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Big Ideas

These blog entries offer some big ideas of lasting value relevant for investing and trading.

Countering High-frequency Traders

How can low-frequency traders save their microscalps from high-frequency traders? In the March 2012 version of their paper entitled “The Volume Clock: Insights into the High Frequency Paradigm”, David Easley, Marcos Lopez de Prado and Maureen O’Hara explore high-frequency trading (HFT) as volume-metered or transaction-metered (rather than time-metered) exploitation of market order processing rules and trading behaviors of others (market microstructure). Low-frequency traders focus on return drivers associated with economic/monetary policy, asset allocation and valuation methods. High-frequency traders fixate on exchange order matching engines, network connections, machine learning, order placement delays and game theory. When market microstructures differ (as for seemingly similar cash and futures equity index markets), HFT strategies differ. Based on relevant research, they conclude that: Keep Reading

A Few Notes on Jackass Investing

Michael Dever (founder of Brandywine Asset Management) introduces his 2011 book, Jackass Investing: Don’t Do It, Profit from It, by stating: “…this book is designed to comfortably provide novice investors with a plan to follow to manage their money – one that they are unlikely to encounter if they are only exposed to the conventional financial wisdom. It’s also intended to provide a rational alternative to the beliefs of experienced investors who may have fallen prey to the myths; written to help you to specifically exploit some of the countless opportunities that are ignored by, and very often created by, the mass of irrational investors who litter the virtual Wall Street landscape. …the most important theme of them all is the fallacy of the myth that ‘There is No Free Lunch.’ In fact there is a free lunch, a veritable free threecourse buffet. It’s called true portfolio diversification…a systematic process that results in a truly balanced diversified portfolio whose returns are derived from a multitude of return drivers.” Using anecdotes and references to some relevant research in describing and refuting financial market myths, he concludes that: Keep Reading

Liquidity Eroding Anomalies?

Are low trading frictions, high trading speed and proliferation of trading strategies elevating market efficiency and thereby extinguishing U.S. stock anomalies? In their March 2012 paper entitled “Trends in the Cross-Section of Expected Stock Returns”, Tarun Chordia, Avanidhar Subrahmanyam and Qing Tong examine the evolution of individual U.S. stock return predictability based on stock/firm characteristics found in the past (mostly during the 1990s) to have predictive power. Characteristics considered include: size; book-to-market ratio; share turnover; cumulative return over five months ending one month ago; cumulative return over six months ending six months ago; monthly return reversal; stock illiquidity; stock price; analyst forecast dispersion; standardized unexpected earnings; and, accounting accruals. They test anomaly evolution across two subperiods using both regressions and long-short portfolios. Using data as available for NYSE-AMEX common stocks during 1976-2009 (with equal subperiods 1976-1992 and 1993- 2009) and for NASDAQ commons stocks during 1983-2009 (with comparable subperiods 1983-1992 and 1993-2009), they find that: Keep Reading

Verdict on Financial Markets Efficiency?

What do three prominent academic experts conclude when they review the body of evidence for and against the Efficient Markets Hypothesis (EMH), and therefore the potential benefit of speculation? In the April 2011 version of their paper entitled “Review of the Efficient Market Theory and Evidence”, Andrew Ang, William Goetzmann and Stephen Schaefer review the theoretical and empirical literature on EMH, with focus on implications for active investment management. They consider a range of markets and tests of both prices and investment managers, noting that EMH has evolved to consider the costs of collecting, analyzing and exploiting market information (trading frictions, financing costs, manager fees). Based on this literature review, they conclude that: Keep Reading

Alternative Portfolio Efficiency Measures

Some experts use the mean-variance analysis of Modern Portfolio Theory (MPT), which penalizes large upside volatility, to measure portfolio efficiency. Others use Second-order Stochastic Dominance (SSD) analysis, purer mathematically than MPT but open to unrealistic investor behavior. Is there a better way? In the February 2012 version of his paper entitled “The Passive Stock Market Portfolio is Highly Inefficient for Almost All Investors”, Thierry Post describes and tests a portfolio efficiency measure based on an Almost Second-order Stochastic Dominance (ASSD) that aims to exclude unrealistic investor behaviors. He applies the measure to a market portfolio (value-weighted average of NYSE, AMEX and NASDAQ stocks) and three alternative sets of ten equity portfolios formed using NYSE decile breakpoints for: (1) market capitalization (size); (2) book-to-market ratio; and, (3) past 11-month return with skip month (momentum). He considers investment horizons of one, 12 and 120 months over sample periods of 1926-2011 and 1963-2011. Using monthly value-weighted returns and contemporaneous stock/firm characteristics from July 1926 through December 2011 (1,026 months), along with the contemporaneous one-month Treasury bill yield as the risk-free rate, he finds that: Keep Reading

The 2000s: A Market Timer’s Decade?

