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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.

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

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

Risk Aspects of Long and Short Futures Trend-following

How do the long and short sides of futures trend-following strategies differently affect portfolio riskiness? In their September 2016 paper entitled “The Long and Short of Trend Followers”, Jarkko Peltomaki, Joakim Agerback and Tor Gudmundsen-Sinclair investigate via linear regression behaviors of the long and short sides of commonly used trend-following strategies across equities, bonds, commodities and currency futures/forwards under different economic conditions. They model trend-following performance by combining two sets of rules: (1) four slow-reacting simple moving average pair crossover rules using 75-225, 100-300, 125-375 or 150-450 daily moving average pairs; and, (2) four fast-reacting moving average breakout rules based on fluctuations around a long-term moving average. They apply the same allocation method for all rules to set a constant initial risk per trade, adjusted daily by scaling inversely with volatility. They examine how long and short trend-following returns depend on economic environment, focusing on interest rates. They assume trading frictions total $30 per contract. Using futures contract data for 22 equity indexes, 15 government bonds, 17 commodities and six currencies relative to the U.S. dollar, and contemporaneous Commodity Trading Advisor (CTA) performance indexes, during 1984 through 2015, they find that: Keep Reading

Globalization Effects on Asset Return Comovement

Is global diversification within asset classes disappearing as worldwide economic and financial integration increases? In their August 2016 paper entitled “Globalization and Asset Returns”, Geert Bekaert, Campbell Harvey, Andrea Kiguel and Xiaozheng Wang examine whether economic and financial integration increases global comovement of country equity, bond and currency exchange market returns. They examine three measures of return comovement for each asset class: average pairwise correlation, average beta relative to the world market and average idiosyncratic volatility. They apply these measures separately to developed markets and emerging markets. Using monthly equity, bond and currency exchange market returns in U.S. dollars for 26 developed markets and 32 emerging markets as available from various inceptions through December 2014, they find that: Keep Reading

Testing 25 U.S. Stock Market Return Predictors

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

Enhancing Stock Market Prediction with Distilled Economic Variables

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

ECRI’s Weekly Leading Index and the Stock Market

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

Economic News Leaks to Some Traders?

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

Gold a Consistent Dynamic Inflation Hedge?

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

Stock Market Capitalization/GNP as Crash Predictor

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

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