Titlebook: Implementing Machine Learning for Finance; A Systematic Approac Tshepo Chris Nokeri Book 2021 Tshepo Chris Nokeri 2021 Machine Learning.Dee

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Univariate Time Series Using Recurrent Neural Nets,zers. Second, it discusses the sequence data problem and how a recurrent neural network (RNN) solves it. Third, the chapter presents a way of designing, developing, and testing the most popular RNN, which is the long short-term memory (LSTM) model. We use the Keras framework for rapid prototyping an

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Stock Clustering,ers are not. High-risk assets are those whose prices change drastically in short periods. To secure capital, investors may select groups of stocks, rather than investing in a single stock or class of stocks. Given that there are many stocks to choose from, investors ordinarily find it arduous to sin

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Market Trend Classification Using ML and DL,he . to predict the possible direction of the market. This chapter presents the nonparametric (or nonlinear) method, also called the .. This prevalent method operates on independent variables and triggers a bounded value. It is suitable when dealing with a categorical dependent variable (a dependent

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Investment Portfolio and Risk Analysis, several machine learning models and deep learning models for robust investment management decision-making. Throughout the book, we alluded to investing in markets involving risk. In this chapter, we present the primitives of investment risk and performance analysis using the Pyfolio package. To ins

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