Implementing Machine Learning for Finance: A Systematic Approach to Predictive Risk and Performance Analysis for Investment Portfolios
暫譯: 金融機器學習實作：投資組合預測風險與績效分析的系統性方法
Nokeri, Tshepo Chris
- 出版商: Apress
- 出版日期: 2021-05-27
- 售價: $2,050
- 貴賓價: 9.5 折 $1,948
- 語言: 英文
- 頁數: 182
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1484271092
- ISBN-13: 9781484271094
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相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
相關主題
商品描述
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 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
Beginning and intermediate data scientists, machine learning engineers, business executives, and finance professionals (such as investment analysts and traders)
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
Beginning and intermediate data scientists, machine learning engineers, business executives, and finance professionals (such as investment analysts and traders)
商品描述(中文翻譯)
將機器學習 (ML) 和深度學習 (DL) 結合於金融交易中，重點放在投資管理上。本書解釋了系統化的投資組合管理、風險分析和績效分析方法，包括使用數據科學程序的預測分析。
本書介紹了模式識別和未來價格預測，這些對時間序列分析模型產生影響，例如自回歸整合移動平均 (ARIMA) 模型、季節性 ARIMA (SARIMA) 模型和加法模型，並涵蓋最小二乘模型和長短期記憶 (LSTM) 模型。它展示了應用高斯隱馬可夫模型的隱藏模式識別和市場狀態預測。本書還涵蓋了 K-Means 模型在股票聚類中的實際應用。它建立了方差-協方差法和模擬法（使用蒙地卡羅模擬）在風險價值估算中的實際應用。它還包括使用邏輯回歸分類器和多層感知器分類器進行市場方向分類。最後，本書呈現了投資組合的績效和風險分析。
在本書結束時，您應該能夠解釋算法交易的運作方式及其在現實世界中的實際應用，並知道如何應用監督式和非監督式的 ML 和 DL 模型來增強投資決策，並實施和優化投資策略和系統。
您將學到什麼
- 了解金融市場和算法交易的基本原理，以及適合系統化投資組合管理的監督式和非監督式學習模型
- 了解特徵工程、數據可視化和超參數優化的概念
- 設計、構建和測試監督式和非監督式的 ML 和 DL 模型
- 發現季節性、趨勢和市場狀態，模擬市場變化和投資策略問題，並預測市場方向和價格
- 結構化和優化投資組合，選擇優秀的資產類別並衡量潛在風險
本書適合誰
初學者和中級數據科學家、機器學習工程師、商業高管以及金融專業人士（如投資分析師和交易員）
作者簡介
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.
作者簡介(中文翻譯)
Tshepo Chris Nokeri 利用大數據、先進分析和人工智慧來促進創新並優化商業表現。在他的實務工作中，他為礦業、石油和製造業的公司提供了複雜的解決方案。他最初完成了資訊管理的學士學位，隨後在威特沃特斯蘭大學（University of the Witwatersrand）獲得商業科學的榮譽學位，並獲得了TATA傑出獎學金和威特研究生優異獎。他被一致授予牛津大學出版社獎。他是Apress書籍《Data Science Revealed: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning》的作者。
