An Intelligent Trading System with Fuzzy Rules and Fuzzy Capital Management

Naranjo, Rodrigo; Meco, Albert; Arroyo, Javier; Santos, Matilde

doi:10.1002/int.21734

Cited by 15 publications

(5 citation statements)

References 32 publications

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“…Rahamneh et al (2010) makes the general case, in comparison to NN, for how fuzzy logic can be used for stock market forecasting. Naranjo et al (2015) shows how fuzzy logic can be used to filter and determine suitable trading signals. Soto et al (2019) shows the power of some of the newer fuzzy logic models, including modern variations of the ANFIS (Adaptive Network-based Fuzzy Inference System) NN approach originally developed by Jang (1993).…”

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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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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“…The control limits u min and u max of the model (7) are defined as (8). Due to the proposed rules only consider the options when the probabilities are 1, that is, that we are sure of knowing the next pattern, the real u k value is obtained by the Sugeno method (6).…”

Section: Trading Decision Based On Fuzzy Logicmentioning

confidence: 99%

“…The complexity in the implementation of these algorithms makes it necessary a high computing capacity to perform on-line trading. In [8], a trading system where fuzzy logic is applied for defining the trading rules and managing the capital to invest. The fuzzy trading system is tested in the two markets (NASDAQ100 and EUROSTOXX) where the main conclusion suggest that the use of fuzzy logic is a good tool for the capital management.…”

Section: Introductionmentioning

confidence: 99%

Investment Portfolio Trading based on Markov Chain and Fuzzy Logic

Ruíz-Cruz

Diaz-Gonzalez

2018

2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI)

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In the present paper, a trading strategy is proposed for a portfolio composed of shares in the stock exchange. The proposed strategy is based mainly on three blocks: 1) a K-means clustering algorithm is used to determine and learn the internal hidden patterns in the time series of stock market prices, 2) a pattern predictor is performed based on a simple Markov chain, and 3) a fuzzy inference system take the decision to trade based on the estimation. The fuzzy inference system is composed by the rules provided by a expert trader. The performance of the trading algorithm is validated through simulations using real prices of the Mexican stock exchange.

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“…The time-window selection is based on claims from Rechenthin and Street (2013), who state that the stock price typically broke the price reversal pattern due to bid-ask bounce after 5 to 10 seconds, and that traces of predictability existed up to 30 minutes, beyond which markets became efficient. Recent studies (Gradojevic and Gençay, 2013;Vella and Ng, 2014a;Naranjo et al, 2015) show the effectiveness of combining moving average signals with fuzzy logic to capture trend information. In this study we use 1 minute stock prices and define the expected mean return, y, at time t, as…”

Section: High Frequency Data and Technical Indicatorsmentioning

confidence: 99%

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Vella

2016

Expert Systems with Applications

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Highlights• We investigate the viability of Type-2 fuzzy systems in high frequency trading.• We propose Type-2 models based on a generalisation of the popular ANFIS model.• Type-2 models score significant risk adjusted performance improvements over Type-1.• Benefits of Type-2 models increase with higher trading frequencies. AbstractIn this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT.

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An Intelligent Trading System with Fuzzy Rules and Fuzzy Capital Management

Cited by 15 publications

References 32 publications

Machine learning in finance: A topic modeling approach

Machine learning in finance: A topic modeling approach

Investment Portfolio Trading based on Markov Chain and Fuzzy Logic

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

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