It is obvious that forecasts are of great importance and widely used in economics and finance. Quite simply, good forecasts lead to good decisions. The importance of forecast evaluation and combination techniques follows immediately-- forecast users naturally have a keen interest in monitoring and improving forecast performance. More generally, forecast evaluation figures prominently in many questions in empirical economics and finance, such as: Are expectations rational? (e.g., Keane and Runkle, 1990; Bonham and Cohen, 1995) Are financial markets efficient? (e.g., Fama, 1970, 1991) Do macroeconomic shocks cause agents to revise their forecasts at all horizons, or just at short- and medium-term horizons? (e.g., Campbell and Mankiw, 1987; Cochrane, 1988) Are observed asset returns "too volatile"? (e.g., Shiller, 1979; LeRoy and Porter, 1981) Are asset returns forecastable over long horizons? (e.g., Fama and French, 1988; Mark, 1995)

Hedge funds are known to exhibit non-linear option-like exposures to standard asset classes and therefore the traditional linear factor model provides limited help in capturing their risk-return tradeoffs. We address this problem by augmenting the traditional model with option-based risk factors. Our results show that a large number of equity-oriented hedge fund strategies exhibit payoffs resembling a short position in a put option on the market index, and therefore bear significant left-tail risk, risk that is ignored by the commonly used mean-variance framework. Using a mean-conditional Value-at-Risk framework, we demonstrate the extent to which the mean-variance framework underestimates the tail risk. Working with the underlying systematic

this paper (based on a different data set) was presented at the 1992 North American Winter Meeting of the Econometric SocietyinNew Orleans, Louisiana.

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Hedge funds are often cited as attractive investments because of their diversification benefits and distinctive risk profiles—in contrast to traditional investments such as stocks and bonds, hedge-fund returns have more complex risk exposures that yield complementary sources of risk premia. This raises the possibility of creating passive replicating portfolios or “clones” using liquid exchange-traded instruments that provide similar risk exposures at lower cost and with greater transparency. Using monthly returns data for 1,610 hedge funds in the TASS database from 1986 to 2005, we estimate linear factor models for individual hedge funds using six common factors, and measure the proportion of the funds ’ expected returns and volatility that are attributable to such factors. For certain hedge-fund style categories, we find that a significant fraction of both can be captured by common factors corresponding to liquid exchange-traded instruments. While the performance of linear clones is often inferior to their hedge-fund counterparts, they perform well enough to warrant serious consideration as passive, transparent, scalable, and lower-cost alternatives to hedge funds.

Since hedge fund returns exhibit non-linear option-like exposures to standard asset classes (Fung and Hsieh (1997a, 2000a)), traditional linear factor models offer limited help in evaluating the performance of hedge funds. We propose a general asset class factor model comprising of excess returns on passive option-based strategies and on buy-and-hold strategies to benchmark the performance of hedge funds. Although, in practice, hedge funds can follow a myriad of dynamic trading strategies, we find that a few simple option writing/buying strategies are able to explain a significant proportion

informs doi 10.1287/mnsc.1060.0520

We introduce trading rules which are selected by an artificially intelligent agent who learns from experience - an Artificial Technical Analyst. It is shown that these rules can lead to the recognition of subtle regularities in return processes whilst reducing data-mining problems inherent in simple rules proposed as model evaluation devices. The relationship between the efficiency of financial markets and the efficacy of technical analysis is investigated and it is shown that the Artificial Technical Analyst can be used to provide a quantifiable measure of market efficiency. The measure is applied to the DJIA daily index from 1962 to 1986 and implications for the behaviour of traditional agents are derived.

Hedge fund managers typically transact in similar asset markets to those used by conventional fund managers. Yet, there is much documented evidence that hedge funds have different return characteristics than those of conventional asset class managers. Some authors have attributed this apparent dilemma to the skill-based nature of hedge fund performance. However, this

This paper benefited from the participants at presentation at the Centre for Hedge Fund Research and Education, London Business School and a forum discussion at Albourne Village. 2 1. Introduction It is a well-documented stylized fact that hedge fund returns differ from the returns of traditional asset classes. Investors looking for alternative return characteristics in hedge funds are clearly concerned about the question of consistency. To go beyond relying on historical hedge fund performance repeating itself, one needs to answer the key question on hedge fund performance: "What is the wind behind this sail?" After all, hedge fund managers typically transact in similar asset markets to traditional managers. How then do they deliver different return characteristics to the very asset classes they trade? We believe the answer to these questions lies in understanding the value of hedgefund strategies and how they can be directly related to traditional asset classes. In a recent paper, Fung and Hsieh (2001a) modeled the unusual return characteristics of trend-following hedge funds. It was first reported in Fung and Hsieh (1997a) that these funds have performance characteristics that resemble straddles on the equity market. They deliver positive returns when the equity markets are at extremes--- both up and down. This is an attractive return profile for diversification purposes. In order to verify that this phenomenon is not merely an empirical regularity, Fung and Hsieh (2001a) explicitly modeled trend-following strategies using traded options. It was shown that the returns from trend-following strategies can be replicated by a dynamically managed option-based strategy known as a "lookback" option. The intuition is as follows. A perfect trend follower is one that buys ...