Following the Crowd: Unveiling the Impact of Macroeconomic Shocks and Monetary Policy Shifts on Herding Dynamics in the Bangladesh Equity Market

Abstract

The study examines the dynamics of herding behavior in relation to macroeconomic shocks and monetary policy shifts in the Bangladesh equity market. By employing robust empirical methodologies across distinct market states including bullish, bearish, crisis, extended crisis, and COVID-19 phases, we first demonstrate that herding prevails under conditions of heightened uncertainty. Based on this foundation, we examine how exchange rate fluctuations and interest rate shifts alongside changes in deposit rates and reserve requirements serve as catalysts for collective investor behavior. The findings demonstrate that depreciation of the domestic currency and reductions in interest rates result in significant intensification of herding during vulnerable market phases. Moreover, monetary policy adjustments, predominantly changes in deposit rates and reserve ratios, trigger coordinated trading responses, especially during bearish and crisis markets. These results reveal the profound sensitivity of frontier equity markets to macro-financial signals and underscore the critical role of policy communication and stability in mitigating destabilizing herd dynamics. By bridging macroeconomic policy and investor psychology in a frontier market context, this research offers practical insights that can help regulators and policymakers improve market resilience.

1. Introduction

Herding behavior has recently gained significant attention in the financial markets among academic researchers since it can exacerbate market inefficiency and volatility, producing speculative bubbles or sudden market crashes (Bikhchandani & Sharma, 2000; Dasgupta et al., 2011; Hwang et al., 2018). The equity market is a fascinating environment to investigate herding dynamics because of its evolving nature and vulnerability to external shocks. Macroeconomic variables and monetary tools may offer a crucial role in shaping the sentiment of equity market participants on their irrational decision-making process. Examining the interaction between herding behavior and these factors, the current study intends to discover the extent to which macroeconomic variables and monetary policy interventions induce collective investor behavior.

The linkage between macroeconomic variables and stock market behavior can be theoretically motivated by the contagion theory. It states that any change in a macroeconomic variable may cause an immediate reaction of financial market participants and can affect the correlated behavior (B. H. Chang & Rajput, 2018). The arbitrage pricing theory (APT) of (Ross, 1976) develops a theoretical framework that describes how a multifactor structure may explain stock price behavior. The APT predicts that when any expected or unexpected new information arrives about macroeconomic fundamentals, it might exert considerable influence on equity market performance using the discount rate, future expected cash flows, or both channels. Two macroeconomic variables that can have an important consequence on the stock market include interest rates and exchange rates. Interest rates can affect stock prices through the discount rate channel. When interest rates change, the required rate of return of investors use to evaluate the present value of future cash flows from stock also changes, which will ultimately impact equity’s intrinsic value. According to the capital asset pricing model (CAPM), we can establish a positive association between interest rate changes and the required rate of return on equity investment. Interest rate variations may control equity prices through a portfolio rebalancing effect. When the yield on bonds declines, investors prefer to switch their investment from fixed income securities to equity, pushing up the prices of equity stocks.

A large number of studies in the literature have investigated the relation between changes in interest rates and equity market return (Ballester et al., 2011; Ferrer et al., 2016; Ferrando et al., 2017; Jareño et al., 2016; Jammazi et al., 2017; Morley, 2007; Moya-Martínez et al., 2015).

The relation between exchange rates and stock market performance is described by traditional macro models of an open economy. Theory shows how exchange rate changes affect the international competitiveness of firms and their earnings, which may impact equity prices (Dornbusch, 1980). The depreciation of the local currency facilitates exporting more attractive goods and enhances foreign demand and hence revenues, which may lead to a rise in equity prices (Gavin, 1989). Another theory that describes the relationship between exchange rate and stock price is the portfolio balance model, which emphasizes the country’s capital account transaction. A vibrant equity market would attract capital inflows into the country through increased foreign portfolio investment, which will appreciate the local currency. The opposite will happen in the case of a downturn in the equity market where investors would sell their portfolio investments to minimize the losses; hence, capital migrates to other the country. In effect, the local currency will depreciate. The following studies examined the relationship between exchange rates and the equity market (Adebiyi et al., 2009; Agrawal et al., 2010; Chinzara, 2011; Cho et al., 2016; Hau & Rey, 2006; Homma et al., 2005; Gong & Dai, 2017; Lawal et al., 2016; Lin & Fu, 2016; Suriani et al., 2015; Tronzano, 2021; Yazdani Varzi et al., 2022; Yadav et al., 2022).

Although herding in equity markets has been extensively studied in behavioral finance research, most of the studies are conducted on developed markets and emphasize market microstructures or firm-specific aspects, including market volatility, up or down markets, returns, and trading volume. There is a paucity of research on the combined impact of macroeconomic shocks and monetary policy changes on herding behavior, especially in frontier markets, where market structures and investor psychology could vary considerably with those of developed equity markets. This study addresses a knowledge gap on how structural vulnerabilities and policy interventions interact to impact investors’ tendencies to follow the collective market behavior.

Being a frontier equity market, Bangladesh is an ideal case for examining herding behavior, where information asymmetry, a lack of institutional depth, and an increased sensitivity to policy and economic shocks can intensify the impact of behavioral biases. In filling this gap, this research examines the combined effect of macroeconomic shocks and monetary policy adjustments in determining herding behavior in the Bangladesh equity market. This market framework provides a rare chance to comprehend investor psychology in a setting where there is high financial growth and policy developments are underway.

This study contributes to the existing herding literature in the following ways. First, the study’s novelty lies in incorporating the combined impact of macroeconomic shocks and monetary policy shifts on herding dynamics. This unexplored empirical evidence from the Bangladesh equity market bridges behavioral finance and macro-financial dynamics. Second, our research provides insights not explored in previous studies by focusing on macroeconomic shocks and monetary policy dimensions with market state classifications like bullish, bearish, crisis, extended crisis, and COVID-19. Third, this is the first study to investigate herding behavior by employing quantile regression, particularly in a frontier equity market setting. This methodological development provides insights into how herding intensity may vary under different distributions of market returns and even within diverse market states.

The remainder of the paper is structured as follows. Section 2 reviews herding behavior and investigates how macroeconomic shocks and monetary policy dynamics induce herding effects. Section 3 describes the dataset and details the methodology applied in the study. Section 4 interprets empirical results, whereas Section 5 discusses the results. Section 6 concludes the study and highlights its practical implications.

2. Literature Review

2.1. General Herding Behavior

Herding is a psychological behavior of market participants to mimic the equity market consensus, thus sacrificing their independent analysis (Vieira & Pereira, 2015; Li et al., 2018). Herding behavior has empirically established evidence of investors’ irrational behavior documented throughout the world since the 1990s. Our objective of this paper is to explore how the effects of macro variables and monetary policy tools induce herding behavior in the Bangladesh equity market. We provide a summary of the herding literature. Herding has been extensively examined in the equity markets by mainly applying two sets of empirical models. The investor-focused herding model incorporates the characteristics of investor-level transaction data and is mostly tested in developed equity markets (Alda & Ferruz, 2016; Lakonishok et al., 1992; Nofsinger & Sias, 1999). The broad market-based herding model uses the information about aggregate market data and is mainly studied in emerging (Tan et al., 2008; Lao & Singh, 2011; Indārs et al., 2019) and frontier equity markets.

Christie and Huang (1995) first empirically investigated market-wide herding behavior using the CSSD model in the US market and found evidence against herding. E. C. Chang et al. (2000) applied a nonlinear model of CSAD to measure equity return dispersion. The results revealed that herding is significantly recorded in South Korea and Taiwan, but not present in the US and Hong Kong. Hwang and Salmon (2004) used different methods to measure herding based on the cross-sectional dispersion of asset sensitivity. They reported significant evidence of herding in the US and South Korea. In the Bangladesh equity market, Khan and Imam (2023) examined herding behavior over the period 2007–2011 and revealed that herding is more prevalent in periods characterized by extreme market movements and large trading volume.

