The stock market returns reflects the performance of the economy and the expectations from the market. In

developing markets like India, stock market performance is not only dependent on the performance of the firms

but also on the economic and social indicators. This paper aims to analyse the compounded and relative effects

of the Human Development Index (HDI), Consumer Price Index (CPI), Wholesale Price Index (WPI), and

Consumer Confidence Index (CCI) on stock market returns in India between the years 2015 and 2025. The

analysis is based on 128 monthly observations of each Bombay Stock Exchange (BSE) and the National Stock

Exchange (NSE) with the help of descriptive statistics, Correlation, Augmented Dickey-Fuller (ADF) unit root

tests, multiple regression (OLS), and Autoregressive Distributed Lag (ARDL) models.

multi-collinearity while maintaining R² = 0.9769 (BSE) and R² = 0.9705 (NSE). All three retained predictors are

significant at p < 0.001. CCI is negatively correlated with BSE (–0.652) and NSE (–0.636) at the bivariate level,

but positively significant in the multivariate regression, consistent with its role as a leading sentiment indicator.

Monthly HDI data was sourced from the UNDP Human Development Data Centre (hdr.undp.org). The study

provides the first empirical evidence in India that human development is a significant positive driver of long-

term stock market performance.

Keywords: Human Development Index (HDI), Consumer Confidence Index (CCI), Consumer Price Index, (CPI),

Wholesale Price Index (WPI), Stock Market Returns, BSE, NSE, ARDL, Time-Series Analysis, India.

Stock market serves as one of the key metrics reflecting the performance of the economy, not only in the present

strategies.

The conventional financial studies have paid attention to a small set of macroeconomic variables including GDP

growth, interest rates or exchange rates in explaining stock market performance. But the Indian market, influenced

by its demographic dividend, expanding consumer base, and goal of development that is driven by policies requires

a wider set of analysis. This paper addresses that gap by considering at the same time four different types of

variables: a developmental index (HDI), two measures of inflation (CPI and WPI), and a behavioural indicator

(CCI).

Human Development Index (HDI): The HDI is an initiative of the UNDP that is released yearly and measures

progress in education health, and income. Increasing HDI is an indicator of human capital and productivity which

operating margins.

Consumer Confidence Index (CCI): This is an index that is surveyed by the Reserve Bank of India and measures

consumer confidence on income, spending and the state of the economy. It is a progressive behavioural measure of

economic action and commercial impetus.

Combining these four dimensions into one analytical model and implementing them on 128 months of data (Apr

2015 - Mar 2025) of both BSE and NSE, this research intends to provide a comparative insight into the drivers of

stock market returns in India.

The connection between inflation and equity returns has been a popular subject of financial literature. The basic

producer inflation on the basis of CPI and WPI, respectively, with CPI having a more direct impact on consumer

demand and corporate revenues whereas WPI has an impact on input costs and operating margins. Indian research

(Sahu and Dhiman, 2011; Naik and Padhi, 2012) proved that there was a negative but lagged correlation between

inflation and Sensex or Nifty returns.

The consumer sentiment as a predictor of financial market performance acquired scholastic backing with the help

of Ludvigson (2004), who demonstrated that consumer confidence survey is rich in information about future returns

of stocks not reflected by conventional macroeconomic indicators. Jansen and Nahuis (2003) observed that there

exists a two-way relationship between consumer confidence and performance in the stock market in the European

economies, which implied that consumer confidence is both a cause and a result of market performance.

Empirical correlations between HDI and stock market performance are not studied in the financial literature.

Majority of the existing studies follow an oblique approach: the higher the HDI, the more the accumulation of

human capital, which spurs productivity, GDP growth, and, finally, corporate profits and share prices. The

endogenous growth theorists Acemoglu and Johnson (2007) and later reasoned that health and education results

have lasting impacts on economic growth that in the long run get passed on to financial markets.

On the national level, countries with a higher level of HDI are more likely to have a developed financial market,

increased investor participation, and higher market capitalization as a ratio of GDP. In the case of India, whose HDI

has steadily increased since 0.624 in 2015, to about 0.680 in 2023, the increase in human capital may be one of the

factors that explain the continued positive trend in BSE and NSE indices during the same timeframe.

