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. 2023 Jan 25;3(2):55. doi: 10.1007/s43546-023-00434-3

An empirical investigation of investor sentiment and volatility of realty sector market in India: an application of the DCC–GARCH model

Naga Pillada 1,3,✉, Sangeetha Rangasamy 2
PMCID: PMC9875763  PMID: 36714500

Abstract

Understanding how an irrational investors’ sentiment affects the realty market returns, especially during the pandemic, is imperative to take any financial decisions. The effect of investor sentiment on the movement of the realty market leading to market volatility is dynamically represented in a numerical form. The study incorporates daily market data and their implicit indices to construct a sector-specific investor sentiment index by using the principal component analysis method. To analyse the relationship between the variables, a quantitative approach is used by incorporating an econometric model—dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC–GARCH). The directionality of the relationship between the variables is assessed by the Diebold–Yilmaz method. This study is done to investigate the return deviation in the realty sector due to sentiment impact during the pandemic in the Indian context. The findings indicate the existence of an asymmetric impact of the sentiment, leading to extreme volatility and returns in the realty sector. The results confirmed the presence of bi-directional relationship between asset returns and investor sentiment and quantified the relationship numerically. This study focused on the development, applicability, and validity of a sentiment index pertaining to the Indian realty sector. This study highlights the impact of a qualitative non-fundamental factor like sentiment as a measurable factor in determining the volatility on market returns.

Keywords: DCC–GARCH, Diebold–Yilmaz test, Investor sentiment index, Indian realty stock market, Principal component analysis

Introduction

The unprecedented onset of COVID-19 has toppled lives globally. It has affected every aspect of human life—physically, psychologically, emotionally, and financially. The pandemic has led to a global crisis. In May 2020, nearly 56,342 cases were recorded (Udhaya et al. 2020). The whole world is operating in fear of financial breakdown. The Indian stock market, though severely impacted, is on its way to recovery. The restrictions set by the Government of India (GOI), especially the lockdown, have led to unemployment and a rise in poverty. It was estimated by the International Labour Organization (ILO) that nearly 2.5 crore jobs were lost globally, and in India the unemployment rate in urban areas was 20.9% during the April–June quarter of 2020(Kumar and Srivastava 2021). Ram and Yadav (2021) estimated that nearly 150–199 million people would fall into poverty during 2021–2022, due to the lockdown which catapulted the industrial collapse, leading to unemployment of lakhs and pushing them below the poverty line. The Indian stock market has also observed a negative investor sentiment. A financial crisis has impacted negatively on the gross domestic product (GDP) and lowered investments (Jindal et al. 2020; Barbate et al. 2021).

In India, the real estate market has been estimated to grow to Rs. 65,000 crores in 2024. Being the highest generator of employment, this sector was estimated to augment the country’s GDP by 13% in 2025 (Sanchaniya 2021). The GOI has made many reforms and policies to improve the real estate sector in India. According to the report by Colliers India, institutional investors will grow by Rs.34500 crores (Indian Real Estate Industry: Overview, Market Size, Growth, Investments 2017).

According to a report by KPMG (A perspective on the Indian real estate sector 2020), the realty sector was classified into various asset classes (Table 1):

Table 1.

Sub-sectors of Indian realty sector and the impact of the pandemic on them

Sub-sectors Impact of the pandemic
1. Retail sub-sector mostly comprises malls Expected growth was from 19 to 25% in 202, but it led to reduced footfall and lowered lease/rental agreements
2. Commercial–office spaces Led to deceleration and re-evaluation of workspace models
3. Hospitality Is badly affected due to travel restrictions and panic. Was supposed to show a growth of 4.2%
4. Housing It tapered sales, pressurized housing operators, and caused contraction of inventory and labour. The sales were lowered from 4 lakh units to 2.8–3 lakh units in 2020–2021 across the top cities
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It has been observed that the majority of change was seen in housing and commercial properties. It has been proposed that the returns of housing real estate portfolios reflect the variation in macroeconomic factors (Shi 2020). Commercial space rentals seem to be going downhill owing to the COVID-19 pandemic. There has been a tremendous reduction in the volume of commercial real estate investment by up to 29%, and inter-regional investment has declined by 69% (Sanchaniya 2021). A link between lowered cap rates, interest rates, and market fluctuations has been studied empirically (Sivitanides et al. 2003). A report by EMIS (2020) gave the impact of COVID-19 on major realty companies such as Mahindra Life spaces, Oberoi Realty Limited, Sobha Limited, etc. This report highlighted the impact of the pandemic on different sub-sectors of the realty sector.

