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Forecasting of Stock Price Using Autoregressive Integrated Moving Average Model
Abstract
Finance sector is highly volatile where the stock prices fluctuate rapidly and it is usually challenging to forecast. The unstable conditions and rapid changes can drastically modify the monetary value of an organization or an individual. Hence, the prediction of stock prices continues to remain as one of the sizzling and vital topics in the applications of data mining in the finance sector. This forecasting is significant as it has the potential to reduce the losses that happen mainly due to erroneous intuitions and blind investment. Moreover, the prediction of stock prices endure to increase in complexity with accumulation of more and more historical data. This paper focuses on American Stock Market (New York Stock Exchange and NASDAQ Stock Exchange). Taking into account the complexity of the prediction, this research proposes Autoregressive Integrated Moving Average (ARIMA) model for estimating the value of future stock prices. ARIMA demonstrated better results for prediction as it can handle the time series data very well which is suitable for forecasting the future stock index.
... (i) Statistical models Jiang et al. [14] utilized the ARIMA model for estimating future stock price values. Since ARIMA is highly adept at handling time series data, the model has shown superior prediction outcomes, making it a good choice for predicting the future stock index. ...
... (iii) Deep Learning models Farnoush Ronaghi et al. [14] have established a hybrid deep-learning technique for stock movement forecasting. The established methods were used to compare the different classification metrics that calculate account productivity and cost levels of transactions to analyze the economic gains in stock market prediction. ...
... In the process of predicting financial variables, the existing large number of high correlation features contains a minimal amount of information, which increases the time complexity [14]; • The SVM approach proved to be computationally expensive for voluminous datasets and unsuitable for forecasting extensive stock data [5]; ...
Forecasting stock market movements is a critical yet challenging endeavor due to the inherent nonlinearity, chaotic behavior, and dynamic nature of financial markets. This study proposes the Autoregressive Integrated Moving Average Ensemble Recurrent Light Gradient Boosting Machine (AR-ERLM), an innovative model designed to enhance the precision and reliability of stock movement predictions. The AR-ERLM integrates ARIMA for identifying linear dependencies, RNN for capturing temporal dynamics, and LightGBM for managing large-scale datasets and non-linear relationships. Using datasets from Netflix, Amazon, and Meta platforms, the model incorporates technical indicators and Google Trends data to construct a comprehensive feature space. Experimental results reveal that the AR-ERLM outperforms benchmark models such as GA-XGBoost, Conv-LSTM, and ANN. For the Netflix dataset, the AR-ERLM achieved an RMSE of 2.35, MSE of 5.54, and MAE of 1.58, surpassing other models in minimizing prediction errors. Moreover, the model demonstrates robust adaptability to real-time data and consistently superior performance across multiple metrics. The findings emphasize AR-ERLM’s potential to enhance predictive accuracy, mitigating overfitting and reducing computational overhead. These implications are crucial for financial institutions and investors seeking reliable tools for risk assessment and decision-making. The study sets the foundation for integrating advanced AI models into financial forecasting, encouraging future exploration of hybrid optimization techniques to further refine predictive capabilities.
... The experimental results showed that the uncertainty of information magnifies momentum profits (Wu & Choudhry, 2018). Jiang and Subramanian (2019) took the securities market of the United States as the research object and proposed an auto-regressive integrated average model to predict the future stocks' value. The experimental results proved that the Auto-regressive Integrated Moving Average (ARIMA) model could provide good results for processing time-series data (Jiang & Subramanian, 2019). ...
... Jiang and Subramanian (2019) took the securities market of the United States as the research object and proposed an auto-regressive integrated average model to predict the future stocks' value. The experimental results proved that the Auto-regressive Integrated Moving Average (ARIMA) model could provide good results for processing time-series data (Jiang & Subramanian, 2019). However, it is worth noting that no matter which method is used in the published academic research, the results are not ideal, or there is still room for improvement. ...
