Accuracy Driven Artificial Neural Networks in Stock Market Prediction

Simon, Selvan; Raoot, Arun; Lake, Vihar

doi:10.5121/ijsc.2012.3203

Cited by 28 publications

(7 citation statements)

References 24 publications

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“…A number of artificial intelligence approaches are generally employed to learn from financial documents, for instance, Support Vector Machines (SVM) [1], [4], [22], Artificial Neural Networks (ANN) [24], k-Nearest Neighbours (kNN) and Naïve Bayes [15]. In [2], Support Vector Regression (SVR) was employed to investigate the impact of financial news 9" " on the Chinese stock market.…”

Section: 4! Machine Learning Techniquesmentioning

confidence: 99%

Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

Shynkevich

McGinnity

Coleman

et al. 2016

Decision Support Systems

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-The market state changes when a new piece of information arrives. It affects decisions made by investors and is considered to be an important data source that can be used for financial forecasting. Recently information derived from news articles has become a part of financial predictive systems. The usage of news articles and their forecasting potential have been extensively researched.However, so far no attempts have been made to utilise different categories of news articles simultaneously. This paper studies how the concurrent, and appropriately weighted, usage of news articles, having different degrees of relevance to the target stock, can improve the performance of financial forecasting and support the decision-making process of investors and traders. Stock price movements are predicted using the multiple kernel learning technique which integrates information extracted from multiple news categories while separate kernels are utilised to analyse each category.News articles are partitioned according to their relevance to the target stock, its sub industry, industry, group industry and sector. The experiments are run on stocks from the Health Care sector and show that increasing the number of relevant news categories used as data sources for financial forecasting improves the performance of the predictive system in comparison with approaches based on a lower number of categories.

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Section: 4! Machine Learning Techniquesmentioning

confidence: 99%

Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

Shynkevich

McGinnity

Coleman

et al. 2016

Decision Support Systems

View full text Add to dashboard Cite

show abstract

“…Further one can divide the complex stock market prediction tasks into simpler subtasks, perform the task and integrate the results to get better performance By applying more than one data mining techniques, say genetic algorithm and neural networks on two different subtasks, we can take the advantages of their strengths. The empirical results obtained shows high level of accuracy for daily stock price prediction with hybridized approach performing better than technical analysis approach [15]. The hybridized approach has the potential to enhance the quality of decision making of investors in the stock market by offering more accurate stock prediction compared to existing technical analysis based approach.…”

Section: B Proposed New Modelmentioning

confidence: 90%

“…In the review of current literature, it is amply clear that artificial neural network can predict the stock market in short term wherein many models support that. 1) Selvan Simon and Arun Rout [15] analyzed competitive ANN model and in improving ANN accuracy in Stock Market forecasting. The factors considered in improving the accuracy of relevant ANN model is by comparing various ANN models, its learning algorithm most suitable for the given application, prediction target and problem situation to get the best result.…”

Section: A Ann Model Developmentmentioning

confidence: 99%

A New Approach to Predict Selective Critical Stock Indices Through Artificial Neural Networks and Chaos Theory

Vinod¹

2016

IJCOT

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Financial markets are generally considered as dynamic entities behaving in a random and chaotic manner posing a challenging problem to equity, commodity and currency forecasters. Adoption ofartificial neural network techniques to forecast such financial marketshas been resorted to by many, howeverwith many shortcomings. The present paper proposes a new model to address the above via a synthesis of integration of a live trading system, marketcrash factors and liquidity parameters with the help of chaos theory of physics and financial, technical analysis.

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“…Different artificial intelligence approaches are employed for learning from financial textual data for predicting the market reaction; examples include Artificial Neural Networks (ANN) [26]; Naïve Bayes [14] and Support Vector Machines (SVM) [1], [5], [24]. In [2], the impact of financial news articles on Chinese stock markets was investigated where Support Vector Regression (SVR) was used to show that releases of online financial news have negative impacts on the market.…”

Section: B Predicting From News Articlesmentioning

confidence: 99%

Predicting Stock Price Movements Based on Different Categories of News Articles

Shynkevich

McGinnity

Coleman

et al. 2015

2015 IEEE Symposium Series on Computational Intelligence

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Publications of financial news articles impact the decisions made by investors and, therefore, change the market state. It makes them an important source of data for financial predictions. Forecasting models based on information derived from news have been recently developed and researched. However, the advantages of combining different categories of news articles have not been investigated. This research paper studies how the results of financial forecasting can be improved when news articles with different levels of relevance to the target stock are used simultaneously. Integration of information extracted from five categories of news articles partitioned by sectors and industries is performed using the multiple kernel learning technique for predicting price movements. News articles are divided into the five categories of relevance to a targeted stock, its sub industry, industry, group industry and sector while separate kernels are employed to analyze each one. The experimental results show that the simultaneous usage of five news categories improves the prediction performance in comparison with methods based on a lower number of news categories.

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Accuracy Driven Artificial Neural Networks in Stock Market Prediction

Abstract: ABSTRACT

Cited by 28 publications

References 24 publications

Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

A New Approach to Predict Selective Critical Stock Indices Through Artificial Neural Networks and Chaos Theory

Predicting Stock Price Movements Based on Different Categories of News Articles

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