Machine Learning for Financial Engineering - Gyorfi, Laszlo; Ottucsak, Gyorgy; Walk, Harro (Singapore : World Scientific, 2012)
This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment. The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and eng.
5. Nonparametric Sequential Prediction of Stationary Time Series L. Gyorfi and Gy. Ottucsak5.1. Introduction; 5.2. Nonparametric Regression Estimation; 5.2.1. The Regression Problem; 5.2.2. Regression Function Estimation and L2 Error; 5.2.3. Partitioning Estimate; 5.2.4. Kernel Estimate; 5.2.5. Nearest-Neighbor Estimate; 5.2.6. Empirical Error Minimization; 5.3. Universally Consistent Predictions: Bounded Y; 5.3.1. Partition-Based Prediction Strategies; 5.3.2. Kernel-Based Prediction Strategies; 5.3.3. Nearest-Neighbor-Based Prediction Strategy; 5.3.4. Generalized Linear Estimates.
5.4. Universally Consistent Predictions: Unbounded Y.
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Keywords: Financial engineering -- Data processing; Machine learning; Investments -- Data processing; Machine learning -- Industrial applications; Science; Computer science; COMPUTERS -- Enterprise Applications -- Business Intelligence Tools; COMPUTERS -- Intelligence (AI) & Semantics; Electronic books; Electronic books