Decision-making model to predict auto-rejection: An implementation of ARIMA for accurate forecasting of stock price volatility during the Covid-19

Suripto, Suripto

doi:10.5267/j.dsl.2022.10.002

Cited by 5 publications

(4 citation statements)

References 34 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The coefficients of parameters for each model were estimated using R software, and the equations of the models are shown in Table 5 by substituting the given coefficients into Equation (6). ARIMA(1,1,0) for the pre-COVID-19 phase, ARIMA(1,1,0) for the post-COVID-19 phase, and ARIMA(1,1,0) for the overall time span phase are given in Equation (11) to Equation (13). y t = 0.9195y t−1 + 0.0669y t−2 + ϵ t , (11) y t = 0.9341y t−1 + 0.0659y t−2 + ϵ t , (12) y t = 0.9670y t−1 + 0.0321y t−2 + ϵ t .…”

Section: Resultsmentioning

confidence: 99%

“…In financial applications, several researchers [13,16,5] found that the ARIMA model was able to capture the patterns and trends of each financial price movement and predict its future values during the COVID-19 pandemic. Nonetheless, despite the outstanding results, few studies have examined the performance of the ARIMA model during the pre-and post-COVID-19 phases, owing to the massive impact of the pandemic on the financial market [17].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

ARIMA Models For Kijang Emas Price Forecasting: Pre- and Post-COVID Analysis

Areh,,

Miswan,

2024

MJMS

View full text Add to dashboard Cite

Gold has significant economic value in a country's economic landscape, serving as a hedge against inflation, particularly during financial turmoil. In Malaysia, gold is known as Kijang Emas and serves as the official bullion gold coin. The price of gold also impacts stock market dynamics, making understanding its fluctuations essential for risk-averse investors. However, the credibility of gold as an investment has been called into question due to price volatility caused by various factors, including the recent upheaval caused by the COVID-19 pandemic. The goal of this study is to explore the effectiveness of the ARIMA model in modelling and forecasting daily Kijang Emas prices in Malaysia from 2012 to 2022, divided into two phases: pre-COVID-19 and post-COVID-19. Model performance was assessed using metrics such as AIC, MAE, and MAPE. The results that the ARIMA model can analyse and forecast Kijang Emas prices, particularly on post-COVID-19 data with high volatility and uncertainty. This insight is valuable for investors seeking to understand market trends and develop future strategies.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

ARIMA Models For Kijang Emas Price Forecasting: Pre- and Post-COVID Analysis

Areh,,

Miswan,

2024

MJMS

View full text Add to dashboard Cite

show abstract

“…The well-known traditional statistics time series forecasting methods, such as ARIMA and its variants 17,[21][22][23][24][25][26][27][28][29] are still used a lot because of their efficiency level. Table 1 presents a summary of ARIMA-based approaches for SPF.…”

Section: Arima Approachesmentioning

confidence: 99%

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

View full text Add to dashboard Cite

We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.

show abstract

“…Additionally, Suripto (2023) recommend that if stakeholders are satisfied with the SME's financial performance and decision-making, it suggests that the SME's capital forecasting process is effective and well-regarded. Thus, the studies highlight the importance of capital forecasting methods, the accuracy of forecasting, factors affecting capital forecasting, and best practices for capital forecasting in SMEs.…”

Section: Capital Forecastingmentioning

confidence: 99%

Does Digital Balanced Scorecards Lead To The Sustainable Performance Amongst The Jordanian SMEs?

Ibrahim¹,

Mohammed²,

Abdulaali³

et al. 2023

J. Professional Business Review

View full text Add to dashboard Cite

Purpose: This study aims to fill up a literature gap related to the sustainable performance in Jordanian SMEs, where the literature focused on limited variables related only to the human capital. Theoretical framework: The researchers provided a suggested model to be implemented in the SMEs in Jordan as one of the industries that must be activated in the country to help the economy. Management accounting system and digital balanced scorecards were the main variables contributing, while variables like capital forecasting and decision making also were playing the role of the dependents variable for the D.BSC. Design/methodology/approach: All of the mentioned variable toward sustainable performance in the Jordanian SMEs in this quantitative research tested via randomly distributing questionnaires among the employees of the industry with returned and valid for analysis of 910 surveys. Using SPSS for descriptive analysis and AMOS for the EFA and CFA. Findings: The results deducted as there is a significant mediation role for D.BSC between MAS and decision making and insignificant mediation role for D.BSC between MAS and capital forecasting. Research, Practical & Social implications: This research is suggesting a model and variables to be used by the SMEs in Jordan. This model will vast the SMEs industry and support the society by affording further employment chances. Originality/value: The paper contributes to the body of knowledge and the Jordanian SMEs industry and Jordanian society. This paper targeting the most important industry in the country currently because such third world countries needed the SMEs referring to the previous studies.

show abstract

Decision-making model to predict auto-rejection: An implementation of ARIMA for accurate forecasting of stock price volatility during the Covid-19

Cited by 5 publications

References 34 publications

ARIMA Models For Kijang Emas Price Forecasting: Pre- and Post-COVID Analysis

ARIMA Models For Kijang Emas Price Forecasting: Pre- and Post-COVID Analysis

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Does Digital Balanced Scorecards Lead To The Sustainable Performance Amongst The Jordanian SMEs?

Contact Info

Product

Resources

About