might react to the project manager’s approaches and how they themselves need to change their approaches towards people. Good managers need to have good effective communication skills with people at all levels of the organization. For communication, managers must also have good relationships with coworkers, patients, competitors, distributors, investors, and others. Relationships offer opportunities for mutual growth, collaboration, innovation, information sharing, and new business development, but they also pose challenges in these respects. The distinguishing factor between a successful manager and an ineffective leader is the ability to effectively manage relationships, so this skill should be developed.

Summing up all of the above, in order to be an effective manager, one should develop strong people skills, self-management skills, and effective communication skills.

1. Introduction

Management is the process of getting things done through people as defined by Mary Parker (1941). Henri Fayol (1916) identified the elements of management which are to forecast and plan, organise, command, coordinate and control.

The key objective of this report was to interview two different managers to examine the extent to which the career paths of these two people whether conform with the concept of the managerial escalator. The first section of the report focuses on background theory and a brief description of the necessary academic and literary terminology. The second part of the report will show summarised findings of the two interviews, including drawn results and facts stated. Following this, discussions on the two managers will further expand on the findings, including clear analysis, comparison with previous research, methods used to obtain data and the shortcomings of the research and methodology. The final part of the report includes a conclusion which discusses whether the research questions and objectives have been answered and achieved.

2. Theory

This section of the report shows a brief explanation of the main concepts and terminology used in the research and discussion.

2.1 Managerial Escalator

“The concept of the managerial escalator is used to explain how specialists become managers” (Rees and Porter, 2015, p5). A specialist may be promoted as a store manager based on their level of competence over the years. Most employees in different organisations tend to make their way up the escalator. A store manager now can be promoted as an area manager in the next five years. This escalator-type progression is very common. (Rees and Porter, 2015)

2.2 Managerial Hybrid

In some situations, people are likely to combine specialist activities while performing managerial responsibilities. Managers and employees who are involved in both managerial and specialist activities are called Managerial Hybrids. (Rees and Porter, 2015)

2.3 Managerial Gap

According to Rees and Porter (2015), the managerial gap is defined as the difference between the amount of time that a person should be spending on managerial activities and the amount of time spent.

2.4 Remedial Strategies

Remedial strategies are crucial in order to deal with the problem of poor managerial performance. They involve dealing with four fundamental causes of ineffective performance which are:

1. Role definition: One of the integral parts of remedial strategies is an accurate role definition. It is necessary to clearly state the managerial aspects of the job for both applicants and those making appointments.
2. Managerial Selection: This is one of the main aspects of why the wrong people are given managerial responsibility. Two key mistakes are considered in this section: 1) failure to recognise the managerial element in jobs and 2) general selection incompetence. (Rees and Porter, 2015)
3. Training and development of managers: This is one of the key strategies in dealing with the problem of poor management. There has been an increase in management training. However, increases in the quantity of training do not always ensure adequate training.
4. Monitoring: This is one of the four key strategies. This applies whether the manager performance is being reviewed. it is important, people who are given managerial responsibilities are also provided help with coaching, appraisals and counselling. (Rees and Porter, 2015)

3. Findings

This section of the report focuses on the findings of two managers interviewed in two different organisations.

3.1 Findings Manager 1

The first person to be interviewed is a Shift Manager at a fast-food restaurant called Roosters Piri Piri. She started as a team member two years ago, working part-time hours alongside studies. Initially, she was trained to be a team member which helped her to carry out regular tasks throughout the day. After six months, one of the shift managers had to leave due to personal problems. To fill out the position quickly, she was approached by the owner of the company to take on the role of the shift manager. As part of her job role, she is responsible for supervising restaurant staff. She is required to deal with customer complaints regularly, providing them with solutions. Other main managerial responsibilities include ensuring store cleanliness and a neat appearance of the public areas both in and out of the store, controlling and monitoring staff labour according to sales and reconciling at the end of a shift; verifying that correct change was given out by the staff.

Alongside her managerial responsibilities, she carries out daily activities such as taking orders through tills as well as over the phone. Some of the other regular tasks include working in the kitchen cooking food, sending out food deliveries to assigned drivers, assisting in preparing and packing food and beverages at the front.

3.2 Findings Manager 2

The second person to be interviewed was a store manager at Domino’s. He has been working in Domino’s for nearly four years. He started as a full-time team member. One year ago, on being recommended by one of the area managers, he applied for the role of the store manager and successfully got accepted. Before attaining the managerial role, he was given on-job training from a few different area managers, which was related to motivating teams and achieving sales targets. Being the manager in one of the leading pizza companies requires tons of multitasking. When asked about the responsibilities, he said no station in the store can be beyond the manager’s ability. Some key responsibility are cooking. This includes making dough and pizzas, cutting and boxing products, and overseeing total quality control for all the food that is being served. In terms of financial aspects, it is crucial that a manager keeps tight controls. He manages labour control; product costs control and waste control. Additionally, he is also held accountable if there is damage, theft or shortages in cash.

He stated most of the time as a manager goes toward meeting sales targets and ensuring labour control. He enjoys working with different kinds of people from around the world as well as motivating them to perform to their best of ability. Furthermore, he said he enjoys being in his current role as it grants him a sense of power. Lastly, he expressed a desire to go higher up in the role in future such as an area manager and build a career at Dominos.

4. Discussions

The next part of this report will discuss the findings of the two managers that were interviewed and how these findings fit in and relate with the concept of Managerial Escalator.

4.1 Discussion Manager 1

The findings of Manager 1 revealed that before her current role as the shift manager, she was a part-time team member in the same company. Shortly after, due to unforeseen circumstances, she was asked to take on the role of the shift manager. This aligns with the concept of Managerial Escalator. Rees and Porter (2015) suggest it is very likely for a specialist to be promoted to a higher level after a certain duration of competent performance. Manager 1 stated that due to her dedication and enthusiasm to her daily tasks in the company, she was appreciated, therefore was assigned the role of manager. This is considered an aspect of managerial selection when staff is appointed as managers merely based on historical performance as specialists. Fortunately, manager 1 did not find any difficulties in coping with managerial responsibilities. She was accompanied by one of the managers in her first few shifts who briefly trained her with important managerial tasks.

