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Big Data and AI Strategies - Machine Learning and Alternative Data Approach to Investing

J.P. Morgan Global Quantitative & Derivatives Strategy Team | May 29, 2017

The new J.P. Morgan report provides a framework for machine learning and big data investing and shows how to trade Global Macro strategies using RavenPack Analytics.

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A “Multi-Topic” Approach to Building Quant Models

RavenPack | May 23, 2017

equities

In this research, we demonstrate how RavenPack’s improved event detection technology and enhanced taxonomy allow investors to systematically identify key topics and market moving events.

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Mean Reversion II: Pairs Trading Strategies

Deutsche Bank | February 16, 2016

equities third party

This Deutsche Bank research shows how to improve pairs trading with RavenPack’s news analytics. Their enhanced signal significantly reduces divergence risk and also boosts the average return per pair.

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Achieve High Capacity Investment Strategies By Trading Economically-Linked Companies

RavenPack | September 21, 2016

equities

We show that supply-chain and competitive landscape information can help create more scalable portfolios from infrequent or sparse signals. We use an earnings sentiment indicator as a signal, constructed using RavenPack Data. We find value in propagating the signal from the source companies to their suppliers and competitors.

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Big Data and AI Strategies - Machine Learning and Alternative Data Approach to Investing

J.P. Morgan Global Quantitative & Derivatives Strategy Team | May 29, 2017

third party global macro

The new J.P. Morgan report provides a framework for machine learning and big data investing and shows how to trade Global Macro strategies using RavenPack Analytics.

Read more Request White Paper

A “Multi-Topic” Approach to Building Quant Models

RavenPack | May 23, 2017

equities

In this research, we demonstrate how RavenPack’s improved event detection technology and enhanced taxonomy allow investors to systematically identify key topics and market moving events.

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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Introducing the New RavenPack Analytics For Equity Trading

RavenPack | April 20, 2017

equities

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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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Big Data and AI Strategies - Machine Learning and Alternative Data Approach to Investing

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A “Multi-Topic” Approach to Building Quant Models

Mean Reversion II: Pairs Trading Strategies

Achieve High Capacity Investment Strategies By Trading Economically-Linked Companies

Big Data and AI Strategies - Machine Learning and Alternative Data Approach to Investing

A “Multi-Topic” Approach to Building Quant Models

Introducing the New RavenPack Analytics For Equity Trading

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