A portfolio optimization model using Genetic Network Programming with control nodes

Chen, Yan; Ohkawa, Etsushi; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

doi:10.1016/j.eswa.2009.02.049

Cited by 25 publications

(6 citation statements)

References 24 publications

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“…Specially, the hybrid model reduces the running time significantly for large size problems. In the same year, [203] proposed a multibrands portfolio optimization model based on genetic network programming (GNP) with control nodes. Their optimization model which consists technical analysis rules, are trained to generate trading advice.…”

Section: Portfolio Managementmentioning

confidence: 99%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

432

192

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Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

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Section: Portfolio Managementmentioning

confidence: 99%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

432

192

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“…We can see that some GP-related stemmed terms appear in the topic keywords, such as 'evolutionari', but in general, the terms are not strong descriptions of the studies clustered in this topic. Chen et al (2009) introduce the potential for Genetic Network Programming, an ML-capable version of GP, in finance. Their forecasting, based on GP applied to a range of technical stock market indicators in the Japanese market, is shown to outperform more common alternative approaches.…”

Section: Nomentioning

confidence: 99%

Machine learning in finance: A topic modeling approach

Aziz

Dowling

Hammami

et al. 2021

Euro Fin Management

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We identify the core topics of research applying machine learning to finance. We use a probabilistic topic modeling approach to make sense of this diverse body of research spanning across multiple disciplines. Through a latent Dirichlet allocation topic modeling technique, we extract 15 coherent research topics that are the focus of 5942 academic studies from 1990 to 2020. We find that these topics can be grouped into four categories: Priceforecasting techniques, financial markets analysis, risk forecasting and financial perspectives. We first describe and structure these topics and then further show how the topic focus has evolved over the last three decades. A notable trend we find is the emergence of text-based machine learning, for example, for sentiment analysis, in recent years. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic EUROPEAN FINANCIAL MANAGEMENTWe thank John A. Doukas, the editor, and an anonymous referee of European Financial Management as the study has enormously benefited from their comments. We also thank Muhammad Farooq Ahmad and participants of IFABS 2019, Angers France for their valuable comments. Saqib Aziz and Michael Dowling acknowledge financial assistance from the B<>COM project: Prospect 2030. The views expressed in this article are those of the authors and all errors are our own. modeling of topics for deep comprehension of a body of literature.

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“…In order to fully test the effectiveness of GNP-URA, the stock trading problem [21] is selected for simulations, which is quite dynamic and challenging to agent-based systems. Generally speaking, there are two types of analysis for this problem: fundamental analysis is to analyze stock prices using the financial information of each company and economic trend, etc; technical analysis studies the past movement of stock prices and predicts their future movement or prices.…”

Section: Simulation On the Stock Trading Problemmentioning

confidence: 99%

Rule pool updating through Sarsa‐learning to improve adaptability in changing environments

Wang

Mabu

Hirasawa

2014

IEEJ Transactions Elec Engng

Self Cite

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Genetic network programming (GNP) is a new evolutionary algorithm using the directed graph as its chromosome. A GNPbased rule accumulation (GNP-RA) method was proposed previously for multiagent control. However, in changing environments where new situations appear frequently, the old rules in the rule pool become incompetent for guiding the agent's actions, and therefore updating them becomes necessary. This paper proposes a more robust rule-based model which can adapt to the environment changes. In order to realize this, Sarsa-learning is used as a tool to update the rules to cope with the unexperienced situations in new environments. Furthermore, Sarsa-learning helps to generate better rules by selecting really important judgments and actions during training. In addition, the ε-greedy policy of Sarsa enables GNP-RA to explore the solutions space sufficiently, generating more rules. Simulations on the tile world problem show that the proposed method outperforms the previous ones, namely GP and reinforcement learning.

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A portfolio optimization model using Genetic Network Programming with control nodes

Cited by 25 publications

References 24 publications

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Machine learning in finance: A topic modeling approach

Rule pool updating through Sarsa‐learning to improve adaptability in changing environments

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