Implementing Machine Learning for Finance

✍ Scribed by Tshepo Chris Nokeri

• Publisher: Apress
• Year: 2021
• Tongue: English
• Category: Library

✦ Synopsis

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

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