University Professor Argimiro Arratia takes a close look at accuracy of Accuity Trading’s sentiment analysis

The advancement of real time analytics has taken another evolutionary step today as Acuity Trading has provided a set of results which reflect its research on the predictability returns of its market sentiment data. Recently, the company entered into a strategic partnership with Spanish edcuational institution Unversitat Politecnica de Catalunya in order to work together […]

The advancement of real time analytics has taken another evolutionary step today as Acuity Trading has provided a set of results which reflect its research on the predictability returns of its market sentiment data.

Recently, the company entered into a strategic partnership with Spanish edcuational institution Unversitat Politecnica de Catalunya in order to work together on research. Led by Professor Argimiro Arratia whose works include Computational Finance: An Introductory Course with R, the research took into account Accuity Trading’s eleven news-based public sentiment indices to identify which sentiment indicator or combination of indicators provided the most reliable forecast.

Andrew Lane, CEO of Acuity Trading today made a commercial statement with regard to the results:

“Until now sentiment-based tools have primarily focussed on bullish and bearish signals but our products cover a number of additional sentiment types which can be equally useful to gauge investor mood. This recent research was commissioned to provide tangible evidence of their independent or combined forecasting capabilities for different asset classes and time series.”

Professor Arratia added:

“Interestingly, the research has also demonstrated that in general, highly popular stocks drive the news whereas less popular stocks are more prone to be driven by the news. The research findings reinforce our view that sentiment can precede market movement.”

Mr. Lane continued:

It also upholds our belief that sentiment data is strongest when combined with other sentiment data sets or alternative data sets to focus the signal. In one example using Apple as the target, Professor Arratia found over 200 instances of forecasted stock returns for one sentiment alone when combined with six other sentiment data sets.

“This makes for truly exciting results and we will be using this ground breaking research to shape our product development and user experience for our clients.”

Professor Arratia has published the full synopsis of his research which is available by request from Unversitat Politecnica de Catalunya

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