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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.

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Social Trading Leader Overconfidence and Influence

Does investing “leader” overconfidence (self-attribution bias) transfer bad trading practices to other non-professional investors who participate in a social trading platform? In their March 2018 paper entitled “Self-Attribution Bias and Overconfidence Among Nonprofessional Traders”, Daniel Czaja and Florian Röder employ data from a large European social trading platform to examine: (1) how self-enhancement (attributing successes to self) and self-protection (attributing failures to external factors) components of self-attribution bias affect non-professional trading performance; and, (2) how social trading platforms transfer any such effects to other non-professional traders. The selected platform lets traders (leaders) manage and comment on virtual portfolios publicly. When enough other traders (followers) express interest in such a portfolio, a business partner of the platform offers a product that replicates its performance. After excluding portfolios managed by professional asset management firms, the authors perform content analysis on leader trading comments to measure the difference between first-person pronouns and third-person pronouns as indicators of self-enhancement and self-protection biases. They then relate leader bias to leader future performance and to inflows of associated investable portfolios from followers. Using daily transaction and performance data for 3,519 social trading portfolios managed by 2,010 European non-professional traders and available for investment for at least 360 days, including 45,623 leader comments, during June 2012 through November 2016, they find that:

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Thaler on Investors

In his January 2018 retrospective “Richard Thaler and the Rise of Behavioral Economics”, Nicholas Barberis reviews the development of behavioral (less than fully rational) models of economics and finance, with focus on Richard Thaler’s contributions. This retrospective summarizes key models that make psychology-based assumptions about: individual preferences; individual beliefs; and, the process by which individuals make decisions. He further segments work on individual preferences into: preferences over riskless choices; preferences over risky choices; time preferences; and, social preferences. From the body of behavioral finance ideas and research since the 1970s, he highlights:

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Beta Males Make Hedge Fund Alpha

Does appearance-based masculinity predict hedge fund manager performance? In their January 2018 paper entitled “Do Alpha Males Deliver Alpha? Testosterone and Hedge Funds”, Yan Lu and Melvyn Teo use facial width-to-height ratio (fWHR) as a positively related proxy for testosterone level to investigate the relationship between male hedge fund manager testosterone level and hedge fund performance. They each year in January sort hedge funds into tenths (deciles) based on fund manager fWHR and then measure the performance of these decile portfolios over the following year. Their main performance metric is 7-factor hedge fund alpha, which corrects for seven risks proxied by: (1) S&P 500 Index excess return; (2) difference between Russell 2000 Index and S&P 500 Index returns; (3) 10-year U.S. Treasury note (T-note) yield, adjusted for duration, minus 3-month U.S. Treasury bill yield; (4) change in spread between Moody’s BAA bond and T-note, adjusted for duration; and, (5-7) excess returns on straddle options portfolios for currencies, commodities and bonds constructed to replicate trend-following strategies in these asset classes. They collect 3,228 hedge fund manager photographs via Google image searches, choosing the best for each manager based on resolution, degree of forward facing and neutrality of expression. They use these photographs to measure fWHR as the distance between the two zygions (width) relative to the distance between the upper lip and the midpoint of the inner ends of the eyebrows (height). Using these fWHRs, monthly net-of-fee returns and assets under management of 3,868 associated live and dead hedge funds, and monthly risk factor values during January 1994 through December 2015, they find that:

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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 intermittently 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 (1947) and contemporaneous December levels of Shiller’s S&P Composite Index through early 2018 (25 distinct doom proximity judgments), we find that: Keep Reading

Ask for Advisor’s Personal Investing Performance?

Are financial advisors expert guides for their client investors? In their December 2017 paper entitled “The Misguided Beliefs of Financial Advisors”, Juhani Linnainmaa, Brian Melzer and Alessandro Previtero compare investing practices/results of Canadian financial advisors to those of their clients, including trading patterns, fees and returns. They estimate account alphas via multi-factor models. Using detailed data from two large Canadian mutual fund dealers (accounting for about 5% of their sector) for 3,276 Canadian financial advisors and their 488,263 clients, and returns and fees for 3,023 associated mutual funds, during January 1999 through December 2013, they find that:

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Crypto-manias?

Are there rational ways to decide whether cryptocurrencies such as Bitcoin are in bubbles? In their December 2017 paper entitled “Datestamping the Bitcoin and Ethereum Bubbles”, Shaen Corbet, Brian Lucey and Larisa Yarovaya test for bubbles in Bitcoin and Ethereum price series. For valuation, they consider three potential cyrptocurrency price drivers:

  1. Blockchain length, reflecting difficulty of finding a new block and receiving payment relative to past difficulty. As more miners engage, the rate of block creation increases, raising the level of difficulty.
  2. Hash rate, indicating speed of blockchain code execution during mining. A higher hash rate increases probability of finding the next block and receiving payment. 
  3. Liquidity, measuring the relationship between cryptocurrency daily returns and volatilities. 

