Equity Premium
Governments are largely insulated from market forces. Companies are not. Investments in stocks therefore carry substantial risk in comparison with holdings of government bonds, notes or bills. The marketplace presumably rewards risk with extra return. How much of a return premium should investors in equities expect? These blog entries examine the equity risk premium as a return benchmark for equity investors.
November 20, 2024 - Equity Premium, Volatility Effects
Can investors safely use leverage to squeeze incremental return from low-volatility/factor-tilted stocks, thereby avoiding underperformance of these stocks during bull markets? In their October 2024 paper entitled “Low-Risk Alpha Without Low Beta”, David Blitz, Clint Howard, Danny Huang and Maarten Jansen exploit the low-volatility anomaly by leveraging multifactor, low-risk, global stock portfolios to a beta of 1.0 while controlling tracking error relative to a capitalization-weighted benchmark. Their portfolio formation rules are:
- The portfolio is long only and fully invested in liquid (large-capitalization) stocks.
- Maximum individual stock weight is the lower of 1.5% or 20 times its benchmark weight.
- Exposure to countries, regions and sectors may deviate at most 10% from benchmark weights.
- Portfolio beta (portfolio-weighted sum of historical stock betas for the last 156 weekly returns) must be less than 0.8 relative to the benchmark.
- Portfolio optimization involves trading off expected returns, benchmark tracking error and turnover. Expected stock returns derive from a multifactor score with 50% for low-risk (equal-weighted combination of past 260-day volatility, 156-week volatility, 260-day beta and 156-week beta), 16.67% for value (net payout yield), 16.67% for quality (gross profits to assets) and 16.67% for momentum (return from 12 months ago to one month ago).
- Use synthetic positions (for example, via equity options) to achieve leverage, with no cash collateral and financing costs equal to the risk-free rate.
- Rebalance at the end of each month but ignore slight deviations from target weights.
They separately discuss impacts of portfolio rebalancing frictions and additional leverage costs/penalties. They focus on developed markets but also look at an emerging markets sample and North American, European and Asia Pacific subsamples. Using daily and monthly data for developed market stocks since December 1985 and emerging market stocks since December 1995, all through December 2023, along with contemporaneous spreads and interest/Treasury bill rates, they find that: Keep Reading
November 18, 2024 - Equity Premium, Gold, Technical Trading
A reader requested a test of the following hypothesis from the article “Gold’s Bluff – Is a 30 Percent Drop Next?” [no longer available]: “Ironically, gold is more than just a hedge against market turmoil. Gold is actually one of the most accurate indicators of the stock market’s long-term direction. The Dow Jones measured in gold is a forward looking indicator.” To test this assertion, we examine relationships between the spot price of gold and the level of the Dow Jones Industrial Average (DJIA). Using monthly data for the spot price of gold in dollars per ounce and DJIA over the period January 1971 through October 2024, we find that: Keep Reading
October 30, 2024 - Bonds, Equity Premium, Strategic Allocation
Do target retirement date funds, offering glidepaths that shift asset allocations away from equities and toward bonds as target dates approach, safely generate attractive returns? To investigate, we consider seven such mutual funds offered by Vanguard, as follows:
We consider as benchmarks SPDR S&P 500 ETF Trust (SPY), iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) and both 80-20 and 60-40 monthly rebalanced SPY-LQD combinations. We look at monthly and annual return statistics, including compound annual growth rate (CAGR) and maximum drawdown (MaxDD). Using monthly total returns for SPY, LQD, three target retirement date funds since October 2003 and four target retirement date funds since June 2006 (limited by Vanguard inception dates), all through September 2024, we find that:
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October 25, 2024 - Animal Spirits, Equity Premium
How do exchange-traded-funds (ETF) focused on supplying renewable energy perform? To investigate, we consider nine of the largest renewable energy ETFs, all currently available, as follows:
We use SPDR S&P 500 (SPY) as a benchmark, assuming investors look at renewable energy stocks to beat the market and not to beat the energy sector. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the nine renewable energy ETFs and SPY as available through September 2024, we find that: Keep Reading
October 16, 2024 - Equity Premium
