The authors effectively argue the case for intrinsic value and DCF based approaches to building Value factor strategies. The traditional value measures, especially the book-to-market ratio, are described as ineffective in today's market environment.
The following factor performance modules have been updated on our Index website.
As a result of the trading required to capture the premiums that drive factor strategies investors may face significant tax liabilities. The challenge for the [...]
We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems.
An efficient way to improve the expected performance of an equity strategy would be to systematically exclude penny stocks, as well with high asset growth and extreme past returns, especially if they have low profitability (and exclude funds that don’t screen out such stocks).
The following factor performance modules have been updated on our Index website.[ref]free access for financial professionals[/ref] Factor Performance Factor Premiums Factor Data Downloads
While both the S&P 500 and the Nikkei indices have recently hit all-time highs, the valuation and balance sheet data we have reviewed indicate that the downside risks in Japanese stocks appear to be far less than the risks in U.S. stocks. Evidence such as this helps explain why legendary investor Warren Buffett has been buying Japanese stocks.
Trading costs, discontinuous trading, missed trades, and other frictions, along with asset management fees can cause a shortfall between live and paper portfolios. The focus of this paper is to test an effective rebalancing method that prioritizes trades with the strongest signals to capture more of the factor premia while reducing turnover and trading costs.
Low short positions come from positive public news, while negative news can drive average short or extremely high short positions
The following factor performance modules have been updated on our Index website.
There is strong empirical evidence demonstrating that momentum (both cross-sectional and time-series) provides information on the cross-section of returns of many risk assets and has generated alpha relative to existing asset pricing models. Ma, Yang, and Ye’s findings provide another test of both robustness and pervasiveness, increasing our confidence that the findings of momentum in asset prices are not a result of data mining.
There is strong empirical evidence demonstrating that momentum (both cross-sectional and time-series) provides information on the cross-section of returns of many risk assets and has generated alpha relative to existing asset pricing models.
Higher volatility is associated with faster, initially stronger reversals, while lower turnover is associated with more persistent, ultimately stronger reversals
To date, the best metric we have for forecasting future equity returns and the ERP is current valuations. An interesting question is whether more complicated methods using newly developed machine learning models can provide superior forecasts.
The following factor performance modules have been updated on our Index website.
Momentum continues to receive much attention from researchers because of the strong empirical evidence.
Can AI models improve on the failures in predicting returns strictly from a practical point of view? In this paper, the possibilities are tested with a battery of AI models including linear regression, dimensional reduction methods, regression trees and neural networks. These machine learning models may be better equipped to address the multidimensional nature of stock returns when compared to traditional sorting and cross-sectional regressions used in factor research. The authors hope to overcome the drawbacks and confirm the results of traditional quant methods. As it turns out, those hopes are only weakly fulfilled by the MLM framework.
The justification for neutralizing sectors in factor strategies is a work in progress. To date, academic researchers haven't had an empirical model to mimic the impact of removing sector "effects" on the measurement and performance of factor strategies. The authors develop and test a two-component model to address the question of, "Is Sector Neutrality in Factor Investing a Mistake?"
While the empirical research on cross-sectional (long-short) momentum has shown that returns have been high, investors have also experienced huge drawdowns—momentum exhibits both high kurtosis and negative skewness. Since 1926 there have been several momentum crashes that featured short, but persistent, periods of highly negative returns. For example, from June to August 1932, the momentum portfolio lost about 91%, followed by a second drawdown from April to July 1933.
The following factor performance modules have been updated on our Index website.[ref]free access for financial professionals[/ref] Factor Performance Factor Premiums Factor Data Downloads
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