An assessment of advertising effectiveness of Indian banks using Koyck model

Abstract

Purpose The problem of differentiation and creating a unique selling proposition is higher in the banking sector, as, any new service or product introduced is very quickly imitated by the competitors. The benefits of advertising have been seen to have long-term effects on the firm’s performance and debate is still on whether the expenses of advertising should be amortized or expensed immediately has been the area of concern for many years. The purpose of this paper is to carry out a comparative analysis of advertising effectiveness on private and public sector banks in India. Design/methodology/approach This study has included 33 listed commercial banks out of 41 listed on S&P BSE 500. Out of 33 banks, 14 banks belong to private sector and 19 banks are public sector banks. Data are extracted for a period of 14 years from 2004 to 2017 from Ace Equity. In total, there are 462 firm-year observations. Interest income, operating income and return on assets are the accounting measures considered in this paper. All the variables are deflated by total assets at the beginning of the period. To assess the effect of advertising on financial measures, distributed lag model is used. Findings The results of Koyck model suggest that it takes lesser time for private sector banks to see a significant change in interest income and return on assets with a change in advertising expenses whereas in case of operating income, the results achieved are opposite. Originality/value This study may be useful from accounting point of view to find out whether advertising creates long-term or short-term impact on financial measures. The study would help in determining the number of years for which advertising expenses can be amortized. With the help of these results, it can be said that advertisement expenses can be capitalized and then expensed over coming years. This means, to some extent advertisement has some long-run impact on financial measures considered in the study. In order to achieve more robust results, this study can be performed on different sectors.

Effect of advertising spending on operating and market performance of banks: empirical evidence from Bangladesh

“…They also suggest capitalizing and amortizing the advertising outlays to obtain future benefits for banks. Their finding is confirmed by Mulchandani et al (2019) for Indian banks and claims that advertising expenditure has a long-term effect on banks' performance to some extent, and it can be capitalized for a few years. These results were opposed by Demsetz (1979), Ayanian (1983) and Erickson and Jacobson (1992).…”

“…However, little empirical evidence exists about the financial performance of advertising spending for banks. Only a couple of studies (such as Chen, 2020; Mulchandani et al , 2019; Acar and Temiz, 2017; Mullineaux and Pyles, 2010) investigate the influence of advertising effectiveness on the banks' financial measures. Moreover, most of them are conducted in the developed economies and uses a variety of accounting measures.…”

Reklam Harcamalarının Ticari Bankaların Rekabet Gücüne Etkisi: Bootstrap Tahminli Veri Zarflama Analizi

“…Advertising expenditures make a positive contribution to the company's revenue thanks to the change in market demand. Many studies in the literature (Oliver and Fumás (2007), Abubakar (2014), Okolo et al (2018), Acar and Temiz (2017), Örs (2003), Mulchandani et al (2019), Ergün and Ergün (2020), Kuzucu and Öztürk (2016)) confirm this positive relationship. On the other hand, it is accepted that expenditures made for advertising are a cost-increasing process for companies.…”

Distributed lags using elastic-net regularization for market response models: focus on predictive and explanatory capacity

“…Some examples of works addressing sales-marketing responses are based on Bayesian regressions (Brown, 1986;Bass et al, 2007), Bayesian hierarchical methods, multivariate linear regressions (Havlena & Graham, 2004), the Kalman Filtering as is the case of Naik et al (1998), Support Vector Machines (SVM) (Viaene et al, 2001) Artificial Neural Networks (ANNs) (Zhang et al, 2009;Guido et al, 2011), nonlinear dynamic time series (Huffaker & Fearne, 2019), dynamic linear model combining multiple submodels for accounting for underlying patterns of advertising (Bruce et al, 2012), Distributed Lag Models (Bass & Clarke, 1972;Clarke, 1976;Weinberg & Weiss, 1982;Rufino, 2008;Mulchandani et al, 2019). Regrettably, it is the rule that these studies solely address in-sample evaluation and, in less often, discuss theoretical assumptions, so they fail to provide sufficient evidence of the validity of their suggestions to practitioners for applying such methods in real-life practice.…”