Investigating Optimal Regimes for Prediction in the Stock Market

Corbelli, Rodrigo; Vellasco, Marley; Veiga, Álvaro

doi:10.1109/cec48606.2020.9185496

Cited by 1 publication

(1 citation statement)

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“…In this way, they can be very useful tools for supporting investors in their investment's decision making. Regarding the stock prices prediction, many methods have been proposed by researchers since the beginning of the stock market [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements

et al. 2021

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In recent years, the area of financial forecasting has attracted high interest due to the emergence of huge data volumes (big data) and the advent of more powerful modeling techniques such as deep learning. To generate the financial forecasts, systems are developed that combine methods from various scientific fields, such as information retrieval, natural language processing and deep learning. In this paper, we present ASPENDYS, a supportive platform for investors that combines various methods from the aforementioned scientific fields aiming to facilitate the management and the decision making of investment actions through personalized recommendations. To accomplish that, the system takes into account both financial data and textual data from news websites and the social networks Twitter and Stocktwits. The financial data are processed using methods of technical analysis and machine learning, while the textual data are analyzed regarding their reliability and then their sentiments towards an investment. As an outcome, investment signals are generated based on the financial data analysis and the sensing of the general sentiment towards a certain investment and are finally recommended to the investors.

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