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.
June 16, 2016 - Economic Indicators
The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for May 2016. The actual total (core) inflation rate for May is slightly higher than (slightly higher than) forecasted.
June 16, 2016 - Economic Indicators
Do changes in the Effective Federal Funds Rate (EFFR), the actual cost of short-term liquidity derived from a combination of market demand and Federal Reserve open market operations designed to maintain the Federal Funds Rate (FFR) target, predictably influence the U.S. stock market over the intermediate term? To investigate, we relate smoothed (volume-weighted median) monthly levels of EFFR to monthly U.S. stock market returns (S&P 500 Index or Russell 2000 Index) over available sample periods. Using monthly data as specified since July 1954 for EFFR and the S&P 500 Index (limited by EFFR) and since September 1987 for the Russell 2000 Index, all through April 2016, we find that: Keep Reading
June 8, 2016 - Economic Indicators
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
June 3, 2016 - Economic Indicators, Strategic Allocation
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
May 17, 2016 - Economic Indicators
The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for April 2016. The actual total (core) inflation rate for April is higher than (about the same as) forecasted.
May 11, 2016 - Bonds, Economic Indicators
A reader commented and asked: “A wide credit spread (the difference in yields between Treasury notes or Treasury bonds and investment grade or junk corporate bonds) indicates fear of bankruptcies or other bad events. A narrow credit spread indicates high expectations for the economy and corporate world. Does the credit spread anticipate stock market behavior?” To investigate, we define the credit spread as the difference in yields between and Moody’s seasoned Baa corporate bonds and 10-year Treasury notes (T-note). Using average daily yields for these instruments by calendar month (a smoothed measurement) and contemporaneous monthly closes of the S&P 500 Index for April 1953 through March 2016 (756 months), we find that: Keep Reading
May 5, 2016 - Economic Indicators
Each week the financial 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 stock market returns. A modern period arguably encompasses 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 daily unadjusted opening and closing levels for SPY during late January 1993 through mid-April 2016 (1,212 weeks), we find that: Keep Reading
May 4, 2016 - Economic Indicators, Equity Premium
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:
- Directional accuracy of forecasts (proportion of forecasts that accurately predict the sign of next-month excess returns).
- 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
March 9, 2016 - Economic Indicators
Each month, the Institute for Supply Management (ISM) compiles results of a survey “sent to more than 375 purchasing executives working in the non-manufacturing industries across the country.” Based on this survey, ISM computes the Non-Manufacturing Index (NMI), “a composite index based on the diffusion indexes for four…indicators with equal weights: Business Activity (seasonally adjusted), New Orders (seasonally adjusted), Employment (seasonally adjusted) and Supplier Deliveries.” ISM releases NMI for a month on the third business day of the following month. Does the monthly level of NMI or the monthly change in NMI predict U.S. stock market returns? Using monthly NMI data and monthly closes of the S&P 500 Index from January 2008 through January 2016 (97 months), we find that: Keep Reading
March 9, 2016 - Economic Indicators
According to the Institute for Supply Management (ISM) ISM, their Manufacturing Report On Business, published since 1931, “is considered by many economists to be the most reliable near-term economic barometer available.” The manufacturing summary component of this report is the Purchasing Managers’ Index (PMI), aggregating monthly inputs from purchasing and supply executives across the U.S. regarding new orders, production, employment, deliveries and inventories. ISM releases PMI for a month at the beginning of the following month. Does PMI, as claimed by some financial experts, predict stock market returns? Using monthly seasonally adjusted PMI data and monthly S&P 500 Index closes from January 1950 through January 2016 (793 months), we find that: Keep Reading