Artificial Intelligence for Financial Markets: The Polymodel Approach (Financial Mathematics and Fintech) - Tapa blanda

Acerca del autor

Thomas Barrau is a Senior Quantitative Researcher working in the hedge fund AXAInvestment Managers Chorus Ltd. He is working on the development of an Equity MarketNeutral portfolio, from the creation of quantitative trading strategies to the portfolioconstruction. Prior to this, he worked at Societe Generale as banker and financial advisorto small businesses, and as CFO in an aerospace company. He holds a PhD in AppliedMathematics from Paris 1 Pantheon-Sorbonne University. Previously, he validated withhonors three different Masters of Science from Aix-Marseille School of Economics,Ca'Foscari University of Venice and Poitiers IAE.

 Raphael Douady is a French mathematician and economist specializing in data science, financial mathematics and chaos theory at the University of Paris I-Panthéon-Sorbonne. He formerly held the Frey Chair of quantitative finance at Stony Brook University and was academic director of the French Laboratory of Excellence on Financial Regulation. He earned his PhD in Hamiltonian dynamics and has more than 25 years of experience in the financial industry. He has particular interest in researching portfolio risks, for which he has developed especially suited powerful nonlinear statistical and data science models, as well as macroeconomics and systemic risk. He founded fin tech firms Riskdata (risk management for the buyside) and Datacore (quantitative portfolio of ETFs) and is Chief Science Officer of NM Fin tech (numerical methods for fixed income trading in China). 

De la contraportada

This book introduces the novel artificial intelligence technique of polymodels and applies it to the prediction of stock returns. The idea of polymodels is to describe a system by its sensitivities to an environment, and to monitor it, imitating what a natural brain does spontaneously. In practice this involves running a collection of non-linear univariate models. This very powerful standalone technique has several advantages over traditional multivariate regressions. With its easy to interpret results, this method provides an ideal preliminary step towards the traditional neural network approach.