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

ISBN-13: 9781484271094 / Angielski / Miękka / 2021 / 182 str.

Tshepo Chris Nokeri

Opis

Intermediate-Advanced user level

Spis treści

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 

O autorze

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.

Blurb

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.

You will:

• 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