Trading algorithmically based on sentiment data is a relatively new field compared to more established approaches. With the explosion of social media and computing power, the analysis of sentiment data has also increased, with some hedge funds committing considerable resources to researching the applications of sentiment data in trading.
However, there is also some skepticism of the value of analyzing social media for trading, so what is sentiment trading all about?
Can sentiment actually be used in trading models and how?
Our guest for this episode, Richard Peterson, has been analyzing sentiment for over 20 years. He started what was probably the world’s first fund specializing in sentiment trading, and now runs a company called MarketPsych, specializing in the collection and analysis of sentiment data.
In our chat you will learn:
- Why sentiment is so important and how it can give traders an edge
- The challenges of using sentiment data in trading models
- The best and worst markets for sentiment analysis as a predictor
- Applications of sentiment analysis in quant models and the future of sentiment analysis
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Resources mentioned in this episode
- Richard can be contacted through his website marketpsych.com or email Richard@marketpsychdata.com
- Elijah DePalma’s research with Thomson Reuters: https://forms.thomsonreuters.com/sentimentandinvestorbehavior/
- Daniel Kahneman’s prospect theory: https://www.princeton.edu/~kahneman/docs/Publications/prospect_theory.pdf
- Lisa Kramer on seasonal affective disorder: http://www.lisakramer.com/aer-sad.pdf
- Free data sets from Quandl: https://www.quandl.com/open-data
- Books mentioned in the show:
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4 September 2016