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Investing Research Articles

Optimal Retirement Glidepath with Trend Following

What are optimal allocations during retirement years for a portfolio of stocks and bonds, without and with a trend following overlay? In their March 2019 paper entitled “Absolute Momentum, Sustainable Withdrawal Rates and Glidepath Investing in US Retirement Portfolios from 1925”, Andrew Clare, James Seaton, Peter Smith and Steve Thomas compare outcomes across two sets of U.S. retirement portfolios since 1925:

  1. Standard – allocations to the S&P 500 Index and a bond index ranging from all stocks to all bonds in increments of 10%, rebalanced at the end of each month.
  2. Trend following – the same portfolios with a trend following overlay that shifts stock index and bond index allocations to U.S. Treasury bills (T-bills) when below respective 10-month simple moving averages at the end of the preceding month.

They consider investment horizons of 2 to 30 years to assess glidepath effects. They consider both U.S. Treasury bonds and U.S. corporate bonds to assess credit effects. For comparison of portfolio outcomes, they use real (inflation-adjusted) returns and focus on Perfect Withdrawal Rate (PWR), the maximum annual withdrawal rate that results in zero terminal value (requiring perfect foresight). Using monthly data for the S&P 500 Index, U.S. government and corporate bond indexes and U.S. inflation during 1926 through 2016, they find that: Keep Reading

Neural Network Software Valuation of Fine Art

Given the uniqueness of fine art objects and uncertainties in demand (at auctions), can investors in paintings get accurate estimates of market values of holdings and potential acquisitions? In their March 2019 paper entitled “Machines and Masterpieces: Predicting Prices in the Art Auction Market”, Mathieu Aubry, Roman Kräussl, Gustavo Manso and Christophe Spaenjers compares accuracies of value estimates for paintings based on: (1) a linear hedonic regression (factor model), (2) neural network software and (3) auction houses. For the first two, they employ 985,188 auctions of paintings during 2008–2014 for in-sample training and 104,404 auctions of paintings during the first half of 2015 for out-of-sample testing. Neural network software inputs include information about artists and paintings (year of creation, materials, size, title and markings), and images of the paintings. Using information about artists/paintings and images and auction house estimates and sales prices for the specified 1,089,592 paintings by about 125,000 artists offered through 372 auction houses during January 2008 through June 2015, they find that:

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Sophisticated Simulation of Intrinsic (Time Series) Momentum

How can investors confidently assess risk of strategy crashes (tail events) when there are so few crashes even in long samples? In their March 2019 paper entitled “Time-Series Momentum: A Monte-Carlo Approach”, Clemens Struck and Enoch Cheng present a Monte-Carlo simulation procedure for strategy backtesting that both preserves time series and cross-sectional return characteristics while diversifying time series simulation inputs. They use this procedure to test intrinsic (absolute or time series) momentum on S&P 500 Index futures and on an equal-weighted multi-class portfolio of 27 futures series. They consider long-short and long-only (long-cash) versions of time series momentum (TSM), with or without volatility adjustment. For testing actual histories, they consider lookback intervals of 1, 3, 6, 9 and 12 months to measure momentum. For simulations, they focus on optimal lookbacks from actual histories and consider multiple time series models. Their in-sample subperiods are 1985-2009 for the S&P 500 Index and February 1989-2009 for the multi-class portfolio. Their out-of-sample subperiod is 2010-2018. They roll each futures series at the end of each month into the next front contract, using spot indexes prior to the availability of some futures. They use buy-and-hold portfolios (with rolling) as benchmarks. Using monthly prices for nine equity indexes, four government bonds, eight commodities and six currencies futures/spot series in U.S. dollars over the specified sample period, they find that:

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Risk Premium Allocation Tail Diversification

Do exposures to long-short factor (alternative risk) premiums (ARP) protect portfolios from stock and bond market crashes? In their February 2019 paper entitled “A Framework for Risk Premia Investing: Anywhere to Hide?”, Kari Vatanen and Antti Suhonen examine weekly correlations of 28 ARP composite returns with those of stocks (MSCI World Equity Market Index), bonds (Barclays Global Treasury Index) and commodities (Bloomberg Commodity Index), overall and during crashes, over an 11-year sample period. They form each ARP composite using both backtested and live data for at least three related strategies from different investment banks, weighted by inverse full-sample volatility and rebalanced weekly. They focus on ARP composite performances when stocks and bonds are weak. Based on findings, they then designate ARP composites as:

  • Offensive (benefiting from high economic growth but suffering from low growth and economic turbulence) or defensive (diversifying risks of offensive strategies).
  • Fundamental (based on investor risk aversion), behavioral (based on typical investor behavior) or structural (based on seasonal asset flows or on market inefficiencies and liquidity imbalances).

Using daily data as specified for long-short alternative risk premium strategies from seven global investment banks during January 2007 to the beginning of May 2018, they find that:

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Weekly Summary of Research Findings: 4/8/19 – 4/12/19

Below is a weekly summary of our research findings for 4/8/19 through 4/12/19. 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

Stock Returns Around Easter

Does the seasonal shift marked by the Easter holiday, with the U.S. stock market closed on the preceding Good Friday, produce anomalous returns? To investigate, we analyze the historical behavior of the S&P 500 Index before and after the holiday. Using daily closing levels of the S&P 500 index for 1950-2018 (69 events), we find that: Keep Reading

EFFR and the Stock Market

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 the intermediate term? 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 February 2019, we find that: Keep Reading

Cautions Regarding Findings Include…

What are common cautions regarding exploitation of academic and practitioner papers on financial markets? To investigate, we collect, collate and summarize our cautions on findings from papers reviewed over the past year. These papers are survivors of screening for relevance to investors of a much larger number of papers, mostly from the Financial Economics Network (FEN) Subject Matter eJournals and Journal of Economic Literature (JEL) Code G1 sections of the Social Sciences Research Network (SSRN). Based on review of cautions in 109 summaries of papers relevant to investors posted during mid-March 2018 through mid-March 2019, we conclude that: Keep Reading

Equity Factor Census

Should investors trust academic equity factor research? In their February 2019 paper entitled “A Census of the Factor Zoo”, Campbell Harvey and Yan Liu announce a comprehensive database of hundreds of equity factors from top academic journals and working papers through January 2019, including a link to citation and download information. They distinguish among six types of common factors and five types of firm characteristic-based factors. They also explore incentives for factor discovery and reasons why many factors are lucky findings that exaggerate expectations and disappoint in live trading. Finally, they announce a project that allows researchers to add published and working papers to the database. Based on their census of published factors and analysis of implications, they conclude that: Keep Reading

Asset Class Momentum Faster During Bear Markets?

A subscriber asked whether the optimal momentum ranking (lookback) interval for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) shrinks during bear markets for U.S. stocks. This strategy each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

To investigate, we compare SACEMS monthly performance statistics when the S&P 500 Index at the previous monthly close is above (bull market) or below (bear market) its 10-month simple moving average. We consider Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners for the baseline SACEMS lookback interval. In a robustness test for the EW Top 3 portfolio, we consider lookback intervals ranging from one to 12 months. Using monthly total (dividend-adjusted) returns for the specified assets since February 2006 (limited by DBC) and the monthly level of the S&P 500 Index since September 2005, all through February 2019, we find that:

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