Facts and Fantasies about Commodity Futures

We construct an equally-weighted index of commodity futures monthly returns over the period between July of 1959 and March of 2004 in order to study simple properties of commodity futures as an asset class. Fully-collateralized commodity futures have historically offered the same return and Sharpe ratio as equities. While the risk premium on commodity futures is essentially the same as equities, commodity futures returns are negatively correlated with equity returns and bond returns. The negative correlation between commodity futures and the other asset classes is due, in significant part, to different behavior over the business cycle. In addition, commodity futures are positively correlated with inflation, unexpected inflation, and changes in expected inflation.

The Fundamentals of Commodity Futures Returns

Commodity futures risk premiums vary across commodities and over time depending on the level of physical inventories, as predicted by the Theory of Storage. Using a comprehensive dataset on 31 commodity futures and physical inventories between 1969 and 2006, we show that the convenience yield is a decreasing, non-linear relationship of inventories. Price measures, such as the futures basis, prior futures returns, and spot returns reflect the state of inventories and are informative about commodity futures risk premiums. The excess returns to Spot and Futures Momentum and Backwardation strategies stem in part from the selection of commodities when inventories are low. Positions of futures markets participants are correlated with prices and inventory signals, but we reject the Keynesian "hedging pressure" hypothesis that these positions are an important determinant of risk premiums.

Long-Term Global Market Correlations

In this paper we examine the correlation structure of the major world equity markets over 150 years. We find that correlations vary considerably through time and are highest during periods of economic and financial integration such as the late 19th and 20th centuries. Our analysis suggests that the diversification benefits to global investing are not constant, and that they are currently low compared to the rest of capital market history. We decompose the diversification benefits into two parts: a component that is due to variation in the average correlation across markets, and a component that is due to the variation in the investment opportunity set. There are periods, like the last two decades, in which the opportunity set expands dramatically, and the benefits to diversification are driven primarily by the existence of marginal markets. For other periods, such as the two decades following World War II, risk reduction is due to low correlations among the major national markets. From this, we infer that periods of globalization have both benefits and drawbacks for international investors. They expand the opportunity set, but diversification relies increasingly on investment in emerging markets.

Global Real Estate Markets - Cycles and Fundamentals

The correlations among international real estate markets are surprisingly high, given the degree to which they are segmented. While industrial, office and retail properties exist all around the world, they are not economic substitutes because of locational specificity. In addition, the broad securitization of real estate property companies has, until recently, lagged that of other types of companies. Never-the-less, international property returns move together in dramatic fashion. In this paper, we use eleven years of global property returns to explore the factors influencing this co-movement. We attribute a substantial amount of the correlation across world property markets to the effects of changes in GNP, suggesting that real estate is a bet on fundamental economic variables which are correlated across countries. A decomposition shows that a local production factor is more important in some countries than in others.

Pairs Trading: Performance of a Relative Value Arbitrage Rule

We test a Wall Street investment strategy known as pairs trading' with daily data over the period 1962 through 1997. Stocks are matched into pairs according to minimum distance in historical normalized price space. We test the profitability of several trading rules with six-month trading periods over the 1962-1997 period, and find average annualized excess returns of up to 12 percent for a number of self-financing portfolios of top pairs. Part of these profits may be due to market microstructure effects. Nevertheless, our historical trading profits exceed a conservative estimate of transaction costs through most of the period. We bootstrap random pairs in order to distinguish pairs trading from pure mean-reversion strategies. The bootstrap results suggest that the pairs' effect differs from previously documented mean reversion profits.

