Implementing Machine Learning for Finance: A Systematic Approach to Predictive Risk and Performance Analysis for Investment Portfolios

Description

Intermediate-Advanced user level 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