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Value Investing Strategy (Strategy Overview)

Allocations for September 2022 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for September 2022 (Final)
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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.

Economic Announcements and VIX

Do economic announcements systematically remove uncertainty from financial markets and thus reliably lower implied volatility indexes? In their September 2010 paper entitled “The Impact of Macroeconomic Announcements on Implied Volatilities”, Roland Füss, Ferdinand Mager and Lu Zhao measure the reactions of the Chicago Board Options Exchange Volatility Index (VIX) and the DAX Volatility Index (VDAX) to U.S. and German macroeconomic announcements. They consider announcements of Gross Domestic Product (GDP), the Producer Price Index (PPI) and the Consumer Price Index (CPI). The measurement interval is apparently close-to-close from the day before to the day of announcement. Using monthly/quarterly macroeconomic announcement dates from January 2005 through December 2009 and contemporaneous daily data for VIX and VDAX (60 months), they find that: Keep Reading

Do Homebuilders Lead the Market?

A reader asked whether the behavior of the stocks of homebuilders anticipate the overall equity market. To check, we first assemble a simple index of the performance of homebuilder stocks as the equally-weighted average monthly return for the stocks of DR Horton, Hovnanian, KB Homes, Lennar, Ryland and Pulte, starting with Pulte in August 1985 and adding the others as they are listed. Comparing these returns with monthly returns for the S&P 500 Index data for August 1985 through March 2012 (320 monthly returns), we find that: Keep Reading

Election Season Stock Market VIX Drivers

Does political drama take over as the principal driver of U.S. stock market implied volatility during election seasons? In their March 2012 paper entitled “U.S. Presidential Elections and Implied Volatility: The Role of Political Uncertainty”, John Goodell and Sami Vähämaa compare the effects of political uncertainty to those of eight other sources of uncertainty on implied stock market volatility (as measured by VIX) during U.S. presidential election campaigns. They define the quadrennial campaign interval as the time from the beginning of February to the beginning of November of election years. They consider two measures of political uncertainty derived from the Iowa Electronic Markets: monthly change in probability of success of the eventual winner; and, monthly change in a measure of how close the race is. They also consider eight competing financial and economic sources of uncertainty as listed below. Using monthly data for these ten variables during the presidential election campaigns of 1992, 1996, 2000, 2004 and 2008 (40 total monthly observations), they find that: Keep Reading

Enhancing Financial Markets Volatility Prediction

Are there economic and financial variables that meaningfully predict return volatilities of financial markets? In their March 2012 paper entitled “A Comprehensive Look at Financial Volatility Prediction by Economic Variables”, Charlotte Christiansen, Maik Schmeling and Andreas Schrimpf investigate the ability of 38 economic and financial variables to predict return volatilities of four asset classes (stocks, foreign exchange, bonds and commodities). Asset class proxies are: (1) the S&P 500 Index; (2) spot levels for a basket of currencies versus the U.S. dollar; (3) 10-year Treasury note futures contract prices; and, (4) the S&P GSCI. They calculate actual (realized) monthly asset class volatilities from daily returns. They construct out-of-sample volatility forecasts based on iterative inception-to-date regressions of volatilities versus predictive variables. They use an autoregressive model (simple realized volatility persistence) as a benchmark. Using monthly data for 13 economic/financial variables and the S&P 500 Index realized volatility over the long period December 1926 through December 2010 (1,009 months) and monthly data for 38 variables and all four asset class volatilities during 1983 through 2010 (366 months), they find that: Keep Reading

Federal Funds Rate Changes Ubiquitously Useful?

Can investors reliably exploit monetary expansion and contraction as signaled by decreases and increases in the Federal Funds Rate (FFR)? In the December 2011 version of his paper entitled “Don’t Fight the Fed!”, Paulo Maio investigates the predictive power of FFR for the equity risk premium and the profitability of trading strategies based on this forecasting power. For example, he tests a rule-based strategy that takes a 1.5X leveraged (-0.5X short) position in a broad U.S. stock market index and a -0.5X short (1.5X long) position in the risk-free rate when the change in the FFR is below (above) some negative (positive) threshold, and otherwise a 1.0X position in the stock market index. He also tests a regression-based strategy that takes the above long (short) position in the stock market index if regression of next-month return versus change in FFR forecasts a positive (negative) return. The benchmark for these tests passively holds a 1.5X long position in the stock market index and a -0.5X position in the risk-free rate. He tests similar strategies on other risk factor and asset class indexes. Using FFR changes, one-month Treasury bill yields and monthly returns for a broad U.S. stock market index and several risk-factor portfolios since August 1954, long-term Treasuries and U.S. corporate bond indexes since August 1954, a commodities index since February 1972 and foreign currencies since July 1978, all through 2010, he finds that: Keep Reading

When and Why of the Size Effect

Does the size effect vary in an usefully predictable way? In the October 2011 revision of his paper entitled “Predicting the Small Stock Premium Over Different Horizons: What Do We Learn About Its Source?”, Valeriy Zakamulin examines whether eight U.S. market/economic variables exploitably predict the small stock premium at monthly, quarterly, semiannual and annual horizons. 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)one-month Treasury bill yield; (7) U.S. Treasuries term premium (30-year bond yield minus one-month bill yield); and, (8) inflation rate. Using monthly data for the potentially predictive variables and for a broad sample of U.S. stocks/firms during January 1927 through December 2010 (1008 months, 252 quarters and 84 years), he finds that: Keep Reading

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

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