Fundamental Valuation

Are there any seasonal, technical or fundamental strategies that reliably time the U.S. stock market as proxied by the S&P 500 Total Return Index? In the February 2018 version of his paper entitled “Investing In The S&P 500 Index: Can Anything Beat the Buy-And-Hold Strategy?”, Hubert Dichtl compares excess returns (relative to the U.S. Treasury bill [T-bill] yield) and Sharpe ratios for investment strategies that time the S&P 500 Index monthly based on each of:

• 4,096 seasonality strategies.
• 24 technical strategies (10 slow-fast moving average crossover rules; 8 intrinsic [time series or absolute] momentum rules; and, 6 on-balance volume rules).
• 18 fundamental variable strategies based on a rolling 180-month regression, with 1950-1965 used to generate initial predictions.

In all cases, when not in stocks, the strategies hold T-bills as a proxy for cash. His main out-of-sample test period is 1966-2014, with emphasis on a “crisis” subsample of 2000-2014. He includes extended tests on seasonality and some technical strategies using 1931-2014. He assumes constant stock index-cash switching frictions of 0.25%. He addresses data snooping bias from testing multiple strategies on the same sample by applying Hansen’s test for superior predictive ability. Using monthly S&P 500 Index levels/total returns and U.S. Treasury bill yields since 1931 and values of fundamental variables since January 1950, all through December 2014, he finds that:

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To determine whether the stock market is expensive or cheap, some experts use aggregate valuation ratios, either trailing or forward-looking, such as earnings-price ratio (E/P) and dividend yield. Operating under a belief that such ratios are mean-reverting, most imminently due to movement of stock prices, these experts expect high (low) future stock market returns when these ratios are high (low). Where are the ratios now? Using recent actual and forecasted earnings and dividend data from Standard & Poor’s, we find that: Keep Reading

Does  applying a simple moving average (SMA) filter improve performance of the “Simple Asset Class ETF Value Strategy” (SACEVS), which seeks diversification across the following three asset class exchange-traded funds (ETF) plus cash according to the relative valuations of term, credit and equity risk premiums?

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

Since many technical traders use a 10-month SMA (SMA10), we test effectiveness of requiring that each of the ETFs pass an SMA10 filter by comparing performances for three scenarios:

1. Baseline – SACEVS as currently tracked.
2. With SMA10 Filter – Run Baseline SACEVS and then apply SMA10 filters to dividend-adjusted prices of ETF allocations. If an allocated ETF is above (below) its SMA10, hold the allocation as specified (Cash). This rule is inapplicable to any Cash allocation.
3. With Half SMA10 Filter – Same as scenario 2, but, if an allocated ETF is above (below) its SMA10, hold the allocation as specified (half the specified allocation and half cash at the T-bill yield).

We focus on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios (using average monthly T-bill yield during a year as the risk-free rate for that year) of SACEVS Best Value and SACEVS Weighted portfolios. We also look at how the SMA rule affects a 60%-40% SPY-TLT benchmark (60-40) portfolio. Using SACEVS historical data and monthly dividend-adjusted closing prices for the asset class proxies and yield for Cash during July 2002 (the earliest all ETFs are available) through November 2019, we find that: Keep Reading

A subscriber proposed an alternative to the strategy tested in “Using P/E10 Thresholds to Time the U.S. Stock Market”, which rebalances a stocks-bonds portfolio based on Shiller cyclically adjusted price-to-earnings ratio (P/E10 or CAPE) thresholds, as follows:

1. If P/E10 > 22, hold 40% stocks and 60% bonds.
2. If 14 < P/E10 < 22, hold 60% stocks and 40% bonds.
3. If P/E10 < 14, hold 80% stocks and 20% bonds.

The alternative strategy (P/E10 Variable Timing) uses linear scaling of the allocation to stocks from 40% to 80% as the P/E10 rises from 14 to 22. To test the alternative, we apply it to SPDR S&P 500 (SPY) since inception in 1993 as stocks and Vanguard Long-Term Treasury Investor Shares (VUSTX) as bonds, with monthly rebalancing/reallocation based on P/E10. We consider gross average monthly and annual returns, standard deviations of monthly and annual returns, compound annual growth rate (CAGR), maximum drawdown (MaxDD), and monthly and annual Sharpe ratio as strategy performance metrics. We use monthly and annual average monthly yield on 3-month U.S. Treasury bills (T-bill) to calculate Sharpe ratios. The benchmark is the original strategy (P/E10 Fixed Timing). Using the specified inputs, allowing a test of nearly 27 years, we find that: Keep Reading

Which equity factors from among those included in the most widely accepted factor models are really important? In their October 2019 paper entitled “Winners from Winners: A Tale of Risk Factors”, Siddhartha Chib, Lingxiao Zhao, Dashan Huang and Guofu Zhou examine what set of equity factors from among the 12 used in four models with wide acceptance best explain behaviors of U.S. stocks. Their starting point is therefore the following market, fundamental and behavioral factors:

They compare 4,095 subsets (models) of these 12 factors models based on: Bayesian posterior probability; out-of-sample return forecasting performance; gross Sharpe ratios of the optimal mean variance factor portfolio; and, ability to explain various stock return anomalies. Using monthly data for the selected factors during January 1974 through December 2018, with the first 10 (last 12) months reserved for Bayesian prior training (out-of-sample testing), they find that: Keep Reading

A subscriber requested verification of a fundamental U.S. stock market timing strategy with rebalancing/reallocation of a stocks-bonds portfolio based on Shiller cyclically adjusted price-to-earnings ratio (P/E10 or CAPE) thresholds, as follows:

1. If P/E10 > 22, hold 40% stocks and 60% bonds.
2. If 14 < P/E10 < 22, hold 60% stocks and 40% bonds.
3. If P/E10 < 14, hold 80% stocks and 20% bonds.

