Abstract
The paper is devoted to the mutual fund performance, tournaments and their interconnection with algorithmic trading. The theoretical research has been conducted with the purpose of understand the nature of tournaments in the industry of mutual funds.
Then, the methodology was designed with the purpose to follow the risk-taking behaviour of competitors in tournaments. This allows us to draw a conclusion that endogenous benchmark is not spread in tournament behavior, while exogenous benchmark has stronger influence.
Introduction
Mutual funds’ functions are limited to helping people have a small share in big corporation. There are two main ways of getting profit from mutual funds, interest or dividends to the fund or the raise of security in value.
There are a lot of advantages of mutual funds, like professional management of investments, affordability, variety and diversification, high convenience and easiness in recordkeeping. Technologies have revolutionized the processes at the financial markets and influenced the trading of financial assets.
Having implemented different electronic platforms and algorithms for trading, some market operators are aimed at competing with others instead of “trading with a counterparty that has no high-speed access”. Therefore, the mutual funds are affected as well. The necessity to adopt algorithmic programs is felt due to the increases of the expected performance.
Tournament behaviour in the mutual fund industry is defined as “the intense competition between fund managers who alter their portfolio risk with the expectation of improving or maintaining their existing ranking relative to the relevant peer group”.
Fund managers try to reach the leading top positions at the end of the year as these activities are used as the basis for the first decade of a new year. There is no much difference between middle and bottom positions in the mutual fund tournament rating, so, “the optimal response of a fund manager to its interim performance is an adjustment of her risk taking”.
Mutual fund tournaments cover “the attempts of rational fund managers to maximize their expected compensation by adjusting the risk of their portfolios in accordance with their relative performance over a year”. According to Wang, “managers increase their portfolio riskiness in response to the gambling incentives, which tends to have a negative effect on fund performance”.
Such behaviour also impacts algorithmic traders as the decisions are based on specific information on the markets and the raise of portfolio riskiness means the variation in prices. Focusing attention on mutual fund performance and tournament behaviour, Lin, Chung, & Lee conducted a deeper research and identified a direct link between competition fund managers and management risk choice.
These activities influence future performance. Lin, Chung, & Lee stress that “the tournament system provides fund managers with the incentives to alter their portfolio risks in an effort to catch up with the market or lock in their yearly performance”.
Beckmann, Lütje, & Rebeggiani have proven that tournament behavior in mutual funds depends on overconfidence, risk assessment and gender differences.
Mutual fund performance on the international arena is conducted via algorithmic platforms in a number of reasons. Algorithmic trading engines are more secure than human trading.
Moreover, algorithmic engines allow monitor and modify funds. It can be concluded that “algorithmic trading systems capitalize on their advantageous ability to process high-speed data feeds and react instantaneously to market movements by submitting corresponding orders or modifying existing ones”.
The spread of algorithmic trading systems in mutual fund tournaments is explained by the dynamic changing of the principles of financial relations, client needs, cost reducing strategies, emergence of new markets, etc.
Investors want to know the post risk-taking effect in the industry of mutual funds as this helps them understand “whether the change of risk-taking alters future fund performance”. This data helps algorithmic system predict the outcome of the affair and make decisions.
Methodology
The increase of declination of the risks may lead to wrong decisions, which, in their turn, are going to influence the situation at the fund market, as “markets always incorporate all available public information” for their needs. To follow the linkage between mutual funds and algorithmic trading, it is necessary to consider the risk-taking behaviour of competitors in tournaments.
Considering closer the data which should be used for the research, it should be mentioned that sample funds should be referred to from France database for 5 past years.
Only domestic equity funds are to be examined with at least 60% hold in common stock. We are not going to take into account specialist funds, closed funds and index funds. We expect to “alter risk-taking behaviour in response to [fund managers’] performance relative to competing fund managers”.
The first step of our research is the examination of the monthly compound return of each fund taken in one tournament year:
, where
is “the monthly change in the fund’s index series value”.
Next, we are going to explore “relationships between interim period performance and subsequent period risk” by means of calculating risk adjusted-return/ratio which represents the change in risk:
, where
- β0 is the intercept,
- β1 is the gradient,
- β2 (W * RTN) denotes winners dummy variables
- β3 (L * RTN) denotes losers dummy variables.
We are going to test two hypotheses, H0: b=0 and H1: b<0. If the estimated coefficient is equal to 0, so we fail to reject the null hypothesis which states that “subsequent period fund risk is independent of ranking period performance”.
If we manage to reject this hypothesis, we will focus on the alternative hypothesis aimed at exploring the central prediction of the tournament hypothesis: “funds with below benchmark returns in the first part of the year (losers) increase their total risk in the remaining part of the year, relative to better performing funds (winners)”.
The age of funds should be related to the mutual fund performance and tournament behavior. Therefore, having classified funs into two categories, ‘young’, which have been existing less than two years, and ‘old’, which have been existing for more than four years.
The hypothesis is as follows, the investors “would be more strongly influenced by poor short-term performance history than for a fund that has been around for some time”. Referencing to the previous equation, we are going to utilize the following augmented dummy variable:
RAR = C + β1 RTN + β2 (W * RTN) + β3(L * RTN) + β0(RTN * W * OLD) +βy (RTN * W * YOUNG) + β0 (RTN * L * OLD) + βy (RTN * L * YOUNG)
Conclusion
Using the data of France mutual funds performance, we have tested a number of hypotheses which might point to the absence of strong evidence of tournament behavior using endogenous benchmark, however, exogenous benchmark is predicted to be used in the tournament behavior.
Furthermore, the impact of the choice of ‘young’ and ‘old’ funds in the mutual funds tournament may be used for changing some parameters in the algorithmic trading system to make the decisions better and more profitable for the investor. Young funds are considered to be more risky, thus, the fund’s performance should be double checked in case of using those.
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