News analytics

News analysis refers to the measurement of the various qualitative and quantitative attributes of textual (unstructured data) news stories. Some of these attributes are: sentiment, relevance, and novelty. Expressing news stories as numbers permits the manipulation of everyday information in a mathematical and statistical way.

News analytics are used in financial modeling, particularly in quantitative and algorithmic trading. They are usually derived through automated text analysis and applied to digital texts using elements from natural language processing and machine learning such as latent semantic analysis, support vector machines, "bag of words" among other techniques.

Applications & Strategies
A large number of companies use news analysis to help them make better business decisions. Academic researchers have become interested in news analysis especially with regards to predicting stock price movements, volatility and traded volume. Provided a set of values such as sentiment and relevance as well as the frequency of news arrivals, it is possible to construct news sentiment scores for multiple asset classes such as equities, Forex, fixed income, and commodities. Sentiment scores can be constructed at various horizons to meet the different needs and objectives of high and low frequency trading strategies, whilst characteristics such as direction and volatility of asset returns as well as the traded volume may be addressed more directly via the construction of tailor-made sentiment scores. Scores are generally constructed as a range of values. For instance, values may range between 0 and 100, where values above and below 50 convey positive and negative sentiment, respectively. Based on such sentiment scores, it should be possible to generate a set of strategies useful for instance within investing, hedging, and order execution.

Absolute Return Strategies
The objective of absolute return strategies is absolute (positive) returns regardless of the direction of the financial market. To meet this objective, such strategies typically involve opportunistic long and short positions in selected instruments with zero or limited market exposure. In statistical terms, absolute return strategies should have very low correlation with the market return. Typically, hedge funds tend to employ absolute return strategies. Below, a few examples show how news analysis can be applied in the absolute return strategy space with the purpose to identify alpha opportunities applying a market neutral strategy or based on volatility trading

Example 1

Scenario: The gap between the news sentiment scores for direction, S, of Company X and Market Y has moved beyond + 20. That is, SX − SY ≥ 20.

Action: Buy the stock on Company X and short the future on Market Y.

Exit Strategy: When the gap in the news sentiment scores for direction of Company X and Market Y has disappeared, SX − SY = 0, sell the stock on Company X and go long the future on Market Y to close the positions.

Example 2

Scenario: The news sentiment score for volatility of Company X goes above 70 out of 100 indicating an expected volatility above the option implied volatility.

Action: Buy a short-dated straddle (the purchase of both a put and a call) on the stock of Company X.

Exit Strategy: Keep the straddle on Company X until expiry or until a certain profit target has been reached

Relative Return Strategies
The objective of relative return strategies is to either replicate (passive management) or outperform (active management) a theoretical passive reference portfolio or benchmark. To meet these objectives such strategies typically involve long positions in selected instruments. In statistical terms, relative return strategies often have high correlation with the market return. Typically, mutual funds tend to employ relative return strategies. Below, a few examples show how news analysis can be applied in the relative return strategy space with the purpose to outperform the market applying a stock picking strategy and by making tactical tilts to ones asset allocation model.

Example 1

Scenario: The news sentiment score for direction of Company X goes above 70 out of 100.

Action: Buy the stock on Company X.

Exit Strategy: When the news sentiment score for direction of Company X falls below 60, sell the stock on Company X to close the position.

Example 2

Scenario: The news sentiment score for direction of Sector Z goes above 70 out of 100.

Action: Include Sector Z as a tactical bet in the asset allocation model.

Exit Strategy: When the news sentiment score for direction of Sector Z falls below 60, remove the tactical bet for Sector Z from the asset allocation model.
(en.wikipedia.org)