The Indian realty sector has seen an increase of 7% (YoY) in institutional investors in the third quarter of 2021 with Mumbai, Delhi, and Bengaluru receiving 77% of the total investment (Indian Real Estate Industry: Overview, Market Size, Growth, Investments…IBEF, 2021). Inflation can be considered as a causative factor leading to the change in investment patterns from equity to realty sectors during a crisis. This could be a reason for the positive growth in the realty sector at specified locations (Leombroni et al. 2020). The impending question in the present investigative study is whether a sectoral sentiment index could be constructed and the possibility of estimating its impact on the realty market returns in the Indian context numerically. The macroeconomic factors considered are GDP, consumer price index (CPI), wholesale price index (WPI), and foreign exchange reserves. Only these factors have been chosen, as they have a measurable impact on returns and sentiment. The existing research and models have been developed for the entire market, considering sentiment for the entire market. However, the impact of investor sentiment on returns from the sectoral index during a crisis (COVID-19) needs to be studied. Hence an attempt is made in this study to develop a model and test for its applicability.

The present study tried to find a solution to the following research questions:

How do we quantify and assess the impact of sentiment index (qualitative factor) on the returns of the Indian realty sector during the pandemic?

The objectives of this study were to investigate the impact of sectoral investor sentiment on the realty sector returns and to check the possibility of constructing a sectoral-specific sentiment index.

This paper is divided into five sections. “Literature review” gives a brief review of the literature. The next section gives the overview of the data and methodology, whereas “Results and findings” deals with the results, followed by the conclusion and future aspects of the study in the final section.

Literature review

Traditional asset pricing models

An asset pricing model gives the relationship between impacting factors and returns (Lalwani and Chakraborty 2020). The traditional theory of asset pricing by Sharpe (1964) gives the linear relationship between returns and market risk (β). It is a single-factor model, which in today’s world is out of context. Despite this model’s poor empirical evaluations, it simplifies the assumptions and makes the calculations easier (Fama and French 2004), but does not hold true in the real world (Rossi 2016). Fama and French disagreed with the fact that only β was sufficient to understand the returns; they believed that size and book-to-market ratio (B/M) also impacted the returns (Fama and French 1992). This made the social scientists use different pricing kernels and include the stochastic discount factor (SDF), as returns are expected discounted payoff (Drobetz 2000; Cochrane 2005). A behavioural insight into asset pricing using SDF was given by Shefrin and Belotti (2007) and Cochrane (2005).

The macroeconomic factors’ impact was studied and this helps in understanding investment opportunities and the appropriate time for investment (Raei et al. 2011). Asset pricing and macroeconomic linkages with empirical framework relating to housing, and exchange rates with a focus on international influence, were done by Claessens and Kose (2017) only to find that they indeed had an impact on pricing.

The traditional model believed in the efficient market hypothesis and did not agree that sentiment was an important factor attributing to investments. Fama and French (2014) believed that they were anomalies despite adding two new factors, investment and profitability, in the original Capital Asset Pricing Model (CAPM). These factors could not explain the macroeconomic effect properly or the investor sentiment. Hence, newer theories and models were developed.

Impact of investor sentiment on the realty sector during the pandemic and sentiment models

The impact of the pandemic on the Indian economy is clearly seen similar to that observed during the 2008 financial crisis. The rise in unemployment, lowered GDP, and inflation are some parameters that can be clearly identified as well as how the strict monetary policies saved the Indian economy from further ramifications (Siddiqui 2019).