Fooling Some of the People All of the Time: The Inefficient Performance and Persistence of Commodity Trading Advisors

Investors face significant barriers in evaluating the performance of hedge funds and commodity trading advisors (CTAs). The only available performance data comes from voluntary reporting to private companies. Funds have incentives to strategically report to these companies, causing these data sets to be severely biased. And, because hedge funds use nonlinear, state-dependent, leveraged strategies, it has proven difficult to determine whether they add value relative to benchmarks. We focus on commodity trading advisors, a subset of hedge funds, and show that during the period 1994-2007 CTA excess returns to investors (i.e., net of fees) averaged 85 basis points per annum over US T-bills, which is insignificantly different from zero. We estimate that CTAs on average earned gross excess returns (i.e., before fees) of 5.4%, which implies that funds captured most of their performance through charging fees. Yet, even before fees we find that CTAs display no alpha relative to simple futures strategies that are in the public domain. We argue that CTAs appear to persist as an asset class despite their poor performance, because they face no market discipline based on credible information. Our evidence suggests that investors' experience of poor performance is not common knowledge.

New Evidence on the First Financial Bubble

The first global financial bubble in stock prices occurred 1720 in Paris, London and the Netherlands. Explanations for these linked bubbles primarily focus on the irrationality of investor speculation and the corresponding stock price behavior of two large firms: the South Sea Company in Great Britain and the Mississippi Company in France. In this paper we examine a broad cross‐section of security price data to evaluate the causes of the bubbles. Using newly collected stock prices for British and Dutch firms in 1720, we find evidence against indiscriminate irrational exuberance and evidence in favor of speculation about two factors: the Atlantic trade and the incorporation of insurance companies. We study the role of innovation in the insurance market by examining market betas and volatilities of new insurance company shares, like (Pastor & Veronesi, Technological Revolutions and Stock Prices, 2009). We find strong evidence for a revolution in the insurance business in 1720. Our findings are consistent with the hypothesis that financial bubbles require a plausible story to justify investor optimism.

Uncovering the Internal Structure of the Indian Financial Market: Cross-correlation behavior in the NSE

The cross-correlations between price fluctuations of 201 frequently traded stocks in the National Stock Exchange (NSE) of India are analyzed in this paper. We use daily closing prices for the period 1996-2006, which coincides with the period of rapid transformation of the market following liberalization. The eigenvalue distribution of the cross-correlation matrix, $\mathbf{C}$, of NSE is found to be similar to that of developed markets, such as the New York Stock Exchange (NYSE): the majority of eigenvalues fall within the bounds expected for a random matrix constructed from mutually uncorrelated time series. Of the few largest eigenvalues that deviate from the bulk, the largest is identified with market-wide movements. The intermediate eigenvalues that occur between the largest and the bulk have been associated in NYSE with specific business sectors with strong intra-group interactions. However, in the Indian market, these deviating eigenvalues are comparatively very few and lie much closer to the bulk. We propose that this is because of the relative lack of distinct sector identity in the market, with the movement of stocks dominantly influenced by the overall market trend. This is shown by explicit construction of the interaction network in the market, first by generating the minimum spanning tree from the unfiltered correlation matrix, and later, using an improved method of generating the graph after filtering out the market mode and random effects from the data. Both methods show, compared to developed markets, the relative absence of clusters of co-moving stocks that belong to the same business sector. This is consistent with the general belief that emerging markets tend to be more correlated than developed markets.Comment: 15 pages, 8 figures, to appear in Proceedings of International Workshop on "Econophysics & Sociophysics of Markets & Networks" (Econophys-Kolkata III), Mar 12-15, 200

Breaking into the blackbox: Trend following, stop losses and the frequency of trading - The case of the S&P500

In this article, we compare a variety of technical trading rules in the context of investing in the S&P500 index. These rules are increasingly popular, both among retail investors and CTAs and similar investment funds. We find that a range of fairly simple rules, including the popular 200-day moving average (MA) trading rule, dominate the long-only, passive investment in the index. In particular, using the latter rule we find that popular stop-loss rules do not add value and that monthly end-of-month investment decision rules are superior to those which trade more frequently: this adds to the growing view that trading can damage your wealth. Finally, we compare the MA rule with a variety of simple fundamental metrics and find the latter far inferior to the technical rules over the last 60 years of investing