Identification of Trading Strategies Using Markov Chains and Statistical Learning Tools - Rodríguez Aguilar, Román; Marmolejo Saucedo, José Antonio (My University, 2021)
Technological advances have modified many operational and strategic areas in companies, the financial sector has been one of the sectors highly influenced by the methods of artificial intelligence and machine learning. The operation in the stock exchanges have used more technological tools to process information and be able to make investment decisions. The main objective is to be able to detect buying and selling opportunities at the right time. Stock markets have traditionally based their decisions on two major approaches, technical analysis and fundamental analysis, with new machine learning and artificial intelligence technologies, these paradigms have been updated making use of additional tools for their analysis. The present work is a proposal for the detection of trading signals in the markets through the use of Markov models and generalized additive models. In order to identify investment opportunities in the stock markets. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords: Generalized additive models (GAM); Machine learning; Markov models; Trading algorithms; Electronic trading; Commerce; Financial markets; Markov chains; Artificial intelligence technologies; Fundamental analysis; Generalized additive model; Investment decisions; Investment opportunities; Selling opportunities; Statistical learning; Technological advances; Investments