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Abnormal Media Attention Impacts Stock Returns

RavenPack | February 21, 2018

We show how our measure of abnormal media attention helps select stocks that are likely to experience strong price movements, resulting in an improvement of average “per trade” returns.

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Value Strategies based on Machine Learning

J.P. Morgan Global Quantitative & Derivatives Strategy Team | August 09, 2017

equities third party

Following the popularity of their Big Data and AI Strategies report, J.P. Morgan demonstrate ideas on implementing machine learning algorithms to stock selection processes based on value strategies. They show how RavenPack data is a useful overlay to their strategy.

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Enhancing Reversal Trading with News and Neutralization

J.P. Morgan Global Quantitative & Derivatives Strategy Team | October 03, 2017

equities third party

In this paper, J.P. Morgan look to neutralize unintended factor exposures in short-term reversals. They find that stocks with lower news volume or with exhibit stronger reversals, and anticipation of earnings news leads to stronger subsequent reversals.

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News Momentum

Michigan State Univ, Rutgers Business School & Prime Quantitative Research LLC | August 02, 2017

intraday trading equities third party

A strategy of news momentum that buys stocks with high news returns and sells stocks with low news returns generates an annualized return of 40.08% in the following week, with a four-factor alpha of 40.44%, controlling for the market, size, value, and momentum.

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Effects of Event Sentiment Aggregation: Sum vs. Mean

RavenPack | May 15, 2018

equities

In particular, we investigate how subtle differences in the way we use the same news information, such as different averaging, can lead to important differences in the characteristics of our portfolio.

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Energy Futures Trading with Machine Learning & Event Detection

RavenPack | May 02, 2018

global macro

We show how combining all models produce solid risk-adjusted returns with lower average bias and without the need to select one particular model.

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Abnormal Media Attention Impacts Stock Returns

RavenPack | February 21, 2018

equities

We show how our measure of abnormal media attention helps select stocks that are likely to experience strong price movements, resulting in an improvement of average “per trade” returns.

Read more Request White Paper

RavenPack Analytics For Equity Trading

RavenPack | April 20, 2017

equities

In this first research on the new RavenPack Analytics (RPA) dataset, we take advantage of the latest innovations in natural language processing, which provide a much deeper textual analysis of unstructured documents. We show how RPA improves the predictive power of our models by taking advantage of our new analytics.

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Earnings Sentiment Consistently Outperforms Consensus

RavenPack | March 16, 2016

equities

In our latest research, we find that our new earnings sentiment indicator derived from real-time news and social media significantly outperforms consensus estimates, both in terms of Information Ratios and annualized returns.

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Thematic Alpha Streams Improve Equity Portfolio Performance

RavenPack | September 22, 2015

equities

We demonstrate one way to combine alpha streams employing RavenPack data derived from news and social media into portfolios that deliver strong risk-adjusted returns and lower turnover than most news-based portfolios.

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Abnormal Media Attention Impacts Stock Returns

Featured Articles

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Popular Articles

Value Strategies based on Machine Learning

Enhancing Reversal Trading with News and Neutralization

News Momentum

Effects of Event Sentiment Aggregation: Sum vs. Mean

Energy Futures Trading with Machine Learning & Event Detection

Abnormal Media Attention Impacts Stock Returns

RavenPack Analytics For Equity Trading

Earnings Sentiment Consistently Outperforms Consensus

Thematic Alpha Streams Improve Equity Portfolio Performance

Discover the RavenPack Platform