The following empirical studies document the prevalence of herding behavior in major equity markets of developed and emerging economies: (Hachicha et al., 2010) in Canada, (Galariotis et al., 2015) the US, and UK; (Clements et al., 2017) in the US; (Espinosa-Méndez & Arias, 2021) in Australia; Tan et al. (2008), (Chiang & Zheng, 2010), and (Chen, 2022) in China; (Lao & Singh, 2011) in India and China; (Kapusuzoglu, 2011) in Turkey; (Rahman et al., 2015) in Saudi Arabia; (Ababio & Mwamba, 2017) in South Africa; (Ah Mand et al., 2023) and (Loang & Ahmad, 2022) in Malaysia; (Bharti & Kumar, 2022) in India, (Jirasakuldech & Emekter, 2021) in Thailand; (Nguyen & Vo, 2024) in Vietnam; (Economou, 2020) in the Balkan region; and (Hassan & Jamil, 2021) in Pakistan.

The literature also provides evidence against herding behavior: (Christie & Huang, 1995; Bangalore Nagendra, 2022) in Ireland; (Khoshsirat & Salari, 2011) in Iran; (Prosad et al., 2012) and (Garg & Jindal, 2014) in India; (Indārs et al., 2019) in Russia; and (Jabeen et al., 2023) in Pakistan.

2.2. Herding and Macroeconomic Variables

Empirical studies on the relationship between macroeconomic variables and stock market performance have been conducted on a large scale with most research studies being aimed at identifying how different macroeconomic variables like interest rates, inflation, exchange rates, and GDP growth affect stock market returns, volatility, and stock market prices around the world. However, minimal research has been performed to understand the influence of these macroeconomic dynamics and herding behavior, namely, the propensity to follow the behavior of other investors instead of basing their decision on their information.

E. C. Chang et al. (2000) investigated herding behavior in the US, Japan, Hong Kong, China, Taiwan, and South Korea and found its presence in two emerging equity markets of Taiwan and South Korea along with partial evidence for its presence in Japan. In addition, they confirm that macroeconomic variables have more significant impact on investor behavior than the firm specific factors in those countries where herding behavior was present. Javaira and Hassan (2015) investigated whether investors in the Pakistan equity market exhibit herding behavior over the period of 2002–2007. Applying the CSSD and CSAD measures, they did report the absence of herding during conditions of high and low trading volume and market volatility. They also found that macroeconomic fundamentals failed to produce any significant impact on herding behavior. Amata et al. (2016) examined the relationship between different macroeconomic variables like interest rate, exchange rate, GDP, herding behavior, and equity market volatility in Kenya using time series data from 2001 to 2014. They found a significant relationship between interest rate and market volatility, while exchange rate, GDP, and herding behavior did not affect market volatility in Kenya.

Jabeen et al. (2022) investigated the relationship between stock returns at the industry level and herding behavior in the Pakistan equity market, and some macroeconomic variables were included as control variables. Using a panel-based pooled mean group (PMG), they revealed evidence of herding in most of the industries and also found an association between macroeconomic control variables and stock returns. Osoolian and Asiayi (2024) analyzed whether the changes in exchange rate can affect herding behavior in the Tehran equity market over the period of 2011–2021. They used the CSAD methodology of E. C. Chang et al. (2000) and found that exchange rate fluctuations tend to lead investors to follow the collective market behaviors, ignoring their own information.

2.3. Herding and Monetary Policy

The association between monetary policy and herding activity can be understood by how anticipation of monetary policy action can affect the investors’ behavior in the equity market. Monetary policy action can influence equity prices through two common economic theories: the discount rate channel and the expected future cash benefits. There are some studies that examined how monetary policy changes can impact equity prices. For example, Bernanke and Kuttner (2005) showed that a 1% change in the federal rate can lead to a 1% change in equity prices. Thorbecke (1997) demonstrated how short-term federal funds rate significantly impacted equity returns. However, a few studies have been performed to examine the relationship between monetary policy and herding behavior in the equity market.

Applying the vector error correction method (VERM) and impulse response functions (IRFs), Wicaksono and Falianty (2022) examined the relationship between herding behavior and monetary policy in the Indonesian equity market. Although the study found that monetary policy can induce herding effects, the influence of US monetary policy on herding tendencies of Indonesian investors was significantly observed. Gong and Dai (2017) investigated how the variation in two macroeconomic variables, namely, interest and exchange rates, influence herding behavior in the Chinese equity market. Using the CSAD model, the results showed that the depreciation of Chinese currency and an increase in interest rates induced herding behavior, particularly in down markets. The study also revealed that monetary policy announcements have a significant influence on herding behavior, implying that a central bank policy may reinforce collective market behavior in the Chinese equity market.

Sibande (2024) investigated herding behavior and monetary policy interactions in the ZAR market by applying the CSAD and CSSD measures and found herding during extreme market events. The results also confirmed that monetary policy announcements triggered herding behavior in bear markets, but these effects are not observed in bull markets. The study by Rinanda et al. (2024) investigated how the US and Indonesian monetary policies impact herding behavior in the Indonesian equity market by considering before and after COVID-19 periods. The findings reveal explicit evidence of the effects of both domestic and federal monetary policies on herding behavior and highlighted the necessity of the alignment of the monetary policies of both countries to reduce this behavioral bias.

Table 1. Summary of empirical studies examining the relationship between macroeconomic and monetary policies and herding behavior.

Author	Country	Methodology	Findings
Impact of Macroeconomic Variables on Herding
E. C. Chang et al. (2000)		CSAD	Herding behavior is influenced by market volatility but not macroeconomic fundamentals.
Javaira and Hassan (2015)	Pakistan	CSSD/CSAD	Macroeconomic variables do not have any influence on herding.
Amata et al. (2016)	Kenya	CSSD	Inflation, exchange rate, and inflation affect volatility, but an association with herding behavior was not found.
Osoolian and Asiayi (2024)	Tehran	CSAD	Exchange rates have a significant impact on herding behavior.
Jabeen et al. (2022)	Pakistan	Pooled Mean Group (PMG)	Herding and macro variables can drive stock market returns.
Impact of Monetary Policy Tools on Herding
Gong and Dai (2017)	China	CSAD	Depreciation of Chinese currency and increased interest rates affect herding, particularly in a down market.
Wicaksono and Falianty (2022)	Indonesia	VERM, IRFs	US monetary policy has a more dominant effect on herding behavior than domestic policy.
Rinanda et al. (2024)	Indonesia	CSAD	US and domestic monetary policies lead to herding behavior in the Indonesia equity market before and after COVID-19 periods.
Sibande (2024)	South African ZAR Market	Quantile Regression	Restrictive monetary policies, especially during extreme market periods, induce herding behavior in the ZAR market.

provides a summary of herding studies related to macroeconomic and monetary policy forces.

Two studies examined herding behavior in the Bangladesh equity market. Both studies did not include all listed companies and analyzed herding, with an emphasis on equity market microstructure characteristics, neglecting the potential combined effects of macroeconomic shocks and monetary policy forces. Moreover, the available literature has not categorized the herding behavior in various market states like bullish, bearish, crisis, extended crisis, and COVID-19 periods. This gap underlines why thorough research is necessary within the context of the Bangladesh equity market.

