LIST OF ABBREVIATIONSABBREVIATIONS ORIGINALCAPM Capital Asset Pricing ModelCPI Consumer prices index CSAD Cross-sectional Absolute Standard Deviation ECO Market economy indicators ER Exc
Current situation of Vietnam's stock market in the period of 2020-2022
Between 2020 and 2022, Vietnam's stock market experienced significant fluctuations due to the economic impact of the COVID-19 pandemic The pandemic severely disrupted Vietnam's export and import activities, leading to a decline in commodity values Key sectors, including tourism, aviation, hotels, and restaurants, faced substantial challenges, resulting in scaled-back operations or closures In early 2020, the VNIndex plummeted to 659.21 points on March 24, marking a 31.4% decline from the end of 2019 and reaching its lowest point in two years However, the market rebounded, with the VNIndex peaking in December 2020, reflecting a 14.87% increase compared to the same period in 2019.
In 2020, VNIndex grew by more than 14%, even higher than Vietnam's GDP growth.
In 2021, Vietnam's stock market experienced significant growth, with the VN-Index rising by 1.34% in December Despite the challenges posed by the COVID-19 pandemic, the market demonstrated resilience, consistently setting new records in both index and trading value By the end of October 2021, the VN-Index had reached 1,444.27 points, reflecting a remarkable increase of 30.8% from the end of 2020, as noted by MB Securities Joint Stock Company in their analysis of the trading session on November 24.
In 2021, the VN-Index rose by 25.24 points (1.72%) to reach 1,488.87 points, while the VN30 index climbed by 32.17 points (2.1%) to 1,565.29 points The market favored buyers, with 287 stocks advancing and 164 declining Liquidity on the HOSE increased to VND 33,763 billion, marking a significant rise despite the challenges posed by the COVID-19 pandemic, which saw market liquidity reach a record high of over one billion USD per session.
In 2022, Vietnam's stock market faced significant challenges due to ongoing pandemic complications, prolonged supply chain disruptions, and government measures to combat inflation Although the market maintained growth momentum from late 2021 at the beginning of the year, it experienced a sharp downturn starting in April, with intermittent recoveries in May, August, and November By December 30, 2022, the VN-Index closed at 1007.09 points, reflecting a 32.8% decline from the end of 2021, while the HNX-Index fell to 205.31 points, down 56.7% Additionally, market liquidity decreased throughout the year, with the average transaction value significantly lower than in previous years.
In 2022, the average trading session on the stock market was VND 20,168 billion, reflecting a 24.1% decrease from 2021 This year also highlighted significant issues within the market, including manipulative trading practices, information concealment, and profit-driven misconduct, underscoring persistent limitations and unhealthy conditions that authorities have addressed with decisive actions.
Results of building market sentiment indicators in Vietnam stock market
Summary statistics of the market sentiment indicators building model
There are 4 research variables in the model, namely RIPO, NIPO, TURN, S, which are taken over time from 2020-2022.
Table 4.1: Descriptive statistics of variables representing market sentiment
Results PCA of the market sentiment indicators building model
4.2.2.1 Matrix of correlation of the market sentiment indicators building model Assumption H0: the correlation coefficient is zero Therefore, if this Sig is less than 5%, we can conclude that the two variables are correlated If this Sig is greater than 5%, then the two variables are not correlated.
In regression analysis, a crucial requirement is that the independent variable must demonstrate a correlation with the dependent variable If correlation analysis reveals no relationship between these variables, the independent variable will be excluded from the regression analysis process.
Table 4.2: Correlation between the variables representing market sentiment indicators có mo
The results of the Pearson correlation analysis show that the independent variables are correlated with each other (sig=1 are considered appropriate to represent variables.
The key component, known as PC1 (Principal Component 1), accounts for the highest percentage of cumulative variance, indicating its superior ability to interpret the data results from the original variables Therefore, PC1 is selected to represent market sentiment indicators Additionally, the eigenvector results reveal that the load vector illustrates the linear relationships between component variables and their corresponding principal components (PCs) The sign of the load vector indicates whether the relationship is inverse or direct, while its value reflects the extent to which each variable explains the principal component.
The NIPO variable significantly influences PCI, while the S variable exhibits an inverse relationship with PC1 Additionally, the RIPO variable shows a substantial value of 68.69% It is essential to verify the suitability of the 3-factor Sent model for accurate analysis.
