IMPLEMENTING MACHINE LEARNING FOR FINANCE : a systematic approach to predictive risk and... performance analysis for investment portfolios

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 Table of Contents About the Author About the Technical Reviewer Acknowledgments Introduction Chapter 1: Introduction to Financial Markets and Algorithmic Trading FX Market Exchange Rate Exchange Rates Quotation Exchange Rate Movement Bids and Offers The Left Bid and Right Offer Rule The Interbank Market The Retail Market Brokerage Desk Dealing Brokers No Desk Dealing Brokers Electronic Communications Network Brokers Straight-Through Processing Brokers Understanding Leverage and Margin The Contract for Difference Trading The Share Market Raising Capital Public Listing Stock Exchange Share Trading Stocks Index Speculative Nature of the Market Techniques for Speculating Market Movement Investment Strategy Management Process Strategy Formulation Modeling Supervised Learning The Parametric Method The Nonparametric Method Binary Classification Multiclass Classification The Ensemble Method Unsupervised Learning Dimension Reduction Cluster Analysis Backtesting Strategy Implementation Strategy Evaluation Algorithmic Trading Chapter 2: Forecasting Using ARIMA, SARIMA, and the Additive Model Time Series in Action Split Data into Training and Test Data Test for White Noise Test for Stationary Autocorrelation Function Partial Autocorrelation Function The Moving Average Smoothing Technique The Exponential Smoothing Technique Rate of Return The ARIMA Model ARIMA Hyperparameter Optimization Develop the ARIMA Model Forecast Using the ARIMA Model The SARIMA Model SARIMA Hyperparameter Optimization Develop a SARIMA Model Forecast Using the ARIMA Model The Additive Model Forecast 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 RNN Model The Recurrent Neural Network Problem The LSTM Model Gates Unfolded LSTM Network Stacked LSTM Network Develop an LSTM Model Using Keras Forecasting Using the LTSM Model Evaluation Conclusion Chapter 4: Discover Market Regimes HMM HMM Application in Finance Develop a GaussianHMM Gaussian Hidden Markov 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 Correlation Methods The Pearson Correlation Method The Covariance Method Pairwise Scatter Plots Eigen Matrix Further Descriptive Statistics Develop the Least Squares Model Model Evaluation Conclusion Chapter 7: Stock Market Simulation Understanding Value at Risk Estimate VAR by Applying 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 Logistic Regression Develop the Logistic Classifier Evaluate a Logistic Classifier Confusion Matrix Classification Report ROC Curve Learning Curve Multilayer Layer Perceptron Architecture Finalize the Model Training and Validation Loss Across Epochs Training and Validation Accuracy Across Epochs Conclusions Chapter 9: Investment Portfolio and Risk Analysis Investment Risk Analysis Pyfolio in Action Performance Statistics Drawback Rate of Returns Annual Rate of Return Rolling Returns Monthly Rate of Returns Conclusions Index 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)