3. Data and Methodology

3.1. Data

In this study, adjusted daily closing prices are used for all companies listed on the Dhaka Stock Exchange (DSE). The price data spanning from January 2010 to December 2021 has been collected from the DSE library. Interest rates and exchange rates are used as macro variables, whereas deposit rates and deposit reserve ratios are considered as monetary policy tools to examine how these factors influence herding behavior in the Bangladesh equity market. The exchange rate data were extracted from Yahoo finance, and interest rate, deposit rates, and deposit reserve ratios were collected from the Bangladesh bank website.

The actual equity return for each company is calculated as follows:

$$R_{i,t}=In\left[{\displaystyle \frac{P_t}{P_{t-1}}}\right]\times 100$$

(1)

where $R_{i,t}$ represents realized equity return of company i at time t. $P_t$ and $P_{t-1}$ represent adjusted closing price of each company at time t and t − 1, respectively.

In order to examine herding behavior, the overall market has been classified into bearish, bullish, crisis, extended crisis, and COVID-19 markets. This helps to provide insights into the ways in which investors’ herding tendencies may change under various market states. This research contributes to the literature by applying Dow Theory to segregate bullish and bearish markets.

The study applied an innovative approach, namely, the application of Dow Theory, to determine bullish and bearish market phases using the patterns of price movements on a sustained basis, not short-term change of returns. According to this theory, the market trends are grouped into primary, secondary, and minor trends. The major trend has the general direction of the market in the long run, over a period of months or years, whereas the minor trend reflects the short-term corrections of the major trend. The smaller trends represent the short-term swings due to market noise or speculation. The theory stipulates that a bullish trend is a type of higher highs and higher lows that are maintained over some time span, and a bearish trend is defined as lower highs and lower lows. There are multiple bullish and bearish intermediate trends developed over the study period depending on these long-term directional movements. These were then combined to have one bullish and bearish market classification to provide analytical consistency. This is a better and more behaviorally based market classification framework than previous works that only report market states as positive or negative returns, which fails to capture the underlying persistence of the trend or sentiment dynamics of investors.

The two classifications of crisis and extended crisis markets were made in order to reflect the unique market conditions of the 2010–2011 market collapse of the Bangladesh equity market that was one of the worst and longest market falls in the history of the capital market in the country. The period of crisis (July 2010–June 2011) is specifically defined as the stage of the market determined by exorbitant price drops, increased volatility, and panic trading activities. The extended crisis period (January 2010–December 2011) was set to capture the likely spillover and adjustment effects prior to and after this sharp contraction and thus included 6 months before and after the central period of crisis. This broad definition enables the study of investor behavior in the run up to and in the recovery of the crisis, during which herding behavior and market sentiment can be noted to develop prior to the observable crash and continue into the period the market is recovering. The study will distinguish between the short-term crisis and the extended crisis phase to offer a more detailed view on how the psychology of the investors will vary across the entire range of market distress, including from the anticipation to response and the gradual stabilization.

3.2. Methodology

The study intends to investigate herding behavior in the Bangladesh equity market by adopting the cross-sectional absolute deviation (CSAD) model of E. C. Chang et al. (2000). The CSAD can be calculated as follows:

$$CSA{D}_t={\displaystyle \frac{1}{N}}{{\displaystyle \sum }}_{i=1}^N\left|R_{i,t}-R_{m,t}\right|$$

(2)

where $R_{i,t}$ represents the observed equity return of security i at time t. $R_{m,t}$ represents the cross-sectional average stock of N returns in the market portfolio at time t.

Herding behavior under general market conditions can be developed as follows:

$$Q_r\left(\tau |X_t\right)=\alpha +{\gamma }_{1,\tau }\left|R_{m,t}\right|+{\gamma }_{2,\tau }{\left(R_{m,t}\right)}^2+{\epsilon }_t$$

(3)

where, $CSA{D}_t$ measures the return dispersion. $\alpha$ represents the constant component. $\left|R_{m,t}\right|$ represents the absolute value of cross-sectional average stock of N returns in the market portfolio at time t. $R_m^2$ represents the squared aggregate market returns, which captures the nonlinear element in the model. Here, $\tau$ presents the quantile level. The coefficients of ${\gamma }_2$ should be significantly negative to confirm herding behavior. The study also examines the asymmetric response of herding behavior conditioned on market returns, market volatility, and market trading volumes. The model formulations are specified as follows:

$$Q_r\left(\tau |X_t\right)=\alpha +{\gamma }_{1,\tau }\left|{R_{m,t}}^{Up}\right|+{\gamma }_{2,\tau }{\left({R_{m,t}}^{Up}\right)}^2+{\epsilon }_t$$

(4)

$$Q_r\left(\tau |X_t\right)=\alpha +{\gamma }_{1,\tau }\left|{R_{m,t}}^{Down}\right|+{\gamma }_{2,\tau }{\left({R_{m,t}}^{Down}\right)}^2+{\epsilon }_t$$

(5)

$$Q_r\left(\tau |X_t\right)=\alpha +{\gamma }_{1,\tau }\left|{R_{m,t}}^{Vol-High}\right|+{\gamma }_{2,\tau }{\left({R_{m,t}}^{Vol-High}\right)}^2+{\epsilon }_t$$

(6)

$$Q_r\left(\tau |X_t\right)=\alpha +{\gamma }_{1,\tau }\left|{R_{m,t}}^{Vol-Low}\right|+{\gamma }_{2,\tau }{\left({R_{m,t}}^{Vol-Low}\right)}^2+{\epsilon }_t$$

(7)

where $\left|R_{m,t}^{Up}\right|$ and $\left|R_{m,t}^{Down}\right|$ represent the absolute value of the equally weighted market returns and are calculated using the returns of all listed securities on day t, when the market is up and down, respectively. $\left|R_{m,t}^{Vol-High}\right|$ and $\left|R_{m,t}^{Vol-Low}\right|$ denote the equally weighted market returns of all listed securities on day t, corresponding to periods of high and low volatility, respectively. The coefficients of ${\gamma }_2$ in each equation should be significantly negative to confirm herding behavior. ${\left(R_{m,t}^{vol-High}\right)}^2$ and ${\left(R_{m,t}^{Vol-Low}\right)}^2$ represent the squared market returns corresponding to these market periods. ${R_{m,t}^{Up}}^2$ and ${R_{m,t}^{Down}}^2$ represent the squared market returns, corresponding to periods of up and down markets, respectively. The coefficients of ${\gamma }_2$ should be significantly negative to confirm herding behavior.

Based on the results of herding behavior, the study tends to investigate the influence of macroeconomic variables on the herding tendency of investors. The macro-based models are specified as follows:

$$CSA{D}_t=\alpha +{\gamma }_1\left|R_{m,t}\right|+{\gamma }_2{R}_{m,t}^2+{\gamma }_3{D}_1^{\mathsf{\Delta }exr+}{R}_{m,t}^2+{\epsilon }_t$$

(8)

$$CSA{D}_t=\alpha +{\gamma }_1\left|R_{m,t}\right|+{\gamma }_2{R}_{m,t}^2+{\gamma }_3{D}_1^{\mathsf{\Delta }exr-}{R}_{m,t}^2+{\epsilon }_t$$

(9)

$$CSA{D}_t=\alpha +{\gamma }_1\left|R_{m,t}\right|+{\gamma }_2{R}_{m,t}^2+{\gamma }_3{D}_1^{\mathsf{\Delta }int+}{R}_{m,t}^2+{\epsilon }_t$$

(10)

$$CSA{D}_t=\alpha +{\gamma }_1\left|R_{m,t}\right|+{\gamma }_2{R}_{m,t}^2+{\gamma }_3{D}_1^{\mathsf{\Delta }int-}{R}_{m,t}^2+{\epsilon }_t$$

(11)

where $\left|R_{m,t}\right|$ represents the absolute value of the equally weighted market returns and is calculated using the returns of all listed securities on day t. $R_{m,t}^2$ represents the market returns squared. $D_1^{\mathsf{\Delta }exr+}$ represents a dummy variable that takes a value of one when the local currency (BDT) depreciates (exchange rate increases) and zero otherwise. $D_2^{\mathsf{\Delta }exr-}$ represents a dummy variable that takes a value of one when the local currency appreciates (exchange rate decreases) and zero otherwise. $D_1^{\mathsf{\Delta }int+}$ represents a dummy variable that takes a value of one when the interest rates increase and zero otherwise. $D_2^{\mathsf{\Delta }int-}$ represents a dummy variable that takes a value of one when the interest rates decrease and zero otherwise. We employ a repo rate as a measure of interest rate. The coefficients ${\gamma }_3$ and ${\gamma }_4$ should be negatively significant to confirm herding behavior.

