Background Information on Behavioral Differences of Mutual Fund Managers Algorithmic Trading and Mutual Funds It should be stressed that algorithmic trading is frequently used by mutual funds because it allows the managers to calculate securities in large amounts with the help of sophisticated mathematical formulas. In particular, this form of trading includes the division of larger amount of shares into smaller groups to provoke quick selling and buying. Therefore, algorithmic trading is used by mutual funds to successfully manage opportunity cost, market impact, and risks (Bergan and Devine, 2005). Automated trading is also used to initiate orders premised on date received electronically to define such aspects as timing, price, and final income. With regard to the above-presented considerations, Chaudhuri (2004) and Khnadani and Lo (2007) state that algorithmic trading enable the mutual fund managers to identify whether their profits can be forecasted in terms of risks and returns obtained at mutual funds. Particular role in defining the connection will be played by random and autoregressive models proposed by Friedman and Shachmurove (1997) for defining the future values of mutual fund performance. Beside greater accuracy of prediction, the risk adjustment rate can be calculated in a less time consuming mode providing more valid results. Impact of Algorithmic Trading on Tournament Behavior It has been commonly recognized that processes of forecasting and modeling are intrinsic, challenging, and fluctuating. These processes are closely associated with a random walk process implying that these correlation series are statistically and economically insignificant (Malkiel, 2003). Such an assumption is premised on the studies of Eugene Fama (1970) who believes that stock markets were significantly efficient in presenting data about individual stocks and about stock market in general. With regard to this theory, there is a close relation between the information available and the extent to which future outcomes are predictable. However, financial researchers and statistics are more inclined to believe that stock prices as well as returns are to an extent predictable (Malkeil, 2003). In particular, Adaptive Market Hypothesis has emerged in an effort to reconcile behavioral and predictable models of stock prices identification. Introduced by Andrew Lo (2004, 2005), the concept applies evolution principles , those of adaptation and natural selection to financial operations. Predictability of mutual fund returns can be analyzed from different perspectives. On the one hand, predictability enables investors to earn extra returns whereas unpredictable processes prevent them from achieving this goal. One the other hand, results base on predictability do not necessarily imply that profits is connected with unfavorable risk; at the same time, unpredictable calculations can lead to profit if their expected risk is favorable (Hallahan et al. 2009). One way or another, the estimated results are uncertain because the forecasting is based on measuring the extent of uncertainty. This means that probability theory should be introduced to define the number of probability to each particular event where the highest certainty is applied to number 1 (Lo, 2004). The assessment of probability level is aimed at achieving Sharpe ratio that reveals the way the return compensates