They then apply ratios constructed from these variables to detect times when price series are substantially disconnected from fundamental drivers. Using Bitcoin data since July 18, 2010 and Ethereum data since July 30, 2015, both through November 9, 2017, they find that: Keep Reading

Aggregate Firm Events as a Stock Return Anomaly

Should investors view stock returns around recurring firm events in aggregate as an exploitable anomaly? In their October 2017 paper entitled “Recurring Firm Events and Predictable Returns: The Within-Firm Time-Series”, Samuel Hartzmark and David Solomon review the body of research on relationships between recurring firm events and future stock returns. They classify events as predictable (1) releases of information or (2) corporate distributions, with some overlap. Information releases include earnings announcements, dividend announcements, earnings seasonality and predictable increases in dividends. Corporate distributions cover dividend ex-days, stock splits and stock dividends. They specify a general trading strategy to exploit these events that is long (short) stocks of applicable firms during months with (without) predictable events. They use market capitalization weighting but, since there are often more stocks in the short side, they scale short side weights downward so that overall long and short sides are equal in dollar value. Based on the body of research and updated analyses based on firm event data and associated stock prices from initial availabilities through December 2016, they conclude that:

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Volatility Patterns as Bubble/Crash Indicators

Does financial market volatility identify bubbles and predict subsequent crashes? In their April 2017 paper entitled “Can We Use Volatility to Diagnose Financial Bubbles? Lessons from 40 Historical Bubbles”, Didier Sornette, Peter Cauwels and Georgi Smilyanov examine price volatility before, during and after 40 financial market bubbles to determine whether realized and/or implied volatility warn of bubble conditions and subsequent crashes. They focus on the following questions:

  1. Does volatility tend to increase during bubble maturation?
  2. Does volatility surge towards the end of a bubble?
  3. Can investors use volatility to diagnose bubbles and forecast their collapse?

They also evaluate credit conditions before and after bubbles to estimate whether leverage generally drives them. Their approach is event-oriented and graphical, centered on index level peaks preceding crashes. Using prices, realized volatilities, implied volatilities and ratios of credit from banks to the private non-financial sector to country GDP as available before, during and after 40 bubbles involving stock indexes, singles stocks, exchange-traded funds, commodity futures and currencies from the October 1929 Dow Jones Industrial Average crash through the September 2011 gold crash, they find that: Keep Reading

Pump-and-Dump via Twitter

Do stock scammers use Twitter to manipulate prices of microcap stocks? In his August 2017 paper entitled “Market Manipulation and Suspicious Stock Recommendations on Social Media”, Thomas Renault performs an event study to analyze returns for microcap stocks around spikes in Twitter activity about the stocks. He identifies tweets about a stock as those containing a dollar sign ($) before its ticker. He identifies Twitter spike events as daily activity (from market close to market close) that exceeds the average of the prior seven days by two standard deviations, with a minimum of 20 tweets from 20 distinct users. He assigns any event occurring on a non-trading day to the next trading day. His baseline analysis is for stocks priced over $0.10 and market capitalization over $1,000,000 at the beginning of the event window, but he tests other thresholds. He considers several ways to define abnormal returns and uses the NASDAQ MicroCap Index as the market return. Using SEC litigation releases during 1996 through 2015 for background and tweet and return data for Over-The-Counter (OTC) microcap stocks during October 5, 2014 through September 1, 2015, he finds that:

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Twitter Activity and Stock Returns

Do changes in Twitter activity about a stock predict its future returns? In their July 2017 paper entitled “Is All that Twitters Gold? Social Media Attention and Stock Returns”, David Rakowski, Sara Shirley and Jeffrey Stark investigate whether Twitter activity: (1) drives noise trading by concentrating investor attention; and, (2) amplifies the effect of fundamental information in news releases. They identify tweets about a stock as those containing a dollar sign ($) before its ticker (such as $AAPL). They focus on daily (midnight-to-midnight) deseasonalized change in tweets, calculated as the residual from a regression on day-of-the-week and month-of-the-year dummy variables. They then test a trading strategy that each day:

  1. Ranks stocks into tenths (deciles) separately by prior-day change in tweets and market capitalization.
  2. At the next market open, takes equally weighted long (short) positions in stocks in both the top decile of change in tweets and the bottom decile of market capitalization (the bottom decile of change in tweets and the bottom decile of market capitalization).
  3. Liquidates positions at the next market close.
  4. Estimates a characteristic-adjusted open-to-close return by subtracting the return of a portfolio of stocks with similar market capitalizations and book-to-market values.

They restrict their sample to stocks in the Russell 3000 Index at some point during the sample period with: price over $5, at least 24 months of data and tweets on at least 10% of days. Using daily Twitter data, firm characteristics, news coverage and open-to-close returns for the specified sample of stocks during 2011 through 2015, they find that: Keep Reading

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