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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.

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

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:

Keep Reading

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

Personal/Social Drivers of Individual Investor Asset Allocation

How strong is investor herding with respect to friends, family and co-workers? In their June 2014 paper entitled “Peer Effects, Personal Characteristics and Asset Allocation”, Annie Zhang, Ben Jacobsen and Ben Marshall examine the roles of personal characteristics (age, gender, wealth and tax rate), peer influence (household, neighbors and coworkers), and financial advice in individual investor asset class allocations and switching decisions. Their data are for individual holders of KiwiSaver accounts in New Zealand (similar to U.S. 401(k) accounts). Asset classes available to KiwiSavers via funds include cash, bonds, equity and real estate. Using KiwiSaver account data for over 40,000 individual investors spanning 28,000 households, 450 neighborhoods and 14,000 employers during July 2007 through June 2011, they find that: Keep Reading

Speculation the Dominant Gold Price Driver?

Are gold price movements predictable? In his December 2013 paper entitled “Gold. The Bursting of a Bubble?”, Tim Verheyden assesses gold price predictability in two ways. First, he applies an autoregressive integrated moving average (ARIMA) model to assess the value of gold price technical analysis (whether past price behavior predicts future price behavior). Second, he examines interaction of gold price with inflation to assess whether the latter drives the former. Using monthly gold prices and U.S. Consumer Price Index data during January 2001 through September 2013, he finds that: Keep Reading

Two Self-destructive Individual Investor Behaviors

What individual investment behaviors are worst? In their January 2014 paper entitled “Which Investment Behaviors Really Matter for Individual Investors?”, Joachim Weber, Steffen Meyer, Benjamin Loos and Andreas Hackethal investigate relationships between the following ten tendencies of individual investors and portfolio performance:

  1. Portfolio turnover: unprogrammed trading volume scaled by portfolio value.
  2. Trade clustering: clustering of investor trades in time.
  3. Disposition effect: selling of winners and holding of losers.
  4. Leading turnover: trading before other investors (same security/same direction).
  5. Forecasting skill: systematically realizing excess returns on purchased securities.
  6. Trend following: buying funds with recent increases in value.
  7. Home bias: preference for German stocks or Germany-focused funds.
  8. Local bias: preference for stocks/funds with nearby headquarters.
  9. Lottery mentality: preference for stocks with low price and high idiosyncratic volatility/skewness.
  10. Under-diversification: holding only a few securities and/or highly correlated securities.

Using trading records, monthly position statements and demographics for 5,000 predominantly German individual investors who use a discount broker spanning January 1999 through November 2011, they find that: Keep Reading

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