Expressing news stories as numbers and metadata permits the manipulation of everyday information in a mathematical and statistical way.
News analytics 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.
The application of sophisticated linguistic analysis to news and social media has grown from an area of research to mature product solutions since 2007.
A large number of companies use news analysis to help them make better business decisions.
[1] Academic researchers have become interested in news analysis especially with regards to predicting stock price movements, volatility and traded volume.
[2][3][4] 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.
To meet this objective, such strategies typically involve opportunistic long and short positions in selected instruments with zero or limited market exposure.
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.
Exit Strategy: When the gap in the news sentiment scores for direction of Company
Action: Buy a short-dated straddle (the purchase of both a put and a call) on the stock of Company
To meet these objectives such strategies typically involve long positions in selected instruments.
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
Exit Strategy: When the news sentiment score for direction of Company
Example 2 Scenario: The news sentiment score for direction of Sector
Other types include Foreign exchange, Shape, Volatility, Sector, Liquidity, Inflation risks, etc.
Example 1 Scenario: The bank operates a VaR model to manage the overall market risk of its portfolio.
Action: Estimate the portfolio covariance matrix taking into account the development of the news sentiment score for volume.
Implement the relevant hedges to bring the VaR of the bank in line with the desired levels.
Action: Estimate the portfolio covariance matrix taking into account the development of the news sentiment score for volume.
applied in the algorithmic trading system, thus taking into account the news sentiment score for volume.
This is followed by the creation of the desired trading distribution forcing greater market participation during the periods of the day when volume is expected to be heaviest.
Being able to express news stories as numbers permits the manipulation of everyday information in a statistical way that allows computers not only to make decisions once made only by humans, but to do so more efficiently.
Since market participants are always looking for an edge, the speed of computer connections and the delivery of news analysis, measured in milliseconds, have become essential.