Andreas S. Weigend – författare

The book is a summary of a time series forecasting competition that was held a number of years ago. It aims to provide a snapshot of the range of new techniques that are used to study time series, both as a reference for experts and as a guide for novices.

The book is a summary of a time series forecasting competition that was held a number of years ago. It aims to provide a snapshot of the range of new techniques that are used to study time series, both as a reference for experts and as a guide for novices.

The result of the 1993 Connectionist Models Summer School, the papers in this volume exemplify the tremendous breadth and depth of research underway in the field of neural networks. Although the slant of the summer school has always leaned toward cognitive science and artificial intelligence, the diverse scientific backgrounds and research interests of accepted students and invited faculty reflect the broad spectrum of areas contributing to neural networks, including artificial intelligence, cognitive science, computer science, engineering, mathematics, neuroscience, and physics. Providing an accurate picture of the state of the art in this fast-moving field, the proceedings of this intense two-week program of lectures, workshops, and informal discussions contains timely and high-quality work by the best and the brightest in the neural networks field.

One of the most exciting applications of neural networks in recent times has been to improve investment performance in the financial markets. Neural networks can be harnessed for this task as they bear many mathematical similarities to the nonlinear econometric models used to study the microstructure of the financial markets.Since 1993, this annual conference has been bringing together investment managers, neural network researchers, statisticians, econometricians, and computer scientists for an insight into the state of the art in this area. The proceedings of the 1995 meeting will give investment managers, institutional investors and analysts a comprehensive look at the most profitable applications of this fascinating new technology in today's financial markets.

One of the most exciting applications of neural networks in recent times has been to improve investment performance in the financial markets. Neural networks can be harnessed for this task as they bear many mathematical similarities to the nonlinear econometric models used to study the microstructure of the financial markets.Since 1993, this annual conference has been bringing together investment managers, neural network researchers, statisticians, econometricians, and computer scientists for an insight into the state of the art in this area. The proceedings of the 1995 meeting will give investment managers, institutional investors and analysts a comprehensive look at the most profitable applications of this fascinating new technology in today's financial markets.

This volume selects the best contributions from the Fourth International Conference on Neural Networks in the Capital Markets (NNCM). The conference brought together academics from several disciplines with strategists and decision makers from the financial industries.The various chapters present and compare new techniques from many areas including data mining, information systems, machine learning, and statistical artificial intelligence. The volume focuses on evaluating their usefulness for problems in computational finance and financial engineering.Applications — risk management; asset allocation; dynamic trading and hedging; forecasting; trading cost control. Markets — equity; foreign exchange; bond; commodity; derivatives; Approaches — data mining; statistical AI; machine learning; Monte Carlo simulation; bootstrapping; genetic algorithms; nonparametric methods; fuzzy logic.The chapters emphasizes in-depth and comparative evaluation with established approaches.