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Big Ideas

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

Index Investing Makes Stock Picking Harder?

How does growth in capitalization-weighted equity index investing affect the stock market? In the December 2014 update of their paper entitled “Indexing and Stock Price Efficiency”, Nan Qin and Vijay Singal examine the relationship between equity index investing (driven passively by liquidity trading and index changes, not actively by information) and stock price efficiency. They estimate equity index (passive) investing from holdings of 591 equity index mutual funds, enhanced index mutual funds, exchange-traded funds and closet indexers. They measure each stock’s passive (non-passive) ownership as the percentage of shares held by these funds (other funds) at the end of each quarter, with the lower bound of passive (non-passive) trading volume the absolute quarterly change in holdings of these (other) funds. They measure stock price efficiency by: (1) magnitude of post-earnings announcement drift (response to new information); and, (2) intraday and daily deviations of price from a random walk. Each quarter, they relate these measures of price inefficiency to level of index ownership across stocks. Using intraday and daily return, earnings announcement and quarterly fund holdings data for S&P 500 Index stocks and size/turnover-matched stocks during 2002 (post-decimalization) through 2013, they find that: Keep Reading

Stock Return Anomalies Just Artifacts of Premium Volatility?

Is it misleading to view factor risk premiums (such as for market, size and value) as constant over time? In his June 2015 paper entitled “Dynamic Risk Premia and Asset Pricing Puzzles”, Andy Jia-Yuh Yeh generates time-varying (dynamic) risk premiums for the Fama-French five-factor asset pricing model and explores whether widely accepted asset pricing anomalies exist after accounting for premium dynamics. Specifically, he applies a filter “trained” by rolling 60-month histories of risk factor returns to generate time-varying series for the market, size, book-to-market, profitability and investment risk factor premiums. He then tests whether widely accepted size, value, momentum, investment, profitability, short-term reversal and long-term reversion stock return anomalies remain statistically significant after accounting for premium time variation. Using monthly returns for U.S. stock factor portfolios from Kenneth French’s library spanning January 1964 through December 2013, he finds that: Keep Reading

Fixing Empirical Finance

What are the most pressing systematic weaknesses in financial research, and how should the investment community address them? In the May 2015 version of his article entitled “The Future of Empirical Finance”, Marcos Lopez de Prado identifies three major problems in empirical finance and proposes ways to mitigate them. Based on his experience and common sense arguments and references to some research, he concludes that: Keep Reading

Inherent Inhibitors of Inference in Financial Markets

Are there intractable weaknesses of historical inference as a tool to predict the behaviors of financial markets? In the May 2015 draft of his article entitled “Beyond Backtesting: The Historical Evidence Trap”, Ulrich Hammerich briefly describes four weaknesses of backtesting more difficult to address than overfitting/snooping, neglect of trading frictions and data quality. He calls these weaknesses the technological trap, the market efficiency trap, the publication trap and the affiliation trap. Based on common sense arguments and references to some past research, he concludes that: Keep Reading

The Case Against Smart Beta Funds?

Smart beta strategies weight stocks according to one or a few historically predictive factors such as value, size, momentum or volatility rather than market capitalization. What are the cautions for investing in smart beta funds? In their April 2015 paper entitled “Smart Beta: Too Good to be True?”, Bruce Jacobs and Kenneth Levy critique the belief that smart beta strategies beat the market simply and cheaply. Using selected observations and citing some past research, they conclude that: Keep Reading

Measuring Extreme Loss Risk

What is the best approach for measuring extreme loss risk? In their April 2015 paper entitled “Why Risk Is So Hard to Measure”, Jon Danielsson and Chen Zhou analyze the robustness of standard extreme loss risk analysis methods. They focus on:

  1. The difference in the reliabilities of forecasts based on Value-at-Risk (VaR) and expected shortfall (ES)
  2. The reliabilities of these forecasts as sample size decreases.
  3. The difference in reliabilities of forecasts based on time scaling of high-frequency data (say, daily) versus overlapping high-frequency data to forecast risk over a many-day holding period.

