Economic Indicators

What drives the price of gold: inflation, stock market behavior, public sentiment? To investigate, we relate spot gold price to non-seasonally adjusted Consumer Price Index (CPI), the S&P 500 Index and University of Michigan Consumer Sentiment. We start sampling in 1975 because: “On March 17, 1968, …the price of gold on the private market was allowed to fluctuate…[, and] in 1975…the price of gold was left to find its free-market level.” We lag CPI and consumer sentiment measurements by one month to ensure they are known to the market when calculating gold returns. Using monthly data from January 1975 (January 1978 for consumer sentiment) through June 2016 (498 or 461 months), we find that: Keep Reading

What variables best predict U.S. stock market returns? In his June 2016 paper entitled “Which Variables Predict and Forecast Stock Market Returns?”, David McMillan examines the power of 25 variables to predict excess return (relative to the 3-month U.S. Treasury bill yield) of Shiller’s S&P Composite Index both in-sample and out-of-sample. He chooses variables based on connectedness to expected cash flow/dividends and risk and assigns them to five groups:

1. Financial ratios: dividend-price, price-to-earnings, cyclically adjusted price-to-earnings (CAPE or P/E10), Tobin’s Q and market capitalization-to-Gross Domestic Product (GDP).
2. Economic:  GDP cycle, GDP acceleration (rate of change in GDP growth), consumption growth, 10-year to 3-month Treasuries term spread and inflation.
3. Labor: wage growth, unemployment, natural rate of unemployment, productivity growth and labor market conditions.
4. Housing: house price growth, house affordability, home ownership, housing supply and new house sales.
5. Other: University of Michigan Consumer Sentiment, Purchasing Managers Index, National Financial Conditions Index, leverage and non-financial leverage.

He employs regressions to test in-sample predictive power. He then tests out-of-sample forecasts starting in 2000 using various forecast methods and accuracy measures and considering both single-variable and multi-variable models. Using the specified data series as available during 1973 through 2014, he finds that: Keep Reading

Financial media often cite Bureau of Labor Statistics (BLS) productivity growth news releases as relevant to investment outlook. Does the quarter-to-quarter change in U.S. labor force productivity predict U.S. stock market behavior? Specifically, does a rise (decline) in productivity portend strong (weak) earnings and therefore an advance (decline) for stocks? Using annualized quarterly changes in non-farm labor productivity from BLS and end-of quarter S&P 500 Index levels during January 1950 through March 2016 (265 quarters), we find that: Keep Reading

Using quarterly data in their April 2013 preliminary paper entitled “Analyzing Federal Reserve Asset Purchases: From Whom Does the Fed Buy?” Seth Carpenter, Selva Demiralp, Jane Ihrig and Elizabeth Klee find that some categories of investors appear to sell U.S. Treasuries to the Federal Reserve and rebalance toward riskier assets (corporate bonds, commercial paper, and municipal debt). Are stocks a part of this process? To investigate, we relate weekly, monthly and quarterly U.S. stock market returns to comparable changes in the Federal Reserve’s System Open Market Account (SOMA) holdings, comprised of U.S. Treasury bills, U.S. Treasury notes and bonds, U.S. Treasury Inflation-Protected Securities (TIP) and Mortgage-Backed Securities (MBS). The Federal Reserve reports these holdings with a small lag. Using weekly (Wednesday close) data for SPDR S&P 500 (SPY) as a stock market proxy and total SOMA holdings during early July 2003 through mid-May 2016, we find that: Keep Reading

Can investors exploit economic data for monthly stock market timing? In their September 2015 paper entitled “Getting the Most Out of Macroeconomic Information for Predicting Excess Stock Returns”, Cem Cakmaklı and Dick van Dijk test whether a model employing 118 economic variables improves prediction of monthly U.S. stock market (S&P 500 Index) excess returns based on conventional valuation ratios (dividend yield and price-earnings ratio) and interest rate indicators (risk-free rate, change in risk-free rate and credit spread). Excess return means above the risk-free rate. They each month apply principal component analysis to distill from the 118 economic variables (or from subsets of these variables with the most individual power to predict S&P 500 Index returns) a small group of independent predictive factors. They then regress next-month S&P 500 Index excess returns linearly on these factors and conventional valuation ratios/interest rate indicators over a rolling 10-year historical window to generate excess return predictions. They measure effectiveness of the economic inputs in two ways:

1. Directional accuracy of forecasts (proportion of forecasts that accurately predict the sign of next-month excess returns).
2. Explicit economic value of forecasts via mean-variance optimal stocks-cash investment strategies that each month range from 200% long to 100% short the stock index depending on monthly excess return predictions as specified and monthly volatility predictions based on daily index returns over the past month, with transaction costs of 0.0%, 0.1% or 0.3%.

