Stock Price Prediction Using ARIMA, Neural Network and LSTM Models

Ho, Ming-Che; Darman, Hazlina; Musa, Sarah

doi:10.1088/1742-6596/1988/1/012041

Cited by 15 publications

(9 citation statements)

References 3 publications

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“…The authors demonstrated that hybrid models achieve better performance than a single model for stock market predictions. In Ho et al (1988), a hybrid method of ARIMA with a neural network and long short-term memory (LSTM) network was applied to predict the Bursa Malaysia stock exchange during the COVID-19 pandemic period.…”

Section: Methodsmentioning

confidence: 99%

Survey of feature selection and extraction techniques for stock market prediction

2023

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In stock market forecasting, the identification of critical features that affect the performance of machine learning (ML) models is crucial to achieve accurate stock price predictions. Several review papers in the literature have focused on various ML, statistical, and deep learning-based methods used in stock market forecasting. However, no survey study has explored feature selection and extraction techniques for stock market forecasting. This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications. We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022. We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles. We also describe the combination of feature analysis techniques and ML methods and evaluate their performance. Moreover, we present other survey articles, stock market input and output data, and analyses based on various factors. We find that correlation criteria, random forest, principal component analysis, and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

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Section: Methodsmentioning

confidence: 99%

Survey of feature selection and extraction techniques for stock market prediction

2023

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“…Many scholars have applied ARIMA and LSTM to analyze FinTech companies. ARIMA and LSTM models have great potential in financial technology stock analysis, and combining multiple data sources and technologies can improve prediction accuracy [9]. Some scholars combine ARIMA and LSTM to predict the future market value of FinTech companies in the United States, and incorporate data preprocessing techniques such as differentiation, normalization, and normalization to improve the accuracy of predictions.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing the ARIMA and LSTM Models on the Stock Price of FinTech Companies

2023

AJBM

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FinTech companies have emerged as a new force in the financial industry in recent years.Their innovative business models, high growth performance, and broad market prospects have attracted the attention and pursuit of numerous investors. Therefore, predicting the future trend of FinTech company stocks is significant for investors. This paper selects PayPal as the prediction target, collects the closing prices from 2018 to 2023, and uses the Auto-regressive Integrated Moving Average Model (ARIMA) and Long Short-Term Memory (LSTM) to compare the accuracy of the prediction results and select the optimal prediction model. Empirical results show that both the ARIMA and the LSTM models have specific effects in predicting the stock prices of the FinTech industry, with the LSTM model having higher prediction accuracy than the ARIMA model. However, it should be noted that stock price prediction is not entirely accurate but based on historical data and market trends. Therefore, when making investment decisions, it is necessary to consider various factors comprehensively and make scientific investment decisions to achieve better investment returns.

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“…Several deep learning-based regression models were built using LSTM networks, and we found that the LSTMbased univariate model, which uses data from a previous week as input for predictions, provides the most accurate result. [1] [2] Additionally, in [5], A hybrid of RNN and LSTM is constructed to make prediction of the stock price trend. Their comparison between the percentage of accuracy of both ARIMA and LSTM used on series data manifested that LSTM outperforms ARIMA.…”

Section: Related Researchmentioning

confidence: 99%

Stock Price Prediction Using Machine Learning Strategies

Chen¹

2023

BCPBM

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Being able to foresee the potential opportunities or crisis in stock market has always been desirable among investors. Especially during the Covid-19 global pandemic, the skill of risk management is of great importance to sustain in such an unstable environment. Apart from various kinds of strategies in traditional business analysis, a robust intelligent system that can correctly predict stock price is desired to determine investment strategies. At present, much related research involve with predicting the stock price trend, and most of them use deep learning methods. Although these research managed to achieve an ideal result of their tasks, seldom surveys focus on the summary of deep learning methods employed in stock price prediction. As a result, the aim of this paper is to summarize the machine learning methods used in forecasting stock price, the development context of the task, and, finally, analyze the development trend of the task based on previous published papers. These papers were classified by deep learning methods, which included Long Short-Term Memory (LSTM); Gated Recurrent Units (GRU); Recurrent Neural Network (RNN); and other hybrid deep learning methods. Furthermore, this paper identifies some of the dataset, variable, model, and results of each article. The survey adopted presents the results through the most used performance metrics: Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and Mean Square Error (MSE).

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Stock Price Prediction Using ARIMA, Neural Network and LSTM Models

Cited by 15 publications

References 3 publications

Survey of feature selection and extraction techniques for stock market prediction

Survey of feature selection and extraction techniques for stock market prediction

Comparing the ARIMA and LSTM Models on the Stock Price of FinTech Companies

Stock Price Prediction Using Machine Learning Strategies

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