I hereby declare that the topic "Effect of market liquidity on the relationship betweenstock liquidity and stock price volatility of independent stocks: case of Real estatestocks in Viet
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Research taskS . Ác LH HH TH TH Thọ TT TH HH Tu nhiệt 6 Noo hố
With the above research objective, the research sets out the following tasks:
This article systematically explores the theoretical foundations of market liquidity, examining its impact on stock price volatility It highlights the intricate relationship between liquidity and the price fluctuations of individual stocks, while also analyzing how overall market liquidity influences this relationship Understanding these dynamics is crucial for investors seeking to navigate the complexities of market behavior and make informed decisions.
- Assess the influence of market liquidity on the relationship between stocks liquidity and stock price volatility of stocks in the Real Estate industry.
- Propose some recommendations for investors.
Based on the research objective, the study poses two research questions as follows:
- How does market liquidity affect the relationship between liquidity and stock price volatility of individual stocks in the Real Estate Stocks?
- What should investors pay attention to when investing in stocks in the Real Estate industry?
Research subject and Scope 7n
Research Subjects oo
This article explores the critical aspects of market liquidity, focusing on how it influences the relationship between stock liquidity and stock price volatility It examines the interplay between stock liquidity and price fluctuations, highlighting the impact of overall market liquidity on these dynamics Understanding these relationships is essential for investors and market analysts aiming to navigate the complexities of stock performance.
- Scope of content: Effect of market liquidity on the relationship between stock liquidity and stock price volatility in the real estate industry on the Vietnamese stock market exchange.
- Spatial scope: Listed stocks in the real estate industry on the Vietnamese stock market exchange.
- Time range: The period from 2019 to 2022.
Data collection methodS - - - (+ vn HT HH TH Hit 7 1.5.2 Data Analysis Methods - - c S2 22111 1111111111111 112111111 11 11 tre 7 1.6 Contribution of research
Qualitative method: Gather materials and refer to previous research papers on the subject topic to develop a convincing arguing strategy and theoretical basis.
Quantitative methods: The study uses secondary data of 75 stocks in the real estate industry which are listed on two exchanges, including HOSE and HNX, over a period from
2019 to 2022 These data are collected from audited financial statements and Fiinpro.
This research systematically reviews documents and data to explore the impact of market liquidity on the relationship between stock liquidity and real estate stock price volatility It defines methods for measuring and constructing data related to market liquidity, stock liquidity, and price movements Utilizing the Vector Error Correction Model (VECM), the study analyzes lag numbers and examines both long-term and short-term relationships between stock liquidity and price volatility, providing a comprehensive analysis of their interconnected dynamics.
This paper makes two major contributions.
Firstly, it adds to the literature on the role of market liquidity, the relationship between stock liquidity and stock volatility
Secondly, this research adds the impacts of market liquidity on this relation in different periods of high and low market liquidity in developing countries like Vietnam.
Finally, this is a useful reference for investors who are interested in stocks of the real estate firms in Vietnam.
Structure of the Study cccccceceseseeeceeceeceseeseeseeeeceeeeseeseeaeceeeeseeaeeaeeneeaes 8
The research paper includes 5 main chapters:
Chapter 2 explores the literature and theoretical framework surrounding market liquidity, highlighting its critical role in influencing stock price volatility It examines the intricate relationship between liquidity and stock price fluctuations, emphasizing how variations in market liquidity can significantly affect this dynamic The chapter underscores the importance of understanding these interactions to better comprehend market behavior and investment strategies.
Literature review and theoretical framework on market liquidity, the
Literature review on market liquidity, the relationship between liquidity
Numerous empirical studies have explored the relationship between stock liquidity and its effects on stock prices, returns, and volatility Additionally, researchers are increasingly focusing on market liquidity and its influence on these key financial dynamics.
Amihud and Mendelson (1986) analyzed NYSE stock data from 1961 to 1980, using the bid-ask spread as a liquidity proxy, and found that a larger spread correlates with higher expected returns, compensating for increased transaction costs However, the relationship between stock liquidity and returns is complex due to the influence of the holding period Pastor and Stambaugh (2003) further emphasized that expected stock returns are cross-sectionally related to how sensitive stocks are to changes in aggregate liquidity, revealing that stocks more responsive to liquidity fluctuations tend to have significantly higher expected returns, even when accounting for market returns, size, and value factors.
