The volume Computational Finance 1999 contains a selection of the papers presented at Computational Finance '99 at the Stern School of Business, New York Univ. in January 1999. This conference is an annual refereed meeting, which was previosly called "Neural Networks in the Capital Markets."In this paper we show how a Bayesian network can be used to represent a traditional financial model of portfolio return. Then we show how expert subjective judgement can be included in the Bayesian network model. The output of the model is the posterior marginal probability distribution of the portfolio return. This posterior return distribution can be used to obtain expected return, return variance, and value-at-risk
According to the modern portfolio theory, the direction of the relationship between the securities i...
The article provides a short overview of methods for constructing mathematical models in the form of...
Bayesian networks are a popular mechanism for dealing with uncertainty in complex situations. They a...
This thesis explores the use of Bayesian networks to develop “views” for a Black-Litterman asset all...
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Portfolio managers and investors have to face the perils of the markets and the trade-off between r...
We live in an era where every human entity, from a simple citizen to the head of an entity as large ...
This thesis concerns portfolio theory from a Bayesian perspective and it includes two papers related...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
An organization’s strategic objectives are accomplished through portfolios. However, the mater...
Bayesian Networks are probabilistic models built from conditional probability tables that relate two...
We propose a systematic factor analysis approach using the Bayesian Network (BN) framework by taking...
We present here a Bayesian framework of risk perception. This framework encompasses plausibility jud...
We propose a novel family of Bayesian learning algorithms for online portfolio selection that overco...
Bayesian networks are a means to study data. A Bayesian network gives structure to data by creating ...
According to the modern portfolio theory, the direction of the relationship between the securities i...
The article provides a short overview of methods for constructing mathematical models in the form of...
Bayesian networks are a popular mechanism for dealing with uncertainty in complex situations. They a...
This thesis explores the use of Bayesian networks to develop “views” for a Black-Litterman asset all...
.......................................................................................................
Portfolio managers and investors have to face the perils of the markets and the trade-off between r...
We live in an era where every human entity, from a simple citizen to the head of an entity as large ...
This thesis concerns portfolio theory from a Bayesian perspective and it includes two papers related...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
An organization’s strategic objectives are accomplished through portfolios. However, the mater...
Bayesian Networks are probabilistic models built from conditional probability tables that relate two...
We propose a systematic factor analysis approach using the Bayesian Network (BN) framework by taking...
We present here a Bayesian framework of risk perception. This framework encompasses plausibility jud...
We propose a novel family of Bayesian learning algorithms for online portfolio selection that overco...
Bayesian networks are a means to study data. A Bayesian network gives structure to data by creating ...
According to the modern portfolio theory, the direction of the relationship between the securities i...
The article provides a short overview of methods for constructing mathematical models in the form of...
Bayesian networks are a popular mechanism for dealing with uncertainty in complex situations. They a...