Do the poor returns and high volatility of the “buy-and-hold-is-dead” U.S. stock market since the beginning of 2000 represent a tailwind for market timers? In other words, is buy-and-hold effective as a benchmark for distinguishing between market timer luck and skill in recent years? To check, we measure the performances of various simple monthly market timing approaches (equal weighting with cash, 10-month simple moving average signals, momentum, and coin-flipping) during the 2000s. Using monthly closes for the dividend-adjusted S&P 500 Depository Receipts (SPY), the 3-month Treasury bill (T-bill) yield and the S&P 500 Index from December 1999 through October 2011 (earlier for S&P 500 Index signal calculations), we find that: Keep Reading

Two Biggest Mistakes of Long-term Investors

How can long-term investors maximize their edge of strategic patience? In their November 2011 paper entitled “Investing for the Long Run”, Andrew Ang and Knut Kjaer offer advice on successful long-term investing (such as by pension funds).  They define a long-term investor as one having no material short-term liabilities or liquidity demands. Using the California Public Employee’s Retirement System and other large institutions as examples, they conclude that: Keep Reading

Mean Reversion of Stock Markets

How long does it take stock markets to revert to their long-run means? In their April 2010 paper entitled “Mean Reversion in International Stock Markets: An Empirical Analysis of the 20 th Century”, Laura Spierdijk, Jacob Bikker and Pieter van den Hoek analyze mean reversion in 17 developed countries (Australia, Belgium, Canada, Denmark, France, Germany, Ireland, Italy, Japan, the Netherlands, Norway, South-Africa, Spain, Sweden, Switzerland, United Kingdom and the United States) over 109 years based on annual data. Using annual levels of 17 country stock market indexes and a composite worldwide index during 1900 through 2008, they find that: Keep Reading

Bull, Bear, Wolf, Sheep…?

The conventional binary animal metaphor for markets is bull (good returns, low volatility) and bear (poor returns, high volatility). Does rigorous analysis of empirical evidence support belief in (just) two market states? In their September 2011 paper entitled “The Number of Regimes Across Asset Returns: Identification and Economic Value”, Mathieu Gatumel and Florian Ielpo apply a regime-switching model and Monte Carlo simulations to determine the likely number of regimes implicit in the returns of 19 asset classes. Their general approach is to increase the number of regimes included in the model until adding a regime no longer materially improves the fit of the model to the actual return distribution. In other words, among statistically equivalent models, they always choose the one with the smallest number of regimes. They discuss the persistence of and performance under each regime discovered.  Using weekly return data for various stock  and bond indexes, currency exchange rates and commodity indexes over the period April 1998 through mid-December 2010 (650 weeks or 12.5 years), they find that: Keep Reading

Return-based Analysis of Demographics as Stock Market Predictor

Analyses such as those described in “Demographic Headwind for U.S. Stock Market?” and “Classic Research: Demography and the Stock Market” assess the impact of demographic changes on the stock market by focusing on market valuation as measured by price-earnings ratio (P/E). What story would a more direct analysis of demographics and stock market returns tell? To investigate, we: (1) collect historical U.S. age demographics; (2) construct an annual series of the ratio of middle-age cohort (ages 40–49) population to the old-age cohort (ages 60–69) population (designated M/O, similar to the metric described in “Demographic Headwind for U.S. Stock Market?”) to capture the joint behavior of presumed equity investors and equity disinvestors; and, (3) relate M/O to annual U.S. stock market returns. Using estimated annual (July 1) age demographics for 1900-2009, 2010 census age demographics, annual S&P 500 Index returns (June 30 to June 30) for 1950 through 2011, annual Dow Jones Industrial Average (DJIA) returns (June 30 – June 30) for 1929 through 2011 and annual Consumer Price Index (CPI) data (June) for 1913 through 2011, we find that: Keep Reading

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