The study also examines how the effect of monetary policy influences the herding effects among investors in the Bangladesh equity market. The models are specified as follows:

$$CSA{D}_t=\alpha +{\gamma }_1\left|R_{m,t}\right|+{\gamma }_2{R}_{m,t}^2+{\phi }_j{D}_j{R}_{m,t}^2+{\epsilon }_t (j=1,2,3,4,5)$$

(12)

where $\left|R_{m,t}\right|$ represents the absolute value of the equally weighted market returns and is calculated using the returns of all listed securities on day t. $R_{m,t}^2$ represents the market returns squared. D₁ to D₄ represent dummy variables that take the value of one in a month when the deposit rate increases, the deposit rate decreases, the deposit reserve ratio increases, and the deposit reserve ratio decreases, respectively, and zero otherwise. D₅ represents a dummy variable that takes the value of one during a monetary policy announcement month and zero otherwise. The coefficients ${\phi }_1$ through ${\phi }_5$ should be negatively significant when monetary policy tools have an impact on herding behavior.

Herding is identified using quantile regression since the method enables an investigation of the complete conditional distribution of the market returns, as opposed to the mean [as is obtained using ordinary least squares (OLS) regression]. Herding behavior can be state dependent; thus, it will not be the same when the market is under very extreme conditions (e.g., when large positive or negative returns happen) or between investors with different risk exposures. This research uses quantile regression to include heterogeneity in market conditions and returns distribution and to obtain a more accurate image of herding among the bottom, median, and upper quantile of returns. This is especially effective in frontier markets such as Bangladesh, where market microstructure aspects, investor behavior, as well as liquidity vary under different conditions in the market.

Once it has been verified that herding does exist using quantile regression, the cross-sectional absolute deviation (CSAD) model is used to investigate how macroeconomic shocks and monetary policy changes will affect herding behavior. The CSAD framework can be used to measure the individual stock return dispersion in comparison to the market return, and this dispersion is lower in the case of herding. The inclusion of macroeconomic and policy variables in the CSAD model helps the study to establish the extent to which external economic shocks or policy interventions induce investor herding behavior. The use of a mix of quantile regression to identify herding and the CSAD to capture external macroeconomic and monetary forces offers a holistic, state-dependent, and policy-relevant perspective of herding dynamics in the environment of a frontier equity market. This two-stage methodological framework confirms the robust presence of herding first before examining its relationship to real macroeconomic and policy shocks, which minimizes the misinterpretation risk.

4. Empirical Results

First, the study presents the herding results by employing the cross-sectional absolute deviation model (CSAD) estimated with quantile regression across diverse market states. Quantile regression takes into account the distributional heterogeneity in herding activity among investors. It suggests that this approach accounts for herding manifestations differently at low, medium, and high levels of equity return dispersions.

In

Table 2. Quantile regression results of nonlinear cross-sectional absolute deviation (CSAD).

Market State	Quantile Levels	α	γ1	γ2
Overall Market	0.25	1.93 ***	0.529	−0.005 ***
	0.50	1.002 ***	0.295	−0.006 ***
	0.75	0.294 ***	0.157 ***	−0.005 ***
Bearish Market	0.25	1.944 ***	0.559 ***	−0.005 ***
	0.50	0.848 ***	0.275 ***	−0.007 ***
	0.75	0.280 ***	0.394 ***	−0.005 ***
Bullish Market	0.25	1.822 ***	0.677 ***	0.185 ***
	0.50	1.20 ***	0.248 ***	0.032
	0.75	0.617 ***	−0.301 ***	0.302 ***
Crisis Market	0.25	2.48 ***	1.41 ***	0.195
	0.50	2.977 ***	−0.137	0.343 ***
	0.75	1.241 ***	−0.103	0.179 ***
Extended Crisis Market	0.25	2.21 ***	0.396 ***	−0.008 ***
	0.50	2.142 ***	0.554 ***	−0.007 ***
	0.75	0.735 ***	0.335 ***	−0.002 ***
COVID-19 Market	0.25	2.47 ***	0.281	0.832 ***
	0.50	1.187 ***	−0.606	1.293 ***
	0.75	0.610 ***	−0.016	−0.012

Note: *** Significant at the 1% level. Regression estimation is performed using the HAC (Newey–West) approach to correct heteroscedasticity and autocorrelation. Source: Calculations are conducted by the author using EViews 13.

, the parameters are estimated using the following regressions with AR(1):

$$Q_r\left(\tau |X_t\right)=\alpha +{\gamma }_{1,\tau }\left|R_{m,t}\right|+{\gamma }_{2,\tau }{\left(R_{m,t}\right)}^2+{\epsilon }_t$$

$\left|R_{m,t}\right|$ represents the absolute value of the equally weighted market returns and is calculated using the returns of all listed securities on day t. $R_{m,t}^2$ represents the squared market returns. The coefficients of ${\gamma }_2$ should be significantly negative to confirm herding behavior. Estimation is performed for the overall, bearish, bullish, crisis (4 July 2010–30 June 2011), extended crisis (4 January 2010–29 December 2012), and COVID-19 markets (8 March 2020–31 December 2021). Bullish and bearish markets are separated using Dow Theory.

Quantile regression in Equation (3) enables us to understand how the herding behaviors of investors vary under different equity return dispersion levels and market states. The findings show that herding behavior was significantly evident in all quantiles of the equity market for the overall, bearish, and extended crisis markets. It demonstrates the persistence of herding effects regardless of the size of the return dispersion distributions in both general and uncertain market scenarios.

4.1. Asymmetric Herding Effects Under Different Market Conditions: Up and Down Markets

This provides the quantile regression results of CSAD measurements under up and down market return conditions.

In

Table 3. Quantile regression results of cross-sectional absolute deviation (CSAD) measurements in up and down markets.

