000			01875nam a22002057a 4500
005			20250211144602.0
008			250211b ||||| |||| 00| 0 eng d
020			_a9781484279090
040			_cAL
041			_aeng
082			_223 _a332.6 _bNOKI
100			_aTshepo Chris Nokeri _9199128
245			_aImplementing machine learning for finance _b: a systematic approach to predictive risk and performance analysis for investment portfolios
260			_aNew York _bApress _c2025
300			_axviii,182p _bPB _c23x15cm.
365			_2General _a6391 _b₹479.20 _c₹ _d₹599.00 _e20% _f6/02/2025
520			_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.
650			_2Economics _aFinancial Economics _9199129
942			_2ddc _cBK
999			_c233751 _d233751