Authors:
- Bridges the gap between finance and data science by presenting a systematic method for structuring, analyzing, and optimizing an investment portfolio and its underlying asset classes
- Covers supervised and unsupervised machine learning (ML) models and deep learning (DL) models, including techniques of testing, validating, and optimizing model performance
- Presents a diverse range of machine learning libraries (such as statsmodels, scikit-learn, Auto ARIMA, and FB Prophet) and covers the Keras DL framework plus the Pyfolio package for portfolio risk analysis and performance analysis
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Table of contents (9 chapters)
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Front Matter
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Back Matter
About this book
The book introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios.
By the end of this book, you should be able to explain how algorithmic trading works and its practical application in the real world, and know how to apply supervised and unsupervised ML and DL models to bolster investment decision making and implement and optimize investment strategies and systems.
What You Will Learn
- Understand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio management
- Know the concepts of feature engineering, data visualization, and hyperparameter optimization
- Design, build, and test supervised and unsupervised ML and DL models
- Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices
- Structure and optimize an investment portfolio with preeminent asset classes and measure the underlying risk
Who This Book Is For
Beginning and intermediate data scientists, machine learning engineers, business executives, and finance professionals (such as investment analysts and traders)
Authors and Affiliations
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Pretoria, South Africa
Tshepo Chris Nokeri
About the author
Bibliographic Information
Book Title: Implementing Machine Learning for Finance
Book Subtitle: A Systematic Approach to Predictive Risk and Performance Analysis for Investment Portfolios
Authors: Tshepo Chris Nokeri
DOI: https://doi.org/10.1007/978-1-4842-7110-0
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Tshepo Chris Nokeri 2021
Softcover ISBN: 978-1-4842-7109-4Published: 27 May 2021
eBook ISBN: 978-1-4842-7110-0Published: 26 May 2021
Edition Number: 1
Number of Pages: XVIII, 182
Number of Illustrations: 53 b/w illustrations
Topics: Machine Learning, Python, Financial Engineering