The backpropagation neural network (BPNN) algorithm of artificial intelligence (AI) is utilized to predict A+H shares price for helping investors reduce the risk of stock investment. First, the genetic algorithm (GA) is used to optimize BPNN, and a model that can predict multi-day stock prices is established. Then, the Principal Component Analysis (PCA) algorithm is introduced to improve the GA-BP model, aiming to provide a practical approach for analyzing the market risks of the A+H shares. The experimental results show that for A shares, the model has the best prediction effect on the price of Bank of China (BC), and the average prediction errors of opening price, maximum price, minimum price, as well as closing price are 0.0236, 0.0262, 0.0294 and 0.0339, respectively. For H shares, the model constructed has the best effect on the price prediction of China Merchants Bank (CMB). The average prediction errors of opening price, maximum price, minimum price and closing price are 0.0276, 0.0422, 0.0194 and 0.0619, respectively.
... Bollerslev proposed the GARCH model and introduced the time-varying variance of financial asset prices [5]. Jiang and Subramanian took the U.S. stock market as the research object and proposed an autoregressive comprehensive average model to predict the value of future stocks [6]. However, traditional statistical forecasting has shortcomings such as prediction lag, large noise, and high correlation of influencing variables. ...
... In the above equation, H represents the number of nodes in the hidden layer, m + n represents the sum of the number of nodes in the input and output layers, and a is a regulation constant between 1 and 10. In the specific context of this study, the input layer nodes m � 21 and output layer nodes n � 1 are substituted in equation (6) to obtain the lower threshold 5. After adding the constant a, we calculate that the optimal number of nodes in the hidden layer falls in a range of [5,15], so we need to test them one by one in this value interval. ...
China’s Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect programs make it possible for investors to trade stocks within specified limits through the two stock exchanges. The A-H share exchange stock market is crucial to the opening of the Mainland market, but few studies have paid attention to the market risks of such stocks. Using deep learning and BP neural network algorithm, this study constructs a three-dimensional A-H share interconnection market risk prediction index system including stock price fundamental indicators, technical indicators, and macro indicators based on the CES300 Index. Taking the CES300 Index return as the output layer indicator, a BP neural network with a 21-10-1 structure is constructed, and the tan-sigmoid transfer function and the LM optimization algorithm training function are used for network training to predict the return of the A-H share interconnected stock market. The mean square error (MSE) converges to 10⁻⁶, and the goodness of fit R reaches 0.9928 and validates the prediction accuracy of the BP neural network model. It provides an efficient and accurate risk prediction model for the A-H share interconnected market, which facilitates the interactive development of the Mainland and Hong Kong markets.
... Since financial time sequence data is characterized by complexity and dynamics, autoregressive integrated moving average (ARIMA) is suitable for stock price forecasting [33]. The first question that is usually answered through econometric analysis is whether the behavior of stock prices is stationary or non-stationary The behavior of stock prices is typically non-stationary and ARIMA models are effective in demeaning the series to obtain stationarity which is the precondition for modeling. ...
An important tool for assisting businesses and investors in making wise market decisions is stock price forecasting. Frequently, forecasting models are needed as they help investors to make the right decisions depending on the tendencies on the stock exchange. Traders, customers and financial institutions have been equally affected by the financial industry in terms of financial strength. Today's advanced artificial intelligence includes machine learning and deep learning algorithms, which are now recreating the new boundaries of the financier's markets. With a focus on the LSTM model, this research conducts a thorough investigation of machine learning-based stock price prediction. Based on this preliminary analysis, the empirical model used historical stock data from Tesla Inc., which has 1693 observations. Besides, the data is preprocessed here where the data is separated, cleaned and then split into training and testing data. This research also employs the Long Short-Term Memory (LSTM) model for this kind of application because it is capable of capturing long-term dependencies-a characteristic which is desirable for most time-series forecasting applications. These three main parameters of evaluation A three-way test includes the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). the performance of the model. The study reveals here that the proposed LSTM model gives the best prediction by having lower MSE, RMSE, and MAE of 0.08, 0.03, and 0.08 than the other models like ARIMA and Decision tree. Such studies have revealed that LSTM is efficient in identifying temporal patterns of stock prices and further directions.