Table 4.8: KMO results 3 factors of market sentiment indicators
The KMO test results indicate that the average KMO coefficients across all three stages exceed 0.5, while the KMO coefficients for the component variables are also favorable, surpassing 0.3 Consequently, this dataset is deemed appropriate for Principal Component Analysis (PCA).
Results of the building of the market sentiment indicators - s5 sôô 42
The PCA analysis reveals that the first major component, with an eigenvalue exceeding 1, accounts for the total variance of the high data set in the Vietnamese stock market Consequently, market sentiment indicators are effectively represented by this primary component during PCA analysis across different periods.
Based on the results of the factor loading (eigenvector) analysis for the first main component, the market sentiment indicators is represented as follows:
Results of building economic indicators on Vietnam stock markef ô<< 43
Summary statistics of the market economy indicators building model
Table 4.9: Descriptive statistics of variables representing market economy a= [ean eee se ed
Results PCA of the market economy indicators building model
4.3.2.1 Matrix of correlation of the market economy indicators building model
In Principal Component Analysis (PCA), a strong correlation among parent variables leads to a reduced composition of the new variable, which may not encompass all the information from the original variables PCA effectively condenses multiple highly correlated variables into a single variable characterized by a lower correlation coefficient.
Table 4.10: Correlation between the variables representing market economy
(Source: STATA result) The degree of correlation between the components of the economic sentiment indicators is less than 0.5, suitable for PCA analysis.
4.3.2.2 The results of main component analysis 3 factors of the market economy indicators building model e Eigenvalue selection result Table 4.11: Results eigenvalues 3 factors of market economy indicators
The analysis reveals that component | is deemed suitable, with a specific value of 1.425 and a cumulative variance of 47.53% of the total data variance Consequently, principal component 1 (PC1) is selected to effectively represent the economic sentiment indicators.
The CPI and OP variables significantly influence PC1, with impacts of 69.6% and 70.3%, respectively, while the ER variable negatively affects PC1 To assess the suitability of the 3-factor Eco model, KMO (Kaiser-Meyer-Olkin) testing is employed, with a KMO coefficient of at least 0.5 deemed acceptable for primary component analysis according to Kaiser (1974).
Table 4.13: KMO results 3 factors of market economy indicators
KMO coefficients of the component variables reach relatively good values (greater than 0.3).Therefore, the dataset is suitable for PCA analysis.
Results of the building of the economic indicators s- 55s 5< s<<sssssss 45 4.4 Result of the model evaluates the influence of market sentiment indicators and
Based on the results of factor loading analysis (eigenvector) for the first main component, the economic sentiment indicators through each period is expressed through each period as follows:
ECO = 0.696*CPI - 0.1448*ER + 0.7033* OP4.4 Result of the model evaluates the influence of market sentiment indicators and market economic indicators on stock valuation
Summary statistics of the model evaluates the influence of market sentiment
Table 4.14: Descriptive statistics of variables in the CAMP model mw 1224 0.008249 0.1245053 -1.002898 0.5349588
1224 0.02936 0.0089735 02082 0.05194 indicators and market economic indicators on stock valuation
To consider the correlation between variables, the correlation coefficient matrix is analyzed using Stata software as follows:
Table 4.15: Matrix of correlation coefficients between variables in the CAMP
Between 2020 and 2022, the analysis revealed a negative correlation between the risk-free rate of return (RF) and economic sentiment indicators (ECO) with the asset return (RI).
Results of regression analysis method POLS in the model evaluates the influence
The Ordinary Least Squares (OLS) model is the most basic form of least squares regression, treating cross-sectional data as homogeneous In this study, OLS regression was conducted using Stata 16 software, yielding the following results.
Table 4.16: Results of regression analysis method POLS in the model evaluates the influence of market sentiment indicators and market economic indicators on stock valuation
Cc represented by *, **, *** respectively The parentheses value is the P-value.
The F-test significance level of 0.0000, which is below the 5% threshold, indicates that the R-squared value of the population is significantly greater than zero This suggests that the regression coefficients in the overall regression equation are not all equal to zero.
R-squared = 0.4077 means that the independent variables explain 40.77% of the variation of the dependent variable.
