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Federal Funds Rate-Stock Market Interactions

A subscriber wondered whether U.S. stock market movements predict Federal Funds Rate (FFR) actions taken by the Federal Reserve open market operations committee. To investigate and evaluate usefulness of findings, we relate three series:

  1. FFR actions per the above source, along with recent and historical committee meeting dates.
  2. S&P 500 Index returns.
  3. Changes in yield for the 10-Year U.S. Constant Maturity Treasury note (T-note).

In constructing the first series, for Federal Reserve open market operations committee meeting dates which do not produce FFR changes, we quantify committee actions as 0%. We ignore committee conference calls that result in no changes in FFR. We calculate the second and third series between committee meeting dates because that irregular interval represents new information to the committee and potential exploitation points for investors. Using data for the three series during January 1990 through March 2018, we find that:

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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:

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Weekly Summary of Research Findings: 4/9/18 – 4/13/18

Below is a weekly summary of our research findings for 4/9/18 through 4/13/18. 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

GDP Growth and Stock Market Returns

The U.S. Bureau of Economic Analysis (BEA) each quarter estimates economic growth via changes in Gross Domestic Product (GDP) and its Personal Consumption Expenditures (PCE), Private Domestic Investment (PDI) and government spending components. BEA releases advance, preliminary and final data about one, two and three months after quarter ends, respectively. Do these estimates of economic growth usefully predict stock market returns? To investigate, we relate economic growth metrics to future stock market index returns. Using quarterly and annual seasonally adjusted nominal GDP data from BEA National Income and Product Accounts Table 1.1.5 as available during January 1929 through December 2017 (about 87 years) and contemporaneous levels of the S&P 500 Index (since 1950 only) and the Dow Jones Industrial Average (DJIA), we find that:

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Simple Volatility-Payout-Momentum Stock Strategy

Is there an easy way for investors to capture jointly the most reliable stock return factor premiums? In their March 2018 paper entitled “The Conservative Formula: Quantitative Investing Made Easy”, Pim van Vliet and David Blitz propose a stock selection strategy based on low return volatility, high net payout yield and strong price momentum. Specifically, at the end of each quarter they:

  1. Segment the then-current 1,000 largest stocks into 500 with the lowest and 500 with the highest 36-month return volatilities.
  2. Within each segment, rank stocks based on total net payout yield (NPY), calculated as dividend yield minus change in shares outstanding divided by its 24-month moving average.
  3. Within each segment, rank stocks based on return from 12 months ago to one month ago (with the skip-month intended to avoid return reversals).
  4. Within the low-volatility segment, average the momentum and NPY ranks for each stock and equally weight the top 100 to reform the Conservative Formula portfolio.
  5. Within the high-volatility segment, average the momentum and NPY ranks for each stock and equally weight the bottom 100 to reform the Speculative Formula portfolio.

Limiting the stock universe to the top 1,000 based on market capitalization suppresses liquidity risk. Limiting screening parameters to three intensely studied factors that require no accounting data mitigates data snooping and data availability risks. They focus on the 1,000 largest U.S. stocks to test a long sample, but also consider the next 1,000 U.S. stocks (mid-caps) and the 1,000 largest stocks from each of Europe, Japan and emerging markets. They further examine: (1) sensitivity to economic conditions doe the long U.S. sample; and, (2) impact of trading frictions in the range 0.1%-0.3% for developed markets and 0.2%-0.6% for emerging markets. Using quarterly prices, dividends and shares outstanding for the contemporaneously largest 1,000 U.S. stocks since 1926, European and Japanese stocks since 1986 and emerging markets stocks since 1991, all through 2016, they find that:

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Best Equity Risk Premium

What are the different ways of estimating the equity risk premium, and which one is best? In his March 2018 paper entitled “Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2018 Edition”, Aswath Damodaran updates a comprehensive overview of equity risk premium estimation and application. He examines why different approaches to estimating the premium disagree and how to choose among them. Using data from multiple countries (but focusing on the U.S.) over long periods through the end of 2017, he concludes that: Keep Reading

10-month vs. 40-week vs. 200-day SMA

A reader proposed: “I would love to see a backtest pitting a 10-month simple moving average (SMA) against a 200-day SMA for SPDR S&P 500 (SPY). I assume trading costs would go through the roof on the latter, but do performance gains offset additional costs?” Others asked about a 40-week SMA. To investigate, we use the three SMAs to time SPY since its inception and compare results. Specifically, we buy (sell) SPY at the close as it crosses above (below) the SMA, anticipating crossing signals such that trades occur at the close on the signal day (assuming calculations can occur just before the close). The baseline SMA calculation series is dividend-adjusted, but we also check use of the non-adjusted series. We assume return on cash is the 13-week U.S. Treasury bill (T-bill) yield (ignoring settlement delays). We use a baseline 0.1% one-way SPY-cash switching frictions and test sensitivity to frictions ranging from 0.0% to 0.5% (but assume dividend reinvestment is frictionless). Using monthly, weekly and daily dividend-adjusted and unadjusted closes for SPY and daily T-bill yield from the end of January 1993 through mid-March 2018, we find that:

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Home Prices and the Stock Market

Homes typically represent a substantial fraction of investor wealth. Are there reliable relationships between U.S. home prices and the U.S. stock market? For example, does a rising stock market stimulate home prices? Do falling home prices point to offsetting liquidation of equity positions. Do homes effectively diversify equity holdings? Using monthly levels of the non-seasonally adjusted S&P/Case-Shiller U.S. National Home Price Index (Home Price Index) and the S&P 500 Index during January 1987 through December 2018 (31 years), and annual median sales prices for existing homes from RealEstateABC.com and the National Association of Realtors spanning 1968 through 2017 (50 years), we find that: Keep Reading

Weekly Summary of Research Findings: 4/2/18 – 4/6/18

Below is a weekly summary of our research findings for 4/2/18 through 4/6/18. 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

Consumer Sentiment and Stock Market Returns

Business media and expert commentators sometimes cite the monthly University of Michigan Consumer Sentiment Index as an indicator of U.S. economic and stock market health, generally interpreting a jump (drop) in sentiment as good (bad) for future consumption and stocks. The release schedule for this indicator is mid-month for a preliminary reading on the current month and end-of-month for a final reading. Is this indicator in fact predictive of U.S. stock market behavior in subsequent months? Using monthly final Consumer Sentiment Index data and monthly levels of the S&P 500 Index during January 1978 through February 2018 (482 monthly sentiment readings), we find that: Keep Reading

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