Numerous studies indicate a positive correlation between trading volume and stock prices across various countries, including the USA, Japan, and the UK (Chen et al., 2001; Lee and Rui, 2002), as well as France, Canada, Italy, Switzerland, the Netherlands, and Hong Kong (Chen et al., 2001), and Hungary and Poland (Gunduz and Hatemi, 2005) However, in Russia and Turkey, stock prices are shown to unidirectionally influence both trading volume and market turnover without reciprocal effects (Gunduz and Hatemi, 2005) Additionally, research by Ariff et al (2016) on liquidity effects on non-bank stock prices in China, India, Korea, and Japan reveals that changes in liquidity positively impact non-bank stock prices, utilizing the dynamic OLS method and single equations for analysis.
As regards to the relationship between stock liquidity and stock volatility or stock price risk, this a bidirectional causality (Bedowska-Sójka and Kliber, 2019) Deuskar (2006)
This article develops a research model linking stock liquidity to stock price volatility by analyzing recent price movements to predict the volatility of risky assets It posits that high volatility correlates with low current returns on risky assets and risk-free rates, resulting in decreased liquidity Consequently, illiquidity exacerbates supply shocks, increasing actual price volatility These findings support liquidity theories and suggest future predictions based on misperceptions of risk tied to options volatility Additionally, research by Chang et al (2017) and Chauhan et al (2017) confirms that stock liquidity mitigates the risk of stock price collapse in the US from 1993 to 2010 and in India, respectively.
Research indicates that increased liquidity enhances the probability of shareholder intervention, compelling managers to disclose unfavorable news rather than suppress it for short-term profit gains This finding is supported by studies conducted by Zhang et al (2018) and Alp et al (2021) in Borsa Istanbul from 2009 to 2019, as well as Tang et al (2022) in China between 2016 and 2017.
Lee and Chung (2017) analyzed Korean stock market data from January 2004 to December 2014, revealing that unanticipated increases in market volatility significantly affect stock returns, particularly when stock liquidity decreases Their findings indicate that a stock's price is more vulnerable to unexpected volatility changes when liquidity providers react strongly to these fluctuations Similarly, Ma et al (2018) found that higher market volatility has a more pronounced effect on returns through liquidity, especially in markets with lower trading values and absent short-selling constraints Notably, this liquidity channel linking market volatility and returns is amplified during crisis periods and in the absence of market makers.
Choi and Munro (2022) emphasize the critical impact of financial market liquidity on investors' reactions to news, which in turn affects market volatility Their research indicates that low market liquidity heightens traders' sensitivity to news, resulting in increased herding behavior due to the influence of others' liquidations Additionally, Gũnter Strobl (2022) presents a model of procyclical market liquidity, illustrating that adverse selection leads to illiquidity in risky investments.
The liquidity of assets is influenced by the market's information structure, with liquidity provision modeled as a recurring game characterized by poor public monitoring Typically, periods of high liquidity and economic growth are succeeded by phases of illiquidity and reduced activity These endogenous liquidity fluctuations arise from the necessity to incentivize investors to generate costly information Helena et al (2023) introduce market-wide liquidity indicators, emphasizing the extremes of the liquidity distribution rather than relying on average individual indicators Their hypothesis posits that changes in market liquidity do not uniformly affect all stocks, and they find that the relationship between market liquidity and market state is highly nonlinear, indicating that market conditions asymmetrically impact the tails of the liquidity distribution.
In Vietnam, numerous authors focus on the relationship between stock liquidity and its effects on firm profitability (Nguyen Anh Phong, 2012), firm valuation (Vo Hoang Oanh, 2013), and stock volatility (Ta Thi Thanh Thuy, 2016; Nguyen Thi Van Hanh and Vo Van Dut, 2021).
