Economic Indicators

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

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

Does the ratio of aggregate U.S. stock market valuation (MV) to U.S. Gross National Product (GNP) or Gross Domestic Product (GDP), the approximate value of goods and services produced by U.S. companies, reliably indicate stock market overvaluation? In their July 2015 paper entitled “Can Warren Buffett Also Predict Equity Market Downturns?”, Sebastien Lleo and William Ziemba investigate whether MV/GNP accurately predicts peak-to-trough declines of more than 10% in daily closes of the S&P 500 Total Return Index within a year. They consider: (1) a simple static model based on a 120% overvaluation threshold; and, (2) two dynamic statistical confidence models based on mean and standard deviation during a four-quarter rolling historical window. They consider both MV/GNP and the logarithm of MV/GNP (relating variable changes) as predictive variables. Using quarterly, seasonally-adjusted GNP and Wilshire 5000 Full Capitalization Price Index level as a proxy for MV (the limiting series) and daily level of the S&P 500 Total Return Index from the fourth quarter of 1970 through the first quarter of 2015 (177 quarters), they find that: Keep Reading

In the introduction to their 2015 book entitled Invest with the Fed: Maximizing Portfolio Performance by Following Federal Reserve Policy, authors Robert Johnson, Gerald Jensen and Luis Garcia-Feijoo state: “Our purpose in writing this book is to provide a general overview of the Fed’s role in the financial markets, but, more important, to offer investors a road map that can be used in designing an investment portfolio that takes account of Fed policy. In detailing our road map for investors, we offer a rationale for each investment strategy along with empirical evidence supporting the efficacy of the strategy. Most important, the recommended strategies come with clear explanations and easy-to-follow descriptions of the processes needed to execute the strategies.” The essential Fed policy discriminator they use is whether monetary conditions are expansive (decreasing discount rate and decreasing federal funds rate), restrictive (increasing discount rate and increasing federal funds rate) or indeterminate (one rate increasing and the other decreasing). Based on their research, they conclude that: Keep Reading

Does fourth quarter global economic data set the stage for asset class returns the next year? In their February 2015 paper entitled “The End-of-the-year Effect: Global Economic Growth and Expected Returns Around the World”, Stig Møller and Jesper Rangvid examine relationships between level of global economic growth and future asset class returns, focusing on growth at the end of the year. Their principle measure of global economic growth is the equally weighted average of quarterly OECD industrial production growth in 12 developed countries. They perform in-sample tests 30 countries and out-of-sample tests for these same 12 countries (for which more data are available). Out-of-sample tests: (1) generate initial parameters from 1970 through 1989 data for testing during 1990 through 2013 period; and, (2) insert a three-month delay between economic growth data and subsequent return calculations to account for publication lag. Using global industrial production growth as specified, annual total returns for 30 country, two regional and world stock indexes, currency spot and one-year forward exchange rates relative to the U.S. dollar, spot prices on 19 commodities, total annual returns for a global government bond index and a U.S. corporate bond index, and country inflation rates as available during 1970 through 2013, they find that: Keep Reading

Is the reward for holding risky bonds material and distinct from the reward for holding stocks and the reward for holding longer term bonds? In their February 2015 paper entitled “Credit Risk Premium: Its Existence and Implications for Asset Allocation”, Attakrit Asvanunt and Scott Richardson measure and explore the predictability and diversification power of the credit (or default) risk premium associated with corporate bonds. They focus on the premium associated with creditworthiness of bonds by first removing the influence of duration/interest rates. They also test whether the credit risk premium diversifies the equity risk premium and the bond term premium. Using data for U.S. corporate bonds, the U.S. stock market, U.S. Treasury securities and economic indicators during 1927 through 2014 and for credit default swaps (CDS) during 2004 through 2014, they find that: Keep Reading

Do any of the various U.S. stock market size and value/growth styles systematically lead or lag the overall market, perhaps because of some underlying business/economic cycle? To investigate, we consider the the following six exchange-traded funds (ETF) that cut across capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

Using monthly dividend-adjusted closing prices for the style ETFs and S&P Depository Receipts (SPY) over the period August 2001 through December 2014 (161 months, limited by data for IWS/IWP), we find that: Keep Reading

Referring to “Leading Economic Index and the Stock Market”, a subscriber inquired about using the Conference Board’s Leading Economic Index (LEI) for the U.S. to generate long-term U.S. stock market timing signals, as follows:  

“How about using the LEI in the following fashion?

Buy when the LEI rises by 1.0 % from its lowest point in the prior six months.
Sell when the LEI falls by 1.5% from its highest point in the last six months.

I used 1% as a buy because bear markets can end abruptly, not because I was torturing the data to confess. You could use 1.5% and I think still have robust results…changes in trend, which are rare, seem to be helpful. I bought the LEI data from the Conference Board and did some testing by hand using the above going back to 1969. I think I found some interesting results. …It gave early sell in 2006… The signal date was the date of the release… Most of the benefit of the trading system comes within the last 14 years.”

Using the monthly change in LEI data from archived Conference Board press releases during June 2002 through October 2014 (146 months), we find that: Keep Reading