Tshepo Chris Nokeri - Implementing Machine Learning for Finance

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Kindly welcome to Implementing Machine Learning for Finance. This book is your guide to mastering machine and deep learning applied to practical, real-world investment strategy problems using Python programming. In this book, you will learn how to properly build and evaluate supervised and unsupervised machine learning and deep learning models adequate for partial algorithmic trading and investment portfolio and risk analysis.

To begin with, it prudently introduces pattern recognition and future price forecasting exerting time-series analysis models, like the autoregressive integrated moving average (ARIMA) model, seasonal ARIMA (SARIMA) model, and additive model, and then it carefully covers the least squares model and the long-short term memory (LSTM) model. Also, it covers hidden pattern recognition and market regime prediction applying the Gaussian hidden Markov model. Third, it presents the practical application of the k-means model in stock clustering. Fourth, it establishes the practical application of the prevalent variance-covariance method and empirical simulation method (using Monte Carlo simulation) for value-at-risk estimation. Fifth, it encloses market direction classification using both the logistic classifier and the multilayer perceptron classifier. Lastly, it promptly presents performance and risk analysis for investment portfolios.

This book targets data scientists, machine learning engineers, and business and finance professionals, including retail investors who want to develop systematic approaches to investment portfolio management, risk analysis, and performance analysis, as well as predictive analytics using data science procedures and tools. Prior to exploring the contents of this book, ensure that you understand the basics of statistics, investment strategy, Python programming, and probability theories. Also, install the packages mentioned in the previous list in your environment.

Writing a single-authored book is demanding, but I received firm support and active encouragement from my family and dear friends. Many heartfelt thanks to the Apress publishing team for all their backing throughout the writing and editing process. Last, humble thanks to all of you reading this; I earnestly hope you find it helpful.