Economic Indicators

Which economic and market variables are most effective in predicting U.S. stock market returns? In his October 2018 paper entitled “Forecasting US Stock Returns”, David McMillan tests 10-year rolling and recursive (inception-to-date) one-quarter-ahead forecasts of S&P 500 Index capital gains and total returns using 18 economic and market variables, as follows: dividend-price ratio; price-earnings ratio; cyclically adjusted price-earnings ratio; payout ratio; Fed model; size premium; value premium; momentum premium; quarterly change in GDP, consumption, investment and CPI; 10-year Treasury note yield minus 3-month Treasury bill yield (term structure); Tobin’s q-ratio; purchasing managers index (PMI); equity allocation; federal government consumption and investment; and, a short moving average. He tests individual variables, four multivariate combinations and and six equal-weighted combinations of individual variable forecasts. He employs both conventional linear statistics and non-linear economic measures of accuracy based on sign and magnitude of forecast errors. He uses the historical mean return as a forecast benchmark. Using quarterly S&P 500 Index returns and data for the above-listed variables during January 1960 through February 2017, he finds that: Keep Reading

Which economic variables are most important for predicting stock returns? In their October 2018 paper entitled “Sparse Macro Factors”, David Rapach and Guofu Zhou apply machine learning to isolate via sparse principal component analysis (PCA) which of 120 economic variables from the FRED-MD database most influence stocks. These variables span output/income, labor market, housing, consumption, orders/inventories, money/credit, yields/exchange rates and inflation. As a preliminary step, they adjust raw economic variables by, where necessary: (1) transforming them to produce stationary series; (2) adjusting for reporting lags of one or two months. They next execute sparse PCA, which sets small component weights to zero, thereby facilitating interpretation of results without sacrificing much predictive power. For comparison, they also extract the first 10 conventional principal components from the same variables. Finally, they use 202 stock portfolios to estimate the influence of sparse and conventional principal components on the cross section of stock returns. Using monthly data for the 120 economic variables and 202 stock portfolios during February 1960 through June 2018, they find that:

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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 liquid, 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 August 2018 (over 25 years), we find that: Keep Reading

Each week the media report U.S. initial and continued unemployment claims (seasonally adjusted) as a potential indicator of future U.S. stock market returns. Do these indicators move the market? To investigate, we focus on weekly changes in unemployment claims during a period of “modern” information dissemination to release-day and next-week stock market returns. By modern period, we mean the history of S&P Depository Receipts (SPY), a proxy for the U.S. stock market. Using relevant news releases and archival data as available from the Department of Labor (DOL) and dividend-adjusted weekly and daily opening and closing levels for SPY during late January 1993 through mid-July 2018 (1,330 weeks), we find that:

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A subscriber proposed: “It would be interesting to do an analysis of the Chemical Activity Barometer [CAB] to see if it has predictive value for the stock market. Either [look] at stock prices when [CAB makes] a two percent pivot down [from a preceding 6-month high] as a sell signal and one percent pivot up as a buy signal…[or when CAB falls] below its x month moving average.” The American Chemistry Council claims that CAB “determines turning points and likely future trends of the wider U.S. economy” and leads other commonly used economic indicators. To investigate its usefulness for U.S. stock market timing, we consider the two proposed strategies, plus two benchmarks, as follows:

1. CAB SMAx Timing – hold stocks (the risk-free asset) when monthly CAB is above (below) its simple moving average (SMA). We consider SMA measurement intervals ranging from two months (SMA2) to 12 months (SMA12).
2. CAB Pivot Timing – hold stocks (the risk-free asset) when monthly CAB most recently crosses 1% above (2% below) its maximum value over the preceding six months. We look at a few alternative pivot thresholds.
3. Buy and Hold (B&H) – buy and hold the S&P Composite Index.
4. Index SMA10 – hold stocks (the risk-free asset) when the S&P Composite Index is above (below) its 10-month SMA (SMA10), assuming signal execution the last month of the SMA measurement interval.