In a nutshell, VaR assesses the probability that a portfolio loses at least a specified amount over a specified holding period, and ES is the expected portfolio return for a specified percentage of the worst losses during a specified holding period. The theoretically soundest sampling approach is to use non-overlapping past holding-period returns, but this approach usually means very small samples. Time scaling uses past high-frequency data once and scales findings to the longer holding period by multiplying by the square root of the holding period. Overlapping data re-uses past high-frequency data many times, thereby creating observations that are clearly not independent. Based on theoretical analysis and intensive Monte Carlo simulation derived from daily returns for a broad sample of liquid U.S. stocks during 1926 through 2014, they conclude that: Keep Reading

Survey of Recent Research on Factors, Regimes and Robustness

Why and how should investors pursue investment premiums associated with factors that explain performance differences among related assets (like common stocks)? In the January 2015 version of his paper entitled “Better Investing Through Factors, Regimes and Sensitivity Analysis”, Cristian Homescu summarizes recent research on: (1) factor-based investing; (2) enhancement of factor-based investing via regime switching models; and, (3) strategy robustness testing. Factor investing means systematic targeting of premiums associated with factors that explain an exploitable portion of return and risk differences among securities within one or several asset classes. Based on recent streams of research, he concludes that:

Keep Reading

Incorporating the Experience of the Financial Crisis

How should financial education incorporate the experience of the 2007-2009 financial crisis? In their May 2014 publication entitled Investment Management: A Science to Teach or an Art fo Learn?, Frank Fabozzi, Sergio Focardi and Caroline Jonas summarize the current approach to teaching finance theory and examine post-crisis criticisms and defenses of this approach via review of textbooks and studies and through interviews with finance professors, asset managers and other market players. Based on these sources, they conclude that: Keep Reading

Retirement Income Modeling Risks

How much can the (in)accuracy of retirement portfolio modeling assumptions affect conclusions about the safety of retirement income? In their December 2014 paper entitled “How Risky is Your Retirement Income Risk Model?”, Patrick Collins, Huy Lam and Josh Stampfli examine potential weaknesses in the following retirement income modeling approaches:

  • Theoretically grounded formulas – often complex with rigid assumptions.
  • Historical backtesting – the future will be like the past, requiring long samples.
  • Bootstrapping (reshuffled historical returns) – provides alternate histories but does not preserve return time series characteristics (such as serial correlation), and requires long samples.
  • Monte Carlo simulation with normal return distributions – sensitive to changes in assumed return statistics and often does not preserve empirical return time series characteristics.
  • Monte Carlo simulation with non-normal return distributions – complex and often does not preserve empirical return time series characteristics.
  • Vector autoregression – better reflects empirical time series characteristics and can incorporate predictive variables, but requires estimation of regression coefficients and is difficult to implement.
  • Regime-switching simulation (multiple interleaved return distributions representing different market states) – complex, requiring estimation of many parameters, and typically involves small samples in terms of number regimes.

They focus on retirement withdrawal sustainability (probability of shortfall) as a risk metric and risks associated with modeling (future asset returns), inflation and longevity assumptions. They employ a series of examples to demonstrate how an overly simple model may distort retirement income risk. Based on analysis and this series of examples, they conclude that: Keep Reading

A Few Notes on A Random Walk Down Wall Street

In the preface to the eleventh (2015) edition of his book entitled A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing, author Burton Malkiel states: “The message of the original edition was a very simple one: Investors would be far better off buying and holding an index fund than attempting to buy and sell individual securities or actively managed mutual funds. …Now, over forty years later, I believe even more strongly in that original thesis… Why, then, an eleventh edition of this book? …The answer is that there have been enormous changes in the financial instruments available to the public… In addition, investors can benefit from a critical analysis of the wealth of new information provided by academic researchers and market professionals… There have been so many bewildering claims about the stock market that it’s important to have a book that sets the record straight.” Based on a survey of financial markets research and his own analyses, he concludes that: Keep Reading

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