Trading and predictive analytics

I attended today's class in the course Trading Strategies and Systems offered by Prof Vasant Dhar from NYU Stern School of Business. Luckily, Vasant is offering the elective course here at the Indian School of Business, so no need for transatlantic travel.

The topic of this class was the use of news in trading. I won't disclose any trade secrets (you'll have to attend the class for that), but here's my point: Trading is a striking example of the distinction between explanation and prediction. Generally, techniques are based on correlations and on "blackbox" predictive models such as neural nets. In particular, text mining and sentiment analysis are used for extracting information from (often unstructured) news articles for the purpose of prediction.

Vasant mentioned the practical advantage of a machine-learning approach for extracting useful content from text over linguistics know-how. This reminded me of a famous comment by Frederick Jelinek, a prominent
Natural Language Processing researcher who passed away recently:

"Whenever I fire a linguist our system performance improves" (Jelinek, 1998)

This comment was based on Jelinek's experience at IBM Research, while working on computer speech recognition and machine translation.

Jelinek's comment did not make linguists happy. He later defended this claim in a paper entitled "Some of My Best Friends are Linguists" by commenting,

"We all hoped that linguists would provide us with needed help. We were never reluctant to include linguistic knowledge or intuition into our systems; if we didn't succeed it was because we didn't fi nd an effi cient way to include it."

Note: there are some disputes regarding the exact wording of the quote ("Anytime a linguist leaves the group the recognition rate goes up") and its timing -- see note #1 in the Wikipedia entry.

Language and psychological state: explain or predict?

Quite a few of my social science colleagues think that predictive modeling is not a kosher tool for theory building. In our 2011 MISQ paper "Predictive Analytics in Information Systems Research" we argue that predictive modeling has a critical role to play not only in theory testing but also in theory building. How does it work? Here's an interesting example:

The new book The Secret Life of Pronouns by the cognitive psychologist Pennebaker is a fascinating read in many ways. The book describes how analysis of written language can be predictive of psychological state. In particular, the author describes an interesting text mining approach that analyzes text written by a person and creates a psychological profile of the writer. In the author's context, the approach is used to study the effect of writing on recovery from psychological trauma. You can get a taste of word analysis on the AnalyzeWords.com website, run by the author and his colleagues, which analyzes the personality of a tweeter.

In the book, Pennebaker describes how the automated analysis of language has shed light on the probability that people who underwent psychological trauma will recuperate. For instance, people who used a moderate amount of negative language were more likely to improve than those who used too little or too much negative language. Or, people who tended to change perspectives in their writing over time (from "I" to "they" or "we") were more likely to improve.

Now comes a key question. In the words of the author (p.14): "Do words reflect a psychological state or do they cause it?". The statistical/data-mining text mining application is obviously a predictive tool that is build on correlations/associations. Yet, by examining when it predicts accurately and studying the reasons for the accurate (or inaccurate) predictions, the predictive tool can shed insightful light on possible explanations, linking results to existing psychological theories and giving ideas for new ones. Then comes the "close the circle", where the predictive modeling is combined with explanatory modeling. For testing the explanatory power of words on psychological state, the way to go is experiments. And indeed, the book describes several such experiments investigating the causal effect of words on psychological state, which seem to indicate that there is no causal relationship.

[Thanks to my text-mining-expert colleague Nitin Indurkhya for introducing me to the book!]