Market Timing Effects Research Articles

Does Market Timing Contribute to the Cattle Cycle?

AbstractRecent evidence suggests that cyclical cattle inventories are driven by exogenous shocks. This article examines a second possible contributing factor to the cattle cycle: a market timing effect that arises from individual attempts to maintain countercyclical inventories. The model uncovers an important conceptual point: to the extent that cycles are driven by exogenous shocks, a representative producer should outperform one who maintains a constant inventory; whereas, for cycles induced by market timing, a representative producer should underperform one with a constant inventory. Simulated net returns over 1974−98 reveal that a constant‐inventory manager significantly outperformed the representative U.S. producer, which indicates that market timing influences the cattle cycle.

Market Timing and Short-Term Interest Rates

Investment Management Inc. in New York (NY 10036). 0th practitioners and academics have devoted considerable time to study of the effect of market timing for returns of different asset classes in asset allocation strategies.' By now, few would disagree that returns are at least predictable, although there is generally no agreement on the reasons for or sources of predictability. There are many different ways to predict returns, including fundamental analysis built upon financial theory; technical analysis; or newer developments in computing technology applying to financial trading, such as fractal analysis, neural networks, genetic algorithms, and fuzzy logic; or a combination of all these techniques.2 A common belief in all market-timing techniques, however, is that patterns in financial markets will repeat themselves, at least in the near fkture within a forecast horizon. Consequently, one of the final steps in buildmg market-timing models is verification of out-of-sample performance. There is also no generally accepted principle for selecting input variables to predict returns. Computer and quantitative-driven techniques, such as genetic algorithms, instead look through several hundred variables in search of a best model.3 One of the most commonly used input variables in all techniques is the short-term interest rate. Wall Street Journul articles frequently cite short-term interest rates as reasons for a market move on a given day. The short-term interest rate is clearly a widely followed asset class return indicator.