Further in the interview, it was revealed she spent 50% of her time undertaking managerial responsibilities such as supervising and training staff as well as dealing with regular complaints from customers. Another 50% of her time, she is carrying out specialist activities such as cooking, serving customers at the front and taking phone orders. When asked whether she has enough time for managerial activities she said due to the small size of the company, she can deal with both specialists and managerial responsibilities simultaneously. This suggests there is no managerial gap for manager 1 as she can work around the clock effectively and efficiently. Furthermore, this finding revealed that she must combine her specialist’s tasks with managerial responsibilities most of the time at work; this suggests she acts as a managerial hybrid. It is clear from her situation that a person can be involved in both specialist and managerial activities.

In terms of monitoring, due to a small hierarchy structure, there is not enough monitoring that takes place. However, the owner keeps an eye on of all the complaints and any other problems that occur during her shift. A key remedial strategy of monitoring applies here, which helps to correct the imbalance in the job. Through monitoring, weak areas are identified which is trained for later.

Towards the end of the interview, it was revealed that manager 1 would continue in this job role until she graduates. She felt that she had developed various skills as a manager by dealing with numerous challenges and responsibilities. She believes this will help her in the short-term and long-term success. Additionally, she expressed a desire for establishing her own company or buying one of the franchises once she graduates. This suggests that manager 1 is aimed to carry along the managerial escalator in the future and may come to an end with all her time on the managerial side of the axis. (Rees and Porter, 2015)

4.2 Discussion Manager 2

The findings of the second manager revealed he has been working in the company for nearly five years and just a year ago he applied for the position of store manager which he successfully got accepted for. This finding aligns with the concept of the managerial escalator as he escalated from the role of the specialist to the store manager. Additionally, he had no previous experience working as a manager in the past. However, he received formal training by area managers as well as went through different on job training programmes within the company. The training provided helps prevent any problems that a specialist might face as a manager.

Furthermore, it was revealed that manager 2 spends 60% of the time on managerial activities that include controlling costs on a day-to-day basis to improve profitability. Addition to, he motivates the team to improve efficiency throughout the store to achieve company’s high standards. He maintains a strong brand image and service standards including uniform/presentation standards and philosophy on customer service and always maintaining and developing product quality. He claims to spend 40% of his time on specialists’ activities. When asked whether he is happy with the time he spends on managerial activities, he said he would like to focus a bit more on managerial aspects of his job and spend maybe 80% of his time undertaking managerial responsibilities. This indicates that the managerial gap for manager 2 is 20% due to the imbalance between his specialists and managerial activities. However, he further stated he is happy to deal with both managerial and specialists as it helps to develop a strong working relationship. This relates to the theory of managerial hybrid when managers need to step in as specialists.

When asked about what he is willing to do in future, he expressed a desire to stay in his current position as a store manager as it will help to expand on his knowledge and skills. He further stated that if an opportunity arises, he would apply for the role of area manager and try his best to go higher up in the company.

Fund flow

To test whether external product markets play an active role in responding to changing in managerial structure, one way is to examine whether shareholders redirect their money away from fund family, reflecting through fund flow measurement. We analyze two measures of net flow. The first measure is the net percentage flow, scales net flows by the total net assets in year t-1 and can be interpreted as an asset growth rate net of appreciation. While most previous papers in the mutual fund flow-performance literature have analyzed only percentage flows, we also focus the dollar measure. We focus on dollar flows for three reasons. First, it is the response of investors to performance (or lack thereof), not the percentage increase in fund size, in which we are interested. Fund manager incentives should be driven by dollar flows and not percentage increases in fund size. Second, the use of dollar flows is more amenable to calculation of elasticities of fund flows with respect to performance. Third, the autocorrelation of fund flows is more directly investigated without the confounding effects of autocorrelation in assets that is present using percentages. The second measure annual net dollar flow in or out of a fund, defined as the annual changes in total net assets minus appreciation. These two net manager flow measures can be viewed as the aggregation of allocation decisions of all the manager’s clients.

Fund flow percentage measure

Regarding to the first fund flow measurement utilising existing data available on the Morningstar direct platform, most flow data are reported as total assets of the fund at the end of the year, these figures contain nuisance in calculating our targeted key variable net inflow/outflow when the returns generated by the mutual fund manager(s) during the year are also part of the component of empirical analysis variables. Therefore I follow most of the previous literatures examining the relationship between fund flows and performance in mitigating nuisance variables, including Jain and Wu (2000); Patel et al. (1994); Sirri and Tufano (1998), where net flow is defined as the net growth in fund assets beyond reinvested dividends. Formally, it is calculated as,

1. NetFlow,t = [TNAi,t – TNAi,t-1* (1+Ri,t)]/ TNAi,t-1, (1)

Where TNA is the year end t’s total net asset of fund I, Rit is the fund i’s return during previous year t. The assumption is that all investor earnings will be reinvested and there is no new fund inflow. Based on this formula, the target variable NETFLOWit is derived which reflects the percentage growth of a fund due to new investment.

Fund flow dollar value measure

We follow dollar value fund flow measure of Fant and O’Neal (2000)’ paper which takes into account all new money invested and any reinvested distributions and is measured in dollars.

1. 〖FLOW〗_(i,t)=〖EA〗_(i,t)- 〖BA〗_(i,t) (〖ENAV〗_(i,t)/〖BNAV〗_(i,t) )

Where

• EA = ending net assets;
• BA = beginning net assets;
• ENAV = ending net asset value; and
• BNAV = beginning net asset value.

Fund flow-performance relationship

The baseline regression model for testing the fund flow performance relationship is to determine whether fund flows are simultaneously a function of their own returns. In this paper, we examine this question mainly in the context of lagged linkages. Prior studies find both fund age and size have a significant impact on flow-performance sensitivity (Chevalier & Ellison, 1997; Jain & Wu, 2000). Size, for example, is important in the flow relationship because larger funds are more likely to attract larger funds. Thus, fund size will correlate for any scale effects induced by the fund flow measure. Similarly, the older, more established funds have a greater potential to attract money flow than do newer funds. Therefore, the age of fund is included in our model. We follow Wang et al. (2015)’s paper and ensure that the results are not biased by age and size factors. Expense has also been incorporated in the literature (Barber et al. 2005; Gil-Bazo and Ruiz-Verdú 2009; Huang et al. 2007); A fund with a higher expense ratio, ceteris paribus, is less attractive to investors compared to that of lower fees. Huang et al. (2007) also consider the effect of fund volatility on money flows. The derivation of fund size, fund age, and expense ratio is very straightforward, however, we need to consider the effect from a market perspective and incorporate the return on the U.S. equity market as a measure for volatility.

