Machine Learning @ RavenPack: Improving Sentiment Models With Better Inputs
At RavenPack we are developing our next-gen platform and we are publishing some of our research findings with the NLP community at large. You can read our latest research below, and take a 2-minute survey to get involved in our BETA program.
Video: Machine Learning-Based Transaction Cost Analysis in Algorithmic Trading
Swagato formulates a methodology using machine learning to sift through troves of order execution data to identify key drivers of algorithm performance and provide actionable recommendations to clients in delivering execution alpha.
Video: The Uses and Misuses of Machine Learning in Finance
Dr. Simonian discusses the differences, advantages, and disadvantages of traditional econometrics vs. financial data science.
Video: Dispelling Myths About Machine Learning for Systematic Investing
Tony reviews some of the main misperceptions and wrong assumptions about applications of Machine Learning in systematic investments.
Big Data and Machine Learning in Investing: Current Misconceptions and the Path Ahead
Rajesh clarifies the difference in the role played by new data vis-a-vis the role played by new analysis techniques. He also classifies data science techniques based on their academic provenance. He argues for certain areas/regimes possessing the most and the least potential for application of big data analysis techniques.
The World of Alphas
An overview of Worldquant's quantitative research process and how the use of AI, Machine Learning & data creates value in finance.
The Neural Networks Survival Kit for Quants
Showcasing examples from portfolio construction to algorithmic trading, this presentation explains neural networks as a non-parametric econometrics technique. Matthew also provides insights and various examples illustrating the tradeoffs between using Deep Q-learning versus supervised deep learning for predictive modeling with signals such as news sentiment.
The Big Data & Machine Learning Revolution: Event takeaways, slides & videos
More than 600 finance professionals registered to attend the London Revolution. An excellent group of top finance professionals shared their latest research and experience with big data and machine learning. The event took place on April 24, 2018 at the Banking Hall, one of the most exquisite venues in Central London. In case you weren't able to attend, presentation slides and video recordings have now been made available.
Machine Intelligence in Capital Markets - Panel
The financial sector is making a massive shift towards machine intelligence in capital markets. Panelists share their experience in using data science and domain expertise in understanding data context. They will address how machine Intelligence in Capital Markets can be useful in creating new alpha signals, as well as in the data generation/preparation process, in portfolio construction or risk management.
Latest Research Papers
Risk as a Mosaic
A Data Mosaic approach to risk management can help identify emerging topics and narratives often missed by traditional approaches.
Developed Markets Sovereign Bonds Investing: Enhancing Style with Sentiment
Real-time news sentiment can significantly enhance the performance of traditional value, carry, momentum, and defensive style factors in the cross-section of developed market sovereign bonds.
Mitigating Risk Using RavenPack Analytics: A Research Roundtable
A growing corpus of research shows that news analytics data has proven useful in forecasting market volatility
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