	Quantile Regression Results from Positive Market Return				Quantile Regression Results from Negative Market Return
	Quantile Levels	α	γ1	γ2	Quantile Levels	α	γ1	γ2
Overall Market	0.25	2.07 ***	0.630 ***	−0.011	0.25	1.97	0.504	−0.003 ***
	0.50	1.28 ***	0.041 ***	0.022	0.50	1.31 ***	0.418 ***	−0.007 ***
	0.75	0.512 ***	0.007	0.051	0.75	1.97 ***	0.882 ***	−0.017 ***
Bearish Market	0.25	2.12 ***	0.765 ***	−0.029	0.25	1.60 ***	0.572 ***	−0.009 ***
	0.50	1.37 ***	0.280 ***	0.030	0.50	2.002 ***	0.522 ***	−0.004 ***
	0.75	0.792 ***	0.102 ***	0.041	0.75	0.418 ***	0.444 ***	−0.004 ***
Bullish Market	0.25	1.85 ***	0.542 ***	−0.153	0.25	1.88 ***	0.454 ***	0.057
	0.50	1.34 ***	0.224	0.095	0.50	1.28 ***	0.139	0.088
	0.75	0.908 ***	−0.597 **	0.484	0.75	0.664 ***	−0.920 **	1.21 ***
Crisis Market	0.25	2.72 ***	−0.142	0.145	0.25	2.690 ***	0.068	0.102
	0.50	2.74 ***	0.072	0.096	0.50	3.15 ***	−0.329	0.379 ***
	0.75	1.37 ***	0.331	−0.023	0.75	1.34 ***	−0.268	0.278
Extended Crisis Market	0.25	2.44 ***	−0.071	0.140 ***	0.25	2.26 ***	0.536 ***	−0.004 ***
	0.50	2.72 ***	−0.136	0.160 ***	0.50	2.17 ***	0.908	−0.015 ***
	0.75	1.62 ***	−0.357	0.142	0.75	0.564 ***	0.500	−0.006 ***
COVID-19 Market	0.25	2.89 ***	0.574	0.407	0.25	2.74 ***	−0.741	1.564 ***
	0.50	3.04 ***	−1.14	2.03 ***	0.50	1.90 ***	−0.901	1.08 ***
	0.75	1.03 ***	−0.37	0.192	0.75	0.899 ***	−0.829	0.762

Note: *** Significant at the 1% level. ** Significant at the 5% level. Regression estimation is performed using the HAC (Newey–West) approach to correct for heteroscedasticity and autocorrelation. Source: Calculations are conducted by the author using EViews.

, the parameters are estimated using the following regressions with AR(1):

$$Q_r\left(\tau |X_t\right)=\alpha +{\gamma }_{1,\tau }\left|{R_{m,t}}^{Up}\right|+{\gamma }_{2,\tau }{\left({R_{m,t}}^{Up}\right)}^2+{\epsilon }_t$$

$$Q_r\left(\tau |X_t\right)=\alpha +{\gamma }_{1,\tau }\left|{R_{m,t}}^{Down}\right|+{\gamma }_{2,\tau }{\left({R_{m,t}}^{Down}\right)}^2+{\epsilon }_t$$

where $\left|R_{m,t}^{Up}\right|$ and $\left|R_{m,t}^{Down}\right|$ represent the absolute value of the equally weighted market returns and are calculated using the returns of all listed securities on day t, corresponding to periods of up and down markets, respectively. ${R_{m,t}^{Up}}^2$ and ${R_{m,t}^{Down}}^2$ represent the squared market returns, corresponding to these periods. The coefficients of ${\gamma }_2$ should be significant and negative to confirm herding behavior. Estimation is performed for the overall, bearish, bullish, crisis (4 July 2010–30 June 2011), extended crisis (4 January 2010–29 December 2012), and COVID-19 markets (8 March 2020–31 December 2021). Bullish and bearish markets are separated using Dow Theory.

Table 3 reports the quantile regression results of CSAD estimated using Equations (4) and (5), separately, for up and down-market days. The results suggest that herding behavior is not present in any of these market classifications during periods of a rising market. However, a distinct asymmetric pattern in herding behavior was consistently observed during periods of a falling market during the entire market, bearish, and extended crisis periods.

4.2. Asymmetric Herding Effects Under Different Market Conditions: High and Low Volatility

We also used the quantile regression approach to measure whether the herding behavior response among investors reflects any asymmetric patterns under conditions of high and low market volatility.

In

Table 4. Quantile regression results of cross-sectional absolute deviation (CSAD) measurements in high and low volatility markets.

	Quantile Regression Results for High Volatility State				Quantile Regression Results for Low Volatility State
	Quantile Levels	α	γ1	γ2	Quantile Levels	α	γ1	γ2
Overall Market	0.25	1.87 ***	0.669	−0.007 ***	0.25	1.99 ***	0.524 ***	0.130 ***
	0.50	1.58 ***	0.728 ***	−0.013 ***	0.50	1.21 ***	0.114	0.155 ***
	0.75	0.284 ***	0.528 ***	−0.008	0.75	0.383 ***	−0.211	0.172 ***
Bearish Market	0.25	1.94 ***	0.700 ***	−0.008 ***	0.25	2.01 ***	0.442 ***	0.140 ***
	0.50	1.89 ***	0.892 ***	−0.014 ***	0.50	1.04 ***	0.043	0.161 ***
	0.75	1.26	0.938 ***	−0.013	0.75	0.313 ***	−0.157	0.166 ***
Bullish Market	0.25	1.84 ***	0.637 ***	0.017	0.25	1.82 ***	1.04 ***	−0.328
	0.50	1.16 ***	0.615	0.008	0.50	1.36 ***	1.07 ***	−0.711
	0.75	1.81 ***	−1.77	1.70 ***	0.75	0.598	−0.684	0.936
Crisis Market	0.25	2.13 ***	0.587	−0.026	0.25	1.54 ***	0.958 **	0.002
	0.50	1.89	0.170	0.114	0.50	1.30 ***	0.875	0.031
	0.75	0.417	−0.176	0.165	0.75	0.987 ***	1.01 **	0.024
Extended Crisis Market	0.25	1.71 ***	0.812 ***	−0.010 ***	0.25	2.43 ***	0.021	0.185 ***
	0.50	1.79 ***	1.21 ***	−0.022 ***	0.50	2.66 ***	−0.234	0.203 *
	0.75	−0.55 ***	1.43 ***	−0.041 ***	0.75	1.31 ***	−0.674 *	0.225 *
COVID-19 Market	0.25	2.49 ***	1.23	0.126	0.25	2.85 ***	0.556	1.96
	0.50	1.43 ***	1.35 ***	0.170	0.50	2.19 ***	−0.977	3.30
	0.75	0.347	0.601	0.081	0.75	0.835 ***	−1.32	3.03

Note: *** Significant at the 1% level. ** Significant at the 5% level. * Significant at the 10% level. Regression estimation is performed using the HAC (Newey–West) approach to correct for heteroscedasticity and autocorrelation. Source: Calculations are conducted by the author using EViews.

, the parameters are estimated using the following regressions with AR(1):

$$Q_r\left(\tau |X_t\right)=\alpha +{\gamma }_{1,\tau }\left|{R_{m,t}}^{Vol-High}\right|+{\gamma }_{2,\tau }{\left({R_{m,t}}^{Vol-High}\right)}^2+{\epsilon }_t$$

$$Q_r\left(\tau |X_t\right)=\alpha +{\gamma }_{1,\tau }\left|{R_{m,t}}^{Vol-Low}\right|+{\gamma }_{2,\tau }{\left({R_{m,t}}^{Vol-Low}\right)}^2+{\epsilon }_t$$

where $\left|R_{m,t}^{Vol-High}\right|$ and $\left|R_{m,t}^{Vol-Low}\right|$ denote the equally weighted market returns of all listed securities on day t, corresponding to periods of high and low volatility, respectively. ${\left(R_{m,t}^{vol-High}\right)}^2$ and ${\left(R_{m,t}^{Vol-Low}\right)}^2$ denote the squared market returns. The coefficients of ${\gamma }_2$ should be significant and negative to confirm herding behavior. Estimation is performed for the overall, bearish, bullish, crisis (4 July 2010–30 June 2011), extended crisis (4 January 2010–29 December 2012), and COVID-19 markets (8 March 2020–31 December 2021). Bullish and bearish markets are separated using Dow Theory.

The results in Table 4 clearly reveal the presence of herding behavior for the entire market and bearish period at the lower and median quantiles. On the other hand, strong evidence of herding is present in extended crisis periods at each quantile level under conditions of high market volatility. Herding prevalence is not present at lower, median, and upper quantiles for any of these markets in a low market volatility state.

