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Implementing Machine Learning for Finance

Implementing Machine Learning for Finance


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International Edition


About the Book

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 S
About the Author: 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.


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Product Details
  • ISBN-13: 9781484271094
  • Publisher: Apress
  • Publisher Imprint: Apress
  • Height: 234 mm
  • No of Pages: 182
  • Spine Width: 11 mm
  • Weight: 340 gr
  • ISBN-10: 1484271092
  • Publisher Date: 29 Sep 2021
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Sub Title: A Systematic Approach to Predictive Risk and Performance Analysis for Investment Portfolios
  • Width: 156 mm


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