The weighted majority algorithm
Information and Computation
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
ϵ-Descending Support Vector Machines for Financial Time Series Forecasting
Neural Processing Letters
On-Line Support Vector Machine Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate on-line support vector regression
Neural Computation
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
How to Better Use Expert Advice
Machine Learning
An Incremental Learning Strategy for Support Vector Regression
Neural Processing Letters
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Prediction, Learning, and Games
Prediction, Learning, and Games
Research on Hybrid ARIMA and Support Vector Machine Model in Short Term Load Forecasting
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
A hybrid model for exchange rate prediction
Decision Support Systems
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
Data stream mining for market-neutral algorithmic trading
Proceedings of the 2008 ACM symposium on Applied computing
Flexible least squares for temporal data mining and statistical arbitrage
Expert Systems with Applications: An International Journal
Incremental learning of support vector machines by classifier combining
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Support vector regression (SVR) is an established non-linear regression technique that has been applied successfully to a variety of predictive problems arising in computational finance, such as forecasting asset returns and volatilities. In real-time applications with streaming data two major issues that need particular care are the inefficiency of batch-mode learning, and the arduous task of training the learning machine in presence of non-stationary behavior. We tackle these issues in the context of algorithmic trading, where sequential decisions need to be made quickly as new data points arrive, and where the data generating process may change continuously with time. We propose a master algorithm that evolves a pool of on-line SVR experts and learns to trade by dynamically weighting the experts' opinions. We report on risk-adjusted returns generated by the hybrid algorithm for two large exchange-traded funds, the iShare S&P 500 and Dow Jones EuroStoxx 50.