We may conclude that significant evidence of herding was consistently observed in the Bangladesh equity market under normal market phenomenon as well as asymmetric scenarios based on high market returns and high volatility. This irrational behavior aligns with the theory of loss aversion driven by flight-to-safety mechanisms during periods of market stress and overreaction to negative shocks forcing panic-driven selling during periods of high volatility, which exacerbates collective market behavior. This significant presence of herding underscores the importance of policy actions to address this behavioral bias to help promote the stable and efficient equity market phenomenon in a frontier market context.

4.3. Nexus Between Herding Behavior and Macroeconomic Variables

This section makes an inventive effort to examine whether variations in interest and exchange rates exert influence on herding behavior in the Bangladesh equity market.

In

Table 5. Regression results of cross-sectional absolute deviation (CSAD) under changes in macroeconomic variables.

Panel A: Effects of change in exchange rates						Equation
	${\gamma }_1$	${\gamma }_2$	${\gamma }_3$	${\gamma }_4$	Adjusted R2
Overall Market	0.748 ***	−0.012 **	−0.066 ***		0.22	8
	0.748 ***	0.053 ***		0.064 ***	0.22	9
Bearish Market	0.830 ***	−0.014 ***	−0.056 ***		0.23	8
	0.831	0.041		0.052 ***	0.24	9
Crisis Market	0.552 ***	−0.017 ***	0.062		0.18	8
	1.69	−0.227		−0.089	0.19	9
Extended Crisis Market	0.987 ***	0.001 ***	−0.005 ***		0.79	8
	0.988	0.044		−0.063 ***	0.83	9
Pane B: Effects of change in interest rates
	${\gamma }_1$	${\gamma }_2$	${\gamma }_3$	${\gamma }_4$	Adjusted R2
Overall Market	0.880 ***	−0.306 ***	0.290		0.78	10
	0.925	−0.186		−0.085 ***	0.82	11
Bearish Market	0.381 ***	0.013 ***	−0.169 ***		0.76	10
	0.378	−0.017		−0.170 ***	0.88	11

Note: *** Significant at the 1% level. ** Significant at the 5% level. The p-values are reported in parentheses. Regression estimation is performed using the HAC (Newey–West) approach to correct for heteroscedasticity and autocorrelation. Source: Calculations are conducted by the author using EViews.

, the parameters are estimated using the following regressions with AR(1):

$$CSA{D}_t=\alpha +{\gamma }_1\left|R_{m,t}\right|+{\gamma }_2{R}_{m,t}^2+{\gamma }_3{D}_1^{\mathsf{\Delta }exr+}{R}_{m,t}^2+{\epsilon }_t$$

$$CSA{D}_t=\alpha +{\gamma }_1\left|R_{m,t}\right|+{\gamma }_2{R}_{m,t}^2+{\gamma }_3{D}_1^{\mathsf{\Delta }exr-}{R}_{m,t}^2+{\epsilon }_t$$

$$CSA{D}_t=\alpha +{\gamma }_1\left|R_{m,t}\right|+{\gamma }_2{R}_{m,t}^2+{\gamma }_3{D}_1^{\mathsf{\Delta }int+}{R}_{m,t}^2+{\epsilon }_t$$

$$CSA{D}_t=\alpha +{\gamma }_1\left|R_{m,t}\right|+{\gamma }_2{R}_{m,t}^2+{\gamma }_3{D}_1^{\mathsf{\Delta }int-}{R}_{m,t}^2+{\epsilon }_t$$

where $\left|R_{m,t}\right|$ represents the absolute value of the equally weighted market returns and is calculated using the returns of all listed securities on day t. $R_{m,t}^2$ represents the market returns squared. $D_1^{\mathsf{\Delta }exr+}$ represents a dummy variable that takes a value of one when the local currency (BDT) depreciates (exchange rate increases) and zero otherwise. $D_2^{\mathsf{\Delta }exr-}$ represents a dummy variable that takes a value of one when the local currency appreciates (exchange rate decreases) and zero otherwise. $D_1^{\mathsf{\Delta }int+}$ represents a dummy variable that takes a value of one when the interest rates increase and zero otherwise. $D_2^{\mathsf{\Delta }int-}$ represents a dummy variable that takes a value of one when the interest rates decrease and zero otherwise. We employ a repo rate as a measure of interest rate. The coefficients ${\gamma }_3$ and ${\gamma }_4$ should be significant and negative to confirm herding behavior. Estimation is performed for the overall market (January 2010–December 2021), bearish market, and extended crisis (4 January 2010–29 December 2012). A bearish market is separated using Dow Theory.

Table 5 furnishes the regression results based on Equations (8)–(11), in which the effects of variations of two macroeconomic variables on herding behavior are displayed. In Panel A, for the overall, bearish, and extended crisis markets, the estimated coefficient ${\gamma }_3$ is significantly negative, whereas ${\gamma }_4$ is insignificant for all markets. These findings suggest that depreciation of the Taka tends to induce herding behavior in the Bangladesh equity market. For a crisis market, we do not confirm the presence of an exchange rate impact on herding behavior. Panel B provides the results of changes in interest rates and indicates that the coefficient ${\gamma }_4$ for the overall and bearish markets is negative and statistically significant. The empirical results manifest that the phenomena of an interest rate decrease and depreciation of the local currency have strong influences on herding behavior in our equity market. We may draw the inference that variations in macroeconomic variables convincingly exert influence on the herding tendency of market participants in the Bangladesh equity market.

4.4. Nexus Between Herding Behavior and the Effects of Monetary Policy

In monetary policy, the interest rate represents the central bank’s stance. Studies that have separately investigated the effects of monetary policy shocks on equity returns include (Bjørnland & Leitemo, 2009; Bernanke & Kuttner, 2005; Gong & Dai, 2017; Thorbecke, 1997).

In this section, we endeavor to examine the effects of monetary policy on investors’ tendency toward herding in the Bangladesh equity market. We analyze the effects of monetary policy from two aspects. One approach is to examine herding behavior around the monetary policy announcement date, and the second approach is to explore herding behavior around changes in monetary policy variables. The effects of monetary policy are investigated with two policy tools: changes in the deposit rate and the deposit reserve ratio.

In

Table 6. Regression results of cross-sectional absolute deviation (CSAD) under the effects of monetary policy shifts.

	${\gamma }_1$	${\gamma }_2$	${\phi }_1$	${\phi }_2$	${\phi }_3$	${\phi }_4$	${\phi }_5$
Overall Market	0.375 ***	0.023 **	−0.164
	0.783 ***	−0.013 ***		0.165
	0.728 ***	0.103 *			−0.116 **
	0.402 ***	0.013 ***				−0.023 ***
	0.403 ***	0.012 ***					−0.046 ***
	${\gamma }_1$	${\gamma }_2$	${\phi }_1$	${\phi }_2$	${\phi }_3$	${\phi }_4$	${\phi }_5$
Bearish Market	0.934 ***	−0.415	0.398
	0.381 ***	0.013 ***		−0.172 ***
	0.386 ***	0.013 ***			−0.029 ***
	0.819 ***	0.085 *				−0.099 **
	0.395 ***	0.013 ***					−0.048 ***
	${\gamma }_1$	${\gamma }_2$	${\phi }_1$	${\phi }_2$	${\phi }_3$	${\phi }_4$	${\phi }_5$
Crisis Market	0.506 ***	0.056	−0.112
	0.506 ***	−0.04 **		0.114
	0.550 ***	−0.060 **			−0.004
	1.63 ***	−0.245				−0.009
	0.542 ***	−0.057 ***					−0.006
	${\gamma }_1$	${\gamma }_2$	${\phi }_1$	${\phi }_2$	${\phi }_3$	${\phi }_4$	${\phi }_5$
Extended Crisis Market	1.00 ***	0.352 **	−0.370 **
	0.448 ***	0.012 ***		0.032
	0.491 ***	0.011 ***			−0.034 ***
	0.498 ***	−0.028 ***				0.038 ***
	0.505 ***	0.010 ***					−0.059 ***

Note: *** Statistically significant at 1%. ** Statistically significant at 5%. * Statistically significant at 10%. Regression estimation is performed using the HAC (Newey-West) approach to correct for heteroscedasticity and autocorrelation. Source: Calculations are conducted by the author using EViews.

