A reality check on technical trading rule profits in the U.S. futures markets

This article investigates the profitability of technical trading rules in U.S. futures markets during the years 1985–2004. Statistical significance of performance across the trading rules is evaluated using White's Bootstrap Reality Check and Hansen's Superior Predictive Ability tests, which can directly measure the effect of data snooping by testing the performance of the best rule in the context of the full universe of technical trading rules. Results show that the best rules generate statistically significant economic profits for only two of 17 futures markets after correcting for data snooping biases. This evidence suggests that technical trading rules generally have not been profitable in the U.S. futures markets. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:633–659, 2010     

Commodity index investment and food prices: does the “Masters Hypothesis” explain recent price spikes?

The “Masters Hypothesis” is the claim that unprecedented buying pressure in recent years from commodity index investors created massive bubbles in food and energy prices. The purpose of this article is to review the evidence from recent studies that investigate the empirical relationship between index investment and price movements in agricultural futures markets. One line of research uses time‐series regression tests, such as Granger causality tests, to investigate the relationship between price movements and index positions. This research provides little evidence in support of the Masters Hypothesis in agricultural futures markets. A second line of research uses cross‐sectional regression tests and studies in this area provide very limited evidence in favor of the Masters Hypothesis for agricultural futures markets. A third line of research investigates whether there is a significant relationship between commodity index trading and the difference, or spread, between futures prices of different contract maturities on the same date. These studies report a range of results depending on the type of test. However, the bulk of the evidence indicates either no relationship or a negative relationship, which is once again inconsistent with the Masters Hypothesis. Overall, this growing body of literature fails to find compelling evidence that buying pressure from commodity index investment in recent years caused a massive bubble in agricultural futures prices. The Masters Hypothesis is simply not a valid characterization of reality.     

Regulatory Circumvention and the Holding Company

A rapidly growing number of households are suffering from energy impoverishment caused by escalating electricity prices, low income, and poor housing energy efficiency. Many households are experiencing considerable hardship in paying energy bills. This manifestation of inequality has followed the global restructuring of electricity sectors, and its incidence has become widespread across Europe, the UK, the US, New Zealand, and Australia. Current policy measures generally resemble "retrospective compensation" rather than addressing the root cause of the problem. This paper argues for a new policy approach that reconfigures electricity price formation in order to address this increasingly embedded social phenomenon.     

Empirical confidence intervals for USDA commodity price forecasts

Conventional procedures for calculating confidence limits of forecasts generated by statistical models provide little guidance for forecasts based on a combination or a consensus process rather than formal models, as is the case with US Department of Agriculture (USDA) forecasts. This study applied and compared several procedures for calculating empirical confidence intervals for USDA forecasts of corn, soybean and wheat prices over the 1980/81 through 2006/07 marketing years. Alternative procedures were compared based on out-of-sample performance over 1995/96 through 2006/07. The results of this study demonstrate that kernel density, quantile distribution and best fitting parametric distribution (logistic) methods provided confidence intervals calibrated at the 80% level prior to harvest and 90% level after harvest. The kernel density-based method appears most accurate both before and after harvest with the final value falling inside the forecast interval 77% of the time before harvest and 92% after harvest, followed by quantile regression (73% and 91% before and after harvest, respectively) logistic distribution (73% and 90% before and after harvest, respectively) and histogram (66% and 84% before and after harvest, respectively). Overall, this study demonstrates that empirical approaches may be used to construct more accurate confidence intervals for USDA corn, soybean and wheat price forecasts.     

Decision Neuroscience

This article presents an introduction to and analysis of an emerging area of research, namely decision neuroscience, whose goal is to integrate research in neuroscience and behavioral decision making. The article includes an exposition of (1) how the exponential accumulation of knowledge in neuroscience can potentially enrich research on decision making, (2) the range of techniques in neuroscience that can be used to shed light on various decision making phenomena, (3) examples of potential research in this emerging area, and (4) some of the challenges readers need to be cognizant of while venturing into this new area of research. The genesis of this workshop session and article was a meeting that Dipankar Chakravarti, Antoine Bechara, and Baba Shiv had one balmy Iowa City summer afternoon in 2003. We coined the term Decision Neuroscience to describe the emerging stream of research outlined in this article.     

Authors’ Reply: Two Paradigms for the Market Value of Liabilities - Discussion by David F. Babbel; Michael Cohen; J. Peter Duran; Luke N. Girard; Thomas S.Y. Ho; Craig Merrill

Abstract

In this article, we express a concern that certain commonly accepted methods of predicting mortality will likely prove to be inadequate in the future. Specifically, the Lee-Carter method, which overall has been empirically successful, uses auto-regressive moving average (ARMA) technology and contains no structural mortality equation. This structure means that no information other than previous history can be introduced. We argue that rapid advances in medical science are taking place and that failure to reflect this information in our projection methodology will make resulting projections unsuitable.