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Short-term Equity Risk More Political Than Economic?

How does news flow interact with short-term stock market return? In their April 2019 paper entitled “Forecasting the Equity Premium: Mind the News!”, Philipp Adämmer and Rainer Schüssler test the ability of a machine learning algorithm, the correlated topic model (CTM), to predict the monthly U.S. equity premium based on information in news articles. Their news inputs consist of about 700,000 articles from the New York Times and the Washington Post during June 1980 through December 2018, with early data used for learning and model calibration and data since January 1999 used for out-of-sample testing. They measure the U.S. stock market equity premium as S&P 500 Index return minus the risk-free rate. Specifically, they each month:

  1. Update news time series arbitrarily segmented into 100 topics (with robustness checks for 75, 125 and 150 topics).
  2. Execute a linear regression to predict the equity premium for each of the 100 topical news flows.
  3. Calculate an average prediction across the 100 regressions.
  4. Update a model (CTMSw) that switches between the best individual topic prediction and the average of 100 predictions, combining the flexibility of model selection with the robustness of model averaging.

They use the inception-to-date (expanding window) average historical equity premium as a benchmark. They include mean-variance optimal portfolio tests that each month allocate to the stock market and the risk-free rate based on either the news model or the historical average equity premium prediction, with the equity return variance computed from either 21-day rolling windows of daily returns or an expanding window of monthly returns. They constrain the equity allocation for this portfolio between 50% short and 150% long, with 0.5% trading frictions. Using the specified news inputs and monthly excess return for the S&P 500 Index during June 1980 through December 2018, they find that:

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Weekly Summary of Research Findings: 5/20/19 – 5/24/19

Below is a weekly summary of our research findings for 5/20/19 through 5/24/19. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Number of Users as Bitcoin Price Driver

How should investors assess whether the market is fairly valuing cryptocurrencies such as Bitcoin? In his March 2019 paper entitled “Bitcoin Spreads Like a Virus”, Timothy Peterson offers a way to value Bitcoin based on Metcalf’s Law (network economics) and  a Gompertz function (often used to describe biological activity). The former model estimates fair price based on number of active users, and the latter model estimates the growth rate of active users. Using findings from prior research plus daily Bitcoin price and active account data from coinmetrics.io and blockchain.info during July 2010 through February 2019, he finds that: Keep Reading

Best Factor Allocation Strategy?

For investors embracing the concept of portfolios based on factor premiums (rather than asset classes), what is the best factor allocation approach? In their March 2019 paper entitled “Factor-Based Allocation: Is There a Superior Strategy?”, Hubert Dichtl, Wolfgang Drobetz and Viktoria-Sophie Wendt search for the best way of combining factors in a portfolio after accounting for bias introduced from snooping many alternative allocation strategies. They consider the following 10 factors (mostly long-short) suitable for a U.S. institutional investor constrained to global equity and fixed income securities: equity, value, size, momentum, quality, low-volatility, term, real rates, credit and high-yield. They construct factors using associated published indexes denominated in U.S. dollars, with 1-month U.S. Treasury bill (T-bill) yield as the risk-free rate. They consider 17 factor allocation strategies: equal weight, minimum variance, equal risk, maximum diversification, volatility timing, reward-to-risk timing, mean-variance optimization without and with shrinkage, Black-Litterman and eight combinations of these strategies. Their test portfolio holds a 100% position in cash and a fully hedged (long-short, or zero net investment) factor portfolio, subject to 0.5% trading frictions on portfolio turnover. Using monthly data required to construct factors and T-bill yield during January 2001 though December 2018, with the first 60 months set aside to estimate strategy inputs, they find that:

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Long/short Equity Mutual Fund Performance Update

How well have long/short equity mutual funds done in recent years? In their April 2019 paper entitled “Hedge Funds Versus Hedged Mutual Funds: An Examination of Long/Short Funds; A Performance Update”, David McCarthy and Brian Wong present an out-of-sample update of a prior performance assessment of long/short equity mutual funds (see “Multialternative Mutual Fund Performance”). They track the same universe as the prior paper and therefore do not include funds launched after January 2013. They construct an equally weighted index of long/short equity mutual funds, rebalanced monthly. They compare performance of this index to those of the S&P 500 Total Return Index, HFRI Equity Hedge Fund Index (HFRI Index) and the Dow Jones Credit Suisse Long/Short Equity Hedge Fund Index (DJ-CS Index). Using monthly returns of 26 live, 14 dead and 4 changed (up to date of change) long/short equity mutual funds established as of January 2013 along with contemporaneous returns for benchmark indexes during July 2013 through December 2018, they find that:

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More International Equity Market Granularity for SACEMS?

