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Economic Indicators

The U.S. economy is a very complex system, with indicators therefore ambiguous and difficult to interpret. To what degree do macroeconomics and the stock market go hand-in-hand, if at all? Do investors/traders: (1) react to economic readings; (2) anticipate them; or, (3) just muddle along, mostly fooled by randomness? These blog entries address relationships between economic indicators and the stock market.

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Mojena Market Timing Model

The Mojena Market Timing strategy (Mojena), developed and maintained by professor Richard Mojena, is a method for timing the broad U.S. stock market based on a combination of many monetary, fundamental, technical and sentiment indicators to predict changes in intermediate-term and long-term market trends. He adjusts the model annually to incorporate new data. Professor Mojena offers a hypothetical backtest of the timing model since 1970 and a live investing test since 1990 based on the S&P 500 Index (with dividends). To test the robustness of the strategy’s performance, we consider a sample period commencing with inception of SPDR S&P 500 (SPY) as a convenient and low-cost proxy for the S&P 500 Index. As benchmarks, we consider both buying and holding SPY (Buy-and-Hold) and trading SPY with crash protection based on the 10-month simple moving average of the S&P 500 Index (SMA10). Using the trade dates from the Mojena Market Timing live test, daily dividend-adjusted closes for SPY and daily yields for 13-week Treasury bills (T-bills) from the end of January 1993 through March 2017 (over 24 years), we find that: Keep Reading

Interpreting Inverted Yield Curves as Economic Indigestion

Is there a straightforward way to interpret the state of the yield curve as a manifestation of how efficiently the economy is processing information? In his March 2017 paper entitled “Simple New Method to Predict Bear Markets (The Entropic Linkage between Equity and Bond Market Dynamics)”, Edgar Parker Jr. presents and tests a way to understand interaction between bond and equity markets based on arrival and consumption of economic information. He employs Shannon entropy to model the economy’s implied information processing ratio (R/C), with interpretations as follows:

  1. R/C ≈ 1: healthy continuously upward-sloping yield curve when information arrival and consumption rates are approximately equal.
  2. R/C >> 1: low end of the yield curve inverts when information is arriving much faster than it can be consumed.
  3. R/C << 1: high end of the yield curve inverts when information is arriving much slower than it can be consumed.

Under the latter two conditions, massive information loss (entropy growth) occurs, and firms cannot confidently plan. These conditions delay/depress economic growth and produce equity bear markets. He tests this approach by matching actual yield curve data with standardized (normal) R and C distributions that both have zero mean and standard deviation one (such that standardized R and C may be negative). Using daily yields for U.S. Treasuries across durations and daily S&P 500 Index levels during 1990 through 2016, he finds that: Keep Reading

Factor Investing and the Business Cycle

What is “under the hood” at quantitative investment firms? In their December 2016 book-length paper entitled “Factor Investing and Asset Allocation: A Business Cycle Perspective”, Vasant Naik, Mukundan Devarajan, Andrew Nowobilski, Sebastien Page and Niels Pedersen examine the process of translating macroeconomic forecasts into alpha-generating portfolios via mean-variance optimization. They address how to: (1) specify the risk factors driving returns in global financial markets; (2) estimate factor returns and volatilities; and, (3) construct an optimal portfolio of factors. They emphasize the primacy of the business cycle in estimating future returns and volatilities of risk factors across multiple asset classes. They also emphasize the importance of market valuations (to identify when price fluctuations create tactical opportunities) in investment decision making. Based on the body of financial markets research over the last 50 years and their own experiences with the investment process, they conclude that: Keep Reading

Deconstructing Industry Stock Return Momentum

Do supply chain (trade network) dynamics explain intermediate-term momentum in industry stock returns? In their December 2016 paper entitled “Feedback Loops in Industry Trade Networks and the Term Structure of Momentum Profits”, Ali Sharifkhani and Mikhail Simutin examine whether industry trading network activities create feedback that induces intermediate-term autocorrelation (echo) in associated stock returns. They apply graph theory to quantify supply-demand relationships within industry trade networks and strength of feedback loops that connect each of 49 industries to itself. They then relate network feedback strength to intermediate-term momentum (industry return from 12 months ago to seven months ago) and short-term momentum (industry return from six months ago to two months ago) for each industry as follows:

  1. Each month, sort the 49 industries into thirds (terciles) by current trade network feedback strength.
  2. Calculate the value-weighted average return of stocks within each industry.
  3. Within each feedback strength tercile, form a hedge portfolio that is long (short) the equal-weighted fifth, or quintile, of industries with the highest (lowest) past returns over each of the two specified momentum measurement intervals.
  4. Calculate average next-month return for each feedback strength-momentum double-sorted hedge portfolio.