Since CAB data extends back to 1912, we use Robert Shiller’s S&P Composite Index to represent the U.S. stock market. For the risk-free rate, we use the 3-month U.S. Treasury bill (T-bill) yield since 1934. Prior to 1934, we use Shiller’s long interest rate minus 1.59% (the average 10-year term premium since 1934). We assume a constant 0.25% friction for switching between stocks and T-bills as signaled. We focus on number of switches, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Using monthly data for CAB, the S&P Composite Stock Index, estimated dividends for the stocks in this index (for calculation of total returns) and estimated long interest rate during January 1912 through December 2017 (about 106 years), and the monthly T-bill yield since January 1934, we find that: Keep Reading

In reaction to “Combine Market Trend and Economic Trend Signals?”, a subscriber suggested adding an interest rate trend signal to those for the U.S. stock market and U.S. unemployment rate for the purpose of timing the S&P 500 Index (SP500). To investigate, we look at combining:

• The 10-month simple moving average (SMA10) for the SP500. The trend is positive (negative) when the index is above (below) its SMA10.
• The 12-month simple moving average (SMA12) for the seasonally adjusted U.S. civilian unemployment rate (UR). The trend is positive (negative) when UR is below (above) its SMA12.
• SMA12 for the average monthly 3-month U.S. Treasury bill yield (T-bill). The trend is positive (negative) when T-bill is below (above) its SMA12.

We consider scenarios when the SP500 trend is positive, the UR trend is positive, the T-bill trend is positive, at least one trend is positive (>=1), at least two trends are positive (>=2) or all three trends are positive (All). For total return calculations, we adjust the SP500 monthly with estimated dividends from the Shiller dataset. When not in the index, we assume return on cash from the broker is the specified T-bill yield. We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics. We use the average monthly T-bill yield during a year as the risk-free rate for that year in Sharpe ratio calculations. While we do not apply any stocks-cash switching frictions, we do calculate the number of switches for each scenario. Using the specified monthly data through October 2017, we find that: Keep Reading

Do changes in the U.S. federal regulatory burden predict U.S. stock market returns? To check, we consider two measures of the regulatory burden:

1. Annual number of pages in the Federal Register (FR) during 1936-2016 – “…in which all newly proposed rules are published along with final rules, executive orders, and other agency notices—provides a sense of the flow of new regulations issued during a given period and suggests how the regulatory burden will grow as Americans try to comply with the new mandates.”
2. Annual number of pages in the Code of Federal Regulations (CFR) during 1975-2016 – “…the codification of all rules and regulations promulgated by federal agencies. Its size…provides a sense of the scope of existing regulations with which American businesses, workers, and consumers must comply.

Specifically, we relate annual changes in these measures to annual returns for the S&P 500 Index. Using the specified regulatory data and annual S&P 500 Index total returns during 1929 through 2016, we find that: Keep Reading

Do hedge funds demonstrate the exploitability of real-time economic data? In their June 2017 paper entitled “Can Hedge Funds Time the Market?”, Michael Brandt, Federico Nucera and Giorgio Valente evaluate whether all or some equity hedge funds vary equity market exposure in response to real-time economic data, and (if so) whether doing so improves their performance. Their proxy for real-time economic data available to a sophisticated investor is the 20-day moving average of an economic growth index derived from principal component analysis of purely as-released industrial output, employment and economic sentiment. They relate this data to hedge fund performance by:

1. Applying a linear regression to measure the sensitivity (economic data beta) of each hedge fund to monthly changes in economic data.
2. Sorting funds into tenths (deciles) based on economic data beta and calculating average next-month equally weighted risk-adjusted performance (7-factor alpha) by decile. The seven monthly factors used for risk adjustment are: equity market excess return; equity size factor; change in 10-year U.S. Treasury note (T-note) yield; change in yield spread between BAA bonds and T-notes; and trend following factor for bonds, currencies and commodities.

Using sample of 2,224 dead and alive equity hedge funds having at least 36 months of net-of-fee returns and average assets under management of at least $10 million, and contemporaneous daily values of the economic growth index, during January 1994 through December 2014, they find that:

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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

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