Add to ORKGAbstract. Gated Bayesian networks (GBNs) are a recently introduced extension of Bayesian networks that aims to model dynamical systems consisting of several distinct phases. In this paper, we present an algo-rithm for semi-automatic learning of GBNs. We use the algorithm to learn GBNs that output buy and sell decisions for use in algorithmic trading systems. We show how using the learnt GBNs can substantially lower risks towards invested capital, while at the same time generating similar or better rewards, compared to the benchmark investment strat-egy buy-and-hold
The volume Computational Finance 1999 contains a selection of the papers presented at Computational ...
In this chapter we propose a financial trading system whose trading strategy is developed by means o...
We propose a novel family of Bayesian learning algorithms for online portfolio selection that overco...
Gated Bayesian networks (GBNs) are a recently introduced extension of Bayesian networks that aims to...
Gated Bayesian networks (GBNs) are an extension of Bayesian networks that aim to model systems that ...
Bayesian networks have grown to become a dominant type of model within the domain of probabilistic g...
Portfolio managers and investors have to face the perils of the markets and the trade-off between r...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
Can we train a stock trading bot that can take decisions in high-entropy envi- ronments like stock m...
This paper describes a new greedy Bayesian search algorithm GBPS and a new combined algorithm PCGBP...
In this thesis, we develop machine learning frameworks that are suitable for algorithmic trading, wh...
In this paper, we revisit the parameter learning problem, namely the estimation of model parameters ...
With the help of this book, you'll build smart algorithmic models using machine learning algorithms ...
Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
The volume Computational Finance 1999 contains a selection of the papers presented at Computational ...
In this chapter we propose a financial trading system whose trading strategy is developed by means o...
We propose a novel family of Bayesian learning algorithms for online portfolio selection that overco...
Gated Bayesian networks (GBNs) are a recently introduced extension of Bayesian networks that aims to...
Gated Bayesian networks (GBNs) are an extension of Bayesian networks that aim to model systems that ...
Bayesian networks have grown to become a dominant type of model within the domain of probabilistic g...
Portfolio managers and investors have to face the perils of the markets and the trade-off between r...
We examine a graphical representation of uncertain knowledge called a Bayesian network. The represen...
Can we train a stock trading bot that can take decisions in high-entropy envi- ronments like stock m...
This paper describes a new greedy Bayesian search algorithm GBPS and a new combined algorithm PCGBP...
In this thesis, we develop machine learning frameworks that are suitable for algorithmic trading, wh...
In this paper, we revisit the parameter learning problem, namely the estimation of model parameters ...
With the help of this book, you'll build smart algorithmic models using machine learning algorithms ...
Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
The volume Computational Finance 1999 contains a selection of the papers presented at Computational ...
In this chapter we propose a financial trading system whose trading strategy is developed by means o...
We propose a novel family of Bayesian learning algorithms for online portfolio selection that overco...