An effective approach for predicting daily stock trading decisions using fuzzy inference systems

Venugopal, R.; Veeramani, C.; Muruganandan, S.

doi:10.1007/s00500-023-09383-3

Soft Comput

2023

DOI: 10.1007/s00500-023-09383-3

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An effective approach for predicting daily stock trading decisions using fuzzy inference systems

R. Venugopal,

C. Veeramani,

S. Muruganandan

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2024

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Cited by 2 publications

References 34 publications

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Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

Orozco-Castañeda,

Alzate-Vargas,

Bedoya-Valencia

2024

Risks

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This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns.

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Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

Orozco-Castañeda,

Alzate-Vargas,

Bedoya-Valencia

2024

Risks

View full text Add to dashboard Cite

show abstract

Online Portfolio Based on Trend Trading Strategy Considering Investor Sentiment Using Text Analysis

Zeng,

Xu,

Zhong

2024

International Journal of Fuzzy System Applications

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Intelligent online portfolios have become an important research topic in the field of quantitative finance. This paper proposes an online portfolio based on trend trading strategy using fuzzy logic technology analysis method and considering investor sentiment. Firstly, the paper uses SVM classification algorithm to analyze stock comment text data in online forums and constructs investor sentiment indicators. Secondly, the paper transforms the heuristic algorithm of technical trading into corresponding fuzzy IF-THEN rules and combines them into a fuzzy investment decision system. Thirdly, the paper constructs a new online portfolio based on trend trading strategy. Finally, the paper uses the text data of investor reviews and stock trading data on the internet for empirical analysis to illustrate the effectiveness of the online portfolio strategy constructed. The results indicate that online portfolios constructed based on trend trading strategies can achieve higher performance than benchmark strategies while considering transaction costs.

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An effective approach for predicting daily stock trading decisions using fuzzy inference systems

Cited by 2 publications

References 34 publications

Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

Online Portfolio Based on Trend Trading Strategy Considering Investor Sentiment Using Text Analysis

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