Equity Premium

Can investors exploit information about business credit tightening/loosening as reported since 1990 in the Federal Reserve’s quarterly Senior Loan Officer Survey to time the U.S. stock market? In the January 2023 draft of his paper entitled “Profitable Timing of the Stock Market with the Senior Loan Officer Survey”, Linus Wilson examines the power of “Net Percentage of Domestic Banks Tightening Standards for Commercial and Industrial Loans to Large and Middle-Market Firms” to predict S&P 500 Index next-quarter returns. A positive (negative) reading means that credit conditions are tightening (loosening) for large and medium-sized firms. Specifically, he relates January survey results to subsequent April-June stock market returns, May survey results to July-September returns, August survey results to October-December returns and November survey results to January-March returns. He considers the full sample of 32 years, two subperiods of 15 years and three subperiods of 10 years. For portfolio tests, he uses the first 15-year subperiod to model allocation decisions to the S&P 500 Index/3-month U.S. Treasury bills (either long-short the stock index or long-only the index) and applies the model to a July 2005 through March 2022 test period. Using quarterly survey results, monthly S&P 500 Index levels and monthly estimated S&P 500 dividends (from Shiller’s data) during April 1990 through March 2022, he finds that: Keep Reading

How do returns of different asset classes recently interact with the Effective Federal Funds Rate (EFFR)? We focus on monthly changes (simple differences) in EFFR  and look at lead-lag relationships between change in EFFR and returns for each of the following 10 exchange-traded fund (ETF) asset class proxies:

• Equities:
  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares MSCI EAFE Index (EFA)
  • iShares MSCI Emerging Markets Index (EEM)
• Bonds:
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares iBoxx $ Investment Grade Corporate Bond (LQD)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
• Real assets:
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)

Using monthly EFFR and monthly dividend-adjusted prices for the 10 ETFs during December 2007 (limited by EMB) through December 2022, we find that: Keep Reading

Past research relating U.S. stock market returns to the party holding the Presidency mostly concludes that Democratic presidents are better for the stock market than Republican presidents. However, Presidents share power conferred by the electorate with Congress. Does historical data confirm that Democratic control of Congress is also better for stock market returns than Republican control of Congress? Is control of the smaller Senate more decisive than control of the House of Representatives? To check, we relate annual U.S. stock market (S&P 500 Index) returns to various combinations of party control of the Presidency, the Senate and the House of Representatives. Using party in power data and annual levels of the S&P 500 Index for December 1927 through December 2022 (95 years), we find that: Keep Reading

A subscriber requested confirmation that a strategy of holding SPDR S&P 500 ETF Trust (SPY) at all times except options expiration week beats holding SPY all the time. To investigate, we look at holding SPY at all times except from the close on the second Friday of each month to the close on the third Friday of each month (Strategy). When the market is closed on Friday, we use the Thursday or next earliest close. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as essential performance statistics. We apply round-trip trading frictions of 0.1% for SPY-cash switches. Given settlement/cash-sweep delays, we assume zero return on cash. Using daily dividend-adjusted closes of SPY from inception in January 1993 through December 2022, we find that: Keep Reading

A subscriber asked about an assertion that bitcoin (BTC) price trend/return predicts return of the S&P 500 Index (SP500). To investigate, we relate BTC returns to SP500 returns at daily, weekly and monthly frequencies. We rationalize the different trading schedules for these two series by excluding BTC trading dates that are not also SP500 trading days. Most results are conceptual, but we test three versions of an SP500 timing strategy based on prior BTC returns focused on compound annual growth rate (CAGR) and maximum drawdown (MaxDD). Using daily SP500 levels and (pruned) BTC prices during 9/17/2014 (limited by the BTC series) through 12/21/2022, we find that:

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Do Federal Funds Rate (FFR) actions taken by the Federal Reserve open market operations committee reliably predict stock market and U.S. Treasuries yield reactions? To investigate, we use the S&P 500 Index as a proxy for the stock market and the yield for the 10-Year U.S. Constant Maturity Treasury note (T-note). We look at index returns and changes in T-note yield during the one and two months after FFR actions, separately for FFR increases and FFR decreases. Using data for the three series during January 1990 through mid-December 2022, we find that:

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How has the price impact associated with stocks entering or leaving the S&P 500 evolved? In their December 2022 paper entitled “The Disappearing Index Effect”, Robin Greenwood and Marco Sammon revisit abnormal returns for S&P 500 additions and deletions and investigate five potential drivers of findings. Using announcement dates, implementation dates and daily returns for 752 S&P 500 additions and 749 S&P 500 deletions during 1980 through 2020, they find that:

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A subscriber requested evaluation of a strategy that seeks to exploit U.S stock market reversion after dips by temporarily applying margin. Specifically, the strategy:

• At all times holds the U.S. stock market.
• When the stock market closes down more than 7% from its high over the past year, augments stock market holdings by applying 50% margin.
• Closes each margin position after two months.

To investigate, we assume:

• The S&P 500 Index represents the U.S. stock market for calculating drawdown over the past year (252 trading days).
• SPDR S&P 500 (SPY) represents the market from a portfolio perspective.
• We start a margin augmentation at the same daily close as the drawdown signal by slightly anticipating the drawdown at the close.
• 50% margin is set at the opening of each augmentation and there is no rebalancing to maintain 50% margin during the two months (42 trading days) it is open.
• If S&P 500 Index drawdown over the past year is still greater than 7% after ending a margin augmentation, we start a new margin augmentation at the next close.
• Baseline margin interest is U.S. Treasury bill (T-bill) yield plus 1%, debited daily.
• Baseline one-way trading frictions for starting and ending margin augmentations are 0.1% of margin account value.
• There are no tax implications of trading.

We use buying and holding SPY without margin augmentation as a benchmark. Using daily levels of the S&P 500 Index, daily dividend-adjusted SPY prices and daily T-bill yields from the end of January 1993 (limited by SPY) through November 2022, we find that: Keep Reading

A subscriber asked whether currency exchange rates exhibit reliable seasonality that may be used to time equities (with a stronger currency implying lower asset prices). To investigate, we look for reliable calendar month effects for the U.S. dollar (USD)-euro exchange rate and for Invesco DB US Dollar Index Bullish Fund (UUP). We further look at how monthly returns for these variables relate to those for SPDR S&P 500 ETF Trust (SPY) as a proxy for the U.S. stock market. Using monthly data for the USD-euro exchange rate since January 1999 and for UUP since March 2007, and corresponding data for SPY, all through November 2022, we find that: Keep Reading

Are models based on advanced machine learning adept at predicting returns for individual emerging market stocks? In the November 2022 version of their paper entitled “Machine Learning and the Cross-section of Emerging Market Stock Returns”, Matthias Hanauer and Tobias Kalsbach compare abilities of machine learning models to predict emerging market stock returns. They consider nine alternatives: two traditional linear models (ordinary least squares and elastic net); two tree-based models (gradient boosted regression trees and random forest); and, five neural networks (one to five layers). Tree-based methods and neural networks identify non-linearities and variable interactions. They further consider a combination of the five neural networks and a combination of all tree-based plus neural network methods. For each model at the end of each month, they rank stocks into country-neutral fifths, or quintiles, based on next-month expected returns and reform a portfolio that is long (short) the quintile with the highest (lowest) expected returns. For tests of long-only net performance, they assume 1-way trading frictions are half the estimated bid-ask spread and apply trading cost mitigation rules. Using returns and 36 accounting/trading variables for 15,152 unique stocks from 32 emerging market countries as included in the MSCI Emerging Markets Index during July 1995 through December 2021 (with out-of-sample testing starting January 2002), they find that:

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