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.