Machine Learning in Business Finance Using Python

This book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own. Contents: Financial Data Correlations: Portfolio Diversification Worked Example: Data Forming Optimal Portfolios Denoising the Correlation Matrix Using a More Accurate Forward Predictor Concluding Thoughts Regression and Regularization: Regression and Regularization Worked Example: Data Linear Regression Prediction Fine-Tuning Hyperparameters Prediction Using Hold-Out Test Set Cross-Validation Concluding Thoughts Corporate Reporting Data and Analyses: Financial Statements Binary Classification Performance Metrics Logistic Regression Worked Example: Data Dimension Reduction and Principal Component Analysis Concluding Thoughts Naïve Bayes, k-NN, and Support Vector Machines: Naïve Bayes k-Nearest Neighbors Algorithm Support Vector Machine Lagrange Duality Solution Support Vector Regression Worked Examples: Data Concluding Thoughts Decision Trees, Random Forest, and Multi-Class Prediction: Decision Tree Method Worked Example DT: Data Random Forest Multi-Class Classification Concluding Thoughts Gradient Boosting, SHAP Values: Gradient Boosting Popular Alternatives Worked Example GB: Data Shapley and SHAP Values Concluding Thoughts Artificial Neural Network I: Forward Propagation Backward Propagation Worked Example: Data Improving the NN and Understanding the Res Concluding Thoughts Artificial Neural Network II: Recurrent Neural Network Worked Example I: Data Variants of RNN Worked Example II: Data Concluding Thoughts Readership: For undergraduate and graduate students in Machine Learning and Algorithms, Quantitative Finance, Computational Finance, Machine Learning, and Business Finance, as well as general public readers who want to improve their general knowledge on Machine Learning.