Volatility measure

To measure volatility, we construct the standard deviation of monthly excess returns or raw returns in the 12-month prior to each quarter t (based on past 12 months):

1. σ_(i.t)= √(∑_(i=1)^T▒〖(r_(i,t)-μ_(i,t))〗^2 ) (2)

Where ri,t denotes standard deviation for fund i for each quarter t. ri,t represents monthly raw return or excess return for fund I for the previous 12 months, and〖 μ〗_(i,t) denotes the average raw return/excess return of fund i during the past 12 months.

Three performance measure

There are many issues that surface when deciding on a set of performance measures to study. The performance evaluation literature is large, and there is considerable debate on as to which measures are most appropriate. Therefore, to provide a more comprehensive approach for fund performance, we use three different measures, namely raw return, the objective-adjusted returns using the capital asset pricing model (CAPM) as the underlying model, and Carhart (1997)’s four-factor model.

Raw return is the most commonly adopted measure of performance. (Benson et al. 2010; Chevalier and Ellison 1997; O’Neal 2004; Sirri and Tufano 1998). We likewise consider this measure because most investors view it as the primary performance measure. The monthly raw return is adjusted for dividend distribution and retrieved from Morningstar Direct database. For each fund in month t, we calculate the fund’s moving average in the past 12 months and use it as a proxy for fund i’s performance in the past year.

The objective-adjusted measure the abnormal performance of a fund relative to the mean performance of other funds within the corresponding investment objective. We adopt Jun (2014)’s method and control for risk differentials across the respective funds by using the capital asset pricing model (CAPM). Therefore, we perform market model regression using fund’s return for the past 24 months to estimate the model parameter α and β. Specifically:

1. R_it – R_ft= α + β_i (R_mt – R_ft) + ε_it, (3)

Where Rit is the ith fund’s raw return in month t, Rft is the risk-free rate (i.e. the one-year interest rate for certified deposits in the U.S.) in month t. and Rmt is the monthly return of the U.S. stock market composite index after dividend distribution adjustment. This regression can be further interpreted that Rit – Rft is he excess return on the security and Rmt – Rft is the market premium. After deriving the target variable α, which is the excess risk-adjusted return (commonly referred to as Jensen’s α), the next step is to compute the monthly risk-adjusted return. Formally, it is calculated as:

1. α_it^CAPM=α_i+e_it (4)

The risk-adjusted abnormal return of the CAPM model can be calculated as the moving average of α_it^CAPM for the past 12 months.

The third measure is the risk-adjusted abnormal return using Carhart’ (1997)’ four-factor model. A number of factors have been shown to influence a fund’s cross-sectional variation in performance, including the fund portfolio s exposure to a market (β) factor, momentum factor, size factor, and market-to-book ratio. Carhart’s methodology is standard for mutual fund studies and incorporates these factors into the performance analysis in order to compute a fund’s risk-adjusted alpha:

1. Rit = αit + bit RMRFt + sit SMBt + hitHMLt + pitPR1YRt+ eit (5)

Where Rit is the fund return in excess of the one-month T-bill return; RMRF is the value-weighted return on aggregated market index of all NYSE, AMX, and NASDAQUE firms over risk-free rate benchmark; SMB (small minus big) is the excess returns for small stock portfolios over that for big stock portfolios, ceteris paribus (e.g. weighted average book-to-market equity); HML (high minus low) is the return differences between high and low book-to-market equity portfolios. PRYR is the momentum factor calculated in Carhart (1997), defining as the difference between the equal-weighted average of firms with the lowest 30% 11-month return lagged one month and the one month lagged equal-weighted average of firms with the highest 30% 11-month return. Specifically, in each month t, we estimate αit, bit, sit, hit, and pit for each fund using the returns over the past 24 months. We then compute the monthly risk-adjusted return of the Carhart’ (1997)’ four-factor model as:

1. α_it^Ch=c_i+ μ_it (6)

The final step is to compute the average of α_it^Ch for the past 12 months.

Previous literature documents that mutual fund flow reacts to past performance in an asymmetric way, customers invest disproportionately in funds with higher performance in the previous period. To provide an overview of the flow-performance relationship, we follow Ippolito (1992)’ paper and rank each fund at the beginning of quarter t based on the average three performance measures in the past 12 months. Each fund is assigned a ranking score from zero for the funds in the worst-performing group to 1 for the funds in the best-performing group. The three segments divide the fund universe in each year by ranked return into the top quintile (HIGH), the middle three quintiles (MID), and the bottom quintile (LOW):

1. LOW=min(〖Rank〗_(t-1),0.2)
2. MID=min(〖Rank〗_(t-1)- LOW,0.6)
3. HIGH=max⁡〖(0,〖Rank〗_(t-1),-0.8)〗

Flows are regressed on performance ranking in the low, medium, and high performance ranged using control variables in regression models.

1. 〖Netflow〗_it= α+ β_1 〖TEAM〗_(i,t-1)+β_2 σ_(i.t-1)+β_3 〖TE〗_(i,t-1)+β_4 〖FL〗_(i,t-1)+β_5 〖BL〗_(i,t-1)+β_6 B_(i,t-1)+β_7 LOW+β_8 MID+β_9 HIGH+β_10 〖Log(TNA〗_(i,t-1))+β_11 〖Log(Age〗_(i,t-1))+β_12 〖Log(Familysize〗_(i,t-1))+〖β_13 V_(i,t-1) P_(i,t-1) 〖VM〗_(i,t-1)+β_14 V_(i,t-1) P_(i,t-1) 〖VH〗_(i,t-1)+β_15 〖FEE〗_(i,t-1) P_(i,t-1)+ε〗_(i,t) (7)

The explanatory variables are defined as: Net flowi,t: Net flows into fund i at quarter t, calculated from Equation (1), Vi,t-1: Riskiness or volatility of fund i at quarter t-1, obtained from Equation (2). TEi,t-1: Total expense ratio incurred for fund I at quarter t-1, which is ratio of total investment that investors pay for the fund operating expenses, FLi,t-1:Fundi’s front-end load at quarter t-1, BLi,t-1:Fundi’s back-end load at quarter t-1, Bi,t-1: Fundi’s 12b_1 expense at quarter t-1, LOW, MID, HIGH: Performance ranks, each quintile captures funds with the corresponding to weighted average of three performance measures as defined earlier this section, Log (TNAi,t-1): Control variable, logarithm of fundi’s size at quarter t-1, Log(Agei,t-1): Control variable, logarithm of fundi’s age at quarter t-1, Log(Familysizei,t − 1) is the log transformation of total net assets under management in the fund family to which the ith fund belongs at the end of quarter t − 1, excluding the net assets of the ith fund; α and ε_(i,t): Constant and residual terms, respectively. Pi,t-1: Fund i’s quarterly excess return or raw return at quarter t-1. All fund and manager controls are lagged by 1 period to exclude their potentially current effect on fund flows.

