This paper focuses on "organization capital," representing intangible assets in a firm's key employees that is not captured by classic value measures such as book-to-market. The authors propose a structural model to analyze the impact of organizational capital on asset prices and argue that shareholders perceive firms with high levels of organizational capital to be riskier than those with more physical capital.
The timelier adoption of new technology and the higher likelihood of large-scale technology adoption make the risk associated with technological innovation more systematic, which in turn increases returns required by investors for technology spillover recipients.
Idiosyncratic volatility (IVOL) is the volatility of a security that cannot be explained by overall market volatility—it is the risk unique to a particular security. IVOL contrasts with systematic risk, which is the risk that affects all securities in a market (such as changes in interest rates or inflation) and, therefore cannot be diversified away. On the other hand, the risks of high IVOL stocks can at least be reduced through diversification.
The Jegadeesh and Titman (1993) paper on momentum established that an equity trading strategy consisting of buying past winners and selling past losers, reliably produced risk-adjusted excess returns. The Jegadeesh results have been replicated in international markets and across asset classes. As this evidence challenged and contradicted widely accepted notions of weak-form market efficiency, the academic community took notice and started churning out research. As a result, a very large number of academic studies were published on momentum. The article summarized here has conveniently summarized 47 articles deemed as the highest quality and published in either the Journal of Finance, the Review of Financial Studies, or the Journal of Financial Economics, all three considered premier journals in the finance discipline. It is difficult to understate the importance of having a well-curated summary of momentum research. Keep it in your library.
Standardized Performance Factor Performance Factor Exposures Factor Premiums Factor Attribution Factor Data Downloads
Over the very long term, while value stocks have been less profitable and have had slower growth in earnings than growth stocks, they have provided higher returns.
The higher returns to high R&D stocks represent compensation for heightened systematic risk not captured in standard asset pricing models.
Trend follower nerd alert: This study is important because it offers a comprehensive analysis of TS momentum strategies, its unifying framework that links performance to underlying variables, and its practical implications for investors seeking to enhance their understanding of momentum investing and improve their portfolio performance.
The following factor performance modules have been updated on our Index website.[ref]free access for financial professionals[/ref] Standardized Performance Factor Performance Factor Exposures Factor Premiums Factor Attribution [...]
Short term return anomalies are generally dismissed in the academic literature "because they seemingly do not survive after accounting for market frictions.” In this research, short term “factors” are taken seriously and the authors argue the standard parameters may not apply for short horizons.
Investment predicts returns because, given expected profitability, high costs of capital imply low net present value of new capital and low investment, and low costs of capital imply high net present value of new capital and high investment.
Factor seasonality reflects its own stock-level equivalent. It is not an independent risk factor.
There are various measures of value and quality, with one being Marx’s gross profits-to-assets.
The following factor performance modules have been updated on our Index website.
The traditional financial theory attributes security returns to market- or factor-based risk, with no role ascribed to other influences. In this research, the authors argue for including investor demand as an additional variable in explaining returns. Can changes in investor demand generate systematic changes in security returns?
The empirical evidence demonstrates that returns to the low-beta anomaly are well explained by exposure to other common factors, and it has only justified investment when low-beta stocks were in the value regime, after periods of strong market and small-cap stock performance, and when they excluded high-beta stocks that had low short interest.
Doug Pugliese, the head of our 1042 QRP business, was recently invited on the ScuttleButt Podcast to discuss the ESOP landscape and the costs and benefits of 1042 QRP transactions. (article on the topic is here).
By using a novel measure of investor attention, generated from InvestingChannel’s clickstream data on online financial news consumption, we can identify broad groups of stocks which are less efficiently priced and therefore where anomalies such as Value and Momentum are likely to produce greater cross sectional differentiation in returns. We also apply these groupings to proprietary ExtractAlpha stock selection signals.
Running regressions on past returns is a great tool for academic researchers who understand this approach's nuance, assumptions, pitfalls, and limitations. However, when factor regressions become part of a sales effort and/or are put in the hands of investors/advisors/DIYers, "the tool can quickly turn you into a fool."
The empirical research demonstrates that, on average, investing in previous winners and short selling previous losers offers highly significant returns that other common risk factors cannot explain. However, momentum also displays huge tail risk, as there are short but persistent periods of highly negative returns. Crashes occur particularly in reversals from bear markets when the momentum portfolio displays a negative market beta and momentum volatility is high.
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