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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.

Size Effect and the Economy

Does the size effect vary with the state of the economy? In his October 2010 paper entitled “The Behaviour of Small Cap vs. Large Cap Stocks in Recessions and Recoveries: Empirical Evidence for the United States and Canada”, Lorne Switzer examines the relative performance of small versus large capitalization stocks around economic peaks and troughs (per NBER business cycle data). Using monthly returns for U.S. (Canadian) stocks starting with January 1926 (1987), associated firm characteristics and contemporaneous economic and equity market benchmark data through August 2010, he finds that: Keep Reading

Trade the Ten O’Clock News?

Can traders reliably play price jumps associated with surprising economic news releases? In their September 2011 paper entitled “Information Driven Price Jumps and Trading Strategy: Evidence from Stock Index Futures”, Hong Miao, Sanjay Ramchander and Kenton Zumwalt examine the relationship between surprises in announcements for eight U.S. macroeconomic indicators and jump returns for Dow Jones Industrial Average (DJIA), NASDAQ Composite Index and S&P 500 Index futures. They define surprises for each economic indicator based on “standardized” values defined as the gap between actual values and consensus forecasts divided by the standard deviation of gaps. They test the profitability of a high-frequency trading strategy constructed to exploit the surprise-jump relationship for 10:00 AM announcements via nearest index futures contracts, taking one-minute long (short) positions during 10:01-10:02 AM after positive (negative) surprises. When there are multiple 10:00 AM announcements, trades trigger only if all surprises have the same direction. They assume trading friction of one tick for each one-way transaction. Using tick-by-tick nearest or next-nearest to maturity (depending on volume) futures contract prices and pre-announcement consensus (median) forecasts and actual values for monthly macroeconomic indicators during 2001 through 2010, they find that: Keep Reading

Shipping Rates and Stock Market Returns

Do international (seaborne) shipping rates offer advance information about stock market behavior? In the July 2011 draft of their paper entitled “Stock Market Returns and Shipping Freight Market Information: Yet Another Puzzle!”, Amir Alizadeh and Gulnur Muradoglu examine whether changes in the Baltic Exchange Dry Bulk Freight Index (BDI) predict stock market returns and compare its predictive power to that of West Texas Intermediate (WTI) crude oil. To investigate economic significance, they test three trading strategies: (1) a Long‐Short strategy that is long (short) stocks when the next-period return forecast is positive (negative); (2) a Long Only strategy that is long stocks (in U.S. Treasury bills) when the next-period return forecast is positive (negative); and, (3) a Short Only strategy that is short stocks (in U.S. Treasury bills) when the next-period return forecast is negative (positive). Using monthly data for BDI, WTI crude oil price, 13 U.S. stock size/sector indexes, 29 international stock market indexes and economic indicators over the period January 1989 (the earliest consistent BDI meaurement) through December 2010, they find that: Keep Reading

Credit as a Tactical Asset Allocation Signal

Does the claim that “credit anticipates and equity confirms” support a trading strategy? In his June 2011 paper entitled “Credit-Informed Tactical Asset Allocation”, David Klein tests a stocks-cash allocation strategy that derives signals from relative valuation of the Bank of America/Merrill Lynch High Yield B index (converted to a default probability) and the Russell 2000 Index (with dividends). The basic premises for the strategy are: (1) stock prices tend to fall when credit spreads rise; and, (2) small capitalization stocks are more sensitive to the credit cycle than large capitalization stocks. The execution of the strategy is to hold stocks (short-term Treasuries) when stocks appear undervalued (overvalued) relative to corporate bonds based on data from a rolling six-month historical interval. Using daily data for the two indexes during May 1997 through April 2011, he finds that: Keep Reading

Probability of Recession and Future Stock Returns

Some time ago, a reader suggested: “Political Calculations has a tool to ‘reckon the odds of U.S. recession in the next 12 months’ based on a formula developed by the Econobrowser from a paper entitled ‘The Yield Curve and Predicting Recessions’ by the Federal Reserve Board’s Jonathan Wright. What about looking at it the other way in trying to gauge the odds of recovery using the tool?” Focusing on the usefulness of the yield curve-based Wright Model (WM) for predicting stock market behavior, we relate its outputs to future stock market returns. Using monthly closes for the 10-year Treasury note yield, the 13-week Treasury bill (T-bill) yield, the Federal Funds Rate (FFR) and dividend-adjusted S&P 500 Depository Receipts (SPY) over the period January 1993 (SPY inception) through May 2011 (221 months), we find that: Keep Reading