In the literature and books on statistics it is stated that if the coefficient VIF 5%, accepts the Ho assumption, the model has no self- correlated phenomena.
CONCLUSION AND RECOMMENDA TIONS .ceccs<essesse 57 5.1 Discuss research results - 5 5 << 2 5 9 0000040010 0 0004089000088 6 57 5.1.1 Comment on the results of building the market sentiment indicators
Comments on the results of the construction of economic indicafors
During the study period, ECO was influenced by 3 variables: inflation, exchange rate and oil price.
The equation ECO = 0.696*CPI - 0.1448*ER + 0.7033*OP highlights that oil prices significantly impact economic sentiment indicators, particularly during the volatile period of 2020-2022, influenced by the Russia-Ukraine conflict This unpredictability in oil prices has led to heightened investor concern Additionally, inflation emerges as the second most influential factor affecting economic indicators, contributing to the overall fluctuations experienced during this timeframe.
Comments about the impact of SENT and ECO on stock market returns
This article explores the influence of market sentiment (SENT) and economic sentiment (ECO) on stock returns and the Capital Asset Pricing Model (CAPM) By analyzing various sentiment factors, the study presents three models that demonstrate how SENT and ECO affect stock performance and investor expectations The findings highlight the significant role of both market and economic sentiments in shaping stock return dynamics.
RI= -0.003 + 0.555RF+0.968(RM-RF)+0.0012SENT+0.0377ECO
The models serve as an enhancement to the traditional Capital Asset Pricing Model (CAPM) by demonstrating that psychological factors, as reflected in market sentiment indicators, significantly influence return on investment (Ri).
Comments about the existence of herd sentiment in Vietnam's stock market 57 5.2 RecommenndafẽOTNS o5 5< 55 555 s55 sSS95 555SS59555555555
In this research paper, in order to accurately verify the existence of herd sentiment in Vietnam's stock market in the period of 2020-2022, the author has built psychological factors
57 affecting asset profitability including market sentiment (SENT) and economic psychology (ECO).
The study investigates the herd behavior of participants in the Vietnamese stock market by employing the methodology established by Chang et al (2000) It analyzes the correlation between various factors, including the degree of dispersion, rate of return, and market yield The research utilizes closing price data from the VN30 stock basket, covering the period from January 1, 2020, to December 2020.
A study conducted on December 31, 2022, utilizing the ordinary least squares (OLS) estimation method, confirms the presence of herd behavior in Vietnam's stock market, aligning with previous research findings Notably, there have been instances where the VN-Index experienced significant rises without any positive economic or corporate news, as well as sharp declines despite the absence of negative reports These observations underscore the persistent influence of herd behavior in the Vietnamese stock market.
Herd behavior can lead to market bubbles and systemic disruptions, stemming from human psychology that cannot be entirely eradicated However, implementing specific strategies can help mitigate herd behavior, fostering a more stable market environment.
5.2 Recommendations 5.2.1 Recommendations for individual investors First, individual investors must improve their understanding of the stock market. Individual investors tend to appreciate their abilities and knowledge Moreover, individual investors also tend to evaluate stocks according to emotions, unreasonable calculations. Therefore, the author recommends that investors need to equip basic knowledge about the stock market before investing.
Investors must enhance their skills in analyzing and evaluating investment philosophies to avoid the pitfalls of crowd investing In Vietnam, numerous social media groups facilitate the exchange of stock and market information among investors However, the overwhelming volume of securities information makes it challenging to verify authenticity, often leading to reliance on potentially misleading data To mitigate risks, investors should approach social media insights with caution and develop the ability to discern valuable information from noise.
5.2.2 Recommendations for Stock Exchanges, securities companies First, constantly improve the organization's operating apparatus in order to operate transparently and effectively Moreover, Stock Exchanges and Securities Companies need to strictly manage customers, or individuals operating in the market under the management of organizations to monitor and react promptly to fraudulent and illegal acts of some entities for improper profiteering Information needs to be arranged to be easy to access, avoiding the phenomenon that only a small part has access to internal information, thereby manipulating the market, causing damage to retail investors who have difficulty accessing information.
To enhance investor knowledge, securities companies must focus on educating clients about investment psychology and the intricacies of the stock market This includes informing investors about potential risks and opportunities, as well as teaching them how to effectively evaluate company information By improving knowledge dissemination, firms can empower investors to make more informed decisions.
