and SGF 2006 for their helpful comments. The first and second authors acknowledge

The 1997-98 financial crises in Asia and elsewhere have brought to the foreground the concern about offshore investment funds and their possible role in exacerbating financial market volatility. Offshore investment funds are alleged to engage in trading behaviors that are different from their onshore counterparts. Because they are less moderated by tax consequences, and are subject to less supervision and regulation, the offshore funds may trade more frequently. They could also engage more aggressively in certain trading patterns such as positive feedback trading or herding that could contribute to greater market volatility. Using a unique data set, we compare the trading behavior in Korea by offshore funds with that of three sets of onshore funds as control groups. There are a number of interesting findings. First, the offshore funds do trade more frequently than their onshore counterparts. Second, however, the offshore funds do not engage in positive feedback trading in a significant way. In contrast, there is strong evidence that the onshore funds from the U.S. and U.K. do. Third, while offshore funds herd, they do so significantly less than the onshore funds during the crisis. In sum, the offshore funds are not especially worrisome monsters relative to the onshore funds.

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Individual investor trading results in systematic and economically large losses. Using a complete trading history of all investors in Taiwan, we document that the aggregate portfolio of individuals suffers an annual performance penalty of 3.8 percentage points. Individual investor losses are equivalent to 2.2 % of Taiwan’s gross domestic product or 2.8% of the total personal income. Virtually all individual trading losses can be traced to their aggressive orders. In contrast, institutions enjoy an annual performance boost of 1.5 percentage points, and both the aggressive and passive trades of institutions are profitable. Foreign institutions garner nearly half of institutional profits. (JEL G11, G14, G15, H31) Financial advisers recommend that individual investors refrain from frequent trading. Investors should buy and hold diversified portfolios, such as low-cost mutual funds. If skill contributes to investment returns, individual investors are obviously at a disadvantage when trading against professionals. What is less clear is just how much do individual investors lose by trading? In this paper, we document that trading in financial markets leads to economically large losses for individual investors and virtually all of the losses of individual investors

Three concepts: stochastic discount factors, multi-beta pricing and mean variance efficiency, are at the core of modern empirical asset pricing. This paper reviews these paradigms and the relations among them, concentrating on conditional asset pricing models where lagged variables serve as instruments for publicly available information. The different paradigms are associated with different empirical methods. We review the variance bounds of Hansen and Jagannathan (1991), concentrating on extensions for conditioning information. Hansen's (1982) Generalized Method of Moments (GMM) is briefly reviewed as an organizing principle. Then, cross-sectional regression approaches as developed by Fama and MacBeth (1973) are reviewed and used to interpret empirical factors, such as those advocated by Fama and French (1993, 1996). Finally, we review the multivariate regression approach, popularized in the finance literature by Gibbons (1982) and others. A regression approach, with a beta pricing formulation, and a GMM approach with a stochastic discount factor formulation, may be considered competing paradigms for empirical work in asset pricing. This discussion clarifies the relations between the various approaches. Finally, we bring the models and methods together, with a review of the recent conditional performance evaluation literature, concentrating on mutual funds and

Stock markets are very important in modern societies and their behaviour have serious implications in a wide spectrum of the world’s population. Investors, governing bodies and the society as a whole could benefit from better understanding of the behaviour of stock markets. The traditional approach to analyze such systems is the use of analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as perfect rationality and homogeneous investors, which threaten the validity of analytical results. This motivates the use of alternative methods. For those reasons, the study of such markets is a fertile field to use the agent-based methodology. In this work, we developed an artificial financial market and used it to study the behaviour of stock markets. In this market, we model technical, fundamental and noise traders. The technical traders are non-simple genetic programming based agents that co-evolve (by means of their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. Such traders are equipped with

Waterloo. The comments of Reo Audette, Mark Kamstra, and other participants are greatly appreciated. I thank the Social Sciences Research Council of Canada for financial support, as well as Chris Fong and, especially, Poh Chung Fong for most capable research assistance. Benchmarking Performance Measures With Perfect-Foresight Asset-Allocation Strategies Popular measures of investment performance do not agree on the relative performance of passive portfolios, mutual funds, or even the seemingly obvious relative abnormal performance of assetallocation strategies generated from portfolio selection models. Moreover, the measures suffer from a number of conceptual and empirical shortcomings. Therefore, in order to better appreciate the ability of the performance measures to detect abnormal returns, this paper investigates whether they correctly recognize the truly amazing abnormal performance of perfectforesight asset-allocation strategies. Unfortunately, although each of the measures recognizes abnormal performance, some of them do not rank the strategies correctly, and others confound Many studies benchmark performance measures using passive portfolios, as passive

∗ We are grateful to an anonymous referee for numerous suggestions that have improved

We investigate the value of active mutual fund management by examining the stockholdings and trades of mutual funds. We find that stocks widely held by funds do not outperform other stocks. However, stocks purchased by funds have significantly higher returns than stocks they sell—this is true for large stocks as well as small stocks, and for value stocks as well as growth stocks. We find that growth-oriented funds exhibit better stock-selection skills than income-oriented funds. Finally, we find only weak evidence that funds with the best past performance have better stock-picking skills than funds with the worst past performance. The Value of Active Mutual Fund Management: An Examination of the Stockholdings and Trades of Fund Managers I.

Problems with performance measurement using information ratios relative to a benchmark are exposed. Random portfolios (that obey constraints but disregard utility) are shown to measure investment skill effectively. Investment mandates can also be based on random portfolios—this allows active fund managers more freedom to implement their ideas, and provides the investor more flexibility to gain utility. The issue of the proper attitude towards tracking error is broached, but left largely undecided. There is also a critique of Fisher’s method of combining p-values that shows Stouffer’s method to be preferable.