Commercial property rents have also been heavily impacted by the pandemic. A regression analysis with macroeconomic variables in the Asian regions was done to understand the ramifications of the pandemic on commercial rents (Allan et al. 2021).

Asset pricing of the housing sector and investor sentiment are two aspects that have a direct impact on each other (Kwakye and Haw 2021). The housing sector comprises a large proportion in an investor’s portfolio and plays a significant role in price jumps of the realty sector as a whole and the market (Kullmann 2001). Hui and Wang (2014) tried to develop a sentiment index to understand investor implications on housing prices and market changes. Kwakye and Haw (2021) analysed the investor sentiment aspect in the housing sector with the help of theories like bubble theory, irrational exuberance theory, and theory of noise trader from behavioural finance. Noise and irrational investor sentiments also play a vital role in stock trading. It impacts the activities of arbitrages leading to underpricing and overpricing of stocks during the crisis (Lemmon and Portniaguina 2002; Baker and Wurgler 2006). The linkage between noise, rational and irrational investors on asset returns, and volatility are not clearly understood. The impact of behaviour and its association with volatility was identified in advanced economies (Brown and Cliff 2004; Qiu and Welch 2006; Lemmon and Portniaguina 2002).

Shiller (2015) dwelled upon the aspect of the irrational exuberance of an investor, which tried to highlight how an investor perceives the risk and future cash flow returns in the housing sector, leading to pricing fluctuations. From the behavioural finance theories, it was inferred that emotions and perceptions play a significant role in financial market movements (Baker and Wurgler 2006, 2007 and Suciu 2015). Investor sentiment has been identified as a relevant factor that influences potential buying behaviour. Especially during the crisis, the trading behaviour of institutional investors was highly correlated with their sentiment in securitized markets (Das et al. 2015).

The onset of the pandemic has made it imperative to study the effect of investor sentiment on asset returns of the realty sector. This study also tries to develop an index and test the applicability of the index for restricted data in the Indian context.

Data and methodology

Investor sentiment has become an important non-fundamental component of analysing asset pricing. In this study, proxies were developed based on Baker and Wurgler (2006), Pandeya and Sehgal (2019), and Haritha and Rashid (2020) models.

The indirect measurement proxies considered for the study are market turnover (TURN), trading volume (TV), share turnover (STR), price/earnings ratio (P/E), and advance and decline ratio (ADV/DEC). These five proxies were chosen to evaluate the impact of irrational investor sentiment on asset pricing of the realty sector in India. The data were collected from financial websites like ACE Analyser and BSE (formerly known as Bombay Stock Exchange) for returns calculations. The period of study is November 2019 to June 2022. The rationale behind choosing that period is to assess the impact of sentiment during and post the pandemic on returns of the realty sector.

GARCH (generalized autoregressive conditional heteroskedastic)/PCA (principal component analysis) models were employed to analyse and understand the return deviation and sentiment index development, respectively. These models were adopted from existing literature and modified to suit the needs of the Indian stock market. Time series models and consumption-based models with a different perspective, which were used in this study, were proposed by Cochrane (2005). In this study, DCC-GARCH (dynamic conditional correlation-generalized autoregressive conditional heteroskedastic) an extended model of Bollerslev's GARCH was employed to assess the volatility in the BSE real estate index due to the sentiment index. For the computation of PCA and GARCH analysis, Jamovi (2.2.2 version) and R-language software were used, respectively.

In this study, a comprehensive econometric model was used to analyse the sentiment of investors. This allowed the researchers to get a bias-free, numerical impact of sentiment on returns, but there are various models which use electronic data and process it to assess the sentiment, especially Twitter data on the stock market using different computer softwares such as Python or RStudio. Tankard et al. (2021) used Twitter sentiment analysis along with natural processing languages to develop a score for the potential bias observed in the comments made intentionally or unintentionally. This clearly proves the existence of biased opinions in the Twitter data, which could be avoided by employing an econometric model (a quantitative approach). Despite developing numerous NLP models for identifying sarcasm or comedy from the available electronic data, the probability of erroneous results is prevalent (Medhat et al. 2014; Wankhade et al. 2022). The possibility of this error, while assessing sentiment, is avoided by employing a quantitative approach using statistical and econometric models, as the sentiment is quantified and estimated numerically.