Can investors reasonably anticipate the signals (stock rankings) for stock anomalies that are based on firm financial information. In their August 2024 paper entitled “Predicting Anomalies”, Boone Bowles, Adam Reed, Matthew Ringgenberg and Jake Thornock investigate whether: (1) stock returns follow predictable patterns before availability of anomaly trading signals; and, (2) anomaly trading signals are themselves predictable. They focus on a set 28 published anomalies that are entirely based on publicly available information in quarterly financial statements. They each quarter for each anomaly reform a hedge portfolio that is long (short) the tenth of stocks with the highest (lowest) expected returns. They consider four models to predict stock rankings for each anomaly: (1) a first-order autoregression that projects strength of signals; (2) a first-order autoregression that projects stock rankings; (3) a machine learning model that uses past anomaly signals and rankings; and, (4) a (martingale) model that assumes anomaly portfolio rankings for next quarter will be the same as current rankings. Using as-published specifications for each of the 28 anomalies plus daily returns and quarterly/annual financial reports for a broad sample of U.S. stocks during January 1990 through December 2019, they find that: Keep Reading
October 15, 2024 - Big Ideas, Equity Premium
Can market efficiency be falling despite ubiquitous data, computing and networking? In his August 2024 paper entitled “The Less-Efficient Market Hypothesis”, Clifford Asness argues that markets have become less efficient in the relative pricing of common stocks over recent decades. To make his argument, he relies on the ratio of expensive stock valuations to cheap stock valuations (the value spread). He considers two versions of this spread, one based on the conventional price-to-book ratio to measure value and the other based on five industry-neutral value metrics. He discusses three potential reasons why the value spread is rising. He closes with advice for value investors. Reflecting on 35 years of research experience, he concludes that:
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October 8, 2024 - Equity Premium, Technical Trading
Short-term reversal is a widely accepted stock return anomaly, with the long-only version glibly termed “buy the dip.” Is short-term reversal readily exploitable? As a live test, we look at the performance of Vesper U.S. Large Cap Short-Term Reversal Strategy ETF (UTRN). This fund seeks to capture bounces of stocks with recent sharp declines by each week:
- Calculating for the 500 largest U.S. stocks a metric similar to the Sharpe ratio but using an asymmetric volatility to find overreaction dips in downtrending stocks (the Chow ratio).
- Initially buying the 25 stocks with the lowest Chow ratios.
- Selling any holdings for which the Chow ratio has risen out of the bottom 50 and replacing them with bottom 25 stocks.
The restriction to large stocks and the differing buy and sell rules suppress trading frictions/portfolio turnover. The benchmark is SPDR S&P 500 ETF Trust (SPY). Using monthly dividend-adjusted returns for UTRN and SPY from the inception of the former in September 2018 through August 2024, we find that: Keep Reading
October 7, 2024 - Big Ideas, Equity Premium
Why have stock return anomalies generally degraded over recent decades? In their August 2024 paper entitled “What Drives Anomaly Decay?”, Jonathan Brogaard, Huong Nguyen, Tālis Putniņš and Yuchen Zhang examine why stock return anomalies decay by:
- Decomposing returns into market-wide, public firm-specific and private firm-specific elements.
- Separating cash flow and discount rate effects within each of these three components.
- Accounting for noise.
This breakdown lets them determine whether changes in anomaly returns over time derive from anomaly publication, identifiable liquidity shocks (such as stock price decimalization) or a more general increase market efficiency. They apply this approach to daily returns of long-short (hedge) portfolios, reformed monthly, for 204 stock return anomalies from Open Source Asset Pricing. Using the required firm characteristics and daily prices for all NYSE/AMEX/NASDAQ common stocks during 1956 through 2021 (an average 4,029 firms per year and a total of 16,966 firms), they find that:
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October 4, 2024 - Bonds, Commodity Futures, Economic Indicators, Equity Premium, Gold, Real Estate
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 end-of-month EFFR and dividend-adjusted prices for the 10 ETFs during December 2007 (limited by EMB) through August 2024, we find that: Keep Reading
October 3, 2024 - Economic Indicators, Equity Premium
Do changes in the Effective Federal Funds Rate (EFFR), the actual cost of short-term liquidity derived from a combination of market demand and Federal Reserve open market operations designed to maintain the Federal Funds Rate (FFR) target, predictably influence the U.S. stock market over horizons up to a few months? To investigate, we relate smoothed (volume-weighted median) monthly levels of EFFR to monthly U.S. stock market returns (S&P 500 Index or Russell 2000 Index) over available sample periods. Using monthly data as specified since July 1954 for EFFR and the S&P 500 Index (limited by EFFR) and since September 1987 for the Russell 2000 Index, all through August 2024, we find that: Keep Reading
