Agricultural and Food Policy, Risk and Uncertainty

The adaptive markets hypothesis posits that trading strategies evolve as traders adapt their behavior to changing circumstances. This paper studies the evolution of trading strategies for a hypothetical trader who chooses portfolios from foreign exchange (forex) technical rules in major and emerging markets, the carry trade, and US equities. The results show that a backtesting procedure to choose optimal portfolios improves upon the performance of nonadaptive rules. We also find that forex trading alone dramatically outperforms the S&P 500, with much larger Sharpe ratios over the whole sample, but there is little gain to coordinating forex and equity strategies, which explains why practitioners consider these tools separately. Forex trading returns dip significantly in the 1990s but recover by the end of the decade and have been markedly superior to an equity position since 1998. Overall, trading rule returns still exist in forex markets—with substantial stability in the types of rules—though they have migrated to emerging markets to a considerable degree.

We analyze the intertemporal stability of excess returns to technical trading rules in the foreign exchange market by conducting true, out-of-sample tests on previously studied rules. The excess returns of the 1970s and 1980s were genuine and not just the result of data mining. But these profit opportunities had disappeared by the early 1990s for filter and moving average rules. Returns to less-studied rules also have declined but have probably not completely disappeared. High volatility prevents precise estimation of mean returns. These regularities are consistent with the Adaptive Markets Hypothesis (Lo (2004)), but not with the Efficient Markets Hypothesis.

This paper argues that inferring long-horizon asset-return predictability from the properties of vector autoregressive (VAR) models on relatively short spans of data is potentially unreliable.We illustrate the problems that can arise by re-examining the findings of Bekaert and Hodrick (1992), who detected evidence of in-sample predictability in international equity and foreign exchange markets using VAR methodology for a variety of countries over the period 1981-1989.The VAR predictions are significantly biased in most out-of-sample forecasts and are conclusively outperformed by a simple benchmark model at horizons of up to six months.This remains true even after corrections for small sample bias and the introduction of Bayesian parameter restrictions.A Monte Carlo analysis indicates that the data are unlikely to have been generated by a stable VAR.This conclusion is supported by an examination of structural break statistics.Implied long-horizon statistics calculated from the VAR parameter estimates are shown to be very unreliable.

The authors investigate the effect of opening a forward or futures market on spot price or real exchange rate variab ility in a two-agent, two-good, two-state, general-equilibrium model. This is shown to depend upon such familiar parameters as substitutio n elasticities, marginal propensities to consume, and degress of risk aversion. The analysis highlights the importance of the income trans fer, which occurs as a result of capital gains and losses in the forw ard market. The authors find some presumption in favor of the view th at opening a forward market reduces spot price variability. The presu mption is strengthened the less risk averse are agents.

Christopher Neely, Paul Weller, Rob Dittmar, Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach, The Journal of Financial and Quantitative Analysis, Vol. 32, No. 4 (Dec., 1997), pp. 405-426

While many technical trading rules are based upon patterns in asset prices, we lack convincing explanations of how and why these patterns arise, and why trading rules based on technical analysis are profitable. This paper provides a model that explains the success of certain trading rules that are based on patterns in past prices. We point to the importance of confirmation bias, which has been shown to play a key role in other types of decision making. Traders who acquire information and trade on the basis of that information tend to bias their interpretation of subsequent information in the direction of their original view. This produces autocorrelations and patterns of price movement that can predict future prices, such as the “head-and-shoulders” and “double-top” patterns. The model also predicts that sequential price jumps for a particular stock will be positively autocorrelated. We test this prediction and find that jumps exhibit statistically and economically significant positive autocorrelations.