Forum on News Analytics

Recently I attended the Forum on News Analytics in London, organized by Carisma from Brunel University. In fact, Brian Sentance wrote a good summary of the event on his blog.

As part of the forum, a panel discussion was held with attendance from Northfield Information Services, Macquarie Equity Research, RavenPack, Thomson-Reuters, Semlab, and Capital IQ ClariFI.

What I mostly found interesting was that the different news analytics providers had chosen to approach the market from different angles. For instance, RavenPack and Reuters offered data feeds and "ready-made" analytics, while Semlab required the end-user to define their own set of rules. From my experience, trying to build the lingusitic models and NLP algorithms yourself is a daunting task and definitely a fulltime job. As a quant, I prefer to focus on the analysis of the news data.

Carisma, Northfield and Macquarie briefly discussed some of their research involving news analytics (all studies were based on RavenPack data). Generally, it seems that most of the practical research out there is based on RavenPack data.

The forum was well attended with people coming from both academia and industry including high and low frequency traders, risk managers, and people focusing on algorithmic execution. I had a chance to talk to people from both camps and there is no doubt that news analytics has surely caught people's attention, and is becoming a hot topic. Especially, it seemed as if people were very much interested in discussing the different techniques that one can use to extract sentiment, but also to use such information to construct triggering events and sentiment factors.

It was definitely an interesting event, and I am looking forward to attending Carisma’s next event "The Interface of Behavioral Finance and Quantitative Finance" and the pre-conference workshop "News Analytics Applied to Trading, Fund Management, and Risk Control" taking place in London early next year.

Sector Rotation Strategies Driven By News Sentiment Indices

Recently, I conducted a study on how to construct sector rotation strategies driven by news sentiment indices. The applied methodology was presented in a previous posting: " Construction of Market Sentiment Indices Using News Sentiment" from August 5th, 2009.

As part of the sector rotation study, I constructed a set of industry sentiment indices, and a market sentiment index as described in a previous posting. The strategy I tested included going long the Top 5 sentiment industries during positive market sentiment regimes (market sentiment index values must be above 50), and short the Bottom 3 sentiment industries during negative market sentiment regimes (market sentiment index values must be below 50).

Interestingly, I found value in every stage of the following four-step procedure:

Step 1: Market Return Momentum Strategy on DJIA

Go long the Dow Jones Industrial Average (DJIA) when the previous month's return has been positive and go short if the previous month's return has been negative (Black-line). Covering the period Feb. 2005 to Sept. 2009, the strategy yielded an annualized return of 11.25% with an Information ratio of 0.73.

Step 2: Industry Return Rotation Strategy with Market Return Overlay

Go long the Top 5 industries when the previous month's DJIA return has been positive and go short the Bottom 3 industries when the previous month's DJIA return has been negative - Momentum (Green-line). Covering the period Feb. 2005 to Sept. 2009, the strategy yielded an annualized return of 18.49% with an Information ratio of 0.95.

Step 3: Industry Return Rotation Strategy with Market Sentiment Overlay

Go long the Top 5 industries when the previous month exhibited positive market sentiment and go short the Bottom 3 industries when the previous month exhibited negative market sentiment (Red-line). Covering the period Feb. 2005 to Sept. 2009, the strategy yielded an annualized return of 27.23% with an Information ratio of 1.37.

Step 4: Industry Sentiment Rotation strategy with Market Sentiment Overlay

Go long the Top 5 industries when the previous month exhibited positive market sentiment and go short the Bottom 3 industries when the previous month exhibited negative market sentiment (Blue-line). Covering the period Feb. 2005 to Sept. 2009, the strategy yielded an annualized return of 29.63% with an Information ratio of 1.80.

The key findings of my study can be summarized as:

• Negative sentiment seems to be a strong leading indicator of future underperformance, while positive sentiment is not as clear a leading indicator of future outperformance.
• Based on an industry sentiment ranking, the Top industries seemed to reach their cumulative return high six months later than the Bottom industries (April 2008 versus September 2007).
• Tracking the sentiment of the Top and Bottom industries could be a valuable input into the creation of a dynamic leverage factor moving more aggressively into ones trading signals in extreme sentiment regimes.
• An industry sentiment rotation strategy with a market sentiment overlay would not only have outperformed an industry return rotation strategy during the 5 year period, but would have done so with a more attractive Information ratio (see above Figure).
  

While more research can be done to refine the methodology and to translate the sentiment index values into actual trading or investment signals, the above results highlights some potentially interesting and profitable relationships between news sentiment and market returns. The full results of my findings are documented in the paper Sector Rotation Strategies Driven By News Sentiment Indices (November, 2009).


Source: RavenPack