All equities global macro intraday trading third party archive

Enhancing Reversals with News and Neutralization - Focus on Japanese Systematic Strategies

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

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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Machine Learning & Event Detection for Trading Energy and Metals Futures

RavenPack | September 19, 2017

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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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 valuation. They show how RavenPack data is a useful overlay to their strategy.

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Abnormal Media Attention Impacts Stock Returns

RavenPack | September 19, 2017

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.

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Enhancing Reversals with News and Neutralization - Focus on Japanese Systematic Strategies

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

equities third party

Read more Request White Paper

Abnormal Media Attention Impacts Stock Returns

RavenPack | September 19, 2017

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

Machine Learning & Event Detection for Trading Energy and Metals Futures

RavenPack | September 19, 2017

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.

Read more Request White Paper

Introducing the New 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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IMPACT STUDY: 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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Enhancing Reversals with News and Neutralization - Focus on Japanese Systematic Strategies

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Machine Learning & Event Detection for Trading Energy and Metals Futures

Value Strategies based on Machine Learning

Abnormal Media Attention Impacts Stock Returns

Enhancing Reversals with News and Neutralization - Focus on Japanese Systematic Strategies

Abnormal Media Attention Impacts Stock Returns

Machine Learning & Event Detection for Trading Energy and Metals Futures

Introducing the New RavenPack Analytics For Equity Trading

IMPACT STUDY: Earnings Sentiment Consistently Outperforms Consensus

Thematic Alpha Streams Improve Equity Portfolio Performance

Discover the RavenPack Platform