A subscriber asked whether more granularity in international equity choices for the “Simple Asset Class ETF Momentum Strategy” (SACEMS), as considered by Decision Moose, would improve performance. To investigate, we replace the iShares MSCI Emerging Markets Index (EEM) and the iShares MSCI EAFE Index (EFA) with four regional international equity exchange-traded funds (ETF). The universe of assets becomes:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Pacific ex Japan (EPP)
iShares MSCI Japan (EWJ)
SPDR Gold Shares (GLD)
iShares Europe (IEV)
iShares Latin America 40 (ILF)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

We compare original (SACEMS Base) and modified (SACEMS Granular), each month picking winners from their respective sets of ETFs based on total returns over a fixed lookback interval. We focus on gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD) and rough gross annual Sharpe ratio (average annual return divided by standard deviation of annual returns) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using daily and monthly total (dividend-adjusted) returns for the specified assets during February 2006 (limited by DBC) through April 2019, we find that: Keep Reading

Stock Returns Around Memorial Day

Does the Memorial Day holiday signal any unusual U.S. stock market return effects? By its definition, this holiday brings with it any effects from three-day weekends and sometimes the turn of the month. Prior to 1971, the U.S. celebrated Memorial Day on May 30. Effective in 1971, Memorial Day became the last Monday in May. To investigate the possibility of short-term effects on stock market returns around Memorial Day, we analyze the historical behavior of the stock market during the three trading days before and the three trading days after the holiday. Using daily closing levels of the S&P 500 Index for 1950 through 2018 (69 observations), we find that: Keep Reading

Weekly Summary of Research Findings: 5/13/19 – 5/17/19

Below is a weekly summary of our research findings for 5/13/19 through 5/17/19. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Financial Experts Ignoring Better Statistical Methods?

Why are expert economic and financial (econometric) forecasters so inaccurate? In his April 2019 presentation package for a graduate course at Cornell entitled “The 7 Reasons Most Econometric Investments Fail”, Marcos Lopez de Prado enumerates shortcomings of standard econometric statistical methods, which concentrate on multivariate linear regressions. In contrast, advanced computational methods that exploit machine learning are ascendant in many other scientific fields, because they avoid (likely unrealistic) assumptions regarding actual data generation (such as linearity). Based on reviews of econometric texts and the body of econometric research, he concludes that: Keep Reading

The Bond King’s Alpha

Did Bill Gross, the Bond King, generate significantly positive alpha during his May 1987 through September 2014 tenure as manager of PIMCO Total Return Fund (Fund)? In their March 2019 paper entitled “Bill Gross’ Alpha: The King Versus the Oracle”, Richard Dewey and Aaron Brown investigate whether Bill Gross generates excess average return after adjusting for market exposures over this tenure. They further compare evaluation of bond market alpha for Bill Gross to evaluation of equity market alpha for Warren Buffett. Following the explanation given by Bill Gross for his outperformance, their factor model of Fund returns includes three long-only market factors: interest rate (Merrill Lynch 10-year Treasury Index), credit (Barclays U.S. Credit Index) and mortgage (Barclays U.S. MBS Index). It also includes a fourth factor that is long U.S. Treasury 5-year notes and short U.S. Treasury 30-year bonds, with weights set to eliminate coupon and roll-down effects of their different durations. Using monthly returns for the Fund and the four model factors, and monthly 1-month U.S. Treasury bill yield as the risk-free rate during June 1987 (first full month of the Fund) through September 2014 (when Gross left the Fund), they find that: Keep Reading

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