Sectoral investor sentiment index construction

The sectoral sentiment index was constructed by using Baker et al. (2012) framework, which was adopted with modifications wherever required. It considered five variables: trading volume, advance–decline ratio, share turnover ratio, price–earnings ratio, and market turnover ratio. The data for the proxies were collected from the top ten BSE-listed companies in the realty market. The following describes the proxies and their calculations.

Proxy introduction: The share turnover ratio (STR) helps in evaluating the number of active traders and investors in the market. It gives the liquidity position of a company. Turnover becomes indispensable while measuring investor sentiment, as optimistic investors accelerate the turnover (Baker and Stein 2004). Haritha and Rishad (2020) defined market turnover (TURN) as “the ratio of trading volume to the number of shares listed on the stock exchange”. It helps in assessing sentiment, as the bearish and bullish movements of the market are indicated by the sentiment (Karpoff 1987), thus making the market turnover ratio a significant factor. The advances and declines ratio (ADR) gives the proportion of the advancing and declining shares, thus giving a glimpse of the market breadth. The trends of the market represent the market movements and indicate the market performance (Brown and Cliff 2004).

The trading volume (TV) acts as a base for calculating proxies and developing sentiment indexes. It helps in estimating the liquidity and indicates that active trading increases the volume (Qiang and Shue-e 2009; Li 2014; Chuang et al. 2010; Haritha and Rishad 2020). Table 2 lists the proxies that have been incorporated in this research with their definitions.

Table 2.

Proxies that have been used in this research study with their definition

Proxies (indirect measurements) and their literature reviews Variable definition
Price to earnings ratio (P/E)—(Khan and Ahmad 2019) Share price/earnings per share
Share turnover ratio (STR)—(Khan and Ahmad 2019), (Haritha and Rishad 2020)

It is calculated as the ratio of daily value to the total number of shares outstanding for that period. It also helps in understanding the liquidity position of a company and gives the gist of the trading activity and interests of investors

Share turnover ratio = traded volume/number of shares outstanding
Trading volume (TV)—(Baker and Wurgler 2006 and 2007), (Pandey and Sehgal 2019) (Khan and Ahmad 2019), (Haritha and Rishad 2020)

TV is the most important value to be calculated, as it is the basis for various calculations

Traded volume = daily volume/number of trading days
Advance/decline ratio (ADV/DEC)—(Pandey and Sehgal 2019) It is the ratio of the total number of companies whose share price increased in a day to companies' shares whose share price decreased on that particular day
Market turnover ratio (TURN)—(Baker and Wurgler 2006) (Pandey and Sehgal 2019) (Khan and Ahmad 2019) (Haritha and Rishad 2020)

Market turnover will be calculated by taking the ratio of volume traded to average market capitalization

TURN = traded volume/average market capitalization
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Index construction

The indirect measurement proxies were calculated and regressed with the chosen, macroeconomic variables (GDP, CPI, WPI, and foreign exchange) to account for their impact. They were tested for stationarity by employing the augmented Dickey–Fuller test (ADF). To develop a sectoral sentiment index, principal component analysis (PCA) was done. For the PCA analysis, the initial loadings are the proxies and their lags and leads, which give a raw-sectoral sentiment index. From this analysis, the factors that were found to be prominent for sentiment index development are P/E, STR, TURN, ADV/DEC ratio, and TV. Then the correlation between raw index–lagged variables–leading variables was calculated using the correlation matrix. The factors which have a higher correlation with the raw index were used to calculate the final sectoral sentiment index.

