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Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust, 2010 IEEE International Conference on (2013)
Alexandria, VA, USA USA
Sept. 8, 2013 to Sept. 14, 2013
pp: 756-761
ABSTRACT
In this paper, we study the problem of benchmarkingreturns of equity funds. We present a novel approach, therandom-trader scheme, to benchmark return of an equity fund duringa specific period. A random trader uses all-in-all-out strategy totrade in the market at random timing with capital being negligibleas compared with the market size.Let $\mathrm{DRRT}$ be the distribution of returns ofrandom traders, and $\mathrm{R_{rt}}$ be a random variablesampling from $\mathrm{DRRT}$. In this paper we model$\mathrm{DRRT}$ as a log-normal distribution, denoted as$\mathrm{s_{DRRT}}$, and provide an efficient algorithm to computethe mean and variance of $\mathrm{s_{DRRT}}$, denoted as$\mathrm{\mu_{DRRT}}$ and $\mathrm{\sigma_{DRRT}}$, respectively.Using TAIEX index as data set, our experiments showed$\mathrm{s_{DRRT}}$ approximates $\mathrm{DRRT}$ well when thelength of given period is one month.We then score each equity fund by the cumulativedistribution function of $\mathrm{s_{DRRT}}$ i.e.,$\mathrm{s}(m;\mathrm{DRRT)%F_{\mathrm{s-index}_{pe}}(r(m;pe);\mu,\sigma)$ =Pr[R_{rt} \le R}(m)]$, where $\mathrm{R}(m)$ denotes the return of the equity fund $m$during the given period. Using the historical data on equity fundsin Taiwan,we observed interesting characteristics.We then score each equity fund by the cumulativedistribution function of $\mathrm{s_{DRRT}}$ i.e.,$\mathrm{s}(m;\mathrm{DRRT)=Pr[R_{rt} \le R}(m)]$, where $\mathrm{R}(m)$ denotes the return of the equity fund $m$during the given period. Using the historical data on equity fundsin Taiwan,we observed interesting characteristics. When analyzing monthly returns, although there are some winnerswho are able to achieve scores higher than $.9$ sometimes, it isdifficult for them to always keep up at the high scores. However,few equity funds showed the stability of scores higher than $.7$,when analyzing long-term returns. Furthermore, there are timeswhen most funds obtain high scores. There are also times when nofunds perform well.
INDEX TERMS
Equity Fund Evaluation; Cumulative Distribution Function Approximation; Rating Equity Funds;,
CITATION

T. Hung, M. Wu, H. Lu and J. Ho, "Rating Equity Funds against Return of Random Traders," 2013 International Conference on Social Computing (SocialCom)(SOCIALCOM), Alexandria, VA, USA, 2013, pp. 756-761.
doi:10.1109/SocialCom.2013.113
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