, the parameters are estimated using the following regressions with AR(1):

$$CSA{D}_t=\alpha +{\gamma }_1\left|R_{m,t}\right|+{\gamma }_2{R}_{m,t}^2+{\phi }_j{D}_j{R}_{m,t}^2+{\epsilon }_t (j=1,2,3,4,5)$$

where $\left|R_{m,t}\right|$ represents the absolute value of the equally weighted market returns and is calculated using the returns of all listed securities on day t. $R_{m,t}^2$ represents the market returns squared. To examine the effects of monetary policy on herding, the study considers five monetary policy dummy variables, each representing a unique policy action. D₁ takes the value of one in a month when the deposit rates increase and zero otherwise. D₂ takes the value of one when the deposit rates decrease and zero otherwise. D₃ and D₄ represent the changes in the deposit reserve ratio, taking the value of one when it increases and decreases, respectively, and zero otherwise. Lastly, D₅ represent a dummy variable that takes the value of one during a monetary policy announcement month, and zero otherwise. The coefficients ${\phi }_1$ through ${\phi }_5$ should be significant and negative when monetary policy tools have an impact on herding behavior. Estimation is performed for the overall (January 2010–December 2021), bearish, and extended crisis markets (4 January 2010–29 December 2012). A bearish market is separated using Dow Theory.

Table 6 discloses the results of the impact of monetary policy tools on herding behavior. The empirical findings communicate that an increase in the bank deposit rate does lead to herding behavior only for the extended crisis market since ${\phi }_1$ is negative and significant, whereas a decrease in the deposit rate does influence herding behavior for the bearish market. The estimated results stipulate that both increases and decreases in the deposit reserve ratio significantly influence herding behavior in the overall, bearish, and extended crisis markets as evident by the significant and negative coefficients of ${\phi }_3$ and ${\phi }_4$, respectively. Like the deposit reserve ratio, similar results are observed in the case of the monetary policy announcement date since the coefficient of ${\phi }_5$ is significant and negative for all markets except the crisis market.

4.5. Robustness of Empirical Findings

The study systematically classifies the Bangladesh equity market into several market-states like bullish, bearish, crisis, extended crisis, and COVID-19. In order to test robustness, the herding results are checked through the application of alternative thresholds to classify the market states. Thus, it is guaranteed that the results are not sensitive to the selected classification scheme.

The 25th, 50th, and 75th quantile regressions are also estimated with market returns. Robustness is validated by the comparison of the results obtained with these quantiles, ensuring that the behavior of herding is always reflected in the entire returns distribution.

The quantile regression model (to identify herding) and the CSAD model (to estimate the effect of the macroeconomic and monetary policy variables) were estimated to include the AR(1) term to explain serial correlations in the returns of the market. The adjustment was made to avoid biasing the estimated coefficients with autocorrelation, which is a common aspect in a financial time-series data.

Although the study used the CSAD model of E. C. Chang et al. (2000) to connect macroeconomic and monetary policy issues to herding effects, the alternative return dispersion measure of CSSD was also tested to provide the robustness of the desired result. This validates the fact that the relationships observed are not exclusive for a given herding measure. As we observe similar results under both CSSD and CSAD, the study only reports the CSAD findings.

5. Discussion of Results

Based on the established empirical herding evidence across different market states in the Bangladesh equity market, this study examines whether changes in key macroeconomic variables and monetary policy tools exert a significant impact on herding phenomenon. The motivation for this investigation lies in the observation that herding was more consistently evident in specific market states, namely, in a bearish market and during extended periods of crisis. These market phases tend to be associated with elevated uncertainty and investor anxiety, which theoretically makes external economic signals more susceptible to equity market movements (Bikhchandani & Sharma, 2000).

5.1. Effects of Macroeconomic Shocks

A macroeconomic perspective reveals that depreciation of the domestic currency is positively related to herding behavior in the overall market and in bearish or extended crisis markets. The findings are consistent with theoretical expectations arising from the behavioral finance and international finance literature. Depreciation of the exchange rate often signals greater macroeconomic instability, potentially increasing investors’ perceptions of risk, particularly in emerging and frontier markets that have a high foreign exchange exposure (Chiang & Zheng, 2010). Herding may intensify in such contexts, as investors abandon individual assessments in favor of mimicking market consensus. This empirical finding reinforces the herding dynamic, indicating that exchange rate volatility acts a prominent informational cue under stressful equity market conditions. Our findings further reveal that a decrease in the policy interest rate has a substantial influence on herding behavior in the overall market and bearish market phase. This finding can be understood by investor psychology and the theory of monetary transmission. In general, a decline in interest rates reduces the opportunity cost associated with equity holding, which may indicate a more accommodative monetary policy. However, such an indicator might trigger investors to overreact under pessimistic market conditions, like the bearish market. Market movements caused by policy changes may lead investors to herd instead of making independent valuation-based decisions. Our results are aligned with empirical studies, which state that expansionary monetary policy may result in investor optimism and collective trading behavior (Balcilar et al., 2013).

Collectively, these findings underline the significance of macro-financial connections in grasping investor behavior. Both depreciating exchange rates and declining interest rates contribute to herding, particularly during vulnerable market phases and not uniformly across all states. Therefore, policymakers and regulators should be vigilant during such equity market environments.

5.2. Effects of Monetary Policy Tools

This study, beyond our macroeconomic analysis, analyzes whether and to what extent monetary policy tools impact herding dynamics in the Bangladesh equity market. The analysis primarily addresses the fundamental policy tools, including the bank deposit reserve ratio and deposit rate, along with the timing of monetary policy announcements. These monetary tools have behavioral implications for the decision making of investors, in particular for markets that experience vulnerability and informational inefficiency, which are traditionally employed to signal policy direction and manage market liquidity.

The results show that any rise in the bank deposit rate may trigger herding in the extended crisis market, whereas a decrease in the deposit rate seems to induce investor herding behavior in the bearish market. These herding trends may reflect investor sensitivity to alternative investment vehicles during high market uncertainty. When the deposit rate increases during a prolonged market crisis, risk-averse investors may take safe refuge in bank deposits, leading to correlated withdrawals from equity investments—a form of reverse herding that still manifests in aggregate levels. Alternatively, investors may interpret a reduction in the deposit rate in bearish markets as a stimulus policy signal, encouraging synchronized re-entry into equity markets, amplifying herding. Our findings also indicate that both increases and decreases in the deposit reserve ratio, a base liquidity regulating monetary tool, strongly induce herding tendencies of investors in the overall as well during bearish and extended crisis markets. This bidirectional effect implies that market participants interpret both contractionary and expansionary monetary policy changes as signals of impending market conditions, triggering coordinated trading. This is consistent with the behavioral finance concept of signal-extraction problems, in which heterogeneous investors are prone to overreacting to policy announcements when they only have limited information processing capacity and tend to infer market sentiment from central bank actions, as noted by Scharfstein and Stein (1990).