The parsimonious sectoral sentiment index

IRSI=0.230PE+0.685STRt-1+0.790TURNt-1+0.419ADVDEC-0.194TVt-1. 1

Sectoral sentiment index impact on realty market index volatility: The sectoral sentiment index was developed, using modified models of Baker and Wurgler (2006 and 2007) and Pandeya and Sehgal (2019), keeping in view the restricted time period. Amongst the various indices available, the chosen index for this study was the S&P BSE Realty Index. The DCC–GARCH model was employed to calculate the impact of the sectoral sentiment index on the BSE realty index market volatility. This model also accounts for the directional correlation.

The GARCH model and its variants could be applied only for those time series, which have a significant ARCH-LM test. Hence, the sectoral sentiment index and S&P BSE realty index returns were tested for volatility clustering and their heteroskedasticity behaviour. It was confirmed for both the realty market index returns and the sentiment index.

For the econometric analysis, the DCC–GARCH model by Engle (1999), an extension of the constant conditional correlation–GARCH by Bollerslev (1986), was employed to assess the dynamic linkages/spillover between two variables. In this context, the impact of the sectoral investor sentiment index on the realty index was assessed.

The time-varying correlations estimated using multivariate models like GARCH usually take in variables that are linear squares and products of returns. Hence, a new model was developed with the flexibility of univariate models and parsimonious models for correlations. The DCC–GARCH is a rather simple two-step process, which gives direct results. It estimates the univariate GARCH results and then finds the correlation. This model helps in estimating the connectedness, directional connectedness, and pairwise linkages (Engle 1999).

This model allows the introduction of a time-varying model, by introducing another parameter which is also time-varying Rt:

Ht=DtRtDt,

where R is a correlation matrix containing the conditional correlations.

Dt = diag{√hi,t}.

The dynamic correlations are evaluated at every juncture by forming a correlation matrix from the following equation:

ρi,j,t=∑s=1t-1λsεi,t-sεj,t-s∑s=1t-1λsεi,t-s2∑s=1t-1λsεj,t-s2=Rti,j.

The returns series of the S&P BSE realty market index returns (RMIR) and the Indian Realty Sentiment Index (IRSI) series were used for the analysis. Since data/series was collected during the period of the pandemic, the realty index returns had been exposed to bad shocks. The investor sentiment has also been exposed to severe panic and shock, leading to visible volatility in the returns.

Causative relationship estimation: There are two methods to estimate the causative relationship between two variables. The representative methods include the Granger causality test, cointegration test, etc., which show only the connectedness neglecting the direction. Diebold and Yilmaz (the other method) developed a framework, wherein the pairwise connectedness and directionality can be measured and expressed numerically (Xiao and Huang 2018). In this research study, a model based on the Xiao and Huang (2018) was used to identify the correlation/connectedness between BSE market index returns and IRSI. The Diebold–Yilmaz test (Diebold and Yilmaz 2012) was conducted to measure the impact of IRSI on S&P BSE Realty market index returns.

Results and findings

The objective of this is to study to estimate the volatility caused by the IRSI on RMIR. The time series data could be assessed by employing statistical and econometric models.

For the sectoral sentiment index, all the proxies chosen were used as factor loadings for PCA analysis. A correlation matrix was developed using the first index, lagged, and current variables of the proxies to get a more appropriate index. The stationarity of the proxies was also estimated. Augmented Dickey–Fuller test (ADF-TEST) for stationarity was conducted for each proxy and the results concluded that all the proxies were stationary (Table 3).

Table 3.

Statistical descriptive measures and ADF-test (p values) results of the proxies

Proxy P/E ratio Share turnover ratio Traded volume Turn ADV/DEC
Mean 1.205801 0.3775 0.02171 0.686 1.4496
Standard deviation 0.8333 0.8104 0.0711 1.4265 1.9535
Variance 0.649 0.6568 0.00506 2.0350 3.8161
ADF-Test 0.027 0.01 0.01 0.01 0.01
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All the statistical and econometric models have a significant p value, affirming the assumptions made about the stationarity (Table 3), volatility (Table 4), and correlation (Table 5). Based on the results arrived from the ADF-test, DCC–GARCH, and Diebold–Yilmaz, necessary changes were made to calculate the proxies as well as to develop the sectoral sentiment index.

