Fuzzy clustering rule-based expert system for stock price movement prediction

Shakeri, Behnoush; Zarandi, M. H. Fazel; Tarimoradi, Mosahar; Turksan, I.B.

doi:10.1109/nafips-wconsc.2015.7284198

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Shakeri et al proposed an expert system based on fuzzy rules. Its applicability to the daily transactions of speculators and traders in the stock market despite the uncertainty and ambiguity of the environmental parameters [ 30 ]. Gray-box models Gray-box models construct the model by model mechanism and optimize the model by using data samples, which guarantees the accuracy of the model [ 5 , 14 ].…”

Section: Literature Reviewmentioning

confidence: 99%

An interpretable model for stock price movement prediction based on the hierarchical belief rule base

Yin,

Zhang,

et al. 2023

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

An interpretable model for stock price movement prediction based on the hierarchical belief rule base

Yin,

Zhang,

et al. 2023

Heliyon

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“…SMA50, EMA20, EMA50, EMA200, UpperBB, LowerBB, RSI, MFI, ATR, Force_Index, MACD, MACD_SL, ADX, OBV, and OBV_EMA [29,30,31]. A total of 22 features have been considered for analysis.…”

Section: Model Architecture and Hyper Parametersmentioning

confidence: 99%

Aggregation and Hybrid Approaches Using Bpso and Bgwo for Feature Selection for Indian Stock Index Price Prediction Using Gru

Gupta,

Singh Jadon

2023

IJEAST

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The stock exchange is a significant component of the economy, and forecasting its development is essential. Several deep-learning time series models based on RNN and its variants are used to forecast the stock market, but their accuracy still needs to be improved. Optimization strategies, such as GA, PSO, GWO, and others, have been used to enhance the reliability of these models. In this proposed paper, we have used the Binary GWO and Binary PSO algorithms to optimize the input characteristics of a two-layer GRU model for forecasting Indian stock index price. To increase the accuracy of these models, we have suggested two distinct approaches: Aggregation Method and the Hybrid method. In the 10- year time frame, the data indicated that the aggregation approach produced superior accuracy than standard Binary GWO and Binary PSO independently. Although the suggested hybrid method did not out perform individual Binary PSO methods in terms of accuracy, it performed better than the Binary GWO model in a 10- year time frame. Moreover, it showed promise for future improvement. The proposed approaches have the potential to make essential contributions to the index of stock price forecasting, particularly in the Indian market.

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“…While Machine learning and AI models have been applied in the financial domain for predicting stock prices [2], but there is a lack of quality literature and research in the application of AI/ML techniques for stock portfolio selection [3]. Decision Support Systems (DSS) to make financial decisions are based on many techniques like Mean Variance (MV) Markowitz method [4], Fuzzy logic based stock selection methods [5], Data Mining based Evolutionary systems [6], Fuzzy Clustering Rule-Based Expert System for Stock Price Movement Prediction [7], Fuzzy Inference systems based on Technical Indicators, Stock Selection based on knowledge discovery rules [8], all these techniques are depended on technical indicators or market statistics which are error prone for long term portfolio selection. Financial DSS based on Fundamental analysis techniques [9] have been proven to be more efficient and accurate [10] when portfolio selection pertains to long term time frame.…”

Section: Introductionmentioning

confidence: 99%

Decision Support System for Stock Portfolio Selection Using Artificial Intelligence and Machine Learning

Patalay¹,

Bandlamudi²

2021

ISI

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Investing in stock market requires in-depth knowledge of finance and stock market dynamics. Stock Portfolio Selection and management involve complex financial analysis and decision making policies. An Individual investor seeking to invest in stock portfolio is need of a support system which can guide him to create a portfolio of stocks based on sound financial analysis. In this paper the authors designed a Financial Decision Support System (DSS) for creating and managing a portfolio of stock which is based on Artificial Intelligence (AI) and Machine learning (ML) and combining the traditional approach of mathematical models. We believe this a unique approach to perform stock portfolio, the results of this study are quite encouraging as the stock portfolios created by the DSS are based on strong financial health indices which in turn are giving Return on Investment (ROI) in the range of more than 11% in the short term and more than 61% in the long term, therefore beating the market index by a factor of 15%. This system has the potential to help millions of Individual Investors who can make their financial decisions on stocks and may eventually contribute to a more efficient financial system.

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Fuzzy clustering rule-based expert system for stock price movement prediction

Cited by 5 publications

References 14 publications

An interpretable model for stock price movement prediction based on the hierarchical belief rule base

An interpretable model for stock price movement prediction based on the hierarchical belief rule base

Aggregation and Hybrid Approaches Using Bpso and Bgwo for Feature Selection for Indian Stock Index Price Prediction Using Gru

Decision Support System for Stock Portfolio Selection Using Artificial Intelligence and Machine Learning

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