UNIVERSITY OF ECONOMICS AND BUSINESSFACULTY OF BANKING AND FINANCE THE IMPACTS OF GEOPOLITICAL RISKS ON THE POSSIBILITY OF HERDING BEHAVIOR IN VIETNAM’S STOCK MARKET IN THE PERIOD 2015-2
Test 1: The existence of herding behavior in the Vietnam’s stock market
Cross-Sectional Absolute Deviation MOE] ôs13 92 E+5kEsseesseersek 24
To test herding behavior in the stock market, my thesis uses the Cross-sectional absolute deviation (CSAD) model This model was built by Chang, Cheng and Khorana (2000).
In their 2000 study, Chang, Cheng, and Khorana utilized absolute deviation to analyze profit dispersion in relation to market returns, differing from the traditional standard deviation approach The Capital Asset Pricing Model (CAPM) suggests that under normal circumstances, returns should rise in accordance with market returns as investors rely on their own analyses However, during periods of significant market volatility, investors often abandon their individual assessments in favor of collective market behavior This herding tendency causes return rates to align more closely with the overall market return, resulting in a unique pattern of dispersion that initially increases but may decrease if herding is pronounced Consequently, the CSAD model has emerged as a popular tool for measuring herding behavior and assessing market return distributions (Youssef and Mokni, 2018; Chang et al., 2020; Chong et al., 2020).
Inheriting Chang and Cheng's (2000) model, the thesis uses the following model:
CSAD, is the absolute value of profit deviation at the time of t, which measures the dispersion of profits.
7, is the price-shifting profit of company shares i at the time of t;
Ymt_ is the average price fluctuation profit of N companies in the portfolio at the time of t.
The author contends that the presence of herd behavior in the market disrupts rational asset valuation models, leading to a disconnect between market return spreads and profitability ratios In such scenarios, investors often mimic the crowd's actions, resulting in a shift from a linear to a non-linear relationship in market dynamics.
On that basis, Chang and Cheng have established a quadratic equation to detect the herd behavior effect as follows:
In a highly volatile market, investor reactions often exceed normal levels, resulting in herd behavior This phenomenon heightens the correlation among profit-generating channels for investors, ultimately diminishing the potential for profitability in the market A negative coefficient for y2 indicates the presence of herd behavior within the stock market.
Therefore, the hypothesis for testing the existence of herding is generally as below.
A positive and statistically significant coefficient for the two variables in the test equation indicates the absence of herding behavior in Vietnam's stock market Conversely, significant price movements prompt similar reactions from investors, leading to increased correlation and reduced dispersion in stock return rates.
(7,1)? in the model takes a negative value (-) and is statistically significant, i.e, there is the existence of herding behavior in the stock market.
3.1.1.2 Structural break The parameters in this static model (CSAD) are however being assumed to remain unchanged during the study period, but in reality, there are many different economic and political events occur that can cause disruptions in the financial data structure, creating structural break Research evidence of parameter changes in macroeconomic time series (Ang and Bekaert, 2002; Stock and Watson, 2006; Andreou and Ghysels, 2009).
Figure 3.1 Volatility of Vietnam's stock market in the period 2015-2020.
Da xuất bản TradingView.com, Tháng Năm 10, 2023 17:18 UTC+7
A structural break refers to a sudden alteration in a data structure over time, impacting the relevance of statistical models that assume data is derived from a consistent process This break can arise from changes in the underlying meaning or parameters influencing the creation of the data series.
David Hendry highlighted the importance of testing the stability of regression coefficients, as their instability can lead to forecasting failures Structural stability, defined as the time-bound invariance of these coefficients, is crucial for the effective application of linear regression models Regular assessments of this stability are essential to ensure accurate predictions and reliable model performance.
Tests of parameter instability and structural changes in regression models are an important part of applied econometric research, which began with Chow (1960), Quandt
(1960) or Adrews & Ploberger (1994) More recently, Bai & Perron (1988, 2003) provided theoretical and computational results that extend beyond Quandt-Adrews by allowing for many unknown structural breaks.