Nguyen Anh Phong (2012) investigated the effects of liquidity risk on the profitability of companies listed on the Vietnam stock exchange from 2007 to 2011 The study measured stock liquidity using three methods: (i) turnover, calculated as the number of shares traded divided by the number of shares outstanding; (ii) the liquidity of a share, defined as the ratio of the average number of shares traded in a month to the average total shares traded in the market for that month; and (iii) the ratio of the average trading value in a month to the average trading value of the entire market The findings indicate a positive relationship between liquidity and profitability, with turnover and average trading value significantly correlating with profitability Furthermore, the liquidity indicators provide a better explanation of stock returns and risks compared to the Capital Asset Pricing Model (CAPM).
Vo Hoang Oanh (2013) investigates the impact of liquidity on securities valuation in Vietnam, focusing on how the liquidity premium influences the risk associated with homogeneous portfolios Analyzing data from non-financial companies listed on the HOSE between December 2006 and December 2012, the study reveals that incorporating a liquidity ratio into the Fama-French three-factor model enhances its explanatory power This finding underscores the significance of liquidity in asset return assessments.
11 liquidity factor has a significant impact on portfolio return To be precise, stock returns are negatively correlated with illiquidity.
In her 2016 study, Ta Thi Thanh Thuy investigates the connection between liquidity and stock price volatility for companies listed on the Ho Chi Minh Stock Exchange during 2014-2015 Utilizing a Vector Autoregression (VAR) model, the research assesses the trends and interdependencies between these variables The findings reveal a positive relationship between stock liquidity and stock price volatility, contradicting existing theories.
Nguyen Thi Van Hanh and Vo Van Dut (2021) investigate the relationship between bank liquidity and stock price volatility among 17 commercial banks listed on HOSE, HNX, and UPCOM from Q1 2006 to Q4 2020 Their study employs a random effect regression model and finds that a higher financial gap positively influences stock price volatility, indicating that increased financial gaps lead to lower bank liquidity and greater stock price fluctuations Additionally, the research identifies that total asset size and exchange rate changes negatively impact bank stock price volatility However, it concludes that stock liquidity does not significantly affect commercial bank stock prices in Vietnam.
The relationship between stock liquidity and volatility has garnered significant attention from Vietnamese scholars, yet the influence of market liquidity on this relationship remains underexplored in Vietnam compared to developed and emerging markets Most research has concentrated on stock liquidity's effects on firm profitability, valuation, and volatility This paper distinguishes itself by examining how market liquidity affects the connection between liquidity and price volatility specifically within Vietnam's real estate sector The real estate market is crucial to the national economy, as it attracts resources, generates fixed assets, and stimulates various economic sectors Since the onset of the COVID-19 pandemic in late 2019, the economy, particularly the real estate market, has faced substantial challenges Notable events in 2022, such as the illicit sale of FLC stocks by the Chairman of FLC Group JSC, further underscore the volatility and complexities within this sector.
The violations related to the bond issuance and trading by the Chairman of Van Thinh Phat Group have profoundly affected both the stock and real estate markets Starting mid-2022, the real estate sector experienced a downward trend due to strict credit policies and inaccuracies in corporate bonds, which negatively impacted investor sentiment and led to prolonged project delays Audited financial statements reveal that real estate companies have seen a significant drop in profits, with some facing losses, while declining equities over multiple trading sessions pose a risk of liquidity loss.
Theoretical framework for market liquidity, the relationship between
2.2.1 Liquidity and stock price volatility
2.2.1.1 Definition and measurement of stock liquidity a Definition of stock liquidity
Stock liquidity is considered as one of the important factors for evaluating investment opportunities.
Sarr and Tonny Lybek (2002) identify five key dimensions of stock liquidity: Tightness, Immediacy, Depth, Breadth, and Resiliency, which can influence price movements away from fundamental values.
Liquidity, as defined by Harris (2003), refers to the capacity to swiftly trade stocks in substantial volumes while incurring minimal transaction costs This concept encompasses three key elements: the speed of trading, the ability to handle large quantities of stocks, and the maintenance of low trading expenses.
