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Value Investing Strategy (Strategy Overview)

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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.

Retail Trading Drives Stock Momentum?

Is retail trading a reliable driver of U.S. stock momentum? In his November 2019 paper entitled “Retail Trading and Momentum Profitability”, Douglas Chung investigates interactions across stocks between current proportion of retail trading and future momentum returns. Specifically, for each month and for each of two recent stock samples, he:

  • Sorts stocks into fifths (quintiles) by current proportion of retail trading.
  • Within each proportion-of-retail-trading quintile:
    • Sorts stocks into sub-quintiles by return from 12 months ago to one month ago.
    • Calculates average next-month returns for an equal-weighted momentum portfolio that is long (short) the sub-quintile of stocks with the highest (lowest) past returns. He also considers other portfolio weighting schemes.
    • Measures alphas of these returns based on various widely accepted single-factor and multi-factor models of stock returns.

He next tests whether proportion of retail trading relates to a gambling motive (lottery trading) by constructing a stock lottery index from inverse of stock price, idiosyncratic volatility, idiosyncratic skewness and recent maximum daily return. In other words, he examines whether the lottery index value for a stock is a proxy for its proportion of retail trading. Using daily data for all NYSE retail orders during March 2004 through December 2014, for small NYSE trades of U.S. common stocks (a proxy for retail trading) during January 1993 through July 2000 and for lottery index inputs during 1940 through 2016, he finds that: Keep Reading

Ways to Beat the Stock Market?

Who beats the stock market and why? In his October 2019 paper entitled “The Five Investor Camps That Try to Beat the Stock Market”, William Ziemba discusses how different categories of investors succeed. For investors pursuing active strategies, he addresses broadly the means of getting an edge and betting well. Based on his academic work and practical experience, he concludes that: Keep Reading

Automation Bias Among Individual Investors

Who do investors trust more, expert advisors or algorithms? In her March 2019 paper entitled “Algorithmic Decision-Making: The Death of Second Opinions?”, Nizan Packin employs a survey conducted on Amazon Mechanical Turk to assess automation bias when making significant investment decisions. Each of four groups of respondents received one of the following four questions (response scale 1 to 5):

  1. “You decide to invest 15% of your savings in the stock market. You find a reputable stockbroker, who makes investment recommendations. How confident are you that you got the best recommendation possible for your investment?”
  2. “You decide to invest 60% of your savings in the stock market. You find a reputable stockbroker, who makes investment recommendations. How confident are you that you got the best recommendation possible for your investment?”
  3. “You decide to invest 15% of your savings in the stock market. You find a reputable online automated investment advisor, who makes investment recommendations. How confident are you that you got the best recommendation possible for your investment?”
  4. “You decide to invest 60% of your savings in the stock market. You find a reputable online automated investment advisor, who makes investment recommendations. How confident are you that you got the best recommendation possible for your investment?”

A followup question asked about level of comfort trusting again the same expert (human or algorithmic) after learning that the initial recommendation resulted in a significant loss. Analyses included controls for respondent age, gender, socioeconomic status, having some college education, race and political ideology (liberal/conservative). Based on 800 total responses to specified survey questions, she finds that: Keep Reading

Vanguard vs. Fidelity Funds

Which fund family is better, Vanguard or Fidelity? In their April 2019 paper entitled “Vanguard versus Fidelity: Multidimensional Comparison of the Index Funds and ETFs of the Two Largest Mutual Fund Families”, Chong Li, Edward Tower and Rhona Zhang compare 21 matched Vanguard and Fidelity fund pairs in five ways: (1) before-tax and after-tax performance, (2) tax efficiency, (3) cost (expense ratio, turnover and short-term redemption fees), (4) diversification and (5) benchmark tracking precision. They consider 10 domestic equity and international equity index mutual funds and 11 sector exchange-trade funds (ETF). Their objective is to aid investors in selecting a fund provider. Using fund performance, cost, holdings and benchmark as of the end of 2018, they find that: Keep Reading

Pump-and-Dump Participation/Losses

A “pump-and-dump” scheme promoter: (1) builds a position in a stock (often a thinly traded penny stock); (2) gooses its price by spreading misleading information; and, (3) liquidates the position once the stock reaches. Who responds to such schemes and what are their returns? In the December 2018 revision of their paper entitled “Who Falls Prey to the Wolf of Wall Street? Investor Participation in Market Manipulation”, Christian Leuz, Steffen Meyer, Maximilian Muhn, Eugene Soltes and Andreas Hackethal investigate pump-and-dump scheme participation rate, purchase size/returns and participant characteristics. Specifically, they explore the intersection of 421 such schemes (both from the responsible German regulatory agency and hand-selected) and trading records/demographics for 113,000 randomly selected individual investors from a major German bank. Using the specified data spanning 2002 through 2015, they find that:

Keep Reading

Rough Net Worth Growth Benchmarks

How fast should individuals plan to grow net worth as they age? To investigate, we examine median levels of household (1) total net worth and (2) net worth excluding home equity from several vintages of U.S. Census Bureau data. We make the following head-of-household age cohort assumptions:

  • “Less than 35 years” means about age 30.
  • “35 to 44 years” means about age 39.
  • “45 to 54 years” means about age 49.
  • “55 to 64 years” means about age 59.
  • “65 to 69 years” means about age 67.
  • “70 to 74 years” means about age 72.
  • “75 and over” means about age 78.

We also assume that wealth growth between these ages is constant via compound annual growth rate (CAGR) calculations. Using median levels of total net worth and net worth excluding home equity from 2000. 2005, 2010 and 2014 Census Bureau summary tables, we find that: Keep Reading

AAII Stock Screens

A reader asked: “The American Association of Individual Investors (AAII) has a lot of strategies they have been paper-trading over many years at Stock Screens. It seems like every strategy builds upon a well-known investing book or otherwise publicized strategy from the last 40 years. Have you ever done an evaluation of those performance results?” According to AAII: “These approaches run the full spectrum, from those that are value-based to those that focus primarily on growth. Some approaches are geared toward large-company stocks, while others uncover micro-sized firms. Most fall somewhere in the middle.” AAII provides performance histories, risk-return statistics and characteristics for all screens. AAII cautions that: “The impact of factors such as commissions, bid-ask spreads, cash dividends, time-slippage (time between the initial decision to buy a stock and the actual purchase) and taxes is not considered. This overstates the reported performance…” Using monthly returns and turnovers for the equally weighted portfolios generated by the 60 screens presented during January 1998 through March 2018 (243 months), along with contemporaneous returns for SPDR S&P 500 (SPY), Vanguard Small Cap Index Fund (NAESX) and Vanguard Total Stock Market Index Fund (VTSMX), we find that: Keep Reading

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:

Keep Reading

Experiences of Retail Currency Traders

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

Mean-Variance Asset Allocation for Individual Investors

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

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