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Big Ideas

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

Accidental Alpha

How can equity weighting strategies and their opposites both outperform the stock market? In the October 2012 version of their paper entitled “The Surprising ‘Alpha’ from Malkiel’s Monkey and Upside-down Strategies”, Rob Arnott, Jason Hsu, Vitali Kalesnik and Phil Tindall challenge beliefs underlying a variety of stock investment strategies that beat a capitalization-weighted benchmark by examining the performance of portfolios based on opposite beliefs. If the original beliefs determine success, then their opposites should underperform. They limit their investigation to long-only stock weightings based on original beliefs and opposites based on inverse weights or complement weights. To ensure portfolio feasibility, they restrict U.S. and global universes to large-capitalization stocks. They reform portfolios at the end of each year. When needed in portfolio construction, they estimate historical parameters (such as volatility) using five years of lagged monthly data. They consider capitalization-weighted, equal-weighted and diversity-weighted benchmarks and use a conventional four-factor (market, size, book-to-market and momentum) model to calculate strategy alphas. They ignore trading frictions. Using monthly returns for the top 1,000 U.S. stocks by market capitalization during 1964 through 2010 and for large-capitalization global stocks during 1991 through 2010, they find that: Keep Reading

Empirical Beta-Return Relationship

Does demand for high-beta stocks by money managers extinguish the risk-return relationship? In his May 2012 paper entitled “Agency-Based Asset Pricing and the Beta Anomaly”, David Blitz investigates whether a volatility preference among stock portfolio managers flattens any relationship between beta and expected returns, thereby invalidating the most widely used asset pricing models. Because institutional investors typically evaluate portfolio managers versus market returns and prohibit or limit leverage, these managers have an incentive (under a belief in reward-for-risk) to focus investments in high-beta stocks with high expected returns. He calculates beta of a stock by regressing its monthly returns (in excess of the risk-free rate) against stock market excess monthly returns over the prior 60 months. Using monthly returns and characteristics for a broad sample of U.S. common stocks during July 1926 through December 2010, along with various benchmark data, he finds that: Keep Reading

Overview of Equity Return Predictors

What is the big picture on stock return predictors? In their May 2012 paper entitled “The Supraview of Return Predictive Signals”, Jeremiah Green, John Hand and Frank Zhang examine aggregate characteristics of 333 signals for which formal research indicates power to predict stock returns. They categorize each signal as accounting-based (from firm financial statements, such as accruals), finance-based (directly or indirectly from stock prices, such as return momentum) or other-based (such as stock buybacks). They standardize across studies via annualization by multiplying daily, weekly, monthly and quarterly returns by 250, 52, 12 and 4, respectively. They compile equal-weighted returns and value-weighted returns separately. They focus on Sharpe ratio as a widely used metric for comparing investment performance. Using a database of predictive signals as published in top-tier U.S. accounting, finance and practitioner journals and as disseminated in academic working papers via the Social Science Research Network (SSRN) during 1970 through 2010, they conclude that: Keep Reading

Persistence of Diversity in Investor/Trader Beliefs

Is there a “correct” (or at least most correct) view of how financial markets work? If so, why do the beliefs of market participants, sophisticated and naive, never converge narrowly to that view? Why do investors disagree so much all the time? The following items offer some ideas, from a generally behavioral perspective, on the persistence of diversity in investor/trader beliefs. Specifically: Keep Reading

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

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