Scholars have been moving toward dynamic, multivariate models, to describe the multifaceted, interactive impacts

of a range of macroeconomic factors on stock returns. The ARDL bounds testing method popularized by Pesarann,

The current literature generally looks at CPI, WPI or CCI alone or only two variables at a time. The literature lacks

a comprehensive model that takes into consideration all four variables (developmental, inflationary and behavioural

dimensions). Human Development Index has not been given much attention within the empirical financial studies,

even though it has a close theoretical relationship with the long-term economic growth. Most financial studies use

HDI as a measure of macroeconomic development as opposed to a measure of financial determinants.

A high number of Indian studies use the static OLS regression without unit roots and cointegration test and without

the use of ARDL models that differentiate short-run adjustments and long-run equilibrium relationships. Although

there is plentiful literature in the field of mature markets, a lack of empirical evidence in India, namely the capital

market, based on a multivariate time series model over a 10-year period after reform (2015-2025) is a weakness.

By comparing both BSE and NSE, serving different segments of the investor population, robustness checks can be

The null hypotheses tested based on the above objectives are:

H01: There is no effect of HDI on stock market returns (BSE and NSE).

The explanatory design is suitable because the research aims to find causal and associational links among

macroeconomic indicators and stock market returns within a specified time frame.

The research covers 10 years between April 2015 and March 2025, considering 128 monthly observations. Table

No.1 shows the sources of data and the frequency of each variable that will be used in the study.

Table No.1 Data Description and sources

The VIF analysis reveals that CPI and HDI exhibit astronomically high VIF values of 211,009 and 210,665

respectively, consistent with their near-perfect correlation of r = 0.9999. At these levels, OLS estimates for CPI and

HDI are statistically meaningless—standard errors are massively inflated, t-statistics deflated, and coefficient signs

may be reversed. Since CPI and HDI are essentially collinear, one must be excluded. CPI is dropped and HDI is

retained because HDI is the theoretically richer variable, capturing education, health, and income dimensions

Table No.3 shows correlation of all the variables of the study. This aids in determining the direction and the strength

Table 6 presents the OLS multiple regression results for BSE and NSE using the revised model (CPI excluded

following VIF analysis). All results use HC3 heteroscedasticity-consistent robust standard errors.

The regression results are highly significant in both markets. F-statistics of 1,381.13 (BSE) and 1,077.91 (NSE)

coefficients were only significant at p < 0.05 due to inflated standard errors from multicollinearity. With CPI

removed and multicollinearity resolved, all coefficients are now precisely estimated with valid standard errors.

The following robustness checks were conducted on the revised model:

Breusch-Pagan Test: LM = 8.07 (p = 0.047) for BSE; LM = 11.07 (p = 0.011) for NSE. Heteroscedasticity

confirmed.

Addressed through HC3 robust standard errors reported in Table 6.

Durbin-Watson Statistic: 0.530 (BSE) and 0.425 (NSE). Positive serial autocorrelation detected, as expected in

monthly macroeconomic time-series. Addressed through the ARDL framework and Newey-West HAC standard

errors in the ARDL specification.

(Source:

Researcher Calculation)

ARDL model was estimated for each of the predictor variable and each of the indices including one period lagged

values. The most important short-run and long-run coefficients of the ARDL are summarized in Table No. 6

Table No. 8 ARDL Model Key Coefficients (NS = Not Significant)

(Source: Researcher Calculation)

The ARDL findings indicate that the lagged dependent variable is the only strongest predictor in both models

(coefficient 0.993), which indicates that there is strong momentum persistence in Indian equity markets. The

statistically significant short-run effect and negative lagged effect of CCI indicate that an increase in consumer

confidence increases market returns in the short run, but this impact is reversed to a degree in the next period as

consumer sentiment will go back to its usual level. The WPI coefficients are not statistically significant in either of

the models and this proves that the impact of WPI on stock returns is indirect, yet consistent.