Sample funds are ranked into three volatility ranks: low (VL), volatility (VM) and high (VH), based on their riskiness calculated using Equation (2). VL captures the bottom third, and VM and VH the middle and top thirds. The volatility rank takes the value of 1 if the fund falls in that rank, 0 otherwise. Two interaction terms between volatility, past quarterly performance and volatility rank dummies: (V_(i,t-1) P_(i,t-1) 〖VM〗_(i,t-1)and V_(i,t-1) P_(i,t-1) 〖VH〗_(i,t-1)) are included. FEEi,t-1:Fundi’s fee charges, which includes of front-end load (FLi,t-1), back-end load (BLi,t-1), 12b_1 expense (Bi,t-1) and pure operating expense (Oi,t-1).FEEi,t-1 represents quantitative variables.

To fulfill a more comprehensive understanding of managerial structure’s effect on fund flow, we hypothesize that team size are also important to fund flow. This conjecture is inspired by Patel and Sarkissian (2017)’s paper, which find that that largest gains in risk-adjusted performance are observed among funds with 3 managers. Therefore, we run the following regression model with a more, substituting team dummy variable with more specific team size:

1. 〖Netflow〗_it= α+ β_1 〖2FM〗_(i,t-1)+β_2 〖3FM〗_(i,t-1)+β_3 〖4FM〗_(i,t-1)+β_4 〖5FM〗_(i,t-1)+β_5 V_(i,t-1)+β_6 〖TE〗_(i,t-1)+β_7 〖FL〗_(i,t-1)+β_8 〖BL〗_(i,t-1)+β_9 B_(i,t-1)+β_10 LOW+β_11 MID+β_12 HIGH+β_13 〖Log(TNA〗_(i,t-1))+β_14 〖Log(Age〗_(i,t-1))+β_15 〖Log(Familysize〗_(i,t-1))+〖β_16 V_(i,t-1) P_(i,t-1) 〖VM〗_(i,t-1)+β_17 V_(i,t-1) P_(i,t-1) 〖VH〗_(i,t-1)+β_n 〖FEE〗_(i,t-1) P_(i,t-1)+ε〗_(i,t) (8)

where 2FMi,t-1 , 3FMi,t-1 , 4FMi,t-1 , and 5FMi,t-1 are dummies that equal 1 if the fund has 2 managers, 3 managers, 4 managers, and 5 or more managers, respectively, at the end of the previous calendar year and 0 otherwise. Other variables are defined as before.

Institutional investor and retail investor (optional)

Previous studies also show that the money flows experienced by retail and institutional funds are likely to vary because of the different markets these sectors serve. Karceski and James (2002) find that while a difference in the performance of retail and institutional funds exists, institutional fund flows are less sensitive to performance than are retail fund flows. Further, they find that the lack of a flow-performance linkage in the institutional funds can be explained by the more sophisticated performance measures that these groups of investors implement. Given that institutional investors are thought to be more professional, experienced, and sophisticated than retail investors, we expect the former to be less likely fooled by performance manipulation. The following equation shows the basic model with manipulation dummy and institutional holdings proportion:

1. 〖Netflow〗_it= α+ β_1 〖TEAM〗_(i,t-1)+β_2 〖TEAM〗_(i,t-1)*〖Lnshld〗_(i,t-1)+β_3 V_(i,t-1)+β_4 〖TE〗_(i,t-1)+β_5 〖FL〗_(i,t-1)+β_6 〖BL〗_(i,t-1)+β_7 B_(i,t-1)+β_8 LOW+β_9 MID+β_10 HIGH+β_11 〖Log(TNA〗_(i,t-1))+β_12 〖Log(Age〗_(i,t-1))+β_13 〖Log(Familysize〗_(i,t-1))+〖β_14 V_(i,t-1) P_(i,t-1) 〖VM〗_(i,t-1)+β_15 V_(i,t-1) P_(i,t-1) 〖VH〗_(i,t-1)+β_16 〖FEE〗_(i,t-1) P_(i,t-1)+ε〗_(i,t)

where 〖Lnshld〗_(i,t-1) is the proportion of institutional holdings of fund i at time t-1. We further incorporate 〖2FM〗_(i,t-1),〖3FM〗_(i,t-1),〖4FM〗_(i,t-1),5〖FM〗_(i,t-1) to replace 〖TEAM〗_(i,t-1).

Managerial structure

Fund complex structure

This figure depicts a standardized mutual fund complex system. Within this governance structure, two relationships of decision delegation are prominently displayed. First, portfolio managers are assigned by the management company under the delegation of shareholders. Second, portfolio managers make decision on the composition of investment portfolios under the delegation of management company. Under these two channels, management companies or fund families may choose single or multiple managers in responsible for the portfolio choice decisions.

