01804nam a22001817a 450000500170000000800410001702000180005804000070007604100080008308200200009110000240011124501410013526000270027630000290030336500610033252011940039365000350158720250211144602.0250211b           ||||| |||| 00| 0 eng d  a9781484279090  cAL  aeng  223a332.6bNOKI  aTshepo Chris Nokeri  aImplementing machine learning for financeb: a systematic approach to predictive risk and performance analysis for investment portfolios  aNew YorkbApressc2025  axviii,182pbPBc23x15cm.  2Generala6391b₹479.20c₹d₹599.00e20%f6/02/2025  aBring 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.  2EconomicsaFinancial Economics