Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models

Farahani, Milad Shahvaroughi; Hajiagha, Seyed Hossein Razavi

doi:10.1007/s00500-021-05775-5

Cited by 77 publications

(28 citation statements)

References 83 publications

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“…Journal of Function Spaces [13]. Kumar and Yadav [14] use the Shanghai Composite Index to calculate the volatility trend of stock prices.…”

Section: The Related Workmentioning

confidence: 99%

Application of Neural Networks in Financial Time Series Forecasting Models

2022

Journal of Function Spaces

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At present, the economic development of the world’s major economies is showing a positive and positive state. Driven by the development of related industries, the development of the financial field is also changing with each passing day. Various activities in the financial industry are in full swing, and the forecasts of related prospects are also full of uncertainties. Summarizing the laws of financial activities through technical means and making accurate predictions of future trends and trends is a hot research direction that relevant researchers pay attention to. Accurate financial forecasts can provide reference for financial activities and decision-making to a certain extent, promote the steady development of the market, and improve the conversion rate of financial profits. As an algorithm model that can simulate the biological visual system, the convolutional neural network can predict the numerical trend of the next period of time based on known data. Therefore, this paper integrates the support vector machine with the established model by establishing a convolutional neural network model and applies the prediction model to the prediction of financial time series data. The experimental results show that the model proposed in this paper can more accurately predict the trend of the stock index.

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“…Journal of Function Spaces [13]. Kumar and Yadav [14] use the Shanghai Composite Index to calculate the volatility trend of stock prices.…”

Section: The Related Workmentioning

confidence: 99%

Application of Neural Networks in Financial Time Series Forecasting Models

2022

Journal of Function Spaces

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“…With regard to the use of neural networks for price prediction, Mehtab and Sen [15] employed different tools, including the neural networks, for the prediction of stock prices and Shahvaroughi Farahani and Razavi Hajiagha [20] studied how neural networks and metaheuristic algorithms can be used for stock prediction. Lastly, Song et al [22] used different types of neural networks to predicts stock prices, Moghaddam et al [18] also employed the neural networks to analyse the NASDAQ stock price and also Lachiheb and Gouider [11] worked with neural network for the prices prediction of the TUNINDEX stocks.…”

Section: Methodsmentioning

confidence: 99%

The dynamics of the prices of the companies of the STOXX Europe 600 Index through the logit model and neural network

Mecchia¹,

Gaudenzi²

2022

Preprint

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The aim of the present work is analysing and understanding the dynamics of the prices of companies, depending on whether they are included or excluded from the STOXX Europe 600 Index. For this reason, data regarding the companies of the Index in question was collected and analysed also through the use of logit models and neural networks in order to find the independent variables that affect the changes in prices and thus determine the dynamics over time.

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“…Mallikarjuna and Rao [17] examine linear, nonlinear, artificial intelligence, frequency domain, and hybrid models to predict stock returns in developed, emerging, and frontier markets. Shahvaroughi [18] uses artificial neural networks to predict stock price indices and uses social spider optimization (SSO) and Bat Algorithm (BA) to train it. Mean absolute error (MAE) is used as an error evaluation criterion.…”

Section: Literature Reviewmentioning

confidence: 99%

The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm

Zheng

Wang

et al. 2021

Algorithms

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Accurate stock market prediction models can provide investors with convenient tools to make better data-based decisions and judgments. Moreover, retail investors and institutional investors could reduce their investment risk by selecting the optimal stock index with the help of these models. Predicting stock index price is one of the most effective tools for risk management and portfolio diversification. The continuous improvement of the accuracy of stock index price forecasts can promote the improvement and maturity of China’s capital market supervision and investment. It is also an important guarantee for China to further accelerate structural reforms and manufacturing transformation and upgrading. In response to this problem, this paper introduces the bat algorithm to optimize the three free parameters of the SVR machine learning model, constructs the BA-SVR hybrid model, and forecasts the closing prices of 18 stock indexes in Chinese stock market. The total sample comes from 15 January 2016 (the 10th trading day in 2016) to 31 December 2020. We select the last 20, 60, and 250 days of whole sample data as test sets for short-term, mid-term, and long-term forecast, respectively. The empirical results show that the BA-SVR model outperforms the polynomial kernel SVR model and sigmoid kernel SVR model without optimized initial parameters. In the robustness test part, we use the stationary time series data after the first-order difference of six selected characteristics to re-predict. Compared with the random forest model and ANN model, the prediction performance of the BA-SVR model is still significant. This paper also provides a new perspective on the methods of stock index forecasting and the application of bat algorithms in the financial field.

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Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models

Cited by 77 publications

References 83 publications

Application of Neural Networks in Financial Time Series Forecasting Models

Application of Neural Networks in Financial Time Series Forecasting Models

The dynamics of the prices of the companies of the STOXX Europe 600 Index through the logit model and neural network

The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm

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