Table 4.

ARCH-test p values of the RMIR and IRSI

Series p value
S&P BSE realty index (RMIR) 0.000
IRSI 0.0157
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Table 5.

The DCC–GARCH analysis of the S&P BSE realty index returns and IRSI

Estimate Std. error p values
DCC–GARCH values S&P BSE realty index returns and sentiment index (IRSI)
 IRSI
  µ 0.8611 0.0382 0.000
  ω 0.6565 0.2225 0.003
  α1 0.1290 0.0630 0.0405
  β1 0.0950 0.2526 0.7067
 S&P BSE market index returns
  µ − 0.001608 0.000750 0.0321
  ω 0.00018 0.000054 0.0456
  α1 0.1992 0.0896 0.02616
  β1 0.5395 0.1917 0.0049
  dcca1 0.05203 0.19178 0.1774
  dccb1 0.92633 0.052422 0.0000*
  Hannan–Quinn − 1.4278
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The returns for the realty market index were calculated as R=log(Pt-Po)/Po. Volatility and heteroskedasticity characteristics of the financial data demand the use of econometric models. Hence, the DCC–GARCH (Engle, 1999) model was employed to capture the volatility and manage the heteroskedasticity of the time series. DCC–GARCH analysis can be applied only to stationary data. Hence, the ADF-test was conducted for the financial series and for the IRSI, and the p values are listed in Table 3. The volatility clustering was observed in both the financial series and is shown in Figs. 1 and 2. The presence of ARCH effects was also assessed and tabulated (Table 4). The presence of ARCH effects was also assessed and tabulated (Table 4). The DCC–GARCH analysis was carried out for both the series of S&P BSE realty index returns (RMIR) and IRSI and the results are tabulated (Table 5).

Fig. 1.

Fig. 1

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Volatility clustering of S&P BSE realty market index returns for the time period (Nov 19 to Jun 22). RMIR is the realty market index returns during the chosen period

Fig. 2.

Fig. 2

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Volatility of IRSI during the period of study. The X-axis represents the time and the Y-axis represents the sentiment index return volatility. It describes the volatility observed. The IRSI index gives the volatility caused by the investor sentiment in the constructed portfolio

The presence of ARCH and GARCH was tested by using a software (R language). The results were significant; hence DCC–GARCH analysis was performed.

Empirical results

In this study, DCC–GARCH was used for the analysis of time series data employed. This model helped in understanding the correlation between the IRSI and S&P BSE market index returns. The dynamic linkages/correlations/spillover are easily identified by employing this model. The estimated results from the DCC–GARCH showed that they were statistically significant (dcca1 and dccb1). The coefficients of ARCH (α) and GARCH (β) were not zero and their sum was close to unity. There is a significant contribution of IRSI to the conditional volatility of the realty market index returns in the long run (dccb1) (Table 5). It can be inferred that portfolio diversification is strongly suggested in the long-run investment to reduce the loss or impact of external factors on returns (Xiao and Huang 2018; Haritha and Rishad 2020).

It can be concluded from this study that sentiment is a critical element influencing market movements. The impact of sentiment might lead to market uncertainty and lowered returns (Fig. 3). From the conditional covariance graph (Fig. 4), it can be inferred that when the sentiment was positive and high, investors believed they would get higher returns, leading to the overvaluation of stocks. During the pandemic, negative sentiments were high, implying negative expectations in market returns. Hence, companies use the positive sentiment period for expansion, by offering initial public offering (IPO) (Haritha and Rishad 2020).

Fig. 3.

Fig. 3

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Conditional correlation between market returns and IRSI for the period of study

Fig. 4.

Fig. 4

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The covariance graph between the market index and sentiment index and vice versa for the 4-year time period. It showcases the trends of volatility in different time periods and helps in understanding how both variables affect each other

To understand and get an accurate estimate of the impact of IRSI on market returns, the Diebold–Yilmaz test was conducted. The results stated that these two variables had a bi-directional relationship. The relation can be understood from Fig. 3.

