Applications of computational intelligence in data-driven trading

Description

"The objective of this book is to introduce the reader to the field of Computational Finance using the framework of Machine Learning as a tool of scientific inquiry. It is an attempt to integrate these two topics: how to use Machine Learning as the tool of choice in solving topical problems in Computational Finance. Readers will learn modern methods used by financial engineers and quantitative analysts to access, process, and interpret data. Throughout, there are case studies that are representative of relevant problems in modern finance. Topics covered include Time Series analysis, forecasting, Dynamic Programming, and Neural Networks"--  Read more... The evolution of trading paradigms -- The role of data in trading and investing -- Artificial intelligence : between myth and reality -- Computational intelligence : a principled approach for the era of data exploration -- How to apply the principles of CI in quantitative finance -- Case study 1 : optimizing trade execution -- Case study 2 : the dynamics of the limit order book -- Case study 3 : applying ML to portfolio management -- Case study 4 : applying ML to market making -- Case study 5 : applications of ml to derivatives valuation -- Case study 6 : using ML for risk management and compliance -- Conclusions and future directions.