| 书目名称 | Implementing Machine Learning for Finance | | 副标题 | A Systematic Approac | | 编辑 | Tshepo Chris Nokeri | | 视频video | | | 概述 | 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 | | 图书封面 |  | | 描述 | Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures..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 en | | 出版日期 | Book 2021 | | 关键词 | Machine Learning; Deep Learning; Python; Finance; Investment Portfolio; Investment Risk Analysis; Stock Ma | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4842-7110-0 | | isbn_softcover | 978-1-4842-7109-4 | | isbn_ebook | 978-1-4842-7110-0 | | copyright | Tshepo Chris Nokeri 2021 |
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发表于 2025-3-21 19:52:45