Risk-taking behaviour: Single manager versus multiple managers

Besides the possible reason for the employment of multiple managers to provide a stable management (e.g. if a manager A leaves the job, manager B can still run the fund), the primary motive for employing more than one manager is to make the portfolio choice decision diversified over the style and judgment of the managers. The diversification of style are most commonly exhibited in team management – where there are multiple individuals who manage the fund together and this style refers that a fund is divided into several sub-accounts that are allocated to different managers who manage their sub-accounts independently. The final investment decision is the result of the aggregation of analyses of the management team rather than of a single manager. Often, it is hard to distinguish between the diversification of style and judgment. For instance, in team management, each team member might specialize in specific sectors so their decisions may be relatively independent. Furthermore, in the case where multiple managers manage different sub-accounts of a fund, they might analyze different, but not completely diverse, subsets of securities, and they would communicate with each other when making an investment decision. Sharpe (1980) first proposed some theoretical justification for the decentralization of investment management. He argued that the employment of multiple managers could reduce the danger of overall fund performance being damaged by the serious decision errors of a single manager. In a follow-up paper, Barry and Starks (1984) proposed an alternative motivation for the employment of multi-managers. They argued that due to risk-sharing arrangements between multiple managers, investors might benefit from the higher risk taken by multiple managers. This section focuses on the analysis of how multiple-manager arrangements affect the risk-taking behaviour of a mutual fund. There are at least two reasons to believe that the number of managers will affect a manager’s ability and willingness to alter the risk of her portfolios. First, a manager who is solely responsible for an investment decision usually is well recognized in the industry. It is unlikely that a person who has been in the industry for a few years only or who has had a poor record could become the sole manager of a fund. The termination risk for these individuals might not be a serious concern for them. Therefore, mid-year loser funds managed by single managers may have a greater incentive to take on higher risk. Second, while a multiple-manager fund, which is a mid-year winner fund, would try to become the top fund by adjusting its portfolio risk, it may be unable to make the adjustment in a timely manner because its managers have to coordinate their decisions. Thus, we would expect that single managers have greater incentives and ability to alter the risk of their portfolios to a larger degree relative to the multiple managers. Loser single managers will be even more aggressive than loser multiple managers. To test this hypothesis, we first estimate the following parametric model,

1. σ_(i,t)= 〖RPM〗_(i,t)+ β_1 σ_(i,t) + β_2 〖AGE〗_(i,t) +β_3 〖Log(SIZE)〗_(i,t) +β_4 〖LG〗_i+ β_5 〖GI〗_i+β_6 〖MULTI〗_(i,t)+β_7 〖MULTI〗_(i,t)*〖RPM〗_(i,t) ∑_(t=1992)^2017▒μ_1 YEARUM_T + ε_(i,t),

where RPMi,t is the relative performance of fund managers i at time t adopting Carhart (1997)’s four-factor model, which is defined in Equation (5). The σi,t is the standard deviation of the return of fund i in period j of the year t. AGEi,t is the age of fund i at time t. 〖Log(SIZE)〗_(i,t) is the logarithm of fund i’s total net assets in period 1 in year t. LGi is a dummy variable which is equal to 1 if fund i’s objective is long-term growth and 0 otherwise. GIi is also a dummy variable which is equal to 1 if fund i’s objective is growth and income and 0 otherwise. In order to examine the difference in risk-taking behaviour of single and multiple managers, we include a dummy variable MULTIi,t which takes the value of one if fund i is managed by multiple managers in year t and zero otherwise. To allow for relative performance sensitivity of adjustment of risk to be different between single managers and multiple managers, we also include an interaction term between RPMit and the dummy variable MULTIi,t. YEARDUMt is the dummy variable which is equal to 1 if the year is year t and 0 otherwise.

Manager turnover

Turnover and fund flow prediction

The preliminary condition for our second hypothesis is that whether turnover has any marginal explanatory power to predict future flow. Kostovetsky and Warner (2015)’ paper use predictive model and find that turnover is associated with improved flow for poor performing funds, suggesting that investors not only pay attention to past returns but also to management changes. Thus, turnover in response to prior poor performance benefits investors, even though the underlying mechanism is not improved return performance. The finding that investor flow responds to manager changes is consistent with evidence presented elsewhere. For example, Massa, Reuter, and Zitzewitz (2010) show that flow falls when the manager of a good performing fund departs. Although flow may largely reflect irrational return chasing, it would not be surprising to also find that such irrational investors pay attention to manager changes. Anecdotal evidence also supports the plausibility of the view that investors pay attention to mutual fund manager changes, which lead to flow fluctuation. Morningstar sometimes has articles about specific changes, and their analysts give facts and opinions about both departing managers and their replacements. Furthermore, changes in Morningstar fund ratings predict fund flow (Del Guercio and Tkac (2008)), so what Morningstar says appears to influence some investors.

Model of multiple managers in manager turnover and fund flow relationship

Similar to a traditional event study of stock returns, an event study on fund flow aims to parsimoniously purge raw fund flow of the influence of all performance and non-performance characteristics other than the manager turnover and thereby isolate the incremental flow due to the managerial structure change from sole to team. To compute flow we estimate a time-series benchmark regression for each individual fund i, in which we take flow and performance control variables into account in the previous period.

1. F_(i,t)=γ+ β_1 〖SF〗_(i,t) +β_2 〖RET〗_(i,t) + β_3 R_(i,t-1)+β_4 〖turnover〗_(t-1)*team+ β_5 〖turnover〗_(t-1)+β_6 team+β_7 F_(t-1)+ε_t,

where Fi,t is the net dollar flow to fund i at month t, RETi,t-1 is fund i’s raw return in month t-1. SFi,t is the aggregate net flow to all funds in the same style category as fund i at month t, Ri,t−1 is fund i’s performance at month t calculated by using Carhart (1997)’s four factor model, and Ft−1 is the net flow to fund i at month t − 1. Team is a dummy variable equals to 1 if fund i has a multiple manager structure such as 2, 3, 4 or more managers take charge of portfolio investment decisions, 0 if fund i is managed by sole manager. The coefficient on turnover, β_5, capture the effect of manager turnover event to fund flow for fund i at month t-1. The coefficient β_4 captures the changes in the metrics that is uniquely associated with the effect of team managerial structure in the event of manager turnover to investors’ perception reflected in fund flow changes. In accordance with our hypothesis two, we expect that if managerial structure has an effect to fund flow when there is managerial turnover, the coefficient β_4 will have a significant negative value.

Team size’s effect on manager turnover event

Since previous study shows that there is a relationship between team size and fund performance. For instance, Mueller (2012) documents that smaller teams display superior performance than larger teams. They further provide evidence that any group composed of 4 or more individuals will see significant increase in coordination costs within the group and diminishing motivation across members of the group. Hamilton et al. (2003) also present a nonlinear benefits of team size, finding that the largest increases in productivity of workers when they join the teams at the early stages of team formation. A profound study of Laughlin et al. (2006) find that when dealing with highly intellective problems, 3-person groups performs better than the best individuals, but more members do not add extra performance gains. In a similar vein, it is reasonable to assume that team size has a nonlinear effect on fund flow when there is manager turnover. Now we examine this effect by running the following regression model:

1. F_(i,t)=γ+ β_1 〖SF〗_(i,t) +β_2 〖RET〗_(i,t) + β_3 R_(i,t-1)+β_4 〖turnover〗_(t-1)*team+ β_5 〖turnover〗_(t-1)+β_6 2FM+〖β_6 3FM+β_6 4FM+β_6 5FM+β〗_7 F_(t-1)+ε_t,

Where 2FMit, 3FMit, 4FMit, and 5FMit are dummies that equal 1 if the fund has 2, 3, 4, and 5 or more managers, respectively, at the end of the calendar year and 0 otherwise. Other variables are defined as before.