The thesis inherited the structural break test according to research by Bai & Perron
A study conducted in 2003 using Eviews 8 software revealed inaccuracies in the breaks associated with the CSAD model, demonstrating that significant market breaks occurred during the study period.
3.1.1.3 Rolling window analysis Rolling window regression is a technique most commonly used in time series analysis of financial data to detect changes in linear regression results, such as the regression coefficient, over time (Wang and Zivot, 2006) This technique uses the same principle as a rolling average, except that a linear regression is applied to each period (window) rather than the mean (Stavroyiannis va Babalos [2017])
The main steps of rolling window regression include:
- Choose the size of the rolling window and a stride that allows the window to switch through different times of the time series.
- Split the data into successive rolling windows and get the values of the independent and dependent variable in each window.
- Use these values to create a linear regression model and calculate regression parameters.
- Use the model to predict the value of the dependent ballin the next window.
Rolling windOW q'ỡQ]SẽS << <5 3111911111911 1191 01g ng ng 27
series or the number of times the window roll is reached.
This thesis conducted a rolling window analysis utilizing Eviews 8 software, following the identification of a structural break in the study data during test 1 The primary objective was to examine the presence of herding behavior in Vietnam's stock market using updated data through rolling windows A t-statistic value of less than -1.96 indicates the existence of herding behavior in the stock market.
Test 2: The impact of geopolitical risks on the possibility of herding behavior in Vietnam’s Stock Market 0111
This thesis analyzes the influence of geopolitical risks on herding behavior in Vietnam's stock market, utilizing the GPR index established by Caldara and Jacoviello (2022) and a probit model The probit model, originally coined by Chester Bliss in 1934 and systematized by John Gaddum in 1933, has its roots in the Weber-Fechner law introduced by Gustav Fechner in 1860, with foundational work rediscovered throughout the 20th century.
The probit regression model is a non-linear statistical approach that estimates the probability of a binary outcome based on independent variables This model is particularly useful for analyzing situations where the dependent variable is categorical, allowing researchers to understand the likelihood of specific events occurring The probit model can be mathematically expressed through a specific formula that outlines its calculations.
In a statistical model, the dependent variable Y indicates the likelihood of an event occurring (Y = 1) based on the independent variables X The variable Z is a linear combination of these independent variables, weighted by coefficients (b0, b1, b2, , bn) To estimate these parameters, the maximum likelihood estimation technique is employed.
The variable that depends on herding behavior is synthesized from the results of the CSAD model, which is the dummy variable that exists herding behavior in the stock market
The Geopolitical Risk (GPR) Index, as defined by Caldara and Iacoviello (2022), indicates whether herding behavior is present (1) or absent (0) This independent variable is based on date-specific data from the GPR index, which has been updated to its latest version available at [matteoiacoviello.com](https://www.matteoiacoviello.com/gpr.htm).
We also ran a probit model using Eviews 8 software to see if geopolitical risks would increase the likelihood of herding behavior If the GPR is positive, it is statistically significant.
Data collection and proCeSSẽnB - - Q1 ng ky 29
Research data - - - Gv 29
This study analyzed secondary data on the closing prices of VN30 stocks and 52 stocks listed on HOSE from January 27, 2015, to December 31, 2019, encompassing 1,233 observations for each variable The objective was to assess the influence of geopolitical risks and the presence of herding behavior prior to 2020, before the onset of the COVID-19 pandemic in Vietnam.
The VN30 index is derived from the 30 largest and most liquid companies in the market, representing over 80% of the total market capitalization and 60% of the overall transaction value of the VN-Index This selection ensures that the VN30 index more accurately reflects the supply-demand dynamics of stocks compared to the VN-Index.
