Fuzzy-neural model with hybrid market indicators for stock forecasting

Adebiyi, Ayodele Ariyo; Ayo, C. K.; Otokiti, S. O.

doi:10.1504/ijef.2011.041342

Cited by 15 publications

(8 citation statements)

References 27 publications

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“…Adebiyi et al ( 2012 ) combine technical and fundamental analysis with ANN and provide results suggesting that the combination improves prediction, consistent with the findings of Yao et al ( 1999 ) and later supported by Sezer et al ( 2017 ). However, Namdari and Li ( 2018 ) report mixed results in terms of forecasting ability of an integrated ANN with fundamental and technical analysis models.…”

Section: Literature Reviewsupporting

confidence: 66%

Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets

2023

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This paper enters the ongoing volatility forecasting debate by examining the ability of a wide range of Machine Learning methods (ML), and specifically Artificial Neural Network (ANN) models. The ANN models are compared against traditional econometric models for ten Asian markets using daily data for the time period from 12 September 1994 to 05 March 2018. The empirical results indicate that ML algorithms, across the range of countries, can better approximate dependencies compared to traditional benchmark models. Notably, the predictive performance of such deep learning models is superior perhaps due to its ability in capturing long-range dependencies. For example, the Neuro Fuzzy models of ANFIS and CANFIS, which outperform the EGARCH model, are more flexible in modelling both asymmetry and long memory properties. This offers new insights for Asian markets. In addition to standard statistics forecast metrics, we also consider risk management measures including the value-at-risk (VaR) average failure rate, the Kupiec LR test, the Christoffersen independence test, the expected shortfall (ES) and the dynamic quantile test. The study concludes that ML algorithms provide improving volatility forecasts in the stock markets of Asia and suggest that this may be a fruitful approach for risk management.

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Section: Literature Reviewsupporting

confidence: 66%

Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets

2023

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“…Adebiyi A. A. et al [ 44 ] used feed forward multilayer perceptron neural network with backpropagation whether incorporating fundamental analysis variables could produce a better performance than technical analysis variables only. The published stock data obtained from the Internet were used.…”

Section: Related Workmentioning

confidence: 99%

A comparative study on effect of news sentiment on stock price prediction with deep learning architecture

et al. 2023

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The accelerated progress in artificial intelligence encourages sophisticated deep learning methods in predicting stock prices. In the meantime, easy accessibility of the stock market in the palm of one’s hand has made its behavior more fuzzy, volatile, and complex than ever. The world is looking at an accurate and reliable model that uses text and numerical data which better represents the market’s highly volatile and non-linear behavior in a broader spectrum. A research gap exists in accurately predicting a target stock’s closing price utilizing the combined numerical and text data. This study uses long short-term memory (LSTM) and gated recurrent unit (GRU) to predict the stock price using stock features alone and incorporating financial news data in conjunction with stock features. The comparative study carried out under identical conditions dispassionately evaluates the importance of incorporating financial news in stock price prediction. Our experiment concludes that incorporating financial news data produces better prediction accuracy than using the stock fundamental features alone. The performances of the model architecture are compared using the standard assessment metrics —Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Correlation Coefficient (R). Furthermore, statistical tests are conducted to further verify the models’ robustness and reliability.

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“…Current state of art provides many articles related to prediction of stock data using technical indicators. However in [28] the author discusses the impact of hybrid indicatorsfundamental, technical and experts' advice in the prediction of stock data. Various features extracted from the mentioned hybrid indicators were fed into a neuro-fuzzy prediction model trained by sigmoid function.…”

Section: Analysis Of Stock Data Using Neural Networkmentioning

confidence: 99%

Prediction and Clustering Techniques used in the Development of Stock Forecasting Model

K P *,

2020

IJITEE

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With the advancement in science and technology more and more real time data is being accumulated in the digital repositories. One such highly accumulating data is stock market data. Prediction of stock market data and its analysis is a challenging task as it is a highly sophisticated time series data vulnerable to sudden changes. The data and its relevance in the real world has attracted the interest of many researchers. Research literature provides many contributions by eminent researchers for analyzing and developing models for stock data. In this research paper an effort has been made present two things. One, building a stock prediction model using artificial neural network using different learning functions and we found that the different learning functions produces different results. In our experiment we have achieved a highest accuracy of 94.55%. The second thing we are trying to do in this article is to provide a detailed information on various techniques used for stock data prediction. More than 50 articles have been studied to present the significant contribution of the researchers. The articles are categorized into two main sections namely prediction methodologies used for stock data analysis and clustering methodologies used for stock data analysis. The sections, prediction methodologies used for analysis and clustering methodologies used for analysis are further explored into eight and four sub categories respectively depending upon the methodologies used. After presenting the detailed analysis, we have also highlighted the research gaps existing in the methodologies discussed in this paper.

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Fuzzy-neural model with hybrid market indicators for stock forecasting

Cited by 15 publications

References 27 publications

Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets

Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets

A comparative study on effect of news sentiment on stock price prediction with deep learning architecture

Prediction and Clustering Techniques used in the Development of Stock Forecasting Model

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