Forecast of the VN30 Index by Day Using a Variable Dimension Reduction Method Based on Kernel Tricks

Thành, Đỗ Văn; Minh, Hai Nguyen

doi:10.1007/978-3-030-92942-8_8

Cited by 1 publication

(3 citation statements)

References 16 publications

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“…In contrast, technical analysis uses historical stock prices and trading volumes to predict market trends, operating under the assumption that past patterns might repeat (Kumbure et al [12]; Lam [14]; Lohrmann [13]. Each approach has its strengths: fundamental analysis is more useful for long-term forecasting, while technical analysis can be more accurate for short-term stock price changes [15]. Nti et al [16] found that some researchers use a combination of both technical and fundamental analysis.…”

Section: Theoretical Background For Stock-market Trends Predictionmentioning

confidence: 99%

“…Pham et al [19] explored the chances that some stock components in the VN30 basket could outperform the index by simultaneously applying Long Short-Term Memory (LSTM) and Ichimoku Cloud trading strategy, selecting three stocks with the best profit potential to sell 10 days later. Do [15] used a variable dimension reduction method based on kernel tricks to forecast daily prices of the VN30 index. Hyndman and Athanasopoulos 2023 advocate for technical approaches, specifically using time-series models, to forecast market trends.…”

Section: Theoretical Background For Stock-market Trends Predictionmentioning

confidence: 99%

“…This methodology yielded a 61% accuracy in predicting the VN30 index's daily price for the next 22 trading days. Do [15] utilized a variable dimension reduction method, employing kernel tricks, to forecast five daily prices of the VN30 index. Using 390 observation days (385 for training and five for testing) and 60 variables, they grouped these into domestic and external factors.…”

Section: Review Of Previous Research On the Vn30 Indexmentioning

confidence: 99%

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Forecasting the VN30 Index: Insights into Vietnam’s Stock Market Trends

Paientko

2024

SIAM

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This research paper examines the potential of forecasting the VN30 index, a prominent benchmark in the Vietnamese stock market, using the Auto Regressive Integrated Moving Average (ARIMA) model. Given the rapid evolution of the Vietnamese stock market since its inception in 2000, it presents unique challenges and opportunities for investors and policymakers. The increasing trading activity and the market's relatively low efficiency make accurate forecasting essential for informed decision-making. The purpose of this study is to determine if the ARIMA model can effectively predict the future values of the VN30 index, providing insights into market trends and assisting stakeholders in navigating the complexities of the Vietnamese stock market. The methodology employed involves a comprehensive approach based on the theory of change and the steps outlined by Hyndman [1], including data visualization, variance stabilization, model selection, and residual analysis. Data used for this study consist of monthly and daily observations of the VN30 index over various periods, allowing for a robust analysis of market behavior. Findings indicate that the ARIMA model can be a valuable tool for forecasting in emerging markets like Vietnam, although challenges related to information transparency and corporate disclosure persist. The results suggest that the VN30 index's future values generally align with ARIMA's prediction interval, offering a degree of confidence for investors and market analysts. However, the study also highlights the need for ongoing improvements in market efficiency and transparency to enhance forecasting accuracy. This paper contributes to the existing literature by demonstrating the applicability of ARIMA in a developing market context and providing practical guidance for investors and policymakers.

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