Conditional volatility: The DCC conditional covariance Fig. 4 depicts the commotions in market volatility of the S&P BSE realty market index returns. The volatility was low in 2019 and that trend changed in 2020, where an upward trend was observed till late 2021. By the mid-2022, the volatility reduced substantially.

Diebold–Yilmaz test: The Diebold–Yilmaz test gives a numerical value along with the direction of cause between two different variables. The test was performed by taking both the series and it was found that the market returns lead to volatility in the sentiment index and vice versa.

From Table 6, it can be inferred that the impact of sentiment index on market returns and vice versa is

2.88+2.72=5.60, 2

Table 6.

Test results of Diebold–Yilmaz: the spillover table has no frequency bands

Realty market index returns (RMIR) Indian realty sentiment index (IRSI) From
Realty market index returns 94.23 5.77 2.88
Indian realty sentiment index 5.43 94.57 2.72
TO 2.72 2.88 5.60
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which is the same as the impact of market returns on sentiment index, thus confirming the bi-directional nature of the connectedness.

Conclusion

A comprehensive study was conducted to understand the impact of the sectoral sentiment index on the realty market index returns. It can be concluded that there was an impact on the S&P BSE realty index returns due to the sectoral investor’s sentiment index development and vice versa. The bi-directionality of their relationship is shown in Table 6. For the index construction, market-oriented proxies were chosen and principal component analysis was used. The investor sentiment could be either positive or negative. The presence of asymmetry in the sentiment led to bearish or bullish market movements. The persistence of the market volatility and its substantial reduction (DCC covariance, Fig. 4) validated the effect of the sectoral sentiment index on the returns of the realty market index. The results help in understanding the role of non-fundamental factors such as sentiment as an important driving factor for causing volatility in the Indian realty equity market.

This study helps in assessing the risk perception as well as the determinants of investment during the period of the pandemic. The sectoral sentiment index was correlated with the stock returns. Hence, there was a sudden increase or decrease in the investment pattern, according to the market movements (bearish/bullish). The results from the study might help investors to understand how a particular sector performs and it also helps in portfolio optimization (Haritha and Rishad 2020). The results from DCC–GARCH state that in the long run, the impact of sentiment on market returns is significant. The connectedness of these two variables has been identified numerically. It can be inferred that there is a requirement for portfolio diversification in the long-run investments to reduce the impact of sentiment on returns (Haritha and Rishad 2020).

The sentiment index developed for the realty sector was found valid. Hence, further research pertaining to different sectors can be explored. The research undertaken is highly specific with a restricted time period. The data for the research were the same as those of the S&P BSE realty index. It did not consider all the realty companies that are listed in BSE. Further research can be done by incorporating all the BSE-listed realty companies for the construction of an index as well as for calculating the returns. A comparative analysis between realty sectors of different economies can be conducted in the future. Only four macroeconomic variables, which were considered relevant for this investigative study, have been used. The impact of other macroeconomic variables on asset returns is yet to be tested.

Acknowledgements

We would like to thank Mr. Sreekumar Nair (Librarian) and CHRIST (Deemed to be University) for providing us with timely access to all online library resources for data collection.

Author contributions

Conceptualization: NP. Methodology: NP. Formula analysis and investigation: NP. Writing—original draft: NP. Writing—review and editing: SR, NP. Funding acquisition: N/A. Resources: NP. Supervision: SR. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

Funding

The authors did not receive support from any organization for the submitted work. The authors have no relevant financial or non-financial interests to disclose.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Ethical approval and informed consent

This article does not contain any studies with human participants performed by any of the authors.

Contributor Information

Naga Pillada, Email: pillada.lakshmanjani@res.christuniversity.in, Email: anjanipilleda508@gmail.com.

Sangeetha Rangasamy, Email: sangeetha.r@christuniversity.in.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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