Further, the analysis demonstrated that monetary policy announcement dates themselves are associated with significant herding effects in the same market states (overall, bearish, and extended crisis). This underscores the significance of announcement timing and communication strategies. During monetary policy announcement days, investors may pay more attention, resulting in increased uncertainty and prompting synchronized decision making, particularly in less efficient markets. This result is in line with empirical findings indicating that central bank communication may shape investor market expectations and behavioral motivation beyond the actual policy changes (Ehrmann & Fratzscher, 2007).

Contrary to the classical view that monetary policy tools solely influence asset prices through interest rates and liquidity channels, our findings signal that investor psychology—specifically herding tendencies—is a consequential and under-recognized transmission mechanism, especially during periods market downturn and stress. The context-specific nature of these effects—manifested prominently during bearish and prolong market crisis environments—highlights the nonlinear and state-dependent interaction between policy interventions and market behavior.

5.3. Comparison of the Results with Similar Studies

Our study results are widely in line with other equity market findings where herding is more pronounced during periods of market stress and monetary policy shocks. Indicatively, Javaira and Hassan (2015) reported stronger herding in the Pakistan equity market during volatile times, whereas Amata et al. (2016) associated herding effects with the macroeconomic instability in Kenya. On the same note, Sibande (2024) observed that herding in the South African (ZAR) market intensified when the market experienced an extreme movement and was also affected by the dynamics of the monetary policy using both the CSAD and CSSD. Consistent with this comparison, our findings indicate herding behavior in Bangladesh is responsive to macroeconomic shocks and monetary policy changes especially in the states of crisis and extended crisis markets. Nevertheless, in contrast to these previous studies, our study provides a more detailed market classification, i.e., bullish, bearish, crisis, extended crisis, and COVID-19 periods. This enables one to have a more detailed description of behavioral responses across various market states.

5.4. Interaction of Global Influences with the Bangladesh Market

The Bangladesh equity market is not in isolation, similar to other frontier markets. External or global factors might have a strong impact on the domestic investor behavior and be a contributor to herding effects. The herding behavior of the Bangladesh equity market is not purely domestic; it is also affected by external and international financial forces. The observation that the depreciation of Bangladeshi Taka has a crucial impact on the herding gives a clear indication of such external connections. The movements of the exchange rates usually indicate the pressure of the global economy, including international capital flows, trade imbalances, and changes in world commodity prices. The depreciation of the Taka is normally an indicator of increasing import prices, inflation, and possible foreign reserve crunch—factors that increase the uncertainty of investors. In this case, investors are likely to go with the general market trends instead of focusing on personal analysis, intensifying herding behavior.

In addition, external shocks tend to incite or exacerbate depreciation, including tightening of the US monetary policy, global oil price spikes, or geopolitical tensions, which disrupt trade and remittances. The analysis of these global events brings in the contagion effect. This causes investors in frontier markets such as Bangladesh to imitate the responses of the foreign market or undertake defensive portfolio adjustments, resulting into synchronous trading movements. Since the effect of exchange rate depreciation on herding is observed, it supports the opinion that the world financial trends and external shocks indirectly affect the psychology of domestic investors in Bangladesh. This observation highlights the need to consider herding as a behavioral phenomenon within the domestic context but also a component of larger global contagion process that is spread via currency and financial channels.

Further, since the presence of foreign investors in the Dhaka Stock Exchange is limited, foreign institutional investors are able to influence the market dynamics due to portfolio rebalancing and risk-off behavior. Under the influence of global outflows, especially those affecting emerging or frontier markets, local investors can take such a move as negative and copy the trading behavior, further increasing the effect of herding under the pressure of outflows in the world markets.

6. Conclusions

Our findings document that macroeconomic variables and monetary policy tools act as key drivers in shaping investors’ herding tendency in Bangladesh equity markets. A decline in the policy interest rate (repo rate) and a depreciation of the Taka (i.e., rising exchange rate) were found to induce herding behaviors in the overall, bearish, and extended crisis markets—conditions where herding was already observed. The results suggest that macroeconomic shocks act as common information signals that trigger uniform investor reactions, especially in times of market stress and uncertainty.

In similar ways, monetary policy tools such as deposit rates and deposit reserve ratios also significantly contribute to herding behavior. A decrease in the deposit rate influenced herding only in bearish markets, whereas an increase in the deposit rate influenced herding only in extended crisis markets. In addition, changes in the deposit reserve ratio—both increases and decreases—significantly affected herding in all three key market states (overall, bearish, and extended crisis markets). The pattern was also mirrored around monetary policy announcement dates, which supports the notion that policy-related events can act as catalysts for collective investor behavior.

These results demonstrate that the herding phenomenon in Bangladesh equity markets is not simply driven by behavioral or sentiment factors; it is also highly responsive to external macroeconomic and monetary policy factors. It appears that investors interpret such signals as indicators of future market direction, resulting in correlated trading decisions. The behavior is particularly pronounced during unstable markets, when investors follow dominant trends rather than relying on individual assessments due to fear and uncertainty.

6.1. Policy Implications

This research has a number of significant regulatory implications on policymakers and market authorities because aggressive changes in macro variables may inadvertently stimulate equity market destabilization through induced herding dynamics in the Bangladesh equity market. The presence of herding behavior, especially in a down market and during times of macroeconomic or monetary instability, highlights the necessity of proactive mechanisms to monitor the market and provide security to investors.

The Securities and Exchange Commission (BSEC) in Bangladesh and other regulators must enhance real time surveillance systems in the market to detect abnormal trade co-movement as well as any possible panic sell-offs. Investor bias is minimized by improving investor education programs by influencing informed and independent decision making.

Regulators might contemplate temporary circuit breakers, short selling caps, or specific liquidity support in periods of macroeconomic uncertainties or acute depreciation of the exchange rate to stabilize the situation and to replenish investor confidence. Furthermore, the central bank and the securities regulator can be more coordinated to ensure that the communication of the monetary policy is aligned with the targets of capital market stability, reducing behavioral spillovers of the policy changes. Lastly, information asymmetry, which is one of the major causes of herding in frontier markets as seen in Bangladesh, can be reduced by facilitating improved institutional variance, better market transparency, and higher standards of information disclosure.

6.2. Limitations

This study highlights certain limitations that should be mentioned. Our analysis covers all listed stocks on the Dhaka Stock Exchange between 2010 and 2021, but the results are only applicable to Bangladesh and may not necessarily be generalizable to other frontier or emerging equity markets with different market structures and investor composition. Second, the study concentrates on the chosen macroeconomic variables (exchange rate, interest rate) and monetary policy indicators (deposit rate, deposit reserve ratio) and does not consider any other possibly significant variables, including inflation, GDP growth, or shock in the global market, which may also affect herding behavior. Lastly, despite the market being classified into different phases to represent distinct market states, unobserved microstructural or firm-level forces may still enforce herding, but it is not well encompassed in the existing framework. The identification of these limitations offers an avenue to future research to increase the coverage of the variables, use alternative modeling techniques, or perform cross-market comparisons to learn more about herding in frontier equity markets.

6.3. Future Research Directions

The results of this research can be extended to future research to investigate a number of extensions that can enhance the understanding of herding dynamics under various market settings. First, the researchers could study the issue of herding by including a broader range of macro-financial variables, including investor sentiment, credit spreads, and monetary transmission variables to measure the greater behavioral and financial connections driving market coordination. Also, further research may utilize more complex econometric or machine learning methods to identify nonlinear herding behaviors, especially when the financial markets are facing a financial crisis or a change in regulatory inflexions. Cross-frontier and emerging equity market comparative analyses may also be interesting to understand the role that institutional frameworks and market efficiency play in the intensity and persistence of herding. Lastly, integrating the behavioral survey data with market-level indicators would provide a more complete picture of the psychological foundations behind collective investor behavior that would lead to a more sophisticated understanding.