The formula for calculating VN30 index is as follows:
Pj: current market price of stock i
Q);: volume/ number of outstanding stocks i
Pp: the initial price of stock i
Qo;: the initial volume of stock i
The formation and development of the Ho Chi Minh Stock Exchange (HOSE)
The State Securities Commission was established on November 28, 1996, and on July
Vietnam's stock market officially launched on July 28, 2000, with the VN Index set at a base value of 100 points Over nearly 20 years, it has emerged as the most effective investment channel, delivering an average annual return of 12.5%.
The Vietnam stock market has experienced significant growth, enhancing its structure and contributing to the development of market economy institutions while fostering international integration It serves as a crucial capital channel for the economy The Ho Chi Minh Stock Exchange (HOSE) was officially established on July 20, 2000, under Decision No 127/1998/QD-TTg, originally known as the Ho Chi Minh City Stock Exchange (HSTC) until it was renamed on August 8, 2007.
Initially, total foreign ownership was restricted to 20% for stocks and 40% for bonds However, to enhance market attractiveness, attract indirect foreign investment, and improve liquidity, the government increased the foreign ownership limit to 30% for stocks in 2003 and subsequently to 49%.
Since 2005, the bond market has seen significant growth, with 111 listed corporations, 55 securities companies, 18 managed funds, and 61 depository funds after seven years of operation Additionally, the Ho Chi Minh Stock Exchange (HOSE) continues to advance its development of Exchange Traded Funds (ETFs).
The Geopolitical Risk Index (GPR Index), created by Caldara and Iacoviello in 2022, utilizes automated text searches from the archives of ten major newspapers, including the Chicago Tribune, Financial Times, and The New York Times This index is calculated by analyzing the monthly count of articles related to negative geopolitical events in relation to the total number of articles published The GPR Index serves as a robust tool for evaluating the effects of geopolitical risks on the economy, politics, and society of various countries.
30 the global The latest version data is updated — and taken https://www.matteoiacoviello.com/gpr.htm for use in the research paper.
Figure 3.2 Recent GPR Index from 1985.
200 ~ bombing US ii Airstrikes on Iraq
1 °° °ằ+ô ÊÊ °° °° f ff gg yg TT.
Source: Charles Schwab, Macrobond, Board of Governors of the Federal Reserve System as of 2/10/2023.
Figure 3.3 Daily GPR Index from 2015 to 2020.
Sample and Variables s11 TH ng ng kệ 32
The study analyzes a sample of 52 stocks listed on the HOSE, with daily data collected from January 27, 2015, to December 31, 2019, resulting in a total of 1,233 observations All transaction data used in this analysis was sourced from Fiinpro prior to the COVID-19 pandemic.
As specified in equation (2), there are three variables in the model The dependent variable (y) is CSAD and the independent variable (y) is market return (rm).
The independent variable utilized in this research is the market return variable, derived from the closing prices of VN30 and HOSE The author calculates the profitability ratio based on the market portfolio's date using a specific formula.
B.: is the close price of the stock index on the t-day.
B._¡: is the close price of the stock index on the previous trading day (t-1).
Dependent variable: used in the research is the average profitability ratio dispersion of securities compared to the profitability ratio of the market portfolio, calculated according to the formula:
N: is the number of companies listed on the market at the time of t. r¡¿: is the profit margin of stock i on day t.
Tm,t: is the return value of the stock index on day t.
In addition, the profit margin of stock i on day t is calculated according to the formula: h 1
The closing price of a stock represents its final trading value at the end of the day, serving as a crucial metric for investors, financial institutions, and organizations that evaluate stock performance and make informed decisions about the company.
RESULTS AND DISCUSSION - - Gv *n ng nHnHkHkhkg 34
Research Results . - - G6 1n TH TH HH HH Hhn 34
4.1.1 Test 1: The existence of herding behavior in Vietnam’s Stock Market in the period 2015-2020.
Use the processed data in Excel to run an equation showing the relationship between profit spread and average market yield in Eviews 8 software, the results obtained as Table 4.1.
