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Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing.
Capital assets pricing model. --- Machine learning --- Finance --- Economic aspects. --- Mathematical models. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Capital asset pricing model --- CAPM (Capital assets pricing model) --- Pricing model, Capital assets --- Capital --- Investments --- Mathematical models --- Advances in Financial Learning. --- Bayesian estimation. --- Bayesian regression. --- Igor Halperin. --- Machine Learning in Finance. --- Marcos Lopez de Prado. --- Matthew Dixon. --- Paul Bilokon. --- Supervised learning. --- asset prices. --- cross-section of stock returns. --- data-driven methods of tuning. --- elastic-net estimator. --- factor models. --- firm fundamentals. --- high-dimensional prediction. --- market efficiency. --- mean-variance optimization framework. --- neural networks. --- out-of-sample performance. --- regularization. --- return predictability. --- ridge regression. --- risk premia estimation. --- trees and random forests. --- Machine learning. --- Financial applications.
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This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.
Research & information: general --- Mathematics & science --- robust optimization --- differential evolution --- ROOT --- optimization framework --- drainage rehabilitation --- overflooding --- pipe breaking --- VCO --- CMOS differential pair --- PVT variations --- Monte Carlo analysis --- multi-objective optimization --- Pareto Tracer --- continuation --- constraint handling --- surrogate modeling --- multiobjective optimization --- evolutionary algorithms --- kriging method --- ensemble method --- adaptive algorithm --- liquid storage tanks --- base excitation --- artificial intelligence --- Multi-Gene Genetic Programming --- computational fluid dynamics --- finite volume method --- JSSP --- CMOSA --- CMOTA --- chaotic perturbation --- fixed point arithmetic --- FP16 --- pseudo random number generator --- incorporation of preferences --- multi-criteria classification --- decision-making process --- multi-objective evolutionary optimization --- outranking relationships --- decision maker profile --- profile assessment --- region of interest approximation --- optimization using preferences --- hybrid evolutionary approach --- forecasting --- Convolutional Neural Network --- LSTM --- COVID-19 --- deep learning --- trust region methods --- multiobjective descent --- derivative-free optimization --- radial basis functions --- fully linear models --- decision making process --- cognitive tasks --- recommender system --- project portfolio selection problem --- usability evaluation --- multi-objective portfolio optimization problem --- trapezoidal fuzzy numbers --- density estimators --- steady state algorithms --- protein structure prediction --- Hybrid Simulated Annealing --- Template-Based Modeling --- structural biology --- Metropolis --- optimization --- linear programming --- energy central --- robust optimization --- differential evolution --- ROOT --- optimization framework --- drainage rehabilitation --- overflooding --- pipe breaking --- VCO --- CMOS differential pair --- PVT variations --- Monte Carlo analysis --- multi-objective optimization --- Pareto Tracer --- continuation --- constraint handling --- surrogate modeling --- multiobjective optimization --- evolutionary algorithms --- kriging method --- ensemble method --- adaptive algorithm --- liquid storage tanks --- base excitation --- artificial intelligence --- Multi-Gene Genetic Programming --- computational fluid dynamics --- finite volume method --- JSSP --- CMOSA --- CMOTA --- chaotic perturbation --- fixed point arithmetic --- FP16 --- pseudo random number generator --- incorporation of preferences --- multi-criteria classification --- decision-making process --- multi-objective evolutionary optimization --- outranking relationships --- decision maker profile --- profile assessment --- region of interest approximation --- optimization using preferences --- hybrid evolutionary approach --- forecasting --- Convolutional Neural Network --- LSTM --- COVID-19 --- deep learning --- trust region methods --- multiobjective descent --- derivative-free optimization --- radial basis functions --- fully linear models --- decision making process --- cognitive tasks --- recommender system --- project portfolio selection problem --- usability evaluation --- multi-objective portfolio optimization problem --- trapezoidal fuzzy numbers --- density estimators --- steady state algorithms --- protein structure prediction --- Hybrid Simulated Annealing --- Template-Based Modeling --- structural biology --- Metropolis --- optimization --- linear programming --- energy central
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This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.
robust optimization --- differential evolution --- ROOT --- optimization framework --- drainage rehabilitation --- overflooding --- pipe breaking --- VCO --- CMOS differential pair --- PVT variations --- Monte Carlo analysis --- multi-objective optimization --- Pareto Tracer --- continuation --- constraint handling --- surrogate modeling --- multiobjective optimization --- evolutionary algorithms --- kriging method --- ensemble method --- adaptive algorithm --- liquid storage tanks --- base excitation --- artificial intelligence --- Multi-Gene Genetic Programming --- computational fluid dynamics --- finite volume method --- JSSP --- CMOSA --- CMOTA --- chaotic perturbation --- fixed point arithmetic --- FP16 --- pseudo random number generator --- incorporation of preferences --- multi-criteria classification --- decision-making process --- multi-objective evolutionary optimization --- outranking relationships --- decision maker profile --- profile assessment --- region of interest approximation --- optimization using preferences --- hybrid evolutionary approach --- forecasting --- Convolutional Neural Network --- LSTM --- COVID-19 --- deep learning --- trust region methods --- multiobjective descent --- derivative-free optimization --- radial basis functions --- fully linear models --- decision making process --- cognitive tasks --- recommender system --- project portfolio selection problem --- usability evaluation --- multi-objective portfolio optimization problem --- trapezoidal fuzzy numbers --- density estimators --- steady state algorithms --- protein structure prediction --- Hybrid Simulated Annealing --- Template-Based Modeling --- structural biology --- Metropolis --- optimization --- linear programming --- energy central
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