he ongoing debate over the competitive effects of advertising is implicitly contesting the issue of economic durability of advertising expenditure (e.g. Ayanian 1975; Comanor and Wilson 1974; Telser 1968, etc. Advertising plays multiple roles in that it is not only used by companies to create awareness among customers for their products and services, but also acts as a tool to build a strong brand image by dramatizing and presenting their products and services in such