Stock Market Prediction Using Artificial Neural Networks

Yang, Bing; Hao, Jian Kun; Zhang, Si Chang

doi:10.4028/www.scientific.net/aef.6-7.1055

Cited by 39 publications

(9 citation statements)

References 9 publications

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“…Bing et al [ 40 ] did an extensive comparison between eight models for prediction of stock value of central bank of Turkey. The six models were based on ANNs, where the number of hidden layers was changed in each model and other two models were based on moving averages.…”

Section: Related Workmentioning

confidence: 99%

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

Rouf

Malik

Sharma

et al. 2022

Computational Intelligence and Neuroscience

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The unprecedented Corona Virus Disease (COVID-19) pandemic has put the world in peril and shifted global landscape in unanticipated ways. The SARSCoV2 virus, which caused the COVID-19 outbreak, first appeared in Wuhan, Hubei Province, China, in December 2019 and quickly spread around the world. This pandemic is not only a global health crisis, but it has caused the major global economic depression. As soon as the virus spread, stock market prices plummeted and volatility increased. Predicting the market during this outbreak has been of substantial importance and is the primary motivation to carry out this work. Given the nonlinearity and dynamic nature of stock data, the prediction of stock market is a challenging task. The machine learning models have proven to be a good choice for the development of effective and efficient prediction systems. In recent years, the application of hyperparameter optimization techniques for the development of highly accurate models has increased significantly. In this study, a customized neural network model is proposed and the power of hyperparameter optimization in modelling stock index prices is explored. A novel dataset is generated using nine standard technical indicators and COVID-19 data. In addition, the primary focus is on the importance of selection of optimal features and their preprocessing. The utilization of multiple feature ranking techniques combined with extensive hyperparameter optimization procedures is comprehensive for the prediction of stock index prices. Moreover, the model is evaluated by comparing it with other models, and results indicate that the proposed model outperforms other models. Given the detailed design methodology, preprocessing, exploratory feature analysis, and hyperparameter optimization procedures, this work gives a significant contribution to stock analysis research community during this pandemic.

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Section: Related Workmentioning

confidence: 99%

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

Rouf

Malik

Sharma

et al. 2022

Computational Intelligence and Neuroscience

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“…Another study used Generalized Feed Forward (GFF) and MLP models for the prediction of the Istanbul Stock Exchange (ISE) market index [100], where the data were taken from the Central Bank of Turkey. A total of eight sets of predictions (six ANN and two MAs) were performed by changing the number of hidden layers.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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

et al. 2021

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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.

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“…Badrul, Zakir and Amjad demonstrates a hybrid model of artificial neural network and fuzzy inference system and used back propagation method for training the neural network and multilayer feed forward network in order to forecast the share values [16] .Multilayer network and the related backpropagation training algorithm is one of the most popular algorithm in artificial neural network [17]. Abhishek, Anshul, Tej and Surya conducted an experiment to stock prediction using back-propagation with feed forward network with an accuracy of 99percent [2] .These all of the works inspire us to apply models in this genre to find remarkable technique.In this recent world everything is automated like E-voting [18,19]…”

Section: Literature Reviewmentioning

confidence: 99%

“…The linear (AR, MA, ARIMA) and non-linear forecasting algorithms (ARCH, GARCH, Neural Networks) both focus on predicting the stock prices for a single company using the daily closing prices [1]. Based on the history data, the neural network model is successfully applied to predict the daily highest, lowest price and closing price of a company stocks in short time, but it is ineffective for predicting the return rate of the stocks [2]. Various features such as stochastic indicator, moving averages, RSI are extracted from the historical stock data to train the ANN model.…”

Section: Introductionmentioning

confidence: 99%

Stock Market Prediction on High-Frequency Data Using ANN

Nova

Mim

Rowshan

et al. 2021

AJRCoS

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A stock market is a place where company shares are traded to the stockbrokers. Stock price prediction is one of the most challenging problems as a high level of accuracy is the key factor in predicting a stock market. Many methods are used to predict the price in the stock market but none of those methods are proved as a consistently acceptable prediction tool due to its volatile nature. In this paper, we proposed Artificial Neural Network (ANN) technique because ANN can generalize and predict data after learning and analyzing from the initial inputs and their relationships. We used feed forward network and backward propagation algorithm to predict stock prices. In this paper, we introduced a method that can find out the future value of stock prices in a particular day based on some input using ANN back propagation algorithm.

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Stock Market Prediction Using Artificial Neural Networks

Cited by 39 publications

References 9 publications

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

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

Stock Market Prediction on High-Frequency Data Using ANN

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