Andreas S. Weigend

"Every time we Google something, Facebook someone, Uber somewhere, or even just turn on a light, we create data that businesses collect and use to make decisions about us. In many ways this has improved our lives, yet, we as individuals do not benefit from this wealth of data as much as we could. Moreover, whether it is a bank evaluating our credit worthiness, an insurance company determining our risk level, or a potential employer deciding whether we get a job, it is likely that this data will be used against us rather than for us. In Data for the People, Andreas Weigend draws on his years as a consultant for commerce, education, healthcare, travel and finance companies to outline how Big Data can work better for all of us. As of today, how much we benefit from Big Data depends on how closely the interests of big companies align with our own. Too often, outdated standards of control and privacy force us into unfair contracts with data companies, but it doesn't have to be this way. Weigend makes a powerful argument that we need to take control of how our data is used to actually make it work for us. Only then can we the people get back more from Big Data than we give it. Big Data is here to stay. Now is the time to find out how we can be empowered by it." -- Publisher's description

how to make our post-privacy economy work for you

Data for the people

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.

Forecasting The Future And Understanding The Past

Time Series Prediction

Andrew W. Lo

Yaser S. Abu-Mostafa

Blake LeBaron

This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. Computational finance, an exciting new cross-disciplinary research area, draws extensively on the tools and techniques of computer science, statistics, information systems, and financial economics. This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. These methods are applied to a wide range of problems in finance, including risk management, asset allocation, style analysis, dynamic trading and hedging, forecasting, and option pricing. The book is based on the sixth annual international conference Computational Finance 1999, held at New York University's Stern School of Business.

Computational Finance 1999

Yaser Abu-Mostafa

A.-Paul N. Refenes

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. Contents:Decision Technologies:Optimization of Trading Systems and Portfolios (J E Moody & L Z Wu)Nonlinear versus Linear Techniques for Selecting Individual Stocks (S Mahfoud et al.)Soft Prediction of Stock Behavior (Y Baram)Risk Management:Validating a Connectionist Model of Financial Diagnosis (P E Pedersen)Neural Networks for Risk Analysis in Stock Price Forecasts (M Klenin)Optimizing Neural Network Classifiers for Bond Rating (A N Skurikhin & A J Surkan)Statistical Learning for Financial Problems:Forecasting Volatility Mispricing (P J Bolland & A N Burgess)Intraday Modeling of the Term Structure of Interest Rates (J T Connor et al.)Modeling of Nonstationary Financial Time Series by Nonparametric Data Selection (G Deco et al.)Foreign Exchange Trading and Analysis:Principal Components Analysis for Modeling Multi-Currency Porfolios (J Utans et al.)Quantization Effects and Cluster Analysis on Foreign Exchange Rates (W M Leung et al.)A Computer Simulation of Currency Market Participantsand other papers Readership: Practitioners and academics who are interested in developments and applications of data mining to finance. keywords:

Andreas S. Weigend

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