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Fintech plays a prominent role in augmenting the financial inclusion of the population and increasing the money supply, which calls for the intervention of monetary policy. This article is an attempt to examine the relationship between the financial inclusion, fintech and monetary policy effectiveness of the Indian economy, within the framework of wealth creation and transmission mechanism through the cost of capital.

On the quarterly data retrieved from multiple sources, autoregressive distributed lagged regression is used to examine the relationship between different variables as explained in four set models; after which the Toda–Yamamoto causality test is employed to capture the direction of the relationship.

The study finds a positive relationship between financial inclusion, fintech and inflation taken as a proxy for Monetary Policy Effectiveness (MPE) in the short as well as in the long run. However, the relationship between fintech and inflation is negative once the cost of capital is included in the models. The causality test exhibits the uni-directional causality from fintech to MPE and MPE to financial inclusion. Bi-directional causality exists between wealth and MPE. Similarly, bank rate and interbank rate are bound by bi-directional causality.

Being financially included facilitates ease and boosts public access to more financial services and credit, leading to increased demand and hence inflation. Hence government and regulators need to take mindful measures to enhance the fintech development and financial inclusion to make the monetary policy effective.

As per the author's best knowledge, this is the first study to examine the relationship between fintech, financial inclusion and monetary policy effectiveness in the context of the Indian economy.

This study aims to conduct a bibliometric analysis to provide a comprehensive picture and identify future research directions to enrich the existing literature on behavioral biases.

The data set comprises 518 articles from the Web of Science database. Performance analysis is used to highlight the significant contributors (authors, institutions, countries and journals) and contributions (highly influential articles) in the field of behavioral biases. In addition, network analysis is used to delve into the conceptual and social structure of the research domain.

The current review has identified four major themes: “Influence of behavioral biases on investment decisions,” “Determinants of home bias,” “Impact of biases on stock market variables” and “Investors’ decision-making under uncertainty.” These themes reveal that a majority of studies have focused on equity markets, and research on other asset classes remains underexplored.

This study extracted data from a single database (Web of Science) to ensure standardization of results. Consequently, future research could broaden the scope of the bibliometric review by incorporating multiple databases.

The novelty of this research is to provide valuable guidance by evaluating the existing literature and advancing the knowledge base on the conceptual and social structure of behavioral biases.

The purpose of this paper is to examine the long‐run relationship between the Indian capital markets and key macroeconomic variables such as interest rates, inflation rate, exchange rates and gross domestic savings (GDS) of Indian economy.

Quarterly time series data spanning the period from January 1995 to December 2008 has been used. The unit root test, the co‐integration test and error correction mechanism (ECM) have been applied to derive the long run and short‐term statistical dynamics.

The findings of the study establish that there is co‐integration between macroeconomic variables and Indian stock indices which is indicative of a long‐run relationship. The ECM shows that the rate of inflation has a significant impact on both the BSE Sensex and the S&P CNX Nifty. Interest rates on the other hand, have a significant impact on S&P CNX Nifty only. However, in case of foreign exchange rate, significant impact is seen only on BSE Sensex. The changing GDS is observed as insignificantly associated with both the BSE Sensex and the S&P CNX Nifty. The paper, on the whole, conclusively establishes that the capital markets indices are dependent on macroeconomic variables even though the same may not be statistically significant in all the cases.

This study emphasises on the impact of macroeconomic variables on the stock market performance of a developing economy, whose performance is measured by these variables.