It’s no secret that hedge fund managers are always looking for new sources of data that will help them in their never-ending quest to beat the market. Quantitative researchers at Bloomberg have been developing innovative methods to help reveal embedded signals in one of the more popular sources of unconventional financial data: sentiment analysis of news stories and social posts.
“Everyone is looking into alternative data sets, sometimes without really understanding their value,” says Dr. Arun Verma, Ph.D., a researcher who leads the Quant solutions team within Bloomberg’s Quantitative Research group, which is headed by Bruno Dupire. “They are looking at data like sentiment, supply chain relationships, and even things like satellite imagery. Often Machine Learning methods are applied to optimize alpha from such data, but a lack of scientific rigor can lead to poor out of sample performance. We avoid the trap of extreme data mining by using robust statistics.”
Read the full story on Tech at Bloomberg, June 14 2017
Alternative Data sets from Bloomberg for social sentiment making its way into algorithmic trading. At Cloudquant have been using it in our backtesting with the intention of improving quantitative trading strategies.
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“Everyone is looking into alternative data sets, sometimes without really understanding their value,” says Dr. Arun Verma, Ph.D.,
