Animal Spirits

Are investors and traders cats, rationally and independently sniffing out returns? Or are they cows, flowing with a herd that must know something? These blog entries relate to behavioral finance, the study of the animal spirits of investing and trading.

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Path Dependence of Satisfying Returns

What makes investors happy with investment returns? In the April 2015 version of their paper entitled “All’s Well That Ends Well? On the Importance of How Returns Are Achieved”, Daniel Grosshans and Stefan Zeisberger employ a series of surveys to investigate how investor satisfaction depends on investment price path. Their main survey asks participants to imagine that they bought three winner stocks (10% terminal gain) and three loser stocks (10% terminal loss) one year ago, with the three in each set having distinct price paths: (1) down-up, (2) straight line (monotonic) and (3) up-down (see the figures below). It also asks how likely participants would be to hold or sell each stock, their minimum selling price and an estimate of the stock’s price after one more year. Using results from surveys of participants recruited via Amazon Mechanical Turk (MTurk) and of students in advanced finance courses, they find that: Keep Reading

A Few Notes on Irrational Exuberance

In the preface to the 2015 Third Edition of Irrational Exuberance, author Robert Shiller states: “…evidence of bubbles has accelerated since the [2007-2009 world financial] crisis. Valuations in the stock and bond markets have reached high levels in the United States and some other countries, and valuations in the housing market have been increasing rapidly in many countries. …The bubbly and apparently unstable situation warrants some concern, although not yet generally as extreme as when the first edition of this book issued a warning about the overpriced and vulnerable stock market…, or when the second edition of this book issued a warning about the overpriced and vulnerable housing market…” Based on his judgement and considerable cited research, he concludes that: Keep Reading

Interactions among Stock Size, Stock Price and the January Effect

Is there an exploitable interaction between a stock’s market capitalization and its price? In their February 2015 paper entitled “Nominal Prices Matter”, Vijay Singal and Jitendra Tayal examine the relationship between stock prices and returns after: (1) controlling for market capitalization (size); (2) isolating the month of January; and, (3) excluding very small stocks. They each year perform double-sorts based on end-of-November data first into ranked tenths (deciles) by size and then within each size decile into price deciles. They calculate returns for January and for the calendar year with and without January. Using monthly prices and end-of-November market capitalizations for the 3,000 largest U.S. common stocks during December 1962 through December 2013, quarterly institutional ownership data for each stock during December 1980 through December 2013, and actual number of shareholders for each stock during 2004 through 2012, they find that: Keep Reading

Betting Against Lottery Stocks

Do lottery traders create the low-volatility (betting-against-beta) effect by overpricing high-beta stocks? In the December 2014 version of their paper entitled “Betting against Beta or Demand for Lottery”, Turan Bali, Stephen Brown, Scott Murray and Yi Tang investigate whether demand for lottery-like stocks drives the empirically low (high) abnormal returns of stocks with high (low) betas. They measure lottery demand for a stock as the average of its five highest daily returns over the past month. They measure beta for a stock as the slope from a regression of its daily excess (relative to the risk-free rate) stock returns versus daily excess stock market returns over the past 12 months. They hypothesize that lottery traders drive current prices of stocks with high lottery demand upward, thereby depressing their expected returns. They further hypothesize that stocks with high lottery demand tend to be high-beta stocks. Using daily and monthly returns and characteristics for a broad sample of U.S. common stocks (excluding those priced under $5), associated firm accounting data and relevant financial variables during July 1963 through December 2012 (594 months), they find that: Keep Reading

Doom and the Stock Market

Is proximity to doom good or bad for the stock market? To measure proximity to doom, we use the “Doomsday Clock” “Minutes-to-Midnight” metric, revised occasionally via the Bulletin of the Atomic Scientists, which “conveys how close we are to destroying our civilization with dangerous technologies of our own making. First and foremost among these are nuclear weapons, but the dangers include climate-changing technologies, emerging biotechnologies, and cybertechnology that could inflict irrevocable harm, whether by intention, miscalculation, or by accident, to our way of life and to the planet.” Using the timeline for the Doomsday Clock since inception and contemporaneous annual returns for the Dow Jones Industrial Average (DJIA) during 1947 through 2014 (22 doom proximity judgments), we find that: Keep Reading

Why Stock Gurus Warn?

Does a need to attract attention distort the information offered by online stock bloggers? Does competition among them suppress or amplify this distortion? In their November 2014 paper entitled “Guru Dreams and Competition: An Anatomy of the Economics of Blogs”, Yi Dong, Massimo Massa and Hong Zhang investigate whether: (1) stock bloggers are informative; and, (2) competition among them enhances the quality of information provided. They start by relating blog activity to two proxies for informed versus liquidity trading. They then test the relationship between future stock returns and blog tone, with focus on tone extremism. Finally, they assess the impact of competition among stock bloggers, defining blog activity as competitive when the number of bloggers covering a stock is among the top fourth across all stocks. Using a hand-collected sample of blog articles covering S&P 1500 stocks during 2006 through 2011, they find that:

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Transient Abnormal Returns for Major College Football Bowl Sponsors

Do U.S. television events that draw large attentive audiences have an effect on the stock prices of main corporate sponsors? In the September 2014 version of his paper entitled “Investor Attention and Stock Prices: Evidence from a Natural Experiment”, Erik Mayer investigates whether main sponsorship of NCAA Division I college football bowl games affects sponsor stock prices during the trading days following the game. Main means that the firm name attaches to the bowl name. He considers three measures of attention: television viewership rating, game score differential (measuring game excitement) and size of competing universities. He uses detailed trading data to determine sources and direction (buy or sell) of trading. Using daily stock prices, trading data and accounting data for 36 publicly traded companies who are the main sponsors of 238 college football bowl games played following the 1991 through 2012 football seasons, and data for the three associated attention measures, he finds that: Keep Reading

Models vs. Experts

Should investors view financial experts as individuals who, through years of study and experience, overcome behavioral biases and reliably add value to investment decisions? In his May 2014 essay entitled “Are You Trying Too Hard?”, Wesley Gray summarizes research that compares the decision-making of experts to the performance of mechanical models across many fields. He highlights the relevance to of this research to investment decision-making. Based on the body of research pitting expert judgment against mechanical models, he concludes that: Keep Reading

Overview of Behavioral Finance

Behavioral finance encompasses research on how investors fall short of a rational ideal in decision-making, and how markets are thereby somewhat inefficient. In his August 2014 paper entitled “Behavioral Finance”, David Hirshleifer examines sources of investor biases and provides an overview of research tying these biases to research on how they affect trading and market prices. Based on theory and the body of behavioral finance research, he concludes that: Keep Reading

Harvesting Volatility Generated by Naive Investors

What is the best way to harvest asset mispricings derived from aggregate overreaction/underreaction by naive investors? In his July 2014 presentation package entitled “Betting On ‘Dumb Volatility’ with ‘Smart Beta'”, Claude Erb examines strategies for exploiting the “dumb volatility” arguably generated by naive investors who buy high and sell low, temporarily driving prices materially above and below fair values. These strategies generally involve periodically rebalancing portfolios to equal weights or some version of fair value weights (smart beta). Using monthly returns for a variety of indexes and funds during December 2004 through June 2014 (since the advent of smart beta research), he finds that: Keep Reading

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