Investing Research Articles

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Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for November 2014. The actual total (core) inflation rate for November is lower than (lower than) forecasted.

The new actual and forecasted inflation rates will flow into Real Earnings Yield Model projections at the end of the month.

Equal Weighting vs. All Feasible Long-only Mean-variance Optimals

Is equal weighting (1/n) of portfolio components a good choice? In their November 2014 paper entitled “Is 1/n Really Better Than Optimal Mean-Variance Portfolio?”, Woo Chang Kim, Yongjae Lee and William Ziemba assess 1/n weighting by comparing its performance to the performances of all feasible mean-variance optimal portfolios for different asset universes. By “all feasible,” they mean many long-only mean-variance optimal portfolios generated by randomly picking the estimated future return-to-variance ratios for assets within a universe. They use Sharpe ratio to measure portfolio performance. They consider  10 asset universes: 10 U.S. equity sectors; 10 U.S. equity industries; eight country equity indexes; three U.S. equity factor portfolios; six U.S. equity styles; 25 U.S. equity styles; 100 U.S. equity styles; 250 large-capitalization U.S. stocks; 250 medium-capitalization U.S. stocks; and, 250 small-capitalization U.S. stocks.They apply mostly annual rebalancing but also consider semiannual and quarterly rebalancing for the three stock universes. They also test 1/n versus capitalization weighting for seven of the 10 universes. Using returns for specified assets at the tested rebalancing frequencies with sample start dates as early as July 1963 and end dates as late as June 2014, they find that: Keep Reading

Usefulness of P/E10 as Stock Market Return Predictor

Is P/E10 (or Cyclically Adjusted Price-Earnings ratio, CAPE) a useful indicator of U.S. stock market valuation? P/E10, as calculated in Robert Shiller’s data set, is the ratio of the inflation-adjusted S&P Composite Index level to the average monthly inflation-adjusted 12-month trailing earnings of index companies over the previous ten years. To investigate its usefulness, we consider in-sample regression and ranking and cumulative performance tests. Using Robert Shiller’s monthly estimates of the nominal and real S&P Composite Index (calculated as average of daily closes during the month), associated dividends, 12-month trailing real earnings and long-term interest rate as available during January 1871 through November 2014, we find that: Keep Reading

Why Stock Gurus Warn?

Does a need to attract attention distort the information offered by online stock bloggers? Does competition among them suppress or amplify this distortion? In their November 2014 paper entitled “Guru Dreams and Competition: An Anatomy of the Economics of Blogs”, Yi Dong, Massimo Massa and Hong Zhang investigate whether: (1) stock bloggers are informative; and, (2) competition among them enhances the quality of information provided. They start by relating blog activity to two proxies for informed versus liquidity trading. They then test the relationship between future stock returns and blog tone, with focus on tone extremism. Finally, they assess the impact of competition among stock bloggers, defining blog activity as competitive when the number of bloggers covering a stock is among the top fourth across all stocks. Using a hand-collected sample of blog articles covering S&P 1500 stocks during 2006 through 2011, they find that:

Keep Reading

Weekly Summary of Research Findings: 12/8/14 – 12/12/14

Below is a weekly summary of our research findings for 12/8/14 through 12/12/14. 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

Monthly Mutual Fund Flow Pattern as Driver of TOTM Effect

Do predictable monthly outflows from and inflows to mutual funds drive the Turn-of-the-Month (TOTM) effect, a concentration of positive stock market returns around the turns of calendar months? In their November 2014 paper entitled “Dash for Cash: Month-End Liquidity Needs and the Predictability of Stock Returns”, Kalle Rinne, Matti Suominen and Lauri Vaittinen explore TOTM with focus on the effects of: (1) month-end flows from mutual funds to retirees and dividend-collecting investors; and, (2) beginning-of-month flows from working investors to mutual funds. To account for trade settlement rules, funds must sell stocks at least three trading days before the end of the month to raise cash for expected month-end outflows. The authors therefore define a TOTM interval from three trading days before through three trading days after the last trading day of the month. They also consider intervals of five trading days before TOTM to measure the effect of fund selling and five trading days after TOTM  to measure reversion from fund buying. Using daily value-weighted, (mostly) total return stock market indexes for the U.S. since 1926 and for 24 other developed markets as available during January 1980 through January 2014, and data for individual U.S. stocks and mutual funds during January 1980 through December 2013, they find that: Keep Reading

Margin Debt as a Stock Market Indicator

Does margin debt serve as an intermediate-term stock market sentiment indicator based on either momentum (with an increase/decrease in margin debt signaling a continuing stock market advance/decline) or reversion (with high/low margin debt signaling a pending reversal)? To investigate, we relate the behavior of NYSE end-of-month margin debt, published with a delay of about a month, with the monthly behavior of the the stock market (S&P 500 Index). Using end-of-month data for the period January 1959 through October 2014 (670 months), we find that: Keep Reading

Use the U.S. LEI for Long-term Stock Market Timing?

Referring to “Leading Economic Index and the Stock Market”, a subscriber inquired about using the Conference Board’s Leading Economic Index (LEI) for the U.S. to generate long-term U.S. stock market timing signals, as follows:  

“How about using the LEI in the following fashion?

Buy when the LEI rises by 1.0 % from its lowest point in the prior six months.
Sell when the LEI falls by 1.5% from its highest point in the last six months.

I used 1% as a buy because bear markets can end abruptly, not because I was torturing the data to confess. You could use 1.5% and I think still have robust results…changes in trend, which are rare, seem to be helpful. I bought the LEI data from the Conference Board and did some testing by hand using the above going back to 1969. I think I found some interesting results. …It gave early sell in 2006… The signal date was the date of the release… Most of the benefit of the trading system comes within the last 14 years.”

Using the monthly change in LEI data from archived Conference Board press releases during June 2002 through October 2014 (146 months), we find that: Keep Reading

Components of U.S. Stock Market Returns by Decade

How do the major components of U.S. stock market performance behave over time? In his October 2014 paper entitled “Long-Term Sources of Investment Returns and a Simple Way to Enhance Equity Returns”, Baijnath Ramraika decomposes long-term returns from the U.S. stock market (as proxied by Robert Shiller’s S&P Composite Index) into four components:

  1. Dividend yield
  2. Inflation
  3. Real average change in 10-year earnings (E10)
  4. Change in the Cyclically Adjusted Price-Earnings ratio (CAPE, or P/E10)

He further segments this decomposition by decade. Using his decomposition by decade for 1881 through 2010 (13 decades), we find that: Keep Reading

Lessons Learned from Attacking CAPM

How diverse are the beliefs of experts on the Capital Asset Pricing Model (CAPM)? In his November paper entitled “CAPM: The Model and 233 Comments about It”, Pablo Fernandez reproduces 52 largely disagreeing and 181 largely agreeing comments solicited from professors, finance professionals and Ph.D. students regarding his prior paper entitled “CAPM: an Absurd Model” (summarized in “Forget CAPM Beta?”). The range of beliefs in the comments is extreme, from

“I was shocked at how horrible your paper is. It is without a doubt the worst excuse for an academic study I have ever seen (and believe me that is saying a lot).”

to

“I totally agree with the absurdity of CAPM model.”

After reflecting on the body of comments, he concludes that: Keep Reading

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