5.3 Research limitations and directions for future studies
Despite extensive efforts in exploring previous research, developing a topic, selecting appropriate research methods, and designing research models with thorough and objective procedures, this study still faces inherent limitations.
Due to the restricted research timeframe, the study has not thoroughly explored various stages of the market Extending the research period will enable a more comprehensive evaluation of Vietnam's young stock market, which has been established and evolving for over 20 years.
The research focused on 34 companies listed on the Ho Chi Minh Stock Exchange, which does not fully capture the complexities of market movements due to the presence of numerous influential companies The team successfully developed market sentiment and economic indicators, addressing the challenges of quantification in this area However, various methods and factors can further enhance these indicators By diversifying market sentiment and economic indicators, stakeholders can gain a more objective perspective, reinforcing the significance of behavioral finance in the broader financial market and specifically in the stock market.
In evaluating the influence of psychological factors on stock valuation, the research team employed the capital asset pricing model (CAPM) to analyze the effects of market sentiment and economic indicators Although CAPM is a foundational model in traditional finance, its effectiveness has sparked considerable debate and controversy Notably, in the Vietnamese market, investors seldom utilize the CAPM model for their trading decisions.
The interpretation of the group's research results may be constrained by a 59 valuation To comprehensively evaluate the impact of field psychology, it is essential to utilize widely recognized models such as the Fama-French 3-factor and 5-factor models, which can enhance diversification and provide a more thorough assessment of psychological factors.
5.3.1 Directions for future studies After reviewing the entire study and giving the limitations and difficulties mentioned above, the author proposes some directions for further research development This thesis represents a relatively simple and comprehensive study of 3 years Therefore, the next research direction can follow a different approach, more specific for each stage of the Vietnamese market With the limitations of research models and methods, subsequent studies can study more deeply and complicatedly to find out the factors affecting the stock market returns.
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APPENDIX APPENDIX I: list of 34 research companies
Company name Asia Commercial Joint Stock Bank Joint Stock Commercial Bank for Investment and Development of Vietnam
BAOVIET INSURANCE CORPORATION Coteccons Construction Joint Stock Company Vietnam Joint Stock Commercial Bank for Industry and Trade
Vietnam Commercial Joint Stock Export Import Bank
FPT Corporation PetroVietnam Gas Joint Stock Corporation Vietnam Rubber Group - Joint Stock Company
Ho Chi Minh City Development Joint Stock Commercial Bank
Hoa Phat Group JSC Khang Dien House Trading and Investment JSC
Military Commercial Joint Stock Bank
Masan Group Corporation Mobile World Investment Corporation
No Va Land Investment Group Corporation
Viet Nam National Petroleum Group Phu Nhuan Jewelry Joint Stock Company
PetroVietnam Power Corporation Refrigeration Electrical Engineering Corporation Saigon Beer - Alcohol - Beverage Corporation
Thanh Thanh Cong - Bien Hoa JSC
SSI Securities Corporation Sai Gon Thuong Tin Commercial Joint Stock Bank Vietnam Technological and Commercial Joint Stock Bank
Stock code ACB BID BVH CTD CTG EIB FPT GAS GVR
HDBHPGKDHMBBMSNMWGNVLPLXPNJPOWREESABSBTSSISTBTCB
26 Hoang Huy Investment Financial Services JSC TCH
27 Tien Phong Commercial Joint Stock Bank TPB
28 Bank for Foreign Trade of Vietnam VCB
30 Vingroup Joint Stock Company VIC
3] Vietjet Aviation Joint Stock Company VIC
32 Viet Nam Dairy Products Joint Stock Company VNM
33 Vietnam Prosperity Joint Stock Commercial Bank VPB
34 Vincom Retail Joint Stock Company VRE
APPENDIX 2: Results run on Stata 16 software
Table 2.1: Results of regression analysis method POLS in the model evaluates the influence of market sentiment indicators and market economic indicators on stock es sanen
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Table 2.2: Results of regression analysis method FEM, REM, ROBUST FEM in the model evaluates the influence of market sentiment indicators and market economic indicators on stock valuation ane ea
Table 2.3: Results of regression analysis method POLS of the model of testing the