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.
Understanding & Overcoming Weak Points of Big Data and Machine Learning Investing
Practitioner’s point of view on what in big data and machine learning investing is challenging and what to do about it. Demystifying the “magic box”, sharing best practices and real-life examples of machine learning application to investing including NLP with RavenPack.
Latest Research Papers
News Sentiment Investment Strategies in APAC - A Case Study
Trading strategies based on Asia Pacific news sentiment produced excess returns for up to a month ahead
Insider Trading Strategies Enhanced by News Sentiment Data
This study shows how insider transaction data can be used to generate profitable trading signals, and enhanced using a News Sentiment overlay
RavenPack News Sentiment Data Outperforms During Coronavirus Crisis
News sentiment data can still enhance alpha strategies and provide downside protection during periods of intense volatility.
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