Economic Indicators

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

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

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

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

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

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

Regarding “Money Supply (M2) and the Stock Market”, a subscriber responded: “I’ve always thought…that both M2 and velocity were needed. If there’s more money, but it is not circulating, then it doesn’t have a chance to have much impact. That’s the situation we have right now for the most part.” The Federal Reserve Bank of St. Louis tracks money velocity based either M1 or M2 money supplies at a quarterly frequency, stating that: “Velocity is a ratio of nominal GDP to a measure of the money supply. It can be thought of as the rate of turnover in the money supply–that is, the number of times one dollar is used to purchase final goods and services included in GDP.” Specifically, the bank calculates money velocity as quarterly nominal GDP divided by average money supply during the quarter. Using quarterly values for seasonally adjusted Velocity of M1, Velocity of M2 and the S&P 500 Index as available from the last quarter of 1958 through the second quarter of 2016 (232 quarters), we find that: Keep Reading

A reader commented: “I couldn’t find an analysis for the M1 money supply similar to the one for M2. How about it? M2 cannot be an accurate money supply measure because it includes non-cash investments such as money market mutual funds. When the stock market corrects and people are exchanging stocks for say, money market mutual fund shares, the M2 figure will actually increase. The money supply is not literally increasing in such cases as no new cash is being created; there is merely an exchange of existing assets. Technically, only increasing the monetary base would increase the money supply, but M1 is a reasonable substitute for that as it includes the cash part of bank reserves.” The M1 money stock consists of funds that are readily accessible for spending: currency in circulation, traveler’s checks, demand deposits and other checkable deposits. Is there a reliable relationship between historical variation in M1 and stock market returns? Using weekly data for seasonally adjusted M1 and the S&P 500 Index during January 1975 through June 2016 (2,165 weeks), we find that: Keep Reading

Some investing experts cite change in money supply as a potentially important driver of future stock market behavior. When the money supply grows (shrinks), they theorize, nominal asset prices tend to go up (down). Or conversely, money supply growth drives inflation, thereby elevating discount rates and depressing equity valuations. One measure of money supply is the M2 money stock, which consists of currency, checking accounts, saving accounts, small certificates of deposit and retail money market mutual funds. Is there a reliable relationship between historical variation in M2 and stock market returns? Using weekly data for seasonally adjusted M2 and the S&P 500 Index during November 1980 through June 2016 (1,861 weeks), 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