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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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Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for March 2018. The actual total (core) inflation rate for March is lower than (higher than) forecasted.

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

Exploitability of Stock Anomalies Worldwide

Are published stock return anomalies exploitable worldwide? In their January 2018 paper entitled “Does it Pay to Follow Anomalies Research? International Evidence”, Ondrej Tobek and Martin Hronec investigate out-of-sample and post-publication performances of 153 cross-sectional stock return anomalies documented in the academic literature, mostly in the top three finance and top three accounting journals. Of the 153 anomalies, 93 involve firm fundamentals, 11 involve firm earnings estimates and 49 involve market frictions. They calculate returns for each anomaly via a hedge portfolio that is long (short) the value-weighted fifth, or quintile, of stocks with the highest (lowest) expected returns for that anomaly. To ensure capacity, they focus on the universe of stocks in the top 90% of NYSE capitalizations. They first examine out-of-sample (after the sample used for discovery but before publication) and post-publication performances of anomalies among U.S. stocks for evidence of performance decay. They then look at anomaly performance outside the U.S. They further test whether strategies that work most widely should be of greatest interest to investors. Finally, they consider a multi-anomaly strategy that each year invests equally in all anomalies that are significant in the U.S. through June, starting in July 1990 for developed country markets and July 2000 for emerging country markets. Using required firm/stock data since July 1963 for the U.S., since January 1987 for Europe, Japan and developed Asia-Pacific and since January 2000 for China and emerging Asia-Pacific, all through December 2016, they find that: Keep Reading

Bonds Lead Stocks?

Are bond market investors generally shrewder than their stock market counterparts, such that bond yield tops (bottoms) anticipate stock market bottoms (tops)? To investigate, we employ both a monthly lead-lag analysis and a comparison of bond yield and stock market tops and bottoms. We define “top” and “bottom” as the highest (lowest) value in a rolling window that extends from 30 months in the past to 30 months in the future (a total window of five years). Using monthly levels of Moody’s yield on seasoned Aaa corporate bonds and the Dow Jones Industrial Average (DJIA) during October 1928 through February 2018 (about 90 years) and monthly levels of the 10-year government bond interest rate and the stock market from Robert Shiller during January 1871 through February 2018 (about 148 years), we find that: Keep Reading

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