1. Mutual fund flow-performance relationship

There has been extensive analysis regarding the flow-performance relationships across a broad range of funds since the 1990s. Ipolito (1992) examine the relationship between investor fund allocation decisions and past performance of mutual funds. Using a large span of data from 1965 to 1984, the author discovers that investors react to new information regarding product quality (prior performance) and respond accordingly. Rational investors exploit this information by allocating investable monies away from lowest ranked funds toward recent good performers considering of transaction costs. Chevalier and Ellison (1997)’ paper finds a similar result and examines funds that make the annual Morningstar best fund list attract more attention by relatively uninformed investors and therefore obtaining higher flows. His research uniquely divides the sample of young (2-5 years) and old (6 years and above) retail funds across period of 1988 to 1994 and uncover a linear fund flow/performance relationship for younger funds and a convex relationship for older funds. The tendency for younger funds to undertake riskier investment to improve performance and to avoid significant outflow contributes to this linear fund flow/ performance relationship.

However, plenty of literatures find a non-linear fund flow/ performance relationship—mutual fund investors chase past performance by rewarding “winners” but failing to punish “losers”. The asymmetric flow–performance relationship for mutual funds has attracted much attention and researchers have investigated this issue from different aspects. For example, Fant and O’Neal, (2014) document an increase in the flow‐performance asymmetry in the second period (1988-1997) that exacerbates the adverse incentive for fund managers to increase portfolio risk. They conclude that, though top performing funds are rewarded with greater fund flows in the sample of 1988 to 1997, the change is due solely to the increase in aggregate fund flows and not to an increased reliance on performance by individual fund investors. While Fant and O’Neal, (2014) have revealed the asymmetric flow-performance relationship, other researchers investigate the rationale behind this phenomenon and the major explanations can be summarized as followed.

1.1 Asymmetric flow/performance relationship

1.1.1 Investor psychological factor

Goetzmann and Peles, (1997) examine this flow/performance relationship by studying the psychological factor of investors using the current psychological model and shows the investor inertia contributes to this asymmetric and convex flow/performance. As a result, the past top performing funds attract disproportionately large inflows in subsequent periods, whereas past poor performers suffer minimal outflows. Similarly, building on the Kahneman and Tversky’s aversion to loss realization theory, Shefrin and Statman’s (1985) place this behaviour pattern into a wider theoretical framework concerning a general disposition to sell winners too early and ride losers too long, which explains the reason for a less manifestation outflow for underperformed fund. Other findings related to investor behavioural bias include Zeckhauser, Patel, and Hendricks (1991), who point to status‐quo bias—the tendency to stick with strategies already adopted and Kahneman and Tversky (1982), who cite regret aversion, which causes investors to regard errors of omission less seriously than errors of commission. Therefore, both institutional and behavioural explanations contribute to the observed asymmetric relation between performance and fund flows.

1.1.2 Transaction fees and search costs

Sirri and Tufano (1998), which is perhaps the most widely cited study of the relation between performance and fund flows. They examine funds from 1971 to 1990 and find no relation between rank performance and fund flows for the quintile of worst performers, but find a positive relation for the upper four quintiles, particularly the top quintile. They suggest that mutual fund investors purchase funds that are easier or less costly for them to identify, such as those with extensive marketing efforts, those receiving more media coverage, and those offered by well-known fund families. In addition, fund flows are fee-sensitive: consumers respond differently to high and low fees as well as to fee increase and decrease, which deters investors to transfer their monies away from underperformed fund. Similarly, Huang et al. (2007) document the transaction costs from purchasing and redeeming fund shares that can hinder investors from removing their monies away from poor performed fund. Moreover, fund characteristics such as age, volatility of past performance, affiliation with a large or ‘star’-producing fund complex, and marketing expenditures affect both the level of fund flows and the sensitivity of flows to past performance. He also models the effect of investor participation costs on the mutual fund flow-performance relationship: the costs of collecting and analyzing information about funds. The authors suggest that participation costs can lead to different flow responses at difference performance levels and, consequently, to an asymmetric flow–performance relationship. Berk and Green (2004) assume a perfectly competitive capital market in which the return to an actively managed fund decreases with its portfolio size. Using variable cost functions for managers, they show that a convex relationship between new investments and past performance exists even in the absence of performance persistence. Lynch and Musto (2003) argue that investment companies can exercise an option to abandon poorly performing strategies and/or fire bad managers. Since poor returns are not likely to be informed about future performance, investors will respond less strongly to bad performance, leading to the convexity in the flow-performance relationship

1.1.3 The investor clientele effect

Guercio and Tkac (2002) compare the relationship between asset flow and performance in the retail mutual fund and fiduciary pension fund segment of the money management industry and relates empirical differences to fundamental differences in the clientele they serve. While pension fund clients punish funds with poor performance by withdrawing assets under management and do not flock disproportionally to recent winners, mutual fund clients do not withdraw assets from funds with poor performance but chase and flock to past winners, displaying an asymmetric performance-flow relationship. Christoffersen and Musto (2002) argue that investors have different demand curves and that the investors of bottom funds are relatively less sensitive to performance and price. Sawicki (2001) investigates the flow–performance relationship using Australian wholesale funds, which are traded primarily by large, institutional investors. She finds that institutional investors in Australia react to recent performance, but the response is not asymmetric.

1.1.4 Star funds and the flow–performance relationship

Gruber (1996) finds evidence that “sophisticated” investors are able to recognize superior management, witnessed by the fact that the flow of new money into and out of mutual funds follows the predictors of future performance. Fund families recognize the importance and the benefits of having popular, well-performing funds include not only the superior performance of their managers but also their funds in general to increase investor inflows and thus increase total net assets managed and management fees. Nanda et al. (2004) also finds a positive spill over effect on the inflows of other family funds resulting from having a star performing fund without the negative effect from a poor performing fund. Using portfolio analysis, they find that factors that enhance the ex ante odds of producing stars are associated with a significantly poorer family performance, which is consistent with lower-ability families pursuing strategies to take advantages of the cash flow response to a star performance. They also find a naïve strategy of chasing families with star performers does not enhance investor return.

