Genetic Algorithms and Genetic Programming in Computational Finance: An Overview of the Book

Chen, Shu‐Heng

doi:10.1007/978-1-4615-0835-9_1

Cited by 61 publications

(76 citation statements)

References 24 publications

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“…(Chen and Wang, 2003). All of these techniques have been applied to modeling agents in the economic literature; see Chen (2002a), Chen (2002b), and Brenner (2006) for a glossary of them.…”

Section: Discussionmentioning

confidence: 99%

On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach

Chen

Tai

2006

Comput Econ

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Abstract. Agent-based Methodology (ABM) is becoming indispensable for the interdisciplinary study of social and economic complex adaptive systems. The essence of ABM lies in the notion of autonomous agents whose behavior may evolve endogenously and can generate and mimic the corresponding complex system dynamics that the ABM is studying. Over the past decade, many Computational Intelligence (CI) methods have been applied to the design of autonomous agents, in particular, their adaptive schemes. This design issue is non-trivial since the chosen adaptive schemes usually have a profound impact on the generated system dynamics. Robert Lucas, one of the most influential modern economic theorists, has suggested using laboratories with human agents, also known as Experimental Economics, to help solve the selection issue. While this is a promising approach, laboratories used in the current experimental economics are not computationally equipped to meet the demands of the selection task. This paper attempts to materialize Lucas' suggestion by establishing a laboratory where human subjects are equipped with the computational power that satisfies the computational equivalence condition.

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“…(Chen and Wang, 2003). All of these techniques have been applied to modeling agents in the economic literature; see Chen (2002a), Chen (2002b), and Brenner (2006) for a glossary of them.…”

Section: Discussionmentioning

confidence: 99%

On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach

Chen

Tai

2006

Comput Econ

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“…Neural networks and genetic programming algorithms are emergent technologies with an increasing number of real-world applications including finance (Lisboa and Vellido (2000) and Chen (2002)). However their numerous limitations and contradictory empirical evidence around their forecasting power are often create scepticism about their use among practitioners.…”

Section: Introductionmentioning

confidence: 99%

Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data

Sermpinis

Laws

Dunis³

2013

The European Journal of Finance

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The motivation for this paper is to investigate the use of a promising class of neural network models, Psi Sigma, when applied to the task of forecasting the one day ahead Value at Risk (VaR) of the oil Brent and gold bullion series using Open-High-Low-Close data. In order to benchmark our results we also consider VaR forecasts from two different neural network designs, the Multilayer Perceptron (MLP) and the Recurrent Neural Network (RNN), a genetic programming algorithm (GP), an Extreme Value Theory model (EVT) along with some traditional techniques such as an ARMA-GJR (1,1) model and the Riskmetrics volatility. The forecasting performance of all models for computing the VaR of the Brent oil and the gold bullion is examined over the period September 2001 to August 2010 using the last year and half of data for out-of-sample testing. The evaluation of our models is done by using a series of backtesting algorithms such as the Christoffersen tests, the violation ratio and our proposed loss function that considers not only the number of violations but also their magnitude.Our results show that the Psi Sigma outperforms all other models in forecasting the VaR of gold and oil at both the 5% and 1% confidence levels, providing an accurate number of independent violations with small magnitude.

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“…There are numerous works that attempt to forecast the future price movements of a stock; several examples can be found in [2], [3]. EDDIE is a well-established genetic programming financial forecasting tool, which has been found to outperform traditional decision rule induction methods, such as C4.5, and return high accuracy results over different international stock markets [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Using Attribute Construction to Improve the Predictability of a GP Financial Forecasting Algorithm

Kampouridis

Otero

2013

2013 Conference on Technologies and Applications of Artificial Intelligence

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Abstract-Financial forecasting is an important area in computational finance. EDDIE 8 is an established Genetic Programming financial forecasting algorithm, which has successfully been applied to a number of international datasets. The purpose of this paper is to further increase the algorithm's predictive performance, by improving its data space representation. In order to achieve this, we use attribute construction to create new (high-level) attributes from the original (low-level) attributes. To examine the effectiveness of the above method, we test the extended EDDIE's predictive performance across 25 datasets and compare it to the performance of two previous EDDIE algorithms. Results show that the introduction of attribute construction benefits the algorithm, allowing EDDIE to explore the use of new attributes to improve its predictive accuracy.

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Genetic Algorithms and Genetic Programming in Computational Finance: An Overview of the Book

Cited by 61 publications

References 24 publications

On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach

On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach

Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data

Using Attribute Construction to Improve the Predictability of a GP Financial Forecasting Algorithm

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