Table 4 1 CSAD model test results.
Variable Coefficient Std Error t-Statistic Prob.
S.E of regression 0.003298 Akaike info criterion -8.589374
Sum squared resid 0.013389 Schwarz criterion -8.581074
Log likelihood 5297.349 Hannan-Quinn criter -8.586252
Source: Results of the author extracted from Eviews 8 software.
Table 4.1 reveals that the coefficient y2 for test 1 is both negative and statistically significant, leading us to reject the null hypothesis Ho: y2=0 in favor of the alternative hypothesis H1 This outcome suggests that herding behavior has been present on the HOSE from 2015 to 2020.
Herding behavior is predominantly observed in developing and emerging countries, as highlighted in chapter 2 Research conducted in various nations, including Taiwan (Kutan & Chen, 2009), Tunisia (Rekik & Boujelbene, 2013), and several Asian developing countries (Hwang & Salmon, 2004), supports this finding.
Equation (2) incorporates two independent variables: market yield (z„) and squared market yield (z2), demonstrating the absence of linear multi-additivity Furthermore, the author has controlled for variance in change by employing a robust standard error model.
4.1.2 Determine structural break by Bai-Perron (2003) method.
The static model presented in equation (2) may produce misleading conclusions regarding herding behavior, as it assumes that the parameters are constant over the entire study period (Balcilar et al., 2013).
This thesis employs the Bai and Perron (2003) method to identify structural breaks in equation (2), permitting up to five breaks in the model The analysis uses a trimming percentage of 5% and a significance level of 0.05, leading to the following results.
Table 4 2 Bai-Perron (2003) structural break test results.
Bai-Perron tests of 1 to M globally determined breaks
Breakpoint variables: ABSR RM_SQUARED
Break test options: Trimming 0.05, Max breaks 5, Sig level 0.05
Allow heterogeneous error distributions across breaks
Scaled Weighted Critical Breaks F-statistic F-statistic F-statistic Value
UDMax statistic* 108.5173 UDMax critical value** 13.27
WDMax statistic* 120.5852 WDMax critical value** 14.19
** Bai-Perron (Econometric Journal, 2003) critical values.
Source: Results of the author extracted from Eviews 8 software.
During the study period from 2015 to 2020, structural breaks were identified, indicating significant changes in the economic environment due to various shocks or crises These fluctuations can lead to ordinary least squares regression estimates that reflect an average relationship, without accounting for the specific impacts of crises or the system's dynamics before or after these shocks To effectively analyze the time-varying nature of herding behavior, the rolling window methodology is employed.
To enhance the accuracy of the CSAD model test results, it is essential to employ rolling window analysis This method allows us to segment the data into conversion windows across various time periods in the time series, facilitating the recalibration of parameters in the regression equation.
A rolling analysis of a time series model is utilized to evaluate the model's stability across different time periods Consistent parameters throughout the sample should yield stable estimates across rolling windows; however, significant deviations in these estimates indicate underlying instability in the model.
In our research, we selected a rolling window of 90 observations, which is aligned with the system's response times and research objectives (Su and Huang, 2009) While there is no definitive guideline for determining the optimal size of the rolling window, as noted by Stavroyiannis and Babalos (2017), it is essential to establish this size before identifying the earliest break, which occurred on August 24, 2015.
The rolling t-statistic method, illustrated in Figure 4.1, identifies significant periods of herding behavior in the stock market from August 21, 2015, to December 19, 2019 Notable instances occurred on specific dates, including January 13, 2016, and August 16-22, 2016, among others, while other periods showed no statistical significance This evidence suggests that individual investors are particularly influenced by market "shocks" and global political events, driving trends of herding behavior during this timeframe.
Figure 4.1 Rolling t-statistics test results.
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Source: Author's results extracted from Eviews 8 and Excel.
