Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for January 2021 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for January 2021 (Final)
1st ETF 2nd ETF 3rd ETF

Investing Expertise

Can analysts, experts and gurus really give you an investing/trading edge? Should you track the advice of as many as possible? Are there ways to tell good ones from bad ones? Recent research indicates that the average “expert” has little to offer individual investors/traders. Finding exceptional advisers is no easier than identifying outperforming stocks. Indiscriminately seeking the output of as many experts as possible is a waste of time. Learning what makes a good expert accurate is worthwhile.

Exploiting Consensus Hedge Fund Conviction Stock Picks

Can investors exploit information about hedge fund stock holdings in SEC Form 13F filings? In their October 2019 paper entitled “Systematic 13F Hedge Fund Alpha”, Mobeen Iqbal, Farouk Jivraj and Luca Angelini investigate whether carefully culled “best ideas” of equity hedge funds produce significantly beat the S&P 500 Total Return (TR) Index. Using quarterly Form 13Fs for U.S. equity long-short, equity market neutral, equity long-only and equity event-driven hedge funds, they measure: individual hedge fund manager conviction regarding a stock based on size of position; and, hedge fund manager consensus regarding a stock based on the number of funds holding it. Using proprietary data, they identify hedge funds exhibiting long-term investment approaches. They then 47 days after the end of each quarter (to ensure availability of Form 13Fs), reform a portfolio from among long-term hedge funds holding at least five stocks, as follows:

  1. Exploit conviction by identifying all stocks comprising at least 7.5% of a fund portfolio.
  2. Exploit consensus by buying the equal-weighted top 50 of these stocks in terms of number of hedge managers holding them. 

Using processed quarterly data from hedge fund Form 13Fs, the specified proprietary data on hedge fund investment approaches and returns for associated stocks during the first quarter of 2004 through the second quarter of 2019, they find that:

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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

Asset Class Return Expectations and Allocations of Sophisticated Investors

What are asset class return expectations and associated portfolio allocations of very sophisticated U.S. investors? In their February 2019 paper entitled “The Return Expectations of Institutional Investors”, Aleksandar Andonov and Joshua Rauh analyze disclosures of expected returns across asset classes among U.S. public pension funds, which hold assets of about $4 trillion (see the first chart below), including fixed income, cash, equities, real assets, hedge funds, private equity and other asset classes. Taking into account past fund performance, they investigate how fund managers estimate future returns. Disclosures also reveal target allocations to asset classes (see the second chart below). Together, expected asset class returns and target allocations allow calculation of expected portfolio returns. Using annual disclosures for 228 U.S. state and local government pension plans during 2014 through 2017, they find that:

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Mutual Fund Managers Harmfully Biased?

Are there relationships between (1) the stock market outlook expressed by a U.S. equity mutual fund manager in semi-annual reports and (2) positioning and performance of that fund? In his October 2019 preliminary paper entitled “Are Professional Investors Prone to Behavioral Biases? Evidence from Mutual Fund Managers”, Mehran Azimi examines these relationships. Specifically, for each such U.S. equity mutual fund semi-annual report, he:

  1. Uses a word list to identify parts of fund reports that may contain stock market outlooks.
  2. Applies machine learning to isolate sentences most likely to present outlooks.
  3. Manually reads and rates these sentences as bearish, neutral or bullish.
  4. Computes fund manager “Belief” as number of bullish sentences minus number of bearish sentences divided by the total number of sentences isolated. Positive (negative) Belief indicates a net bullish (bearish) outlook.

He then employs regressions to relate fund manager Belief to fund last-year return, asset allocation, portfolio risk and next-year 4-factor (adjusting for market, size, book-to-market and momentum) alpha. Using 40,731 semi-annual reports for U.S. equity mutual funds and associated fund characteristics, holdings and returns during February 2006 through December 2018, he finds that:

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Overview and Mitigation of Financial Biases

What are ways to mitigate biases that interfere with rational investment decision-making? In their September 2019 paper entitled “The Psychology of Financial Professionals and Their Clients”, Kent Baker, Greg Filbeck and Victor Ricciardi describe common psychological biases and suggest ways to overcome them. Based on their knowledge and experience, they conclude that: Keep Reading

