AI-powered growth concentrates among larger firms and is associated with higher industry concentration. Our results highlight that new technologies like AI can contribute to growth and superstar firms through product innovation.
The following factor performance modules have been updated on our Index website.
Trailing twelve-month P/E ratios account for 91% of the variation in analysts’ price targets. We construct a new kind of asset-pricing model around this fact and show that it explains the market response to earnings surprises.
While the skewness metric did demonstrate that it could select funds with managers skilled a security selection, the fund’s expenses and implementation meant that the fund was just about able to cover its expenses, and that was before the negative impact of active management on after-tax returns—and the finding was not statistically significant at even the 10% level of confidence.
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
This paper provides new evidence on the efficacy of prioritizing transactions so as to focus portfolio turnover on the trades that offer the strongest signals and hence the highest potential performance impact.
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.
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