Weimin Liu (2006) describes stock liquidity as the ability to trade large numbers of stocks quickly with low cost, with little effect on price.
Stock market liquidity, as defined by Brennan et al (2012), is the market's capacity to efficiently absorb a substantial volume of securities with minimal execution costs in a short timeframe, while maintaining stable stock prices.
Stock liquidity refers to the capacity to buy or sell a significant volume of a company's shares quickly and with minimal cost, as outlined by Holden et al (2014) Effective measurement of stock liquidity is crucial for investors and market analysts to assess the ease of trading and the associated costs.
Empirical studies have employed various liquidity measures to assess stock market liquidity, focusing on key characteristics such as depth (volume or quantity), breadth (price impact), immediacy (speed), and transaction costs (spread) These liquidity metrics are derived from both high-frequency intraday data and low-frequency data collected daily, weekly, monthly, quarterly, or yearly.
+ A one-way measure that represents one aspect of stock liquidity
Tarun Chordia et al (2000) measure stock liquidity based on the corresponding formula:
Quoted spread (QSPR) Pa- Pp $
Proportional quoted spread (PQSPR) (Pa- Ps)/Pm
Effective spread (ESPR) 2* |Pr - Pm| $
Proportional effective spread (PESPR) 2*|Pr - Pm| / Pr
In which: lo P: Stock price fe) t: actual transaction ° PA: ask price fe) PB: bid price o PM=PA-PB
Stock liquidity is primarily determined by trading activity, with higher trading volumes indicating stronger liquidity This relationship highlights that increased stock trading volume correlates with enhanced trading activity, while lower volumes suggest weaker liquidity.
Stoll's 2000 study analyzes the price spread during transactions, utilizing half difference determination to standardize the friction level inherent in these transactions This approach accounts for the transaction costs involved, providing a clearer understanding of the sell volume (Qa) and order volume (Qs).
14 fe) Ais ask price lo Bis bid price
The half spread is associated with each transaction in the underlying transaction database The daily average of the half spread is weighted by the number oftrades at each spread.
Based on the approach of Stoll (2000), Michael Gruning (2011) measures price spread according to the formula:
The bid-ask spread, calculated as (Ask Price - Bid Price) / 2 * Closing Price, signifies the immediate transaction cost for investors, as noted by Michael Aitken and Forde (2003) To buy or sell a stock, investors must navigate this spread by accepting existing ask or bid orders For small investors, this calculation serves as an effective measure of stock liquidity, allowing for comparisons across different price levels when expressed as a percentage of the stock price (relative spread) However, minimum tick rules can restrict the applicability of this ratio among stocks within the same tick category but varying significantly in price Additionally, for larger investors, the relative spread may not accurately reflect the true trading costs, potentially leading to an overestimation of liquidity.
Pastor and Stambaugh (2003) measure liquidity based on the residual function of the transaction value regression function affected by stock returns (,)
Sarr and Lybek (2006) calculate the spread measure as the absolute difference between the buying and selling prices or as a percentage of the difference spread.
The ask price (Pa) and bid price (Ps) are crucial in determining the percentage spread, which highlights that a larger spread is less burdensome when prices are higher, facilitating easier comparisons across different markets Additionally, dealers adjust their bid and ask prices due to uncertainties regarding the equilibrium price.
Francisco Muñoz (2012) evaluates stock liquidity using two primary methods, with trading volume serving as the main indicator This metric is derived from daily data reflecting both the number of shares traded and the total shares outstanding for the company.
In which: ° Dg is the number of days of transactions in the quarter.
The second liquidity measure is the industry-adjusted trading volume, calculated by dividing a firm's trading volume by that of its industry This multi-dimensional metric influences various factors, including price fluctuations, trading volume, and transaction duration.
Yakov Amihud et al (1997) introduced the liquidity ratio (LR), also known as the Amivest measure, to assess the relationship between trading volume and stock price changes A higher LR indicates increased market liquidity and depth, reflecting more robust trading activity In this context, Vj represents the trading volume, and Rj denotes the return on stock j for day t, with the summation occurring over the estimation period.
