Implementing Machine Learning for Finance
Apress (Verlag)
978-1-4842-7109-4 (ISBN)
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)
Tshepo Chris Nokeri harnesses big data, advanced analytics, and artificial intelligence to foster innovation and optimize business performance. In his functional work, he has delivered complex solutions to companies in the mining, petroleum, and manufacturing industries. He initially completed a bachelor’s degree in information management. He then graduated with an honors degree in business science at the University of the Witwatersrand on a TATA Prestigious Scholarship and a Wits Postgraduate Merit Award. They unanimously awarded him the Oxford University Press Prize. He has authored the Apress book Data Science Revealed: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning.
Chapter 1: Introduction to Financial Markets and Algorithmic Trading.- Chapter 2: Forecasting Using ARIMA, SARIMA, and the Additive Model .- Chapter 3: Univariate Time Series Using Recurrent Neural Nets .- Chapter 4: Discover Market Regimes .- Chapter 5: Stock Clustering.- Chapter 6: Future Price Prediction Using Linear Regression.- Chapter 7: Stock Market Simulation.- Chapter 8: Market Trend Classification Using ML and DL.- Chapter 9: Investment Portfolio and Risk Analysis.
Erscheinungsdatum | 07.06.2021 |
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Zusatzinfo | 53 Illustrations, black and white; XVIII, 182 p. 53 illus. |
Verlagsort | Berkley |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung | |
Schlagworte | algorithmic trading • Deep learning • Finance • Investment portfolio • Investment Risk Analysis • machine learning • Python • Stock Market • Supervised Machine Learning |
ISBN-10 | 1-4842-7109-2 / 1484271092 |
ISBN-13 | 978-1-4842-7109-4 / 9781484271094 |
Zustand | Neuware |
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