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Big Ideas

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

Stock Return Model Snooping

How special is the Fama-French three-factor model (market, size, book-to-market ratio) compared to other possible three-factor models? In their November 2012 paper entitled “Firm Characteristics and Empirical Factor Models: a Data-Mining Experiment”, Leonid Kogan and Mary Tian systematically compare explanatory breadth for all 351 three-factor and 2,925 four-factor (linear) models for predicting stock returns that can be formed from 27 firm characteristics other than size and book-to-market ratio. They measure explanatory breadth of a model by how well it captures the average future return differences across value-weighted deciles from annual sorts on the characteristics not used in the model. Using monthly returns and annual/quarterly firm characteristics for a broad sample of non-financial U.S. stocks during 1971 through 2011, they find that: Keep Reading

A Few Notes on The Physics of Wall Street

James Weatherall, physicist, mathematician and philosopher, introduces his 2012 book, The Physics of Wall Street: A Brief History of Predicting the Unpredictable, by stating: “This book tells the story of physicists in finance. …It is about how the quants came to be, and about how to understand the ‘complex mathematical models’ that have become central to modern finance.” Tracing the historical stream of key contributions by physicists and mathematicians to finance, he concludes that: Keep Reading

The Illiquidity Premium Worldwide

Can investors systematically earn a premium by holding relatively illiquid assets? In their December 2012 paper entitled “The Illiquidity Premium: International Evidence”, Yakov Amihud, Allaudeen Hameed, Wenjin Kang and Huiping Zhang examine the illiquidity premium in 26 developed and 19 emerging equity markets. They measure illiquidity as the average ratio of absolute daily stock return to trading volume (price impact per monetary volume traded). They define the illiquidity premium as the average gross return in excess of the risk-free rate for volatility-controlled portfolios that are long (short) high-illiquidity (low-illiquidity) stocks. Specifically, every three months, they: (1) sort stocks into three equal groups (terciles) based on return volatility (standard deviation of daily returns) over a lagged, rolling three-month window; (2) within each volatility tercile, sort stocks into fifths (quintiles) based on illiquidity over the same window; (3) skip one month to avoid any short-term reversal; and, (4) calculate the illiquidity premium as the average of returns of three portfolios that are long (short) the high-illiquidity (low-illiquidity) quintile within each volatility tercile. They groom the sample by excluding stocks that trade infrequently or exhibit extreme movements. They consider equal, value and monetary volume weightings for portfolios. Using daily price, trading volume and shares outstanding data for common stocks in 45 countries, along with estimates of market, size and book-to-market risk factors, during 1990 through 2011 (22 years), they find that: Keep Reading

When Stock Picking Works

When should an investor favor picking individual stocks over holding a stock index fund? In their November 2012 paper entitled “On Diversification”, Ben Jacobsen and Frans de Roon derive from Modern Portfolio Theory simple rules to compare concentrated investment in a portfolio of one or a few stocks to a broad, diversified (value-weighted) benchmark portfolio. The essential rule is that a concentrated portfolio is preferable to the benchmark portfolio if the product of its expected Sharpe ratio and the expected correlation of its returns with the benchmark’s returns exceeds the expected Sharpe ratio of the benchmark. They apply derivative thumb rules to real stocks to determine conditions under which stock picking is preferable to buying and holding a diversified benchmark portfolio. Using theoretical derivations and monthly returns and fundamentals for the 500 largest non-financial companies as of the end of the sample period with a history of at least five years during 1926 through 2011, they find that: Keep Reading

A Few Notes on Antifragile

Nassim Taleb introduces his 2012 book, Antifragile, Things That Gain from Disorder, as “…my central work. I’ve had only one master idea, each time taken to the next step, the last step–this book–being more like a big jump. I am reconnected to my ‘practical self,’ my soul of a practitioner, as this is a merger of my entire history as practitioner and ‘volatility specialist’ combined with my intellectual and philosophical interests in randomness and uncertainty… …the relationship of this book to The Black Swan would be as follows:…Antifragile would be the main volume and The Black Swan its backup of sorts, and a theoretical one, perhaps even its junior appendix. Why? Because The Black Swan (and its predecessor, Fooled by Randomness) were written to convince us of a dire situation, and worked hard at it; this one starts from the position that one does not need convincing that (a) Black Swans dominate society and history (and people, because of ex post rationalization, think themselves capable of understanding them); (b) as a consequence, we don’t quite know what’s going on, particularly under severe nonlinearities…” For investors, key points are: Keep Reading

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

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