Siniša Bogdan et al (2012) utilize the Turnover measure to assess stock liquidity, noting that it does not account for transaction costs, which can vary across different stocks According to the authors, Turnover is calculated using a specific formula.
Turnover (VK) is calculated as the price (pn) in transaction n at time t multiplied by the number of traded stocks (Vn) and the total number of transactions (Nt) at that time According to Andrew W Lo and Jiang Wang (2000), turnover can also be expressed as the aggregate turnover (AT), which is the total number of stocks traded divided by the total number of issued stocks In this context, the traded stock volume of stock i at time t (vit) is considered alongside the total number of issued stocks (It) for that stock A higher aggregate turnover indicates greater liquidity in the market.
This measure indicates the free float of each stock, reflecting the proportion of total issued shares that are traded daily throughout the year A higher percentage signifies greater liquidity for the stock.
2.2.1.2 Definition and measurement of stock price volatility a Definition of stock price volatility
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Based on the research objectives and tasks mentioned above, the study focuses mainly on the following issues:
The study establishes a theoretical framework for market liquidity, examining its impact on stock price volatility It explores the relationship between liquidity and the price volatility of individual stocks, highlighting how market liquidity influences this dynamic.
Secondly, the study evaluates the situation of real estate stocks and the stock market in the period of 2019-2020
Thirdly, the study assesses the influence of market liquidity on the relationship between stocks liquidity and stock price volatility of stocks in the Real Estate industry.
Finally, the study discusses the research results to make recommendations and solutions for investors.
Based on the research objectives and content presented in the previous chapter, the research determines the research process including 04 basic steps as follows:
(i) Step 1: Determine the research problem.
An overview of domestic and international research reveals a significant interest among Vietnamese scholars in the relationship between stock liquidity and volatility, particularly in the context of Real Estate Stocks While the influence of market liquidity on this relationship has been extensively studied in developed and emerging markets, it remains underexplored in Vietnam This study aims to address two key questions: (i) How does market liquidity impact the relationship between liquidity and stock price volatility of individual stocks in the Real Estate sector?; and (ii) What considerations should investors keep in mind when investing in Real Estate stocks?
In this study, both quantitative and qualitative research methods are employed The qualitative approach aims to clarify theories and address issues concerning the impact of market liquidity on the relationship between stock liquidity and real estate stock price volatility, ultimately providing recommendations for investors Meanwhile, the quantitative method focuses on analyzing the relationship between stock liquidity and price volatility throughout the research period, thereby illustrating the influence of market liquidity on this dynamic.
The qualitative research results offer valuable insights into the state of real estate stocks and the broader stock market in Vietnam Utilizing time series data, the Vector Error Correction Model (VECM) is employed to analyze the relationship between liquidity and price volatility of real estate stocks from 2019 to 2022 VECM is preferred due to its ability to identify the number of lags and assess both long-run and short-run relationships The analysis follows four key stages: first, conducting a unit root test to assess the stationarity of the three variables; second, selecting the optimal number of lags using criteria like the Akaike Information Criterion (AIC) and the Schwarz Criterion (SC); third, performing the Johansen cointegration test with the maximal eigenvalue and trace tests; and fourth, applying the VECM or Granger causality test to evaluate the long-run and short-run dynamics between liquidity and price volatility in Vietnam's real estate sector.
(Hi) Step 3: Collect data and run research model
The study analyzes secondary data from 75 real estate stocks listed on the HOSE and HNX exchanges, including those in the VN30 index, over the period from 2019 to 2022 Data was sourced from audited financial statements and Fiinpro.
This research investigates the effects of market liquidity on the relationship between liquidity and price volatility in 75 real estate stocks in Vietnam from 2019 to 2022 The study compares this relationship across two sub-periods: January 2019 to March 2022 and March 2022 to December 2022 To mitigate the influence of stock exchange regulations on price fluctuations—specifically the 7%, 10%, and 15% limits for the Ho Chi Minh City Stock Exchange (HOSE), Hanoi Stock Exchange (HNX), and UPCOM, respectively—the research analyzes stock market volatility over weekly intervals This results in a total of 207 weeks, leading to 621 observations included in the analysis from January 2019 to December 2022.
