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Big Ideas

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

A Few Notes on DIY Financial Advisor

Wesley Gray, Jack Vogel and David Foulke preface their 2015 book, DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth, by stating that: “This book is a synopsis of our research findings developed while serving as a consultant and asset manager for large family offices. …Our book is meant to be an educational journey that slowly builds confidence in one’s own ability to manage a portfolio. In our book, we explore a potential solution that can be applicable to a wide variety of investors, from the ultra-high-net-worth to middle-class individual, all of whom are focused on similar goals  of preserving and growing their capital over time.” Based on their research, they conclude that: Keep Reading

Pros and Cons of New Technology-enabled Indexes

What are pros and cons of extending the definition of financial index beyond conventional market capitalization (buy-and-hold) weighting? In the October 2015 draft of his paper entitled “What Is an Index?”, Andrew Lo proposes that any portfolio satisfying three properties should be considered an index: (1) transparent (public and verifiable); (2) investable (realistic and liquid benchmark); and, (3) entirely rules-based (allowing no judgment/discretion). He calls indexes that are not weighted by market capitalization dynamic indexes (requiring frequent rebalancing). He distinguishes between active investing and active risk management. He also addresses the elevated risk of snooping bias as dynamic indexes proliferate. Based on a broader perspective on indexes, he concludes that: Keep Reading

A Few Notes on Systematic Trading

Robert Carver introduces his 2015 book, Systematic Trading: A Unique New Method for Designing Trading and Investing Systems, by stating that: “I don’t believe there is any magic system that will automatically make you huge profits, and you should be wary of anyone who says otherwise, especially if they want to sell it to you. Instead, success in systematic trading is mostly down to avoiding common mistakes such as over complicating your system, being too optimistic about likely returns, taking excessive risks, and trading too often. I will help you avoid these errors. This won’t guarantee returns, but it will make failure less likely. My framework…can be adapted to meet your needs. …Each element of the framework has been carefully designed… I’ll explain the available options, which I prefer, and why.” Based on his experience as a trader/portfolio manager and specific research, he concludes that: Keep Reading

Sociology of Financial Markets Research?

What does a large online repository of research on financial markets say about community interactions? In the August 2015 version of his article entitled “Recent Trends in Empirical Finance”, Marcos Lopez de Prado measures trends in level of research activity, topical emphasis, level of interest as measured by downloads and level of collaboration. Based on data for 128,897 research papers by 72,070 authors posted on SSRN’s Financial Economics Network (as of June 4, 2015), he finds that: Keep Reading

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

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