Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets
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DRL trading agents build a multi-factor model and provide algorithmic trading strategies, which are difficult for human traders.
“Taking many complex financial factors into account, DRL trading agents build a multi-factor model and provide algorithmic trading strategies, which are difficult for human traders [3, 47, 24, 22].”
From FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance (2020) · cites this paper as [3]
A DRL trading agent builds a multi-factor model to trade automatically, which is difficult for human traders to accomplish.
“Incorporating many financial factors, as shown in Fig. 1, a DRL trading agent builds a multi-factor model to trade automatically, which is difficult for human traders to accomplish [4, 53].”
From FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance (2021) · cites this paper as [4]
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Sorted by citations
Deep reinforcement learning for automated stock trading: an ensemble strategy
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“Section 5 gives the empirical results of all the models considered and investigates the possibility of improving their performance with the introduction of a sophisticated trading strategy while section 6 provides some concluding remarks.”
BackgroundAn intelligent short term stock trading fuzzy system for assisting investors in portfolio management
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“In this study, the Sharpe Ratio is used in the training period as the fitness, similar to previous studies [2, 10, 41].”
MethodHYBRID FUZZY NEURAL NETWORK TO PREDICT PRICE DIRECTION IN THE GERMAN DAX-30 INDEX
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“Among the pioneering studies some of the most outstanding are Leung, Daouk, & Chen (2000), Kim and Han (2000), Kim (2003), Bekiros (2010) and Kara, Boyacioglu, and Baykan (2011).”
BackgroundInfluentialFinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
Liu, X.-Y., Yang, H., Chen, Q., Zhang, R., Yang, L., Xiao, B., & Wang, C. (2025) · doi:10.2139/ssrn.3737859 · Cited by 51 · see citing works
“Taking many complex financial factors into account, DRL trading agents build a multi-factor model and provide algorithmic trading strategies, which are difficult for human traders [3, 47, 24, 22].”
Revealing Pairs-trading opportunities with long short-term memory networks
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“These factors may include macroeconomic factors, a trader’s psychology, fundamental elements, market sentiment, and so forth [63,64].”
BackgroundAutomated Trading Point Forecasting Based on Bicluster Mining and Fuzzy Inference
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“[16] S. D. Bekiros, “Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets,” Eur.”
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“Among them, Bekiros (2010a) introduced a hybrid neuro-fuzzy system that uses technical analysis in order to predict the direction of the market on the next trading day, for ten stock indices from various areas of the world.”
MethodEmpirical Analysis of Automated Stock Trading Using Deep Reinforcement Learning
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“DRL trading agents can build multi-factor models by considering various financial factors, which makes it possible to perform algorithmic trading strategies that are difficult for human traders [19], [20], [21], [22].”
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Fuzzy and Grey Forecasting Techniques and Their Applications in Production Systems
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“Bekiros (2010) introduced a hybrid neuro-fuzzy system for decision-making and trading under various uncertainty.”
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“Similarly, Bekiros (2010) proposed a hybrid neuro-fuzzy system for predicting the direction of the market on the next trading day.”
Background¿Han sido los mercados bursátiles eficientes informacionalmente?
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Huo, Y., Jin, M., & You, S. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2493 · Cited by 3 · see citing works
Iraqi Stock Market Prediction using Proposed Model of Convolution Neural Network
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“Forecasting the direction of a stock's price is critical for value investors (Bekiros, 2010).”
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A Parameterized Intuitionistic Type-2 Fuzzy Inference System with Particle Swarm Optimization
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“For instance, the time-series financial data (can refer to [24,25]) may also be examined by using proposed IT-2 FIS in future research.”
BackgroundZhang, C., & Pan, H. (2014) · doi:10.3968/j.mse.1913035x20140801.4274 · Cited by 2 · see citing works
Applications of Modern Mathematics in Economics and Finance
Dymowa, L. (2011) · Intelligent systems reference library · doi:10.1007/978-3-642-17719-4_2 · Cited by 1 · see citing works
The Future of Fuzzy Sets in Finance: New Challenges in Machine Learning and Explainable AI
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Automated trading based on biclustering mining and fuzzy modeling
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“…[23] [24] [25] [26] [27] [28] [29] and intelligent computing, more and more technique such as support vector machine (SVM), artificial neural networks (ANN), fuzzy modelˈ genetic algorithm and particle swarm optimization (PSO) are used for forecasting stock trends [30] [31] [32] [33] [34] [35].”
MethodMULTIPLES AND STOCK PRICE, NEW APPROACH FOR RELATIVE VALUATION THROUGH NEURAL NETWORK
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A Model of Financial Bubbles and Drawdowns with Non-local Behavioral Self-ReferencingPreprint
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Deep Reinforcement Learning for Automated of Asian Stocks Trading
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Evaluating Machine Learning and Deep Learning Models for Accurate Stock Price Prediction
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“The study [5] provides evidence of the potential of hybrid neurofuzzy systems in financial market prediction, particularly in complex dynamic time series.”
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Trading Policy Optimization Using Deep Reinforcement Learning for the Dhaka Stock Exchange
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“This has proved especially true for new markets, which exhibit high volatility and inefficiencies for structural reasons [1].”
Ensemble Deep Reinforcement Learning (DRL) for Adaptive Crypto Trading with Risk Control
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“Fischer’s survey [21] identified RL’s risk management shortfalls, and Bekiros [22] and Zhang et al. [23] adapted techniques, albeit not in DRL applications.”
(2018) · arXiv (Cornell University) · Cited by 74 · see citing works
- Authors
- Bekiros, S. D.
- Affiliations
- European University Institute (IT)
- Corresponding author
- Bekiros, Stelios D.
- ORCID
- Bekiros, Stelios D.
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2010-04
- Volume
- 202
- Issue
- 1
- Pages
- 285-293
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W1997231007
- Cited by
- 53
- References
- 85
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 6 · 2023: 4 · 2022: 3 · 2021: 7 · 2020: 5 · 2019: 4 · 2018: 2 · 2017: 2 · 2016: 2 · 2015: 2 · 2014: 4 · 2013: 2 · 2012: 3
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Journal of Operational Research (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Cadmus - EUI Research Repository (European University Institute) (submittedVersion, public-domain)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- fuzzy neural network, bounded rationality, technical trading, stock market prediction, behavioral finance, emerging stock markets, transaction costs, recurrent neural networks
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