Using industry input-output network trade data as issued (partly every five years and partly annual) and monthly industry component stock returns/capitalizations for 49 U.S. industries since 1972, and related analyst coverage data since 1984, all through December 2014, they find that: Keep Reading

Economic Uncertainty as a Stock Return Factor

Do specific stocks react differently to economic uncertainty? In their December 2016 paper entitled “Is Economic Uncertainty Priced in the Cross-Section of Stock Returns?”, Turan Bali, Stephen Brown and Yi Tang investigate the role of economic uncertainty in the cross-sectional pricing of individual stocks. They measure economic uncertainty monthly as an aggregation of the volatilities of the unpredictable components of a large number of economic indicators (see the chart below). They then calculate each stock’s sensitivity to economic uncertainty by regressing next-month returns versus economic uncertainty over rolling 60-month windows. Finally, sort stocks into tenths (deciles) by economic uncertainty regression betas and measure economic uncertainty factor returns as the difference in next-month average returns of stocks in extreme deciles. They test robustness via multiple factor models of stock returns and many control variables. Using monthly economic uncertain index data, monthly returns for a broad sample of U.S. stocks and monthly values of control variables during July 1972 through December 2014, they find that: Keep Reading

Dollar-Euro Exchange Rate, U.S. Stocks and Gold

Do changes in the dollar-euro exchange rate reliably interact with the U.S. stock market and gold? For example, do declines in the dollar relative to the euro indicate increases in the dollar value of hard assets? Are the interactions coincident or exploitably predictive? To investigate, we relate changes in the dollar-euro exchange rate to returns for U.S. stock indexes and spot gold. Using end-of-month and end-of-week values of the dollar-euro exchange rate, levels of the S&P 500 Index and Russell 2000 Index and spot prices for gold during January 1999 (limited by the exchange rate series) through October 2016, we find that: Keep Reading

Housing Starts and Future Stock Market/REIT Returns

Each month, the Census Bureau announces and the financial media report U.S. housing starts as a potential indicator of future U.S. stock market returns. Release date is about two weeks after the month being reported. Moreover, new releases may substantially revise recent past releases, so that the Census Bureau historical data set effectively has a longer lag. Does this economic indicator convey useful information about future returns for the broad U.S. stock market or for Real Estate Investment Trusts (REIT)? To investigate, we relate returns for the S&P 500 Index and for the FTSE NAREIT All REITs total return index to changes in housing starts at the monthly release frequency. Using monthly data for the S&P 500 Index and for seasonally adjusted annualized housing starts starting in January 1959, and for the FTSE NAREIT index starting in December 1971, all through September 2016, we find that:
Keep Reading

New Home Sales and Future Stock Market/REIT Returns

Each month, the Census Bureau announces and the financial media report U.S. new home sales as a potential indicator of future U.S. stock market returns. Release date is about three weeks after the month being reported. Moreover, new releases may substantially revise recent past releases, so that the Census Bureau historical data set effectively has a longer lag. Does this economic indicator convey useful information about future returns for the broad U.S. stock market or for Real Estate Investment Trusts (REIT)? To investigate, we relate returns for the S&P 500 Index and for the FTSE NAREIT All REITs total return index to changes in new home sales at the monthly release frequency. Using monthly data for the S&P 500 Index and for seasonally adjusted annualized new homes sales starting in January 1963, and for the FTSE NAREIT index starting in December 1971, all through September 2016, we find that: Keep Reading

CPI and Stocks Over the Short and Intermediate Terms

Do investors reliably react over short and intermediate terms to changes in the U.S. Consumer Price Index (CPI), a logical measure of the wealth discount rate? Using monthly total and core (excluding food and energy) CPI releases (for all items, not seasonally adjusted) from the Bureau of Labor Statistics (BLS) and contemporaneous S&P 500 Index open and close data for the period mid-January 1994 (the earliest for which release dates are available) through mid-October 2016 (274 releases), we find that: Keep Reading

Effects of Deflation on Stock Market Returns/Valuation

Does the stock market perform poorly in a deflationary environment? In the September 2016 version of his paper entitled “Deflation and Stock Prices”, Michael Clemens explores relationships between change in the Consumer Price Index (CPI) and each of stock market return and stock market valuation. He defines four deflation/inflation regimes based on ranges of annualized average monthly change in CPI over the previous 12 months. He considers both contemporaneous (last 12 months) and future (next 12 months) stock market returns. He measures stock market price-earnings ratio (P/E) as the average of Robert Shiller’s Cyclically Adjusted Price-Earnings Ratio (CAPE or P/E10), 12-month historical P/E and 12-month future P/E known with perfect foresight. Using Shiller’s U.S. monthly data spanning January 1871 through February 2016 and shorter, recent samples for Japan (January 2001 through February 2016) and Switzerland (January 2005 through February 2016), he finds that: Keep Reading

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