... Autoregressive integrated moving average (ARIMA) models are a popular choice for stock price prediction due to their ability to handle the complex and dynamic nature of financial time series data [22]. Stock prices often exhibit non-stationary behavior, and ARIMA models excel at differencing the data to achieve stationarity, making them suitable for modeling. ...
The volatile and non-linear nature of stock market data, particularly in the post-pandemic era, poses significant challenges for accurate financial forecasting. To address these challenges, this research develops advanced deep learning and machine learning algorithms to predict financial trends, quantify risks, and forecast stock prices, focusing on the technology sector. Our study seeks to answer the following question: “Which deep learning and supervised machine learning algorithms are the most accurate and efficient in predicting economic trends and stock market prices, and under what conditions do they perform best?” We focus on two advanced recurrent neural network (RNN) models, long short-term memory (LSTM) and Gated Recurrent Unit (GRU), to evaluate their efficiency in predicting technology industry stock prices. Additionally, we integrate statistical methods such as autoregressive integrated moving average (ARIMA) and Facebook Prophet and machine learning algorithms like Extreme Gradient Boosting (XGBoost) to enhance the robustness of our predictions. Unlike classical statistical algorithms, LSTM and GRU models can identify and retain important data sequences, enabling more accurate predictions. Our experimental results show that the GRU model outperforms the LSTM model in terms of prediction accuracy and training time across multiple metrics such as RMSE and MAE. This study offers crucial insights into the predictive capabilities of deep learning models and advanced machine learning techniques for financial forecasting, highlighting the potential of GRU and XGBoost for more accurate and efficient stock price prediction in the technology sector.
... The correlogram of ACF and PACF has been framed by taking 1 st difference. By analyzing the correlogram, it has been found that the spikes in ACF and PACF are significant at 3,9,13,15,17,18,24,35,47, and 131 lags. Considering these lags, 100 models have been framed by taking different AR and MA terms. ...
... The correlogram of ACF and PACF has been framed by taking 1 st difference. By analyzing the correlogram, it has been found that the spikes in ACF and PACF are significant at 3,9,13,15,17,18,24,35,47, and 131 lags. Considering these lags, 100 models have been framed by taking different AR and MA terms. ...
Many investors in order to predict stock prices use various techniques like fundamental analysis and technical analysis and sometimes rely on the discussions provided by
various stock market analysts. ARIMA is a part of time-series analysis under prediction algorithms, and this paper attempts to predict the share prices of selected pharmaceutical companies in India, listed under NIFTY100, using the ARIMA model. A
sample size of 782 time-series observations from January 1, 2017, to December 31, 2019
for each selected pharmaceutical firm has been considered to frame the ARIMA model.
ADF test is used to verify whether the data are stationary or not. For ARIMA model
estimation, significant spikes in the correlogram of ACF and PACF have been observed,
and many models have been framed taking different AR and MA terms for each selected company. After that, 5 best models have been selected, and necessary inculcation of various AR and MA terms has been made to adjust the models and choose the
best adjusted ARIMA model for each firm based on Volatility, adjusted R-squared, and
Akaike Information Criterion. The results could be used to analyze the stock prices and
their prediction in-depth in future research efforts.
Environmental, social, and governance (ESG) criteria is gaining immense importance among investors while evaluating the companies for investment. After corporate governance and corporate social responsibility, ESG practices have been the watchword in the Indian corporate environment. During the pandemic, these ESG funds grew incredibly popular among other investment avenues. Financial forecasting is an important topic in finance and economics which has spurred the interest of researchers over a period of time to develop better predictive models. The autoregressive integrated moving average (ARIMA) models have been explored in this study which attempts to forecast the NAV of the ESG mutual funds for the period ranging from January 2022 to December 2022. The paper examines the stationarity and estimates and selects the best ARIMA model for predicting the NAV of selected ESG funds. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction and can compete favourably with the existing techniques for financial forecasting.
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