The benchmark is an annually rebalanced 60% stocks-40% bonds portfolio (60-40). To assess reasonableness of the P/E10 thresholds chosen, we use P/E10 monthly levels since 1881 and S&P 500 Index monthly returns since 1927. To verify and assess robustness of the specified strategy (P/E10 Timing), we apply it to SPDR S&P 500 (SPY) since inception in 1993 as stocks and Vanguard Long-Term Treasury Investor Shares (VUSTX) as bonds, with monthly rebalancing/reallocation based on P/E10. We consider gross average monthly and annual returns, standard deviations of monthly and annual returns, compound annual growth rate (CAGR), maximum drawdown (MaxDD), and monthly and annual Sharpe ratio as strategy performance metrics. We use monthly and annual average monthly yield on 3-month U.S. Treasury bills (T-bill) to calculate Sharpe ratios. As an additional benchmark, we include a simple technical strategy that is in SPY when prior-month S&P 500 Index is above its 10-month simple moving average and VUSTX when it is below (SPY SMA10). Using the specified inputs, allowing a P/E10 Timing test of nearly 27 years, we find that: Keep Reading

Does including a measure of asset valuation as a qualifier improve the performance of intrinsic (absolute or time series) momentum? In their October 2019 paper entitled “Carry and Time-Series Momentum: A Match Made in Heaven”, Marat Molyboga, Junkai Qian and Chaohua He investigate modification of an intrinsic momentum strategy as applied to futures using the sign of the basis (difference between nearest and next-nearest futures prices) for four asset classes: equity indexes (12 series), fixed income (18 series), currencies (7 series) and commodities (28 series). Their benchmark intrinsic momentum strategy is long (short) assets with positive (negative) returns over the last 12 months, with either: (1) equal allocations to assets, or (2) dynamic allocations that each month target 40% annualized volatility for each contract series. The modified strategy limits long (short) positions to assets with positive (negative) prior-month basis. They account for frictions due to portfolio rebalancing and rolling of contracts using cost estimates from a prior study. They focus on Sharpe ratio to assess strategy performance. Using monthly returns for 65 relatively liquid futures contract series during January 1975 through December 2016, they find that:

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Does careful accounting for transitory expenses in SEC Form 10-Ks provide a better view of future firm/stock performance than that provided by Generally Accepted Accounting Practices (GAAP) earnings per share (EPS)? In their October 2019 paper entitled “Core Earnings: New Data and Evidence”, Ethan Rouen, Eric So and Charles Wang define Core Earnings, which adds to GAAP 10-K net non-operating expenses related to: (1) acquisitions, (2) currency exchange adjustments, discontinued operations, (4) legal or regulatory events, (5) pension adjustments, (6) restructuring, (7) gains and losses designated “other” by firms and (8) other unclassified gains and losses deemed non-operating. Using a dataset compiled by a combination of human analysts and machine learning that identifies and classifies quantitative disclosures in 10-Ks of Russell 3000 firms, and associated stock prices, during 1998 through 2017, they find that:

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A subscriber asked about the lead-lag relationship between S&P 500 earnings and S&P 500 Index returns. To investigate, we relate actual aggregate S&P 500 operating and as-reported earnings to S&P 500 Index returns at both quarterly and annual frequencies. Earnings forecasts are available well in advance of returns. Actual earnings releases for a quarter occur throughout the next quarter. Using quarterly S&P 500 earnings and index levels during March 1988 through June 2019 and September 2019, respectively, we find that: Keep Reading

Will equity funds “managed” by artificial intelligence (AI) outperform human investors? To investigate, we consider the performance of AI Powered Equity ETF (AIEQ), which “seeks long-term capital appreciation within risk constraints commensurate with broad market US equity indices.” Per the offeror, the EquBot model supporting AIEQ: “…leverages IBM’s Watson AI to conduct an objective, fundamental analysis of U.S.-listed common stocks and real estate investment trusts…based on up to ten years of historical data and apply that analysis to recent economic and news data. Each day, the EquBot Model…identifies approximately 30 to 125 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights… The EquBot model limits the weight of any individual company to 10%. At times, a significant portion of the Fund’s assets may consist of cash and cash equivalents.” We use SPDR S&P 500 (SPY) as a simple benchmark for AIEQ performance. Using daily dividend-adjusted closes of AIEQ and SPY from AIEQ inception (October 18, 2017) through September 2019, we find that: Keep Reading