An Empirical Examination on Forecasting VN30 Short-Term Uptrend Stocks Using LSTM along with the Ichimoku Cloud Trading Strategy

Hai, Pham Ngoc; Hieu, Hoang Trung; Hung, Phan Duy

doi:10.1007/978-981-19-2130-8_19

Cited by 3 publications

(2 citation statements)

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“…Bui [18] used the rolling window method to forecast the Stock Price Movement Direction (SPMD) of 30 stocks in the VN30 basket, comparing Support Vector Machines (SVM), logistic regression, and Artificial Neural Networks (ANN). 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.…”

Section: Theoretical Background For Stock-market Trends Predictionmentioning

confidence: 99%

“…Long Short-Term Memory (LSTM) has also been used in forecasting within the Vietnamese stock market. Pham NH et al [19] applied LSTM and Ichimoku Cloud trading strategy to explore whether some stocks within the VN30 basket could outperform the index. They chose three stocks with the best profit potential, holding them for 10 days before selling, with the process repeated from 2015 to 2020.…”

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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Section: Theoretical Background For Stock-market Trends Predictionmentioning

confidence: 99%

Section: Review Of Previous Research On the Vn30 Indexmentioning

confidence: 99%