4.1.4 Test 2: The impact of geopolitical risks on the possibility of herding behavior in Vietnam’s Stock Market in the period 2015-2020.
The herding behavior of investors during periods of uncertainty raises important questions about its underlying causes Recently, escalating geopolitical risks have become a significant concern for businesses and the broader financial market This thesis explores the potential impact of these geopolitical risks on investor herding behavior, aiming to understand their influence on market dynamics.
Table 4 3 Probit Model test results.
Herding Coef St Err t- p- [95% Interval] Sig value value Conf
Mean dependent var -0.266 SD dependent var 0.442
Pseudo r-squared 0.001 Number of obs 1143
Akaike crit (AIC) 1326.280 Bayesian crit.(BIC) 1336.363
Source: Results of the author extracted from Eviews 8 software.
The analysis reveals that a positive Geopolitical Risk (GPR) value of 0.431 is statistically significant, indicating a strong correlation with herding behavior among investors, which is represented by a negative value of -0.483, also statistically significant This suggests that heightened geopolitical risks lead to increased imitation of trading actions among investors, aligning with previous research Notably, studies by Nikkinen and Vahamaa (2010) and Burch et al (2003) highlight how events like terrorist attacks can significantly alter investor expectations regarding future profits and risk premiums, as well as negatively impact overall investor sentiment.
4.2 Discussion The research results indicate that geopolitical risks are likely to increase the likelihood of herding behavior among individual investors in Vietnam's stock market in the
Between 2015 and 2020, significant political events such as wars, terrorism, and epidemics led investors to make irrational decisions driven by fear and uncertainty This environment fostered herding behavior, where investors imitated each other's trading actions, further amplifying market volatility.
Geopolitical risk significantly impacts the stock markets of dominant economies like the U.S., China, and Russia For instance, the U.S market faced a crisis during the Iraq war, while Russian indices dropped notably amid the Ukraine crisis and subsequent sanctions in 2014 (Nivorozhkin and Castagneto-Gissey, 2016) Similarly, the 2018 trade war between China and the U.S led to declines in China's major stock markets (De Nicola et al 2019) This trend is also evident in the Vietnamese stock market.
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This study investigates the influence of geopolitical risks on investor herding behavior in Vietnam's stock market, specifically analyzing the VN30 index and stocks on HOSE from January 27, 2015, to December 31, 2019 The selected timeframe allows for a clear assessment of geopolitical impacts and crowd sentiment prior to the onset of the COVID-19 pandemic in Vietnam Utilizing the CSAD model, the research confirms the presence of herding behavior on the Ho Chi Minh City Stock Exchange before the pandemic.
19 This result fully supports the research of Hwang & Salmon (2004), Kutan & Chen (2009), Rekik & Boujelbene (2013) on the existence of herding behavior that is more likely to occur in developing and emerging countries However, the static model with all data corresponding to 1233 observations is assumed to be unchanged parameters during the study This is not really true when a series of events about markets and politics, constantly happening, seriously affecting the stability of the economy Therefore, the author continues to investigate structural breaks in the CSAD regression equation using the Bai-Perron method (2003) The results found structural breaks between 2015 and 2020, so the authors used rolling window analysis to further examine the existence of herding behavior in the new data The results show that herding behavior is mostly present during this period This is also very consistent with investment practices in Vietnam, where investors always depend too much on herding behavior (Dang Van Dan, 2016) because they are mainly vulnerable and easily influenced by many factors.
The study offers crucial empirical evidence regarding the presence and effects of herding behavior on investor decisions within Vietnam's stock market These findings enhance the understanding of herding behavior's influence in developing countries like Vietnam, providing market participants with insights into the causes and mechanisms behind this phenomenon This awareness can assist investors in making more informed decisions when herding behavior arises.
