Generative Adversarial Network to evaluate quantity of information in financial markets

【Author】 Santoro, Domenico; Grilli, Luca

【Source】NEURAL COMPUTING & APPLICATIONS

【影响因子】5.102

【Abstract】Nowadays, the information obtainable from the markets are potentially limitless. Economic theory has always supported the possible advantage obtainable from having more information than competitors, however quantifying the advantage that these can give has always been a problem. In particular, in this paper we study the amount of information obtainable from the markets taking into account only the time series of the prices, through the use of a specific Generative Adversarial Network. We consider two types of financial instruments traded on the market, stocks and cryptocurrencies: the first are traded in a market subject to opening and closing hours, whereas cryptocurrencies are traded in a 24/7 market. Our goal is to use this GAN to be able to "convert" the amount of information that the different instruments can have in discriminative and predictive power, useful to improve forecast. Finally, we demonstrate that by using the initial dataset with the 5 most important feature useds by traders, the prices of cryptocurrencies present higher discriminatory and predictive power than stocks, while by adding a feature the situation can be completely reversed.

【Keywords】Generative adversarial network; Deep learning applications; Market information analysis; Stock price analysis

【发表时间】

【收录时间】2022-06-07

【文献类型】实证性文章

【主题类别】

区块链治理-市场治理-数字货币