“Buy These Stocks for 2019” Forward Test

When media recommend stocks, should investors pay attention? To check, we look at performance of stock recommendations for 2019 from December 2018 articles in several publications. Specifically, we test:

For each source, we form equally weighted portfolios of recommended stocks at the close on 12/31/2018 and hold without rebalancing. For a broader perspective, we form an equally weighted portfolio of all recommended stocks (Overall). Because the sample period is very short, we focus on daily performance statistics, but also look at cumulative returns. We use SPDR S&P 500 (SPY) as a benchmark. Using daily dividend-adjusted prices of the 74 recommended stocks and SPY during 12/31/2018 through October 15, 2019, we find that: Keep Reading

Investment Strategy Development Tournaments?

Is there a way that asset managers can share knowledge/data across proprietary boundaries with many researchers to advance development of investment strategies? In their September 2019 paper entitled “Crowdsourced Investment Research through Tournaments”, Marcos Lopez de Prado and Frank Fabozzi describe highly structured tournaments as a crowdsourcing paradigm for investment research. In each such tournament, the organizer poses one investment challenge as a forecasting problem and provides abstracted and obfuscated data. Contestants pay an entry fee, develop models and provide forecasts, retaining model ownership by running calculations on their own hardware/software. Based on this hypothetical tournament setup and their experience, they conclude that:

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Contents of Investment Advisor Portfolios

What should investors expect to see in a typical investment advisor’s model portfolio? In their July 2019 paper entitled “Factors and Advisors Portfolios”, Brian Lawler, Andrew Ang, Brett Mossman and Patrick Nolan examine patterns and factor exposures in detailed holdings for a large number of model portfolios from many types of investment advisors. When holdings are funds, they examine contents of the funds. They assess exposures to economic growth, real interest rates and inflation. Within equity holdings, they assess exposures to size, value, momentum, quality and volatility factors. Using holdings of 9,940 model portfolios provided by investment advisors during October 2017 through September 2018, they find that: Keep Reading

Investors vs. Matched Robo-investors

Would retail investors improve portfolio performance by using robo-advisors to manage holdings they have selected? In their July 2019 paper entitled “Artificial Intelligence Alter Egos:Who benefits from Robo-investing?”, Catherine D’Hondt, Rudy De Winne, Eric Ghysels and Steve Raymond compare performances of portfolios held by each of a large sample of actual individual investors to that of a robo-investor constrained to the stocks and exchange-traded funds (ETF) held by that investor over a rolling 2-year historical window. They consider three robo-investor strategies:

  1. Mean-variance optimization with guiding average and variance estimates based straightforwardly on 2-year rolling historical windows and parameters set to maximize Sharpe ratio.
  2. Mean-variance optimization guided by machine learning algorithms and sophisticated covariance estimators, with two variations in variance estimation.
  3. Equal weight.

Robo-investors may hold cash, but they may not sell short, with focus on quarterly portfolio rebalancing. They measure portfolio performance monthly and exclude trading frictions. Using common stock/exchange-traded fund (ETF) trading records for 20,622 individual Belgian brokerage accounts during January 2003 through March 2012, they find that:

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Performance of Analyst Short-term Trade Ideas

Do short-term trade ideas of professional stock analysts have merit? In their July 2019 paper entitled “Are Analyst Trade Ideas Valuable?”, Justin Birru, Sinan Gokkaya, Xi Liu and René Stulz examine the price impact of analyst trade ideas, which differ from stock ratings in that trade ideas:

  1. Have horizons of only one week to three months.
  2. Reflect expected short-term price changes in response to upcoming news (firm catalysts) or short-term overreaction/underreaction to recent news (temporary mispricing).
  3. May be opposite in direction from the analyst’s rating on a stock.
  4. Are typically issued on days with no firm news and no other analyst reports.

They estimate abnormal returns of trade ideas based on an equally weighted portfolios of stocks with similar size, book-to-market ratio and momentum characteristics. For trade idea buy and sell portfolios, they add a new stock at the close on the trading day after idea announcement and rebalance the portfolio on any day a stock is added or removed. Using a manually constructed sample of 4,167 buy ideas and 367 sell ideas from 688 analysts at 77 brokers involving 1,619 unique stocks during 2000 through 2015, they find that: Keep Reading

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