Liquidity is a crucial factor for investors, reflecting how swiftly they can enter and exit market positions In this study, liquidity is quantified using turnover ratios (L/Q), which are determined by dividing the total shares traded over a specific period by the average number of outstanding shares during that same timeframe A higher turnover ratio indicates improved liquidity in the stock market, benefiting investors.
The VN30 index, established in June 2012, comprises the top 30 large-cap and liquid stocks on the Ho Chi Minh City Stock Exchange (HOSE) and serves as a key indicator of the Vietnamese stock market, representing over 70% of the country's capital market Consequently, market liquidity is assessed based on this significant index.
While: e LIQyt : Liquidity of VN30 on week t. ° N : Number of individual stocks in VN30 (N0). ° TƯ,¿: Average number of share i traded during week t (Stock i belongs to
VN30). ° 0S;:Outstanding share i on week t (Stock i belongs to VN30).
Similarly, the formula of the liquidiy of real estate stocks is described as below:
The liquidity of real estate stocks during week t is influenced by the average number of shares traded for each of the 75 individual stocks in the industry Additionally, the outstanding shares for each stock during that week play a crucial role in determining overall liquidity.
Stock volatility refers to the percentage change in a stock's price over a specific period This research employs standard deviation as the key metric for assessing stock volatility The formula used to calculate this measure is outlined below.
The volatility of the real stock market during week t is represented by VOL; while the return of stock S on day i of that week is denoted as r,¿ The average return of stock S throughout week t is indicated by 7,¿, and n signifies the number of trading days within that week.
So, the volatiliy of real estate stocks is calculated as below formular:
While: e VOLaz„¿: The volatility of real estate stocks on week t. ° VOL,;: The volatility of the real estate stock j on week. ° mm : Number of stocks in the real estate industry.
The Vector Error Correction Model (VECM) method for data analysis consists of four key stages: first, conducting a unit root test to assess the stationarity of the variables; second, selecting appropriate lags; third, performing the Johansen cointegration test, which utilizes both the maximal eigenvalue test and the trace test This methodology is rooted in the vector autoregression (VAR) of order p.
The equation Ye = H + A1y¿T1 + + ApYe—p + Et represents a vector autoregression (VAR) model, where yz is an nx1 vector of variables integrated of order one, denoted as I(1), and & signifies an nx1 vector of innovations This VAR model can be reformulated for further analysis.
If the coefficient matrix II has a reduced rank \( r < n \), there exist \( n \times r \) matrices \( A \) and \( B \), both with rank \( r \), such that \( II = A B \) and \( B' y_t \) is stationary Here, \( r \) represents the number of cointegrating relationships, with the elements of \( A \) serving as the adjustment parameters in the vector error correction model, while each column of \( B \) corresponds to a cointegrating vector For a specified \( r \), the maximum likelihood estimator of \( B \) identifies the combination of \( y_{t-1} \) that results in the \( r \) largest canonical correlations of \( \Delta y_t \) with \( y_{t-1} \), after accounting for lagged differences and deterministic variables Johansen introduces two likelihood ratio tests—the trace test and the maximum eigenvalue test—to assess the significance of these canonical correlations and determine the reduced rank of the matrix.
Imax = —Tin(1 — Âr+1) ° Vector error correction model (VECM) has the form:
AX, = Xp + TAX + +TAX pa + Ủy
Where, AX; is a vector of n different variables.
(iv) Step 4: Synthesizing, discussing, and making recommendations.
This study analyzes the impact of market liquidity on the relationship between stock liquidity and stock price volatility in the Vietnamese market, highlighting key results, limitations, and underlying causes It also compares these findings with previous research, culminating in actionable recommendations and solutions for investors based on the research outcomes.
4.1 Liquidity and Price volatility of real estate stocks in Vietnam from 2019 to 2022
Figure 4.1 Liquidity & Price volatility of real estate stocks from 2019 - 2022
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===== Price volatility of real estate stocks ===== Liquidity of real estate stocks
Source: Compiled by the author