It has been well-documented that value stocks have provided higher expected returns than growth stocks. However, there is a great debate about the source of that premium: Is it risk-based or is it related to [...]
Conventional wisdom can be defined as ideas that are so accepted that they go unquestioned. Unfortunately, conventional wisdom is often wrong. Two great examples are that millions of people once believed the conventional wisdom that [...]
The academic research has generally found valuations, such as the earnings yield (E/P) (or the CAPE 10 earnings yield) and valuation spreads, have predictive value in terms of future returns. The higher the earnings yield, [...]
Now that the Federal Reserve has begun the process of raising interest rates, and has announced their intention to begin to unwind their policy of quantitative easing (reducing the amount of bonds in their portfolio, [...]
In their seminal 1993 paper, “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency,” Narasimhan Jegadeesh and Sheridan Titman reported significant returns to buying winners and selling losers in the U.S. equity [...]
Similar to some better-known factors like size and value, time-series momentum is a factor that historically has demonstrated above average excess returns. Time-series momentum, also called trend-momentum or absolute momentum, is measured by a portfolio long assets that have had recent positive returns and short assets that have had recent negative returns. Compare this to the traditional (cross-sectional) momentum factor that considers recent asset performance only relative to other assets. The academic evidence suggests that inclusion of a strategy targeting time-series momentum in a portfolio improves the portfolio’s risk-adjusted returns.
As my co-author Andrew Berkin, the director of research for Bridgeway Capital Management, and I explain in our new book, “Your Complete Guide to Factor-Based Investing,” there is considerable evidence of cross-sectional return predictability. Citing [...]
The Holy Grail for mutual fund investors is the ability to identify in advance, which of the active mutual funds (or ETFs nowadays) will outperform in the future. The evidence suggests this task is almost [...]
One of the great debates in finance is whether the source of the value premium is risk-based or a behavioral anomaly. In our book, “Your Complete Guide to Factor-Based Investing,” my co-author Andrew Berkin and [...]
Momentum is the tendency for assets that have performed well (poorly) in the recent past to continue to perform well (poorly) in the future, at least for a short period of time. The momentum effect [...]
David Smith, Na Wang, Ying Wang and Edward Zychowicz contribute to the literature on momentum with their paper, “Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry,” which was published in the December 2016 issue of the Journal of Financial and Quantitative Analysis. Their work examines how investor sentiment affects the effectiveness of technical analysis strategies (which include the use of moving averages as well as momentum) used by hedge funds (which are considered sophisticated investors). The study was motivated by prior research that has focused on “investor sentiment,” which is the propensity of individuals to trade on noise and emotions rather than facts. Sentiment causes investors to have beliefs about future cash flows and investment risks that aren’t justified. Two researchers, Malcolm Baker and Jeffrey Wurgler, constructed an investor sentiment index based on six measures: trading volume as measured by NYSE turnover; the dividend premium (the difference between the average market-to-book ratio of dividend-payers and non-payers); the closed-end fund discount; the number and first-day returns of IPOs; and the equity share in new issues. Data is available at through Wurgler and New York University.
Before proceeding, it’s important to note that beta and volatility are related, though not the same. Beta depends on volatility and correlation to the market, whereas volatility is related to idiosyncratic risk (see here for an explanation of how to calculate the different measures). The superior performance of low-volatility and low-beta stocks was first documented in the literature in the 1970s — by Fischer Black (in 1972) among others — even before the size and value premiums were “discovered.” And the low-volatility anomaly has been shown to exist in equity markets around the world. Interestingly, this finding is true not only for stocks, but for bonds as well. In other words, it has been pervasive.
