Individual Investing

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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How do individual currency traders view their trading experience? In his June 2016 paper entitled “Retail FX Trader Survey Results”, Chris Davison reports results of an anonymous survey of retail currency traders asking 14 questions about the way they trade. He elicited participants via posts on two online currency trading forums: Forex Factory and MyFXbook. Using responses from 133 traders during late November 2015 through late April 2016, he finds that: Keep Reading

Can individual investors practically implement mean-variance optimization in a multi-asset class context? In their April 2016 paper entitled “Asset Allocation: A Recommendation for Resolving the Collision between Theory and Practice”, Larry Prather, James McCown and Ron Shaw describe how individual investors can construct and maintain a low-cost optimal (maximum Sharpe ratio) multi-class portfolio via the Excel Solver function. They consider four criteria in selecting asset class proxies: (1) market capitalization-weighted coverage of a wide variety of investable assets; (2) small initial investment; (3) low annual expenses; and, (4) versions that investors can short. Based on these criteria, they select five Vanguard index mutual funds and three precious metals:

• Vanguard Total Stock Market Index Fund Investor Shares (VTSMX), capturing the U.S. equity market.
• Vanguard Total International Stock Index Fund Investor Shares (VGTSX), representing 98% of the capitalization of non-U.S. equity markets.
• Vanguard Emerging Markets Stock Index Fund Investor Shares (VEIEX), supplementing VGTSX to better capture emerging market equities.
• Vanguard Total Bond Market Index Fund Investor Shares (VBMFX), providing broad exposure to U.S. investment grade bonds.
• Vanguard REIT Index Fund Investor Shares (VGSIX), providing broad exposure to U.S. Real Estate Investment Trusts (REIT).
• Spot gold, platinum and palladium, offering safe haven and currency exchange rate protection.

These mutual funds and metals have exchange-traded fund (ETF) analogs, supporting optimization with short selling. They assume a constant risk-free rate of 3%. Using daily mutual fund returns and spot metals prices during September 1998 through June 2015, they find that: Keep Reading

How should investors assess the risk of financial advisor misconduct? In their March 2016 paper entitled “The Market for Financial Adviser Misconduct”, Mark Egan, Gregor Matvos and Amit Seru investigate the recent extent of misconduct among registered financial advisors (“advisors”) and financial advisory firms in the U.S. Their data include employment history, customer disputes, disclosed investigations and disciplinary events (civil, criminal and regulatory). Using information on 1.2 million registered financial advisors (644,277 current and 638,528 former) during 2005 through 2015, they find that: Keep Reading

Mebane Faber states in the first chapter of his 2016 book Invest with the House: Hacking the Top Hedge Funds: “We make two assumptions…: 1. There are active managers that can beat the market… 2. Superior active managers can be identified. …There is a general feeling that the market can’t be beat, and it is tough to get past that belief. A big challenge is separating luck from skill. But would anyone deny that some people are better than others at stock picking? Just like any other profession, the investment field has top experts who are paid handsomely for what they do. …You have access to the stock picks made by fund managers who often spend millions of dollars and every waking moment thinking and obsessing about the financial markets. …The best ones know everything there is to know about a company before they invest. …You can then build a stable of these managers and use them…for stock ideas to research and possibly implement in your own portfolio.” Based on prior research/experience and performances of the top ten (long) holdings from quarterly Form 13F filings for selected fund managers during January 2000 through December 2014, he concludes that: Keep Reading

Do SeekingAlpha and StockTwits offer valuable stock-picking information? In their March 2015 paper entitled “Crowds on Wall Street: Extracting Value from Collaborative Investing Platforms”, Gang Wang, Tianyi Wang, Bolun Wang, Divya Sambasivan, Zengbin Zhang, Haitao Zheng and Ben Zhao evaluate the stock-picking expertise available via SeekingAlpha and StockTwits. They tailor stock sentiment measures for these sources and relate these measures to future stock and stock market performance. They test ranking of author informativeness both directly via future stock returns and indirectly by level of reader interaction (comments). They then test strategies for exploiting sentiments of top authors. Finally, they summarize responses to a May 2014 survey of 500 SeekingAlpha authors (95 responses) and 500 non-contributing SeekingAlpha users (104 responses). Using SeekingAlpha content from launch in 2004 through March 2014, StockTwits content from launch in 2009 through February 2014 and daily returns (not including dividends) from associated individual stocks and S&P 500 SPDR (SPY) as a market proxy, they find that: Keep Reading

Do individual investors exhibit good or bad timing in stock transactions of recent years? In the August 2015 version of their paper entitled “Fool’s Mate: What Does CHESS [Clearing House Electronic Subregister System] Tell Us About Individual Investor Trading Performance?”, Reza Bradrania, Andrew Grant, Joakim Westerholm and Wei Wu examine the short-term performance of stocks with unusual buying or selling pressure among individual Australian investors, Australian institutions or non-Australian (foreign) institutions. They define unusual pressure as the fifths (quintiles) of stocks with the most extreme buy/sell imbalances. Using daily closing stock holdings (capturing positions held at least overnight) aggregated by investor category and price/volume data for each of 2,841 Australian stocks during January 2009 through mid-August 2014, they find that: Keep Reading

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

Does the average mutual fund investor accrue the average fund performance, or do investor timing practices alter the equation? In their July 2014 paper entitled “Timing Poorly: A Guide to Generating Poor Returns While Investing in Successful Strategies“, Jason Hsu, Brett Myers and Ryan Whitby compare the average dollar-weighted and buy-and-hold returns of different U.S. equity mutual fund styles, with focus on the value style. Dollar weighting adjusts the return stream based on the timing and magnitude of fund flows and is a more accurate measure than buy-and-hold of the returns realized by fund investors who may trade in and out of funds. Using monthly returns, monthly total assets and quarterly fund style information for a broad sample of U.S. equity mutual funds during 1991 through 2013, they find that: Keep Reading

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