Implementing Machine Learning for Finance

A Systematic Approach to Predictive Risk and Performance Analysis for Investment Portfolios
1. Auflage 2021
ISBN: 978-1-4842-7109-4
Verlag: Apress

Buch, Englisch, 182 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 312 g

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

• 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)

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Nokeri, Tshepo Chris

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Chapter 1: Introduction to the Financial Markets and Algorithmic Trading Foreign exchange market - Exchange rate - Exchange rates quotation The Interbank market The retail market Brokerage - Understanding leverage and margin - Contract for difference trading The share market 

Raising capital - Public listing - Stock exchange - Share trading Speculative nature of foreign exchange market Techniques for speculating market movement Algorithmic trading - Supervised machine learning The parametric method - The non-parametric method Binary classification Multiclass classification - The ensemble method - Unsupervised learning - Deep learning - Dimension reduction 
Chapter 2: Forecasting Using ARIMA, SARIMA and Additive Model Time series in action Split data into training and test data Test for stationary Test for white noise Autocorrelation function Partial autocorrelation function The moving averages smoothing technique The exponential smoothing technique Rate of return The ARIMA Model ARIMA Hyperparameter Optimization - Develop the ARIMA model - Forecast prices using the ARIMA model  The SARIMA model - Develop SARIMA model - Forecast using the SARIMA model Additive model - Develop the additive model - Forecast prices the additive model - Seasonal decomposition Conclusion 
Chapter 3: Univariate Time Series using Recurrent Neural Nets What is deep learning? Activation function Loss function Optimize an artificial neural network The sequential data problem 

The recurrent net model The recurrent net problem The LSTM model Gates Unfolded LSTM network Stacked LSTM network LSTM in action - Split data into training, test and validation - Normalize data - Develop LSTM model - Forecasting using the LSTM - Model evaluation - Training and validation loss across epochs - Training and validation accuracy across epochs Conclusion 
Chapter 4: Discover Market Regimes HMM HMM application in finance - Develop GaussianHMM Mean and variance Expected returns and volumes Conclusions 
Chapter 5: Stock Clustering Investment Portfolio Diversification Stock market volatility K-Means clustering K-Means in practice Conclusions 
Chapter 6: Future Price Prediction using Linear Regression Linear Regression in Practice Detect missing values Pearson correlation Covariance Pairwise scatter plot Eigen matrix Split data into training and test data. Normalize data Least squares model hyperparameter optimization Step 1: Fit least squares model with default hyperparameters Step 2: Determine the mean and standard deviation of the cross-validation scores Step 3: Determine Hyper-parameters that yield the best score. Develop least squares model Find an intercept Find the estimated coefficient Test least squares model performance using SciKit-Learn Plotting actual values and predicted values Conclusion 
Chapter 7: Stock Market Simulation Understanding value at risk Estimate VAR using the Variance-Covariance Method Understanding Monte Carlo Application of Monte Carlo simulation in finance - Run Monte Carlo simulation - Plot simulations Conclusions 
Chapter 8: Market Trend Classification using ML and DL Classification in practice Data preprocessing Split Data into training and test data Logistic regression - Finalize a logistic classifier - Evaluate a logistic classifier - Learning curve Multilayer layer perceptron - Architecture - Finalize model - Training and validation loss across epochs - Training and validation accuracy across epochs Conclusions 
Chapter 9: Investment Portfolio and Risk Analysis Investment Investment Analysis Investment Risk Management Investment Portfolio Management Pyfolio in action Performance statistics Drawback Rate of returns Annual rate of return Rolling returns - Monthly rate of returns Conclusions 

Über Autor(innen)

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.