An interpretable neuro-fuzzy approach to stock price forecasting

Rajab, Sharifa; Sharma, Vinod

doi:10.1007/s00500-017-2800-7

Cited by 32 publications

(22 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [29] developed a fuzzy logic approach to analyze the sentiments on social media for SMP. Furthermore, several studies have used hybrid fuzzy approaches for SMP [115][116][117].…”

Section: Fuzzy Algorithms (Fa)mentioning

confidence: 99%

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

et al. 2021

View full text Add to dashboard Cite

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.

show abstract

Section: Fuzzy Algorithms (Fa)mentioning

confidence: 99%

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

et al. 2021

View full text Add to dashboard Cite

show abstract

“…A comparison of the complexity of time and space has shown that the proposed method is better than existing methods. Rajab and Sharma [22] focused on the Bombay Stock Exchange, CNX Nifty, and S&P 500 and proposed an effective neurofuzzy model for their prediction.…”

Section: Review Of the Scientific Literaturementioning

confidence: 99%

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Janková

Dostál

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Extensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. However, type-1 fuzzy sets cannot fully capture the uncertainty associated with stock market developments due to their limited descriptiveness. This paper fills a scientific gap and focuses on type-2 fuzzy logic applied to stock markets. Type-2 fuzzy sets may include additional uncertainty resulting from unclear, uncertain, or inaccurate financial data through which model inputs are calculated. Here we propose four methods based on type-2 fuzzy logic, which differ in the level of uncertainty contained in fuzzy sets and compared with the type-1 fuzzy model. The case study aims to create a model to support investment decisions in Exchange-Traded Funds (ETFs) listed on international equity markets. The created models of type-2 fuzzy logic are compared with the classic type-1 fuzzy logic model. Based on the results of the comparison, it can be said that type-2 fuzzy logic with dual fuzzy sets is able to better describe data from financial time series and provides more accurate outputs. The results reflect the capability and effectiveness of the approach proposed in this document. However, the performance of type-2 fuzzy logic models decreases with the inclusion of increasing uncertainty in fuzzy sets. For further research, it would be appropriate to examine the different levels of uncertainty in the input parameters themselves and monitor the performance of such a modified model.

show abstract

“…Rajab and Sharma [5] proposed an interpretable neuro-fuzzy approach to stock price forecasting applied to various exchange series.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting

Vlasenko

Vynokurova

et al. 2019

Data

View full text Add to dashboard Cite

Neuro-fuzzy models have a proven record of successful application in finance. Forecasting future values is a crucial element of successful decision making in trading. In this paper, a novel ensemble neuro-fuzzy model is proposed to overcome limitations and improve the previously successfully applied a five-layer multidimensional Gaussian neuro-fuzzy model and its learning. The proposed solution allows skipping the error-prone hyperparameters selection process and shows better accuracy results in real life financial data.

show abstract

An interpretable neuro-fuzzy approach to stock price forecasting

Cited by 32 publications

References 36 publications

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting

Contact Info

Product

Resources

About