ARIMA Versus ANN—A Comparative Study of Predictive Modelling Techniques to Determine Stock Price

Maheswari, B. Uma; Sujatha, R.; Fantina, S.; Mansurali, A.

doi:10.1007/978-981-15-9689-6_35

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…In the research, Multilayer Feedback Artificial Neural Network (MLF-ANN) model was preferred due to its success in forecasting financial time series (Bahrammirzaee, 2010;Ismail et al, 2018;Maheswari et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…Despite the limited number of studies on green bond markets, there is a massive literature on the forecasting of financial asset prices with Artificial Neural Networks (ANN). Studies prove that ANN can predict financial asset prices or values with high accuracy (Tealab et al, 2017) and its prediction performance is superior to linear models (Ma, 2020;Maheswari et al, 2021).…”

Section: Related Studiesmentioning

confidence: 99%

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Green Bonds in Climate Finance and Forecasting of Corporate Green Bond Index Value With Artificial Intelligence

ÇETİN>

2022

Journal of Research in Business

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The effects of global climate change and increasing environmental awareness have led to an increase in the significance of climate projects and, accordingly, climate finance and green bonds. Despite the increasing significance, the fact that the price forecasting studies on green bonds are extremely scarce has been the main motivation of this study. The aim of this paper is to forecast the corporate green bond prices with the Artificial Neural Network model and to determine the predictor by addressing the conceptual framework of green bonds. For this purpose, the Multi-Layer Feedback Artificial Neural Network (MLF-ANN) model, in which S&P 500 index prices are determined as input and S&P green bond index prices as output, is designed. To determine whether the conventional bond prices are the predictor of the corporate green bonds, the S&P 500 index was used as the sole input of the forecasting model. The findings show that corporate green bond prices are forecasted with 1.13% Mean Absolute Percentage Error (MAPE) and 98.93% Regression Determination Coefficient (R2). The results of the research provide data to maximize profits and/or minimize risk for green bond investors and market makers, while providing insight into the effectiveness of green bonds in financing climate projects for policy makers. This paper is the first study in the literature in terms of proving the effectiveness of the MLF-ANN model in forecasting corporate green bonds and revealing that conventional bonds are predictor of green bonds. Thus, it is expected that the study will shed light on future studies.

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Section: Methodsmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Green Bonds in Climate Finance and Forecasting of Corporate Green Bond Index Value With Artificial Intelligence

ÇETİN>

2022

Journal of Research in Business

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“…In addition, it should be noted that ARIMA models rely on the assumption of stationarity in the underlying time series. However, this assumption may not hold true for stock prices, as they frequently demonstrate trends and fluctuating volatilities throughout time [20]. Another challenge that arises is the selection of optimal parameters for the ARIMA model, a task that demands expert knowledge and can be time-consuming.…”

Section: Application Of Autoregressive Integrated Moving Average (Arima)mentioning

confidence: 99%

Comparative Analysis of Linear Regression, Polynomial Regression, and ARIMA Model for Short-term Stock Price Forecasting

2023

AEMPS

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This research investigates the effectiveness of three prominent stock price prediction methodologies: Linear Regression, Polynomial Regression, and AutoRegressive Integrated Moving Average (ARIMA) model. The study leverages one and a half years of historical data from Apple, Tesla, Amazon, and Nike stocks to predict average prices over the ensuing 14 days. The predictive efficacy of each model is tested against actual data, revealing their respective strengths and limitations. Linear Regression offers an overview of stock trends with limited intricacy, while Polynomial Regression delivers a more comprehensive understanding of price variations and cyclical trends. However, Polynomial Regression's reliability for predictions remains uncertain. In contrast, the ARIMA model, designed explicitly for short-term forecasting, demonstrates superior accuracy, correctly predicting seven out of eight scenarios. It should be noted that this is despite its assumptions of linearity and stationarity. The findings underscore the complexity of accurate stock market prediction and highlight the ARIMA model's reliability for short-term forecasts. Therefore, understanding the strengths and weaknesses of each model is crucial for improving stock price prediction techniques and for better grasping the complex dynamics of volatile stock markets.

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On supervised learning to model and predict cattle weight in precision livestock breeding

Biase¹,

Albertini²,

Mello³

2022

Computers and Electronics in Agriculture

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ARIMA Versus ANN—A Comparative Study of Predictive Modelling Techniques to Determine Stock Price

Cited by 6 publications

References 18 publications

Green Bonds in Climate Finance and Forecasting of Corporate Green Bond Index Value With Artificial Intelligence

Green Bonds in Climate Finance and Forecasting of Corporate Green Bond Index Value With Artificial Intelligence

Comparative Analysis of Linear Regression, Polynomial Regression, and ARIMA Model for Short-term Stock Price Forecasting

On supervised learning to model and predict cattle weight in precision livestock breeding

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