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A Machine Learning-Based Trading Strategy Using Sentiment Analysis Data
In this white paper, Lucena found that constructing portfolios using RavenPack sentiment indicators jointly with traditional factors can result in significant outperformance versus...
A “Multi-Topics” Approach to Building Quant Models
In this research, RavenPack demonstrate how their improved event detection technology allow investors to systematically identify key topics and market-moving events.
Bond over Big Data - Trading bond futures (& FX) with RavenPack news data
The author uses the RavenPack Analytics Global Macro data to create news-based economic indices (NBESI) for the U.S., E.Z, U.K. and Japan which they then test against sovereign bond futures prices.
Can anyone solve the smile problem?
Within this paper the authors explore whether the smile problem can be solved and provide a general reflection of the problem.
CUDA Programming Using Wolfram Finance Platform
This document describes the benefits of CUDA integration in Wolfram Finance Platform and provides some applications for which it is suitable.
Dead on Arrival - Living Wills and Liquidity
With surprisingly little fanfare, the Federal Reserve and other prudential regulators completely rejected the ‘Living Wills’ submitted by eleven of the largest U.S. Bank Holding Companies (BHCs).
How to use Natural Language Processing for Multi-Topic Quant Investing
In this article, Peter Hafez discusses how investors need the right tools to cut through the noise to uncover the signal behind the latest move in the markets.
IMPACT STUDY: Earnings Sentiment Consistently Outperforms Consensus
In RavenPack’s latest research, we find that their new earnings sentiment indicator derived from real-time news and social media significantly outperforms consensus estimates.
Index-tracking Portfolio Optimization Model
In the present tutorial report Guillermo Navas-Palencia examines the theory and computational aspects behind the index-tracking portfolio optimization model.