Predicting Variation in the Size Effect

Does the size effect vary in a predictable way? In the May 2011 version of his paper entitled “Explaining the Dynamics of the Size Premium”, Valeriy Zakamulin investigates relationships between eight market/economic variables and the size effect in U.S. stocks to identify the best model of size effect variation. The eight variables are: (1) stock market return; (2) stock market dividend yield; (3) equity value premium; (4) stock return momentum; (5) default spread  (Moody’s BAA-AAA corporate bond yield spread); (6) Treasury bill yield; (7) U.S. Treasuries term premium  (30-year bond yield minus one-month bill yield); and, (8) inflation rate. He then tests the exploitability of the best model via a strategy that switches between small-capitalization and large-capitalization stocks out of sample based on inception-to-date historical data. Using annual data for the eight potentially predictive variables and annual and monthly data for the magnitude of the size effect among NYSE, AMEX and NASDAQ stocks as available over the period 1927 through 2009 (83 years), he finds that: Keep Reading

Fed Model Respecified?

The Fed Model relates the aggregate earnings yield (E/P) of the stock market to Treasury bond or bill yields under the assumption that investors view equities and government bonds as competing ways to achieve yield. Might supply (company management), rather than demand (investors), more precisely drive the relationship between E/P and interest rates? In the April 2011 (incomplete) draft of his paper entitled “Understanding the Fed Model, Capital Structure, and then Some”, J.H. Timmer argues that the stock market earnings yield tends to equilibrium not with the government bond yield but with the average after-tax corporate bond yield as companies adjust capital structure (mix of equity and bonds) to maximize earnings per share. SEC Rule 10b-18 (explicitly allowing share repurchases) enabled fine adjustment toward equilibrium as of 1982. Using annual estimates of one-year forward earnings yields and corporate bond yields for a subset of S&P 500 companies and assuming a constant corporate tax rate of 30% over the period 1968 through 2006, he finds that: Keep Reading

Lead-lag Relationships for Stocks, FFR and Treasuries

Are there reliable lead-lag relationships among stock market returns, changes in the Federal Funds Rate (FFR) and changes in Treasury bond yields? In their February 2011 paper entitled “The US Stock Market Leads the Federal Funds Rate and Treasury Bond Yields”, Kun Guo, Wei-Xing Zhou, Si-Wei Cheng and Didier Sornette apply a new “thermal optimal path” method to test whether: (1) U.S. stock market returns and changes in U.S. Treasury instrument yields have negative correlation; and, (2) FFR as a proxy for U.S. monetary policy predicts U.S. stock market returns. The thermal optimal path method applies statistical methods of thermodynamics to determine the most likely relationship between stock market returns and FFR/yields. Using both monthly and weekly time series for the S&P 500 Index, FFR and U.S. Treasury instrument yields grouped by short-term (three months to three years) and long-term (five years to 20 years) maturities over the period August 2000 through February 2010 (115 months), they find that: Keep Reading

Predicting Stock Market Returns Based on Fixed Business Cycle

Does the concept of an idealized fixed business cycle help predict stock market returns? In his recent paper entitled “Forecasting 2011 Using U.S. Precedents: A Simple Analysis of Equity Market Performance”, Thomas Hall examines the performance of major U.S. stock market indexes at fixed intervals after business cycle troughs and extrapolates results to predict U.S. stock market returns for 2011. For extrapolation, he employs a regression relating returns during months 19-30 after business cycle troughs (equating to calendar year 2011 for the most recent trough) to returns during the immediately preceding months 1-18 after troughs. Using National Bureau of Economic Research business cycle trough months and monthly closes of the Dow Jones Industrial Average and the S&P 500 Index for trough months and months 19 and 30 after troughs as available since 1926 (14 and eight troughs before June 2009, respectively), he finds that: Keep Reading

Baltic Dry Index as Return Predictor

Do variations in the Baltic Dry Index (BDI), a weighted average of the Baltic Exchange shipping cost indexes for the four largest dry-vessel classes, serve as an early measure of global demand for raw materials and thereby predict asset class returns? In the January 2011 version of their paper entitled “The Baltic Dry Index as a Predictor of Global Stock Returns, Commodity Returns, and Global Economic Activity”, Gurdip Bakshi, George Panayotov and Georgios Skoulakis investigate the ability of BDI to predict stock market and commodity market returns. They focus on three-month changes in BDI as a predictor to smooth the high volatility of the monthly series. Using monthly BDI levels and returns for four MSCI regional stock indexes, 19 developed country stock indexes, 12 emerging country stock indexes, three spot commodity indexes and industrial production data for 20 countries mostly over the period May 1985 through September 2010 (305 months), they find that: Keep Reading

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