1.2 Fund flow volatility – performance relationship

Rakowaski (2010) provide a detailed analysis of the impact of daily mutual fund flow volatility on fund performance and document a significant negative relationship between the volatility of daily fund flows and cross-sectional differences in risk-adjusted-performance. The negative relationship is strongest for domestic equity funds. This study collect data from several sources from March 2000 until October 2000 and use cross-sectional regression analysis to find that flow volatility remains significant after correcting for funds’ turnover, suggesting it is not simply the increase trading by fund managers that drives the link between flow volatility and performance.

Wang et al. (2018) study this relationship from another perspective. He examines the impact of fund volatility and fee charges on flow-performance sensitivity. They use quarterly data for January 1999 to December 2011 from CRSP Survivor-bias free mutual fund database and test flow-performance sensitivity under three phases: pre-GFC (January 1999-June 2007); GFC period (July 2007-March 2009); and post GFC period (April 2009-December 2011). They find that investors react negatively to fund volatility which means that low volatility funds experience the greatest flow-performance relationship, implying they generate greater net flows in relation to past performance. They also identify the impact of pure operating expense. High expenses are an additional burden born by investors and weaken flow-performance sensitivity, which implies that funds with higher pure operating expense generate less net flows. It is suggested that investors dislike fee charges and learn to avoid them and their reaction is stronger post-GFC. In addition, this paper concludes that advertising effects increase investors’ awareness and encourage greater net flows even in sluggish time.

2. Mutual fund managerial structure

Over the past two decades, team-based portfolio management has become very popular in the U.S. mutual fund industry. For example, in 2010, more than 70% of all U.S. domestic equity mutual funds were managed by “teams” of portfolio managers compared to only 30% in 1992 (Patel and Sarissian, 2017). Researchers are attempting to find the rationale behind this striking trend. This section review literatures from performance and risk dimensions of the pros and cons for team and sole managerial structure.

2.1 Managerial structure and fund performance

Previous literatures explain this trend predominantly from the fund performance viewpoint. A prevalent view is multiple manager fund outperforms sole managers. For example, Han et al. (2008) develop a model where fund performance is driven by organizational design (team or solo-managed) and managerial ability. In their model, team structure leads to better performance by improving information quality, suggesting a positive relation between fund performance and team management. They use empirical evidence to prove that team-managed funds outperform solo-managed funds by 23–38 basis points per year, but only after controlling for managerial self-selection. However, in their framework superior managers may self-select into solo-managed funds where they are not subject to the “dilution in the reward from superior performance” attendant in team structures. Consequently, it is possible that a solo-managed fund may yield better performance than an otherwise equivalent team-managed fund. Similarly, Patel and Sarkissian (2017) argue large discrepancies of the data used by previous studies and challenge the previous conclusions of no performance benefits from team management. Utilising more accurate and detailed managerial-level Morningstar Direct data from 1992 to 2010 in U.S., they find that team-managed funds outperform single-managed funds across various performance metrics, and fund benefit the most from teams of 3 portfolio managers.

Contrary with Han et al. (2008) and Patel and Sarkissian (2017) s’ finding, other empirical studies find little evidence of performance benefits of teamwork in the fund industry. For instance, Prather and Middleton (2002) and Prather and Middleton (2006) examine performance differences between solo and team-managed funds in the context of classical versus behavioural decision-making theory. The former is predicated on the assumption that all decision-makers are rational, and that management structure does not impact performance outcomes. On the other hand, behavioural decision-making theory suggests that team management leads to more integrated information gathering and decision processes with fewer biases, more consistency, more comprehensive use of information and rationality, all of which should lead to better performance outcomes. Prather and Middleton (2002) and Prather and Middleton (2006) empirical results support the classical decision-making framework as they find an insignificant difference in the performance outcomes between team-managed and solo-managed mutual funds. Similarly, Bliss et al. (2008) provide an empirical examination of whether funds managed by individuals perform differently from funds managed by teams. Using a sample of about 3,000 equity mutual funds over a 12-year horizon, the authors find that although the number of funds managed by teams has grown at seven times the rate of funds managed by individuals, no significant difference in risk-adjusted performance is observed between team-managed and individually managed funds. More specifically, Chen et al. (2004) document that team-managed funds underperform their solo-manager counterparts by an average of 48 basis points per year. Therefore, these literatures show ambiguous results on the literature reviews of relationship between managerial structure and performances.

2.2 Investment risk and fund flow volatility for different managerial structure

On the other hand, the extant academic literature highlights the benefits of group decision-making from risk perspectives. For instance, Sharpe (1981), Barry and Starks (1984), and Sah and Stiglitz (1991) argue that teams in the fund management industry achieve a diversification of style and judgment that reduces portfolio risk, thus inducing better performance. In particular, Barry and Starks (1984) address the investor’s decision to employ multiple managers for the management of investment funds. Instead of the common believes that specialization of managers (If investment managers specialize and produce special insights into the likely success of certain industries or limited groups of securities, then it is reasonable to employ managers to invest funds in their areas of specialty) and diversification of managers (to protect against the possibility that a particular manager might make a serious error in the management of funds, one can diversify funds among managers, hence ‘washing out’ the danger of overall fund performance being seriously damaged by the bad fortune of a single manager.) can be the potential explanation for a favourable team-managed structure, besides, this paper shows that risk sharing considerations may be sufficient and demonstrate that one aspect of the principal-agent relationship, and can influence the decision to use multiple managers.

In addition, Qiu (2003) analyses the difference in the risk-taking behaviour of funds managed by multiple managers and single managers. Single managers have greater incentives to undertake higher portfolio risk investment in order to catch up with the top fund. Their results support the notion that multiple managers provide an effective way of reducing the risk-taking incentives of funds in response to their relative performance. In general, single-manager funds are more aggressive and tend to take higher risk exposure than team-managed funds. In a more comprehensive study, Bliss et al. (2008) exhibit the team-managed advantage compared with sole managed fund from risk, cost, and flow perspectives. They find that team-managed funds are significantly less risky and exhibit lower turnover, the total cost of owning a team-managed mutual fund is, on average, nearly 50 bps lower per year than the cost of owning an individually managed mutual fund and team-managed funds attract significantly greater investor flows than individually managed funds, even after controlling for performance, risk, and expenses.

