Michigan State Univ, Rutgers Business School & Prime Quantitative Research LLC
| August 02, 2017
The authors decompose daily stock returns into news- and non-news driven components, using a comprehensive sample of intraday firm-level news arrivals matched with high-frequency movements of their stock prices.
The extensive literature on return predictability has established an interesting array of facts regarding the dynamics of individual stock returns. In particular, whereas short-horizon stock returns within the past month and long-horizon returns in the past 3-5 years exhibit
reversals, returns in the period of 3-12 months show a pattern of continuation in the subsequent 3-12 months. This finding on the stock price momentum has received widespread attention, and generated substantial controversy among financial economists regarding its
implications for market efficiency.
In this paper, the authors exploit this insight to contribute to the literature on return predictability.
To better understand
the source of the news momentum
, we follow Lo and MacKinlay (1990), decomposing the expected news momentum profit into three components...
The authors examine the relation between news return and future stock returns.
And they start with a simple news momentum strategy using univariate sorts, and then perform multivariate regressions to further assess the incremental information contained in news returns. Next,
they compare the news momentum strategy with other investment strategies
, such as size, value, price momentum, and short-term return reversal, and conclude this section with an analysis of transaction costs.
In this subsection, the authors examine the cross-sectional determinants of the news momentum effect to shed further light on its nature. Specifically, they study whether
the performance of the news momentum strategy concentrates
among stocks with certain characteristics, including firm size, analyst coverage, volatility, illiquidity, and past returns.
In this section, the authors perform an in-depth analysis of overnight news, which has received relatively little attention in the literature. Using an intraday event study approach, they find compelling evidence for delayed reaction to overnight news, which constitutes more than half of our sample. It lends further support to underreaction as the
main driver of news
The strategy’s profitability is driven by positive serial correlations in individual stock returns, and is particularly pronounced for overnight and weekend news and among small firms.
The white paper's conclusion suggests that investor under-reaction to news, coupled with limits to arbitrage, drives
Please use your business email. If you don't have one, please email us at email@example.com.
We will process your personal data with the purpose of managing your personal account on
RavenPack and offering our services. You can exercise your rights of access, rectification,
erasure, restriction of processing, data portability and objection by emailing us at firstname.lastname@example.org. For more information, you can
Your request has been recorded and a team member will be in touch soon.
High inflation has returned in developed markets after decades of lying low. In our latest paper, we show how to build an inflation-based asset allocation strategy using sentiment data and we illustrate that sentiment-based strategies outperform models that depend merely on past observed inflation values.
This year's RavenPack Research Symposium brought two intense days of knowledge sharing in London and New York, from 25 top experts in natural language processing, quantitative investing and machine learning. Together, we explored how firms can leverage new language models to generate alpha, better manage risk and respond to calls for more sustainable investment practices.
Human capital is at the heart of value creation. Our latest research demonstrates how unprecedented workforce insights, sourced from over 200 million job postings, can generate more alpha.