Papers by Francesco Parrella
Many approaches for obtaining systems with intelligent behavior are based on components that lear... more Many approaches for obtaining systems with intelligent behavior are based on components that learn automatically from previous experience. The development of these learning techniques is the objective of the area of research known as machine learning. During the last decade, researchers have produced numerous and outstanding advances in this area, boosted by the successful application of machine learning techniques. This thesis presents one of this techniques, an online version of the algorithm for training the support vector machine for regression and also how it has been extended in order to be more flexible for the hyper parameter estimation. Furthermore the algorithm has been compared with a batch implementation
Data Mining for Algorithmic Asset Management
Springer eBooks, Oct 2, 2008
ABSTRACT Statistical arbitrage refers to a class of algorithmic trading systems implementing data... more ABSTRACT Statistical arbitrage refers to a class of algorithmic trading systems implementing data mining strategies. In this chapter we describe a computational framework for statistical arbitrage based on support vector regression. The algorithm learns the fair price of the security under management by minimining a regularized ε-insensitive loss function in an on-line fashion, using the most recent market information acquired by means of streaming financial data. The difficult issue of adaptive learning in non-stationary environments is addressed by adopting an ensemble learning approach, where a meta-algorithm strategically combines the opinion of a pool of experts. Experimental results based on nearly seven years of historical data for the iShare S&P 500 ETF demonstrate that satisfactory risk-adjusted returns can be achieved by the data mining system even after transaction costs.
Springer eBooks, Jan 7, 2009
Support vector regression (SVR) is an established non-linear regression technique that has been s... more Support vector regression (SVR) is an established non-linear regression technique that has been successfully applied 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 Don Jones EuroStoxx 50.
Data mining for algorithmic asset management: an ensemble learning approach
onlinesvr.altervista.org
Algorithmic asset management refers to the use of expert systems that enter trading orders withou... more Algorithmic asset management refers to the use of expert systems that enter trading orders without any user intervention. In particular, market-neutral sys-tems aim at generating positive returns regardless of underlying market conditions. In this chapter we describe an ...
Many approaches for obtaining systems with intelligent behavior are based on components that lear... more Many approaches for obtaining systems with intelligent behavior are based on components that learn automatically from previous experience. The development of these learning techniques is the objective of the area of research known as machine learning. During the last decade, researchers have produced numerous and outstanding advances in this area, boosted by the successful application of machine learning techniques. This thesis presents one of this techniques, an online version of the algorithm for training the support vector machine for regression and also how it has been extended in order to be more flexible for the hyper parameter estimation. Furthermore the algorithm has been compared with a batch implementation and tested in a real application at the Department of Information, Systematics and Telematics in Genoa.
Statistical arbitrage refers to a class of algorithmic trading systems implementing data mining s... more Statistical arbitrage refers to a class of algorithmic trading systems implementing data mining strategies. In this chapter we describe a computational framework for statistical arbitrage based on support vector regression. The algorithm learns the fair price of the security under management by minimining a regularized ε-insensitive loss function in an on-line fashion, using the most recent market information acquired by means of streaming financial data. The difficult issue of adaptive learning in non-stationary environments is addressed by adopting an ensemble learning approach, where a meta-algorithm strategically combines the opinion of a pool of experts. Experimental results based on nearly seven years of historical data for the iShare S&P 500 ETF demonstrate that satisfactory risk-adjusted returns can be achieved by the data mining system even after transaction costs.
Support vector regression (SVR) is an established non-linear regression technique that has been a... more 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.
Data Mining for Algorithmic Asset Management
Data Mining for Business Applications, 2009
Statistical arbitrage refers to a class of algorithmic trading systems implementing data mining s... more Statistical arbitrage refers to a class of algorithmic trading systems implementing data mining strategies. In this chapter we describe a computational framework for statistical arbitrage based on support vector regression. The algorithm learns the fair price of the security ...
Uploads
Papers by Francesco Parrella