Acuity Trading Unveils Comprehensive Research Study on Sentiment Analysis

Acuity Trading has just announced its latest research findings on the predictability returns of its market sentiment data platform, which were conducted with the help of BarcelonaTech, according to an Acuity statement.

Acuity Trading specializes in Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision-making. In the trading space, analytics are applied in a predictive manner in an attempt to forecast the price more accurately. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analy Read this Term and sentiment-based trading systems. The group focuses exclusively on bringing big data solutions to the broader retail investment community and caters to both online brokers and platform providers.

Earlier this month, Acuity Trading underwent collaboration with the Barcelona’s Universitat Politècnica de Catalunya (BarcelonaTech). The public university has earned a reputation for being at the forefront of research in the region – the impetus behind the partnership was to assist academics and delve deeper into computational finance.

In its latest study done at BarcelonaTech, which was spearheaded by Professor Argimiro Arratia, Acuity Trading utilized eleven news-based public sentiment indices to identify which sentiment indicator or combination of indicators ultimately yielded the most reliable forecast. Such information can be instrumental for financial service providers or brokers who are looking to utilize big data in their business.

The results and subsequent study conducted at BarcelonaTech found Acuity’s Financial Volatility Volatility In finance, volatility refers to the amount of change in the rate of a financial instrument, such as commodities, currencies, or stocks, over a given time period. Essentially, volatility describes the nature of an instrument’s fluctuation; a highly volatile security equates to large fluctuations in price, and a low volatile security equates to timid fluctuations in price. Volatility is an important statistical indicator used by financial traders to assist them in developing trading systems. Trad In finance, volatility refers to the amount of change in the rate of a financial instrument, such as commodities, currencies, or stocks, over a given time period. Essentially, volatility describes the nature of an instrument’s fluctuation; a highly volatile security equates to large fluctuations in price, and a low volatile security equates to timid fluctuations in price. Volatility is an important statistical indicator used by financial traders to assist them in developing trading systems. Trad Read this Term index not only to be highly accurate in its methodology, but a suitable means for viably predicting returns of any financial time series sampled on a daily basis, with the variable Fear being a strong forecaster.

The findings also found that dependent on a given target series, its forecasting capabilities, and more specifically Acuity’s indices dealing with Fear, Financial Up, Financial Down and Frequency, proved to be tangible measures with strongly correlated results across sentiment data sets.

According to Andrew Lane, CEO of Acuity Trading, in a recent statement on the findings, “Until now sentiment-based tools have primarily focused 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.”

“The research findings reinforce our view that sentiment can precede market movement. 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, Arratia found over 200 instances of forecasted stock returns for one sentiment alone when combined with six other sentiment data sets. This is truly exciting results and we will be using this ground breaking research to shape our product development and user experience for our clients,” added Mr. Lane.