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.

Page 1 of 1012345678910

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

Categorical Versus Stock-specific Thinking

How much do equity investors leave on the table by focusing on categories of stocks (industry or style) and paying little attention to individual stocks? In her July 2013 paper entitled “Categorical Thinking in Portfolio Choice”, Swasti Gupta-Mukherjee investigates whether such simplifying categorical thinking is economically beneficial and what factors magnify or diminish it. She focuses on managers of U.S. equity mutual funds. She defines a Categorical Thinking Index (CTI) as relative emphasis on category-wide information relative to stock-specific information for making portfolio adjustments. She implements CTI based on two-digit SIC codes (broad industries) as categories. Each quarter, she measures CTI for a mutual fund based on the explanatory power of past industry returns for portfolio changes relative to the explanatory power of past individual stock returns for these changes. Using monthly returns, quarterly holdings and style (benchmark) representations for 2,812 U.S. equity mutual funds, along with associated industry and individual stock returns, during 1990 to 2011, she finds that: Keep Reading

Investor Perception/Anticipation of Tail Events

How do individuals perceive and position for Black Swans? In his March 2013 paper entitled “The Psychology of Tail Events: Progress and Challenges”, Nicholas Barberis employs a two-step framework to summarize recent research on the psychology of tail events. He first addresses belief about the probability of a tail event. He then covers actions/decisions based on this belief, with focus on the concept of probability weighting. Based on the available body of research, he finds that: Keep Reading

Socially Amplified Trading?

How do relevant electronic social networks affect individual investing? In their March 2012 paper entitled “Facebook Finance: How Social Interaction Propagates Active Investing”, Rawley Heimer and David Simon investigate the propagation of active investing strategies within a Facebook-like social network of retail foreign exchange traders. Registered users of this free network (who must have a qualified foreign exchange broker account) have access to: (1) an indicator of the aggregate positions of the entire network in specific currency pairs; and, (2) a real-time view of the trading activity of mutually accepted “friends.” The network receives information about user trades instantly from qualified brokers. Using a complete record of activities within this network involving more than 5,500 foreign exchange traders, two million time-stamped trades and 140,000 messages and friendships mostly between February 2009 and December 2010, they find that: Keep Reading

Individual Investors in Bull and Bear Markets

How do individual investors adjust trading behaviors during bull and bear markets? Are any such adjustments advantageous? In their December 2011 paper entitled “Don’t Confuse Brains with a Bull Market: Attribution Bias, Market Condition, and Trading Behavior of Individual Investors”, Zhen Shi and Na Wang examine the trading behaviors of individual investors during different market conditions. They apply a regime switching model to the Chinese stock market to identify: a normal market during January 2005 through August 2006; a bull market during September 2006 through October 2007; and, a bear market during November 2007 through November 2008. They define excessiveness of trading based on two measures: (1) the performance of stocks bought versus that of stocks sold; and, (2) the relationship between portfolio turnover and performance. Using the trading records of 15,040 randomly selected individual Chinese investors during January 2005 through November 2008 (2,357,959 trades), they find that: Keep Reading

Page 1 of 1012345678910
Avoiding Investment Strategy Flame-outs eBook
Login
Current Momentum Winners

ETF Momentum Signal
for September 2014 (Final)

Momentum ETF Winner

Second Place ETF

Third Place ETF

Gross Momentum Portfolio Gains
(Since August 2006)
Top 1 ETF Top 2 ETFs
222% 229%
Top 3 ETFs SPY
221% 81%
Strategy Overview
Recent Research
Popular Posts
Popular Subscriber-Only Posts