Efficient market hypothesis and calendar effects: Empirical evidences from the Vietnam stock markets

Khanh, Pham Dan; Dat, Pham Thanh

doi:10.5267/j.ac.2020.5.005

Cited by 6 publications

(4 citation statements)

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“…The second set of parameters (2,0,0) [12] is for the seasonal part of the ARIMA model. The first number (2) indicates that there are two autoregressive terms included in the seasonal part of the model. This means that the model uses the last two years of data to predict the future value.…”

Section: Saim000598 4(5)2024mentioning

confidence: 99%

“…The Vietnamese stock market, which began operations in July 2000, has rapidly evolved but is still considered young and not entirely efficient [2][3][4]. Despite these challenges, there is a significant potential for growth, as highlighted by Bui [5], and the increasing number of trading accounts from 2.3 million at the end of 2019 to 7 million by April 30, 2023 (Vietnam Securities Depository [6]), signals a robust interest in this market.…”

Section: Introductionmentioning

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: Saim000598 4(5)2024mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Paientko

2024

SIAM

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“…According to the EMH theory, an efficient market is stock prices that reflect all information, including financial statements. This case causes investors not to get more profit because there is no hidden information [118], [119], [120].…”

Section: Hypothesis Developmentmentioning

confidence: 99%

Investor Response to Financial Performance and Business Prospect

Juniarti¹,

Chandra²,

Tandyono³

2021

Advances in Economics, Business and Management Research

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“…However, specific tests have demonstrated that the market is occasionally inefficient, in which predicting SPMD is possible [2], [3]. On the other hand, creating the required and adequate circumstances for an efficient market is challenging, particularly in a developing market like Vietnam [4]- [6]. Thus, predicting SPMD is quite doable for the Vietnam market.…”

Section: Introductionmentioning

confidence: 99%

Forecasting stock price movement direction by machine learning algorithm

Khoa

Huynh

2022

IJECE

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<p><span lang="EN-US">Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of powerful algorithms features in machine learning and artificial intelligence has opened a promising new direction. This study compares the predictive ability of three forecasting models, including <a name="_Hlk106797328"></a>support vector machine (SVM), artificial neural networks (ANN), and logistic regression. The data used is those of the stocks in the VN30 basket with a holding period of one day. With the rolling window method, this study got a highly predictive SVM with an average accuracy of 92.48%.</span></p>

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Efficient market hypothesis and calendar effects: Empirical evidences from the Vietnam stock markets

Cited by 6 publications

References 1 publication

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

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

Investor Response to Financial Performance and Business Prospect

Forecasting stock price movement direction by machine learning algorithm

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