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)

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In this paper, we analyze the economic value of predicting index returns as well as volatility. On the basis of fairly simple linear models, estimated recursively, we produce genuine out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data from 1954 to 1998, we test the statistical significance of return and volatility predictability and examine the economic value of a number of alternative trading strategies.

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.

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This paper combines techniques drawn from the literature on evolutionary optimization algorithms along with bootstrap based statistical tests. Bootstrapping is used as a general framework for estimating objectives out of sample by redrawing subsets from a training sample. Evolution is used to search the large number of potential network architectures. The combination of these two methods creates a network estimation and selection procedure which finds parsimonious network structures which generalize well. The bootstrap methodology also allows for objective functions other than usual least squares, since it can estimate the in sample bias for any function. Examples are given for forecasting chaotic time series contaminated with noise. 1 1

The contingency table literature on tests for dependence among discrete multi-category variables is extensive. Standard tests assume, however, that draws are independent and only limited results exist on the effect of serial dependency a problem that is important in areas such as economics, finance, medical trials and meteorology. This paper proposes new tests of independence based on canonical correlations from dynamically augmented reduced rank regressions. The tests allow for an arbitrary number of categories as well as multi-way tables of arbitrary dimension and are robust in the presence of serial dependencies that take the form of finite-order Markov processes. For three-way or higher order tables we propose new tests of joint and marginal independence. Monte Carlo experiments show that the proposed tests have good finite sample properties. An empirical application to microeconomic survey data on firms’ forecasts of changes to their production and prices demonstrates the importance of correcting for serial dependencies in predictability tests.

In this paper, we develop a methodology to identify money managers who have private information about future asset returns. The methodology does not rely on a specific risk model, such as the Sharpe ratio, CAPM, or APT. Instead, it relies on the observation that returns generated by managers with private information cannot be replicated by those without it. Using managers ’ trading records, we develop distribution-free tests that can identify such managers. We show that our approach is general with regard to the nature of private information the managers may have, and with regard to the trading strategies they may follow.

The statistical forecasting efficiency of new crop corn and soybean futures is the topic of frequent academic inquiry. However, few studies address the usefulness of these forecasts to economic agents ’ decision making. Each year Central Illinois producers are faced with the decision to plant either corn or soybeans on marginal acreage. Agronomic concerns aside, these decisions hinge on the expected relative return of corn versus soybeans, which is largely a function of expected new crop prices. Do new crop futures prices reliably guide producers into the correct production decision? The results suggest that over the entire period of the analysis, futures markets provide only marginal decision-making information to the producer; however, more recent signals do appear to be useful. Further analysis explores several possible factors that could explain why the signals have improved so significantly since 1985. The Forecasting Value of New Crop Futures: A Decision-Making Framework