CONCLUSION AND RECOMMENDATIONS Ăn SSs he 42 5.1 Summary of research results and Recommendations .- - -ôô- 42
Limitations of the present study and directions for future research
of 5 years and focusing on stocks in the VN30, the results partly cannot accurately reflect the nature of investor sentiment in the market Vietnam's stock market Therefore, the next research direction needs a longer observation sample and a closer analysis of the various complex financial behaviors of investors trading in the Vietnam's stock market to be able to make more specific policy recommendations Secondly, this thesis only focuses on analyzing data in the Vietname's stock market, it is necessary to expand the research area as the developed market (US stock market with Dow Jones and S&P500 indexes, European stock
The study examines various stock market indexes, including the FTSE 100, DAX, and CAC 40, alongside emerging markets like the Shanghai Stock Exchange Future research should incorporate control variables to analyze the effects of the COVID-19 pandemic and macroeconomic factors on individual countries' markets.
Arin, K P., Ciferri, D., & Spagnolo, N (2008) The price of terror: The effects of terrorism on stock market returns and volatility Economics Letters, 101(3), 164-
Research has shown that time-varying rare disaster risks significantly influence stock returns, as discussed by Berkman, Jacobsen, and Lee (2011) in the Journal of Financial Economics Additionally, Bialkowski, Gottschalk, and Wisniewski (2008) explored the impact of national elections on stock market volatility, revealing notable fluctuations in market behavior surrounding these events These studies underscore the complex relationship between economic events and stock market dynamics.
Brenner, M., Pasquariello, P., & Subrahmanyam, M (2009) On the Volatility and Comovement of U.S Financial Markets around Macroeconomic News Announcements Journal of Financial and Quantitative Analysis, 44(6), 1265-
1289 doi:10.1017/S002210900999038X Brites, C (2020) Early Intervention on Autism: What Do We Need to Know? Psychology, 11, 1081-1090 doi: 10.4236/psych.2020.118071.
Brick, T., & Wickstrửm, B (2004) The Economic Consequences of Terror: Guest Editors’ Introduction European Journal of Political Economy, 20(2), 293-300. doi:10.1016/j.ejpoleco.2004.03.004
Caldara, D and Jacoviello, M (2022) “Measuring geopolitical risk,” American
Economic Chang, C., McAleer, M., & Wang, Y (2020) Herding behaviour in energy stock markets during the global financial crisis, SARS, and ongoing COVID-19*.
Renewable and Sustainable Energy Reviews, 134, 110349. doi:10.1016/j.rser.2020.110349 Chira I, Adams M and Thornton B (2008) Behavioral Bias Within The Decision Making Process, Journal of Business and Economic Research, 6(8)
Chen, A H., Robinson, K J., & Siems, T F (2004) The wealth effects from a subordinated debt policy: Evidence from passage of the Gramm-leach-bliley act.Review of Financial Economics, 13(1-2), 103-119 doi:10.1016/s1058- 3300(03)00025-9
Chesney, M., Reshetar, G., & Karaman, M (2011) The impact of terrorism on Financial Markets: An empirical study Journal of Banking & Finance, 35(2), 253-
267 doi:10.1016/j.jbankfin.2010.07.026 Chiang, T C (2021) Geopolitical risk, economic policy uncertainty and asset returns in Chinese Financial Markets China Finance Review International, 11(4), 474-501 doi:10.1108/cfri-08-2020-0115
A study by Chong et al (2020) investigates whether the variability of stock returns in China affects the herding behavior of traders in both local markets and among China's trading partners Published in the Journal of International Financial Markets, Institutions and Money, the research highlights the interconnectedness of global financial markets and the potential influence of China's stock market on trading patterns elsewhere The findings suggest that fluctuations in China's stock returns may lead to synchronized trading behaviors, impacting market dynamics beyond its borders.
