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Individual Investing

What does it take for an individual investor to survive and thrive while swimming with the institutional and hedge fund sharks in financial market waters? Is it better to be a slow-moving, unobtrusive bottom-feeder or a nimble remora sharing a shark’s meal? These blog entries cover success and failure factors for individual investors.

Following the Leaders On SeekingAlpha and StockTwits

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

Individual Investor Trade Timing Performance

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

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

Investor Return versus Mutual Fund Performance

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

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

Individual Investor Equity Market Timing

Should investors believe that they can usefully time the stock market? If so, how big might “usefully” be? In their July 2014 paper entitled “Can Individual Investors Time Bubbles?”, Jussi Keppo, Tyler Shumway and Daniel Weagley investigate persistence in the ability of individual Finnish investors to time the stock market, with focus on timing of two bubbles/crashes. They measure investor timing performance by relating monthly flows into and out of the investor’s portfolio to next-month and next-quarter returns of the value-weighted HEX 25 Index (now the OMX Helsinki 25). They test for persistence by comparing an investor’s relative timing performance in the first half of the sample period (January 1995 through March 2002) to that in the second half (April 2002 through June 2009). They treat January 2000 and October 2007 as beginnings of market crashes and focus on whether an investor performed well during the 12 months before and after each peak. Using data on all trades by 1,386,540 individual Finnish investors during January 1995 through June 2009, they find that: Keep Reading

Active Beats Buy-and-Hold?

Do individuals who actively reallocate funds within their pension accounts outperform passive counterparts? In the March 2014 update of their paper entitled “Individual Investor Activity and Performance”, Magnus Dahlquist, Jose Vicente Martinez and Paul Soderlind examine the activity and performance of individual participants in Sweden’s Premium Pension System. This system allows individual participants to reallocate among available mutual funds on a daily basis with no switching fees/impediments. Information about the 1,230 funds offered during the sample period includes type (fixed income, balanced, life-cycle and equity), return and risk measured at several horizons, fee and major holdings. Most are equity funds, about half of which invest primarily in international equities. The government assigns individuals who make no choice to a default fund. Using daily net returns, fund trades and demographics for 70,755 individuals (from a random draw of individuals in the system over the entire period) and contemporaneous returns for several benchmarks during September 2000 through May 2010, they find that: Keep Reading

Technical Analysis a Drag?

Does technical analysis boost or depress performance for individual investors? In their February 2014 paper entitled “Technical Analysis and Individual Investors”, Arvid Hoffmann and Hersh Shefrin combine actual trading histories and results of a survey to investigate the use of technical analysis by individual investors. The 2006 survey solicits objectives, strategies and traits from a large group of individual clients of an online Dutch discount broker. The survey explicitly asks about use technical analysis and/or fundamental analysis. The authors use actual trading records to measure individual investment performance. Using 5500 survey responses matched to detailed trading histories spanning January 2000 through March 2006, they find 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

An Edge for Attentive Traders After Hours?

Can investors quickly exploit surprising after-hours firm earnings/revenue announcements by trading after hours? In the January 2014 version of his paper entitled “Slow Price Adjustment to Public News in After-Hours Trading”, Jiasun Li investigates after-hours (4:00 pm to 8:00 pm) responses of stock prices to surprising after-hours quarterly earnings announcements. He defines a positive (negative) surprise as neither revenue nor earnings below (above) consensus estimates and at least one of them above (below). He specifies a trading strategy that buys (sells) positive (negative) surprises and holds to the end of after-hours trading. He examines delays between earnings announcement and trade initiation of up to 15 minutes. He calculates returns with actual trade prices, taking into account effective bid-ask spreads. Using (cleaned) tick-by-tick after-hours stock price data for 5,881 surprising after-hours announcements associated with reasonably liquid trading during 2002 through 2012, he finds that: Keep Reading

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