Bar, Kempf, Ruenzi (2011) analyze the behavioural differences between fund-managed teams and single managers and provided empirical evidence by examining investment decisions of mutual fund managers to support the diversification of opinion theory and reject the group shift theory which suggests that the opinion of team members shifts towards the opinion of the dominant person in a team. They find that team managers follow less extreme investment styles, hold less risky portfolios and exhibit lower industry concentration within their portfolios than single managers, which eventually allows them to achieve less extreme performance outcomes and these result hold after taking into account the impact of fund and family characteristics as well as manager characteristics. Their conclusions of more stable and conservative behaviour among team-managed funds and the growth of team management in the mutual fund industry are consistent with increased demand for stability among institutional investors. Stein (2002) argues that in decision settings that involve soft information (e.g., based on research, relationships) a decentralized setting where a solo-manager is in charge is more effective than a more complex organization setting where the decision is reviewed by multiple managers and requires individual managers to persuade other managers. Chen, et al. (2004) apply this concept to mutual fund management structures and argue that organizational design costs are minimal for solo-manager led funds but not for team-managed funds. As investing becomes more complicated with so many new opportunities arising from new industries, markets and companies, team-managed funds make more sense (Kovaleski, 2000). This paper provides a potential explanation for team managerial trend: team-managed mutual funds are on the rise as mutual fund companies look for strength in numbers and try to avoid falling victim to ‘stars’ who leave, which he explains that strong performance can lead to turnover among star managers.

3. Manager turnover

Literatures related to mutual fund manager turnover can be classified into two broad categories, the determinants of replacement of fund manager and consequences of manager turnover.

3.1 Determinants of fund manager turnover

Khorana (1996) studies the relation between managerial replacement and prior fund performance. He finds evidence supporting the presence of an inverse relation between the probability of fund manager replacement and past performance, using the growth rate in the fund’s assets and portfolio returns. However, this relationship is proved to be very weak. In Kostovetsky and Warner (2015)’s paper, they provide one potential explanation for this weak relationship: mutual fund manager turnover data are noisy – it is difficult to distinguish between forced and voluntary departures, biasing downward any estimate of the true turnover–performance relation. They find a significantly stronger connections between manager departures and prior underperformance than previous studies. In addition, they find that characteristic-adjusted returns (incorporating manager tenure) going back as far as 5 years are statistically significant determinants of manager turnover.

Chevalier and Ellison (1999) re-examine the performance replacement relation with special focus on the age of the fund manager. They find that younger managers are more likely to experience replacement if the fund’s systematic or unsystematic risk deviates from the average risk level of other funds in the matched investment objective. Ding and Wermers (2005) construct a comprehensive data set on portfolio managers and find evidence that experienced large‐fund managers with better track‐records outperform their size, book to market, and momentum benchmarks. They also report that higher numbers of independent directors predict a better future performance and higher likelihood of replacement of underperforming managers.

Fricke and Eric (2015) examines the relationship between board holdings, compensation and the turnover of underperforming mutual fund managers. Based on 2003 data collected from 606 mutual funds, their results provide evidence that underperforming fund managers have a lower probability of being replaced when their boards have lower holdings and higher compensation. Bryant and Lonnie (2012) first to link managerial turnover to mutual fund managerial structure in a manner that indicates the strong presence of a conflict of interests between investors and fund sponsors in an area of fund governance where we have been led to believe there are strong and well-functioning mechanisms to guard against the exploitation of investors. In addition, the conflict of interests affects the replacement decision, as high expense ratio fund managers have a lower probability of replacement for a given level of underperformance.

In addition, Barron et al. (2013) investigates the effect of Morningstar ratings on mutual fund manager replacement find that not only do Morningstar ratings affect the likelihood fund managers are replaced, but that Morningstar ratings are better predictors of manager replacement than alternative measures of fund performance. They also examine the changes in the management structure of funds that are made in conjunction with manager replacement in response to poor performance.

3.2 Manager turnover and post-replacement performance and fund flow

Prior works present a picture of the consequences of portfolio manager changes. In a firm level setting, Denis and Denis (1995) examine the impact of CEO turnover on the post-replacement performance of the firm. For the subsample of managers experiencing forced replacement, they document forced resignations of top managers are preceded by large and significant declines in operating performance but significant improvements in post-replacement operating performance. However, they find that forced turnovers occur after prolonged periods of poor performance, which leads to a substantial loss in shareholder wealth. On the other hand, normal retirements are followed by small increases in operating income and are also subjective to a slightly higher than normal incidence of post-turnover corporate control activity.

In a particular mutual fund industry, Khorana (2001) examine the impact of mutual fund manager replacement on subsequent fund performance and document significant improvements in post-replacement performance relative to the past performance of the fund. On the other hand, the replacement of overperforming managers results in deterioration in post-replacement performance. They also document that the level of portfolio turnover activity decreases significantly in the post-replacement period and the replacement of poor performers is preceded by significant decreases in net new inflows in the fund. The performance flow relation suggests that replacement of the poorly performing fund managers is preceded by significantly lower asset flows, hence limiting the ability of funds to earn higher investment advisory fees in the pre-replacement years. These findings also suggest that external product markets can play an important role in affecting the managerial replacement decision. Therefore, the replacement of poorly performing managers tends to be a value-enhancing activity for both the investment advisors and shareholders of the fund. In a similar vein, Kostovetsky and Warner (2015) find evidence that flow improves after management is replaced. This suggests that fund investors react positively to changes in management, and fund sponsors may cater to investors to attract inflow or minimize outflow. However, they do not find significant improvements in returns, which is contradict to Korana (2001)’s finding, documenting a significant improvement in post-replacement performance.

Deuskar et al. (2011) relate the turnover of money managers to the career opportunities in the money management industry to understand the turnover of mutual fund managers during a special time period when the land scape of the asset management industry is undergoing an extreme makeover due to the rapid growth of hedge fund. They find that mutual funds can retain managers with good performance in the face of competition from a growing hedge fund industry. On the other hand, poor performers are more likely to leave the mutual fund industry. A small fraction of these poor performers finds jobs with smaller and younger hedge fund companies, especially when the hedge fund industry is growing rapidly. Analogously, a small fraction of the better-performing mutual fund managers is retained by allowing them to manage a hedge fund side-by-side.