Big Ideas

Is there a way to deal with structural breaks more precisely than just expressing vague skepticism about the usefulness of old data? In the April 2007 draft of their paper entitled “How Useful Are Historical Data for Forecasting the Long-run Equity Return Distribution?”, John Maheu and Thomas McCurdy describe and test a methodology for identifying and calibrating structural breaks in long-term excess equity returns. Using monthly U.S. equity return and risk-free rate data for the period February 1885 through December 2003, they conclude that: Keep Reading

Both the academic community and practitioners generate large numbers of studies, formal and informal, analyzing and forecasting financial markets. In this blog, we offer an organization of financial markets research by topics such as The Value Premium and Buybacks and Secondaries. Are there other organizing principles that might convey a more fundamental understanding? Reflecting on the hundreds of studies we have reviewed and the limitations of this research with regard to practical application, here is another framework for thinking about financial markets research: Keep Reading

Financial econometrics gives empirical life (and death) to financial market models. Where has this rapidly growing branch of economics been, where is it now and where is it going? In the October 2006 revision of his article entitled “Financial Econometrics”, Andrew Lo provides an introduction to four decades of the field’s most influential academic papers. Some of his key points are: Keep Reading

Are investing results ultimately just good or bad luck? In conflict with intuition perhaps derived from an essential human sense of self-worth, the strictest form of the Efficient Markets Hypothesis (EMH) says yes. Where has the EMH been and where is it going? In his recent article for The New Palgrave: A Dictionary of Economics entitled “Efficient Markets Hypothesis”, Andrew Lo assesses the past and the future of the EMH. Some of his key points are: Keep Reading

Many equity market researchers assume conventional three-factor (market return, size, book-to-market) and four-factor (plus momentum) models as standards of comparison for discovery of new sources of abnormal returns. Are they the best standards? Could they be derivatives of more economically fundamental sources of differences among individual stock returns? In their March 2007 paper entitled “Too Many Factors! Do We Need Them All?”, Soosung Hwang and Chensheng Lu seek to identify the minimum number of economically fundamental factors needed to explain why different stocks generate different returns. They investigate 16 factors (12 firm characteristics and four macroeconomic measures) that others have found to explain such return differences. Their principal test is to measure returns from zero-cost portfolios that are long stocks with high (top third) values and short stocks with low (bottom third) values of evaluated factors. Using data for a large sample of non-financial stocks during 1963-2005 and contemporaneous macroeconomic data, they conclude that: Keep Reading

Many investors and analysts use the Sharpe ratio (mean excess return per unit of risk) as a field-leveling measure of investment performance. Does this variable reliably indicate the best portfolio? In his brief January 2007 summary paper entitled “Beware the Sharpe Ratio”, Steve Christie applies the Generalized Method of Moments to test the portfolio discrimination power of the Sharpe ratio. Using two monthly data sets spanning 24 years for a set of multi-asset class portfolios created from index series and 18 years for a large group of mutual funds, he concludes that: Keep Reading

Attempting to follow long stock market trends is a common investment approach, with much guru attention focused on calling long-term tops and bottoms. Is this approach meaningful for investors as an avenue to improve upon buy-and-hold performance? In the December 2006 version of his paper entitled “Analyzing Regime Switching in Stock Returns: An Investment Perspective”, Jun Tu investigates the potential importance to investors of exploiting differences between bull and bear markets within a Bayesian framework that accommodates considerable uncertainty. Using monthly value-weighted stock return and volatility data for July 1963 to February 2006 (512 observations), he finds that: Keep Reading

Can the laws of physics and information theory help explain human psychology, specifically as exhibited by investors? In the December 2005 update of his paper entitled “The Physical Foundation of Human Mind and a New Theory of Investment”, Jing Chen: (1) builds upon the similarities between the mathematics of information theory and of physical entropy to explain certain human thinking patterns; and, (2) uses this synthesis to unify understanding of the behavior of financial markets. He posits that human thinking patterns are adaptations evolved (mostly in hunter/gatherer mode) to acquire efficiently the resources needed for survival, as constrained by physical laws. In a mostly theoretical discussion, he offers the following insights: Keep Reading

We have selected for retrospective review a few all-time “best selling” research papers of the past few years from the General Financial Markets category of the Social Science Research Network (SSRN). Here we summarize the August 2003 paper entitled “A Theory of Large Fluctuations in Stock Market Activity” (download count nearly 2,700) by Xavier Gabaix, Parameswaran Gopikrishnan, Vasiliki Plerou and Eugene Stanley. Why do stock prices vary more than company fundamentals? Why do stock markets crash? This paper proposes a theory of large stock market movements based upon a linkage between market activity and the size distribution of large financial institutions. Motivated by empirical findings that stock returns, trading volumes, number of trades, price impacts of trades and sizes of large investors all have power law distributions, the authors propose that: Keep Reading

Because economics and financial markets lack mature theoretical (deductive) foundations, these fields involve largely empirical (inductive) work. The principal mathematical tools in this pursuit derive from probability and statistics. In his 2003 book Probability Theory, The Logic of Science, E.T. Jaynes presents in textbook form his own evolutionary growth in the understanding of probability theory. His approach is Bayesian, in that he views probabilities as conceptually distinct from frequencies of occurrence and probability theory as synonymous with the process of inductive inquiry. He emphasizes iterative “plausible reasoning” as the kernel of probability theory. He offers a few summarizing points relevant to equity investors/traders, as follows: Keep Reading