Using monthly values of the 118 economic variables (lagged one month to assure availability), conventional ratios/indicators and monthly and daily S&P 500 Index levels during January 1967 through December 2014, they find that: Keep Reading

Financial market commentators and media sometimes cite the Economic Cycle Research Institute’s (ECRI) U.S. Weekly Leading Index (WLI) as an important economic indicator, implying that it is predictive of future stock market performance. According to ECRI, WLI “has a moderate lead over cyclical turns in U.S. economic activity.” ECRI publicly releases a preliminary (revised) WLI value with a one-week (two-week) lag. Does this indicator usefully predict U.S. stock market returns? Using WLI values for January 1967 through January 2016 and contemporaneous weekly levels of the S&P 500 Index, we find that: Keep Reading

Can small (unconnected) investors compete in trades on economic news? In the February 2016 draft of her paper entitled “Is Someone Front-Running You Around News Releases?”, Irene Aldridge examines U.S. stock price, volatility and trading activity around ISM Manufacturing Index and Construction Spending news releases (which occur while the stock market is open). Media violations of embargoes on pre-release distribution of such news, intended to promote widespread simultaneous scheduled release, could influence this activity. She uses average price response of Russell 3000 stocks as a market reaction metric. She considers news “direction” relative either to prior-month value (increase or decrease) or to consensus forecast (above or below). Using one-minute trading data for Russell 3000 Index stocks around monthly ISM Manufacturing Index and Construction Spending announcements during January 2013 through October 2015, she finds that: Keep Reading

A subscriber suggested that the Federal Reserve Bank of Chicago’s National Financial Conditions Index (NFCI) may be a useful U.S. stock market predictor. NFCI “provides a comprehensive weekly update on U.S. financial conditions in money markets, debt and equity markets, and the traditional and ‘shadow’ banking systems.” It consists of 105 inputs, including the S&P 500 Implied Volatility Index (VIX) and Senior Loan Officer Survey results. Positive (negative) values indicate tight (loose) financial conditions, with degree measured in standard deviations from the mean. The Chicago Fed releases NFCI each week as of Friday on the following Wednesday at 8:30 a.m. ET (or Thursday if Wednesday is a holiday). To investigate its usefulness as a U.S. stock market predictor, we relate NFCI and changes in NFCI to future S&P 500 Index returns. Using weekly levels of NFCI and daily and weekly closes of the S&P 500 Index during January 1973 through December 2015, we find that: Keep Reading

Is gold a consistent hedge against inflation? In their October 2015 preliminary paper entitled “Is Gold a Hedge Against Inflation? A Wavelet Time-Frequency Perspective”, Thomas Conlon, Brian Lucey and Gazi Salah Uddin examine the inflation-hedging properties of gold over an extended period at different measurement frequencies (investment horizons) in four economies (U.S., UK, Switzerland and Japan). They consider both realized and unexpected inflation. They also test the inflation-hedging ability of gold futures and gold stocks. Using monthly consumer price indexes (not seasonally adjusted) for the four countries and monthly returns for spot gold (bullion) in the four associated currencies since January 1968, monthly survey-based U.S. inflation expectations since January 1978, and monthly returns on the Philadelphia Gold and Silver Index (XAU) as a proxy for gold stocks since January 1984, all through December 2014, they find that: Keep Reading

A reader, citing the book Ahead of the Curve by Joseph Ellis, inquired about the hypothesis that consumer spending drives economic cycles and is therefore a leading indicator for the stock market. For example, Mr. Ellis presents a chart showing annual change in Personal Consumption Expenditures (PCE) and annual change in S&P 500 operating earnings based on quarterly data. The chart also shows bear markets for U.S. stocks. The chart discussion states: “Most bear markets begin…when the Y/Y [year-over-year] rate of growth in consumer spending is peaking… Most bear markets then proceed as (the rate of growth in) consumer spending continues to slow, and are largely over by the time recessions…are under way. Importantly, most bear markets end…when consumer spending and S&P 500 profits are at, or even prior to, their worst Y/Y comparisons.” Does PCE usefully predict stock market behavior? The Bureau of Economic Analysis (BEA) releases seasonally adjusted, annualized total PCE monthly with a lag of about one month (Summary Table 2.85, Line 1: “Personal Income and Its Disposition, Monthly”). Using this series and monthly S&P 500 Index levels for January 1959 through August 2015 (680 months), we find that…

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