Christie, W G., & Huang, R D (1995) Following the pied piper: Do individual returns herd around the market? Financial Analysts Journal, 51(4), 31-37. doi:10.2469/faj.v51.n4.1918
Demirer, R., & Kutan, A M (2006) Does herding behavior exist in Chinese stock markets? Journal of International Financial Markets, Institutions and Money, 16(2), 123-142 doi:10.1016/j.intfin.2005.01.002
Devenow A and Welch I (1996) Rational crowd psychology in financial markets, European Economic Review, 40, 603-615
Edrington, L H., & Lee, J H (1993) How markets process information: News releases and_ volatility The Journal of Finance, 48(4), 1161-1191. doi:10.1111/j.1540-6261.1993.tb04750.x
Elder, J., Miao, H., & Ramchander, S (2012) Impact of macroeconomic news on metal futures Journal of Banking & Finance, 36(1), 51-65. doi:10.1016/j.jbankfin.2011.06.007
Elsayed, A.H., Helmi, M.H (2021) Volatility transmission and spillover dynamics across financial markets: the role of geopolitical risk Ann Oper Res 305, 1-22 doi: 10.1007/s10479-021-04081-5
In his 2022 study, H.S Emsen explores the impact of geopolitical risks and political uncertainties on stock markets, focusing on developing Asian countries Utilizing a new generation panel data analysis, the research highlights country-specific responses to these risks and uncertainties, providing valuable insights into market behavior in the context of political dynamics The findings, published in the Journal of Process Management and New Technologies, underscore the significance of understanding local factors when assessing stock market volatility in the region.
2, 2022, pp 82-101 Froot, K.A., Scharfstein, D.S., Stein, J.C., 1992 Herd on the street: Informational ineciencies in a market with short-term speculation Journal of Finance 47,
Garcia, D (2013) Sentiment during recessions The Journal of Finance, 68(3), 1267-1300 doi:10.1111/jofi.12027
Huang, T., Wu, F., Yu, J., & Zhang, B (2015) International political risk and government bond pricing.Journal of Banking & Finance,55, 393-405. doi:10.1016/j.jbankfin.2014.08.003
Manela, A, & Moreira, A (2017) News implied volatility and disaster concerns Journal of Financial Economics,123(1), 137-162. doi:10.1016/j.jfineco.2016.01.032
My, T N., & Truong, H H (2011) Herding behaviour in an emerging stock market: Empirical evidence from Vietnam Research Journal of Business Management, 5(2), 51-76 doi:10.3923/rjbm.2011.51.76
McQueen, G., Pinegar, M.A., Thorley, S., 1996 Delayed reaction to good news and the cross-autocorrelation of portfolio returns Journal of Finance 51, 8894919.
Newey, W.K., West, K.D., 1987 A simple positive semi-de®nite, heteroscedasticity and autocorrelation consistent covariance matrix Econometrica 55, 703+708.
Nikkinen, J, & Vahamaa, S (2010) Terrorism and stock market sentiment Financial Review, 45(2), 263-275 doi:10.1111/j.1540-
Pastor, L, & Veronesi, P (2013) Political uncertainty and Risk Premia Journal of Financial Economics, 110(3), 520-545 doi:10.1016/j.jfineco.2013.08.007
The research by Rigobon and Sack (2005) explores the impact of war risk on U.S financial markets, highlighting significant correlations between geopolitical events and market fluctuations Additionally, Smales (2019) investigates the spillover effects of geopolitical risk on volatility in oil and stock markets, providing insights into the interconnectedness of these financial sectors Both studies underscore the importance of understanding geopolitical influences in financial analysis and investment strategies.
414134 Phung A (2010) Behavioral Finance, University of Alberta Riter J.R (2003) Behavioral Finance, Pacific-Basin Finance Journal, 11(4), 429- 437
Rajan, R.G., 1994 Why credit policies uctuate: A theory and some evidence. Quarterly Journal of Economics 436, 399+442.
