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Investing Research Articles

Misleading Mutual Fund Classifications?

Are Morningstar mutual fund profiles accurate? In their October 2019 paper entitled “Don’t Take Their Word For It: The Misclassification of Bond Mutual Funds”, Huaizhi Chen, Lauren Cohen and Umit Gurun examine whether aggregate credit risks of actual of U.S. fixed income (corporate bond) mutual fund portfolios match those presented by Morningstar in respective fund profiles. They focus on recent data (first quarter of 2017 through second quarter of 2019), during which Morningstar includes percentages of fund holdings by risk category. Using Morningstar profiles, actual holdings as reported to the SEC, detailed credit ratings of holdings and returns for 1,294 U.S. corporate bond funds during January 2003 through June 2019, they find that:

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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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Hold Stocks Only After All-time Market Highs?

A subscriber asked for verification of the finding in “Is Buying Stocks at an All-Time High a Good Idea?” that it is not only a good idea, but a great one, including comparison to a moving average crossover rule. To investigate, we use the S&P 500 Index as a proxy for the U.S. stock market and test a strategy that holds SPDR S&P 500 (SPY) when the S&P 500 Index stands at an all-time high at the end of last month and otherwise holds Vanguard Long-Term Treasury Fund Investor Shares (VUSTX). We compare results to buying and holding SPY, buying and holding VUSTX, and holding SPY (VUSTX) when the S&P 500 Index is above (below) its 10-month simple moving average (SMA10) at the end of last month. We assume 0.1% switching frictions. We compute average net monthly return, standard deviation of monthly returns, net monthly Sharpe ratio (with monthly T-bill yield as the risk-free rate), net compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key strategy performance metrics. We calculate the number of switches for each scenario to indicate sensitivities to switching frictions and taxes. Using monthly closes for the S&P 500 Index, SPY and VUSTX during January 1993 (inception of SPY) through October 2019, we find that:

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Weekly Summary of Research Findings: 11/18/19 – 11/22/19

Below is a weekly summary of our research findings for 11/18/19 through 11/22/19. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

U.S. Stock Market Returns Around Thanksgiving

Does the Thanksgiving holiday, a time of families celebrating plenty, give U.S. stock investors a sense of optimism that translates into stock returns? To investigate, we analyze the historical behavior of the S&P 500 Index during the three trading days before and the three trading days after the holiday. Using daily closing levels of the S&P 500 Index for 1950-2018 (69 events), we find that: Keep Reading

Combining Economic Policy Uncertainty and Stock Market Trend

A subscriber requested, as in “Combine Market Trend and Economic Trend Signals?”, testing of a strategy that combines: (1) U.S. Economic Policy Uncertainty (EPU) Index, as described and tested separately in “Economic Policy Uncertainty and the Stock Market”; and, (2) U.S. stock market trend. We consider two such combinations. The first combines:

  • 10-month simple moving average (SMA10) for the broad U.S. stock market as proxied by the S&P 500 Index. The trend is bullish (bearish) when the index is above (below) its SMA10 at the end of last month.
  • Sign of the change in EPU Index last month. A positive (negative) sign is bearish (bullish).

The second combines:

  • SMA10 for the S&P 500 Index as above.
  • 12-month simple moving average (SMA12) for the EPU Index. The trend is bullish (bearish) when the EPU Index is below (above) its SMA12 at the end of last month.

We consider alternative timing strategies that hold SPDR S&P 500 (SPY) when: the S&P 500 Index SMA10 is bullish; the EPU Index indicator is bullish; either indicator for a combination is bullish; or, both indicators for a combination are bullish. When not in SPY, we use the 3-month U.S. Treasury bill (T-bill) yield as the return on cash, with 0.1% switching frictions. We assume all indicators for a given month can be accurately estimated for signal execution at the market close the same month. We compute average net monthly return, standard deviation of monthly returns, net monthly Sharpe ratio (with monthly T-bill yield as the risk-free rate), net compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key strategy performance metrics. We calculate the number of switches for each scenario to indicate sensitivities to switching frictions and taxes. Using monthly values for the EPU Index, the S&P 500 Index, SPY and T-bill yield during January 1993 (inception of SPY) through October 2019, we find that:

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Extra Attention to Earliest Quarterly Earnings Announcements

Does the market react most strongly to the earliest quarterly earnings announcements? In their October 2019 paper entitled “Calendar Rotations: A New Approach for Studying the Impact of Timing using Earnings Announcements”, Suzie Noh, Eric So and Rodrigo Verdi study effects of the relative order of U.S. firm quarterly earnings announcements, which vary systematically for some firms according to the day of the week of the first day of a month. Specifically, they qualify firms by identifying those firms that exhibit systematic earnings announcement schedules (such as Friday of the fourth week after quarter ends, sometimes set in firm bylaws) for at least four consecutive same fiscal quarters. They then for each firm each fiscal quarter:

  • Calculate EA Order, ranking of earnings announcement date divided by number of firms with the same fiscal quarter-end.
  • Compute change in EA Order compared to the same fiscal quarter last year, indicating a calendar acceleration or delay in announcement. Positive (negative) change in EA Order indicates delay (acceleration)
  • Examine effects of change in EA Order on media coverage (number of articles), stock trading volume and stock return from one trading day before to one trading day after earnings announcement.

Using sample of 76,622 firm-quarters during 2004 through 2017, they find that: Keep Reading

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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Multi-year ETF Momentum

Do U.S. equity exchange-traded funds (ETF) exhibit long-term momentum? In their October 2019 paper entitled “ETF Momentum”, Frank Li, Melvyn Teo and Chloe Yang investigate future performance of U.S. equity ETFs sorted on multi-year past returns. Each month starting August 2004, they:

  1. Sort selected ETFs into tenths (deciles) based on returns over the past two, three or four years, with focus on three years.
  2. Reform an equal-weighted (EW) or value-weighted (VW) portfolio that is long (short) the decile with the highest (lowest) past returns, with focus on value-weighted.

They then evaluate performances of deciles and long-short portfolios based on raw return, 4-factor (adjusting for market, size, book-to-market and momentum) alpha and 5-factor (replacing momentum with profitability and investment) alpha. Using monthly returns, market capitalizations and net asset values for all U.S. equity ETFs with capitalizations greater than $20 million and share price greater than one dollar during August 2000 through June 2018, they find that: Keep Reading

Weekly Summary of Research Findings: 11/11/19 – 11/15/19

Below is a weekly summary of our research findings for 11/11/19 through 11/15/19. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

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