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Value Investing Strategy (Strategy Overview)

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Momentum Investing Strategy (Strategy Overview)

Allocations for July 2020 (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.

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

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

Federal Funds Rate Size Effect?

A reader noted and asked: “I have seen on the net that it is better to be in large capitalization stocks when the Federal Funds Rate (FFR) target is increasing and small capitalization stocks when the FFR target is decreasing. Is there any serious study about this belief?” An argument supporting this proposition is that investors view small firms as more sensitive to changes in interest rates than large firms. Using FFR target actions and daily closes of the S&P 500 Index (representing large firms) and the Russell 2000 Index (representing small firms) for January 1990 through December 2010, we find that: Keep Reading

Federal Funds Rate and the Stock Market

Media commentators and expert advisors sometimes cite cuts and hikes in the Federal Funds Rate (FFR) as an indicator of U.S. stock market prospects, with decreases (increases) in FFR acting as a stimulant (depressant). Does the overall U.S. stock market respond systematically to loosening and tightening of credit as measured by cuts and hikes in FFR? Using FFR target changes and daily S&P 500 Index levels over the period January 1990 through December 2010, encompassing 76 changes in FFR (46 cuts and 31 hikes), we find that: Keep Reading

Testing Bond Allocation Strategies

Can investors anticipate long-term changes in the interest rate environment accurately enough to support active management of bond portfolios? In their September 2010 paper entitled “Gains from Active Bond Portfolio Management Strategies”, Naomi Boyd and Jeffrey Mercer investigate the effectiveness of using Federal Reserve policy signals for two types of bond allocation timing strategies: (1) increasing (decreasing) portfolio duration in anticipation of rate decreases (increases); and, (2) anticipating narrowing or widening of the yield spreads between categories of bonds with different credit ratings. They assume that a falling (rising) interest rate interval begins the month after an Federal Open Market Committee (FOMC) bank discount rate decrease (increase) that follows an increase (a decrease) and ends the month after the next discount rate increase (decrease). Using FOMC announcements and monthly total returns for U.S. 30-day Treasury Bill (T-bill), U.S. Intermediate-term Government Bond, U.S. Long-term Government Bond, U.S. Long-term Corporate Bond and Domestic High-yield Corporate Bond indexes spanning 1973-2006, they find that: Keep Reading

Industrial Metals as Asymmetric Equity Return Predictors

Do investors view industrial metal price changes differently during good times and bad times? In the August 2010 draft of their paper entitled “Return Predictability When News Means Different Things in Different Times”, Ben Jacobsen, Ben Marshall and Nuttawat Visaltanachoti explore how the power of aluminum, copper, lead, nickel and zinc price changes to predict stock returns differs between economic expansions and contractions. For the U.S., they consider two indicators of the economic state, NBER business cycles and the Chicago Fed National Activity Index. Using futures prices for aluminum, copper, nickel, lead and zinc since 1991, 1977, 1995, 1977 and 1991, respectively, levels of the Goldman Sachs industrial metal index since 1977 and contemporaneous data for the S&P 500 Index and 13 economic indicators through June 2010, they find that: Keep Reading

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