Tan, L., Chiang, T C., Mason, J R., & Nelling, E (2008) Herding behavior in Chinese stock markets: An examination of A and B shares Pacific-Basin Finance Journal, 16(1-2), 61-77 doi:10.1016/j.pacfin.2007.04.004
Tetlock, P C (2007) Giving content to investor sentiment: The role of media in the stock market The Journal of Finance, 62(3), 1139-1168 doi:10.1111/j.1540- 6261.2007.01232.x
UNCTAD (2018) Invesment and new industrial policies World Investment Report. United Nations, New York and Geneva.
Youssef, M., & Mokni, K (2018) On the effect of herding behavior on dependence structure between stock markets: Evidence from GCC countries Journal of Behavioral and Experimental Finance, 20, 52-63 doi:10.1016/j.jbef.2018.07.003
References in Vietnamese Đào.T.T.B (2020) Kiểm định sự tồn tại và mức độ của tâm lý đám đông trên thi trường chứng khoán Việt Nam
Lê Anh Tuấn và Phạm Thị Kiều Khanh (2022) đã nghiên cứu tác động của rủi ro địa chính trị đến sự ổn định tài chính của các ngân hàng thương mại ở Châu Á Nghiên cứu này được đăng trên Tạp chí Châu Á, nhấn mạnh mối liên hệ giữa các yếu tố địa chính trị và tình hình tài chính của các ngân hàng, từ đó cung cấp cái nhìn sâu sắc về sự ảnh hưởng của biến động địa chính trị đối với hệ thống ngân hàng trong khu vực.
Ngô.T.T (2014) Do lường hành vi bầy đàn trên thị trường chứng khoán Việt Nam.
Nguyễn Đức Hiển, Đàm Văn Huế, Ngô Duy & Nguyễn Ngọc Trâm (2012), 'Các dạng thiên lệch hành vi của nhà đầu tư cá nhân', Kinh tế và Phát triển, 180, 77-85.
Nguyễn Trọng Tài (2016), ‘Tam lý nhà đầu tư trên thi trường tài chính Việt Nam’,
Tạp chí Ngân hàng, từ http://tapchinganhang.gov.vn/tam-ly-nha-dautu-tren-thi- truong-tai-chinh-viet-nam.htm
Trần Thị Hải Lý (2010) đã nghiên cứu về hành vi bầy đàn trên thị trường chứng khoán Việt Nam, phân tích nguyên nhân và đề xuất một số giải pháp để cải thiện tình hình Bài viết được đăng trên Tạp chí Tài chính và Phát triển, số 6 Nghiên cứu này đóng góp vào việc hiểu rõ hơn về tâm lý nhà đầu tư và cách thức hoạt động của thị trường chứng khoán tại Việt Nam.
Trần Nam Trung (2011) trong luận văn Thạc sỹ của mình đã phân tích tác động của các yếu tố hành vi đến quyết định đầu tư của cá nhân trên thị trường chứng khoán Hồ Chí Minh Nghiên cứu được thực hiện tại Trường Đại học Kinh tế TP.HCM, cung cấp cái nhìn sâu sắc về cách mà tâm lý và hành vi của nhà đầu tư ảnh hưởng đến quyết định đầu tư của họ.
Bài viết của Trần Phương Thảo và Lê Anh Tuấn (2020) nghiên cứu về rủi ro địa chính trị và nguy cơ mất khả năng thanh toán tại các doanh nghiệp ở Đông Nam Á Nghiên cứu cung cấp bằng chứng thực nghiệm cho thấy mối liên hệ giữa các yếu tố địa chính trị và tình hình tài chính của doanh nghiệp, nhấn mạnh tầm quan trọng của việc quản lý rủi ro trong bối cảnh kinh tế hiện nay Kết quả nghiên cứu được công bố trên Tạp chí Nghiên cứu Kinh tế và Kinh doanh Châu Á, khẳng định sự cần thiết phải theo dõi các yếu tố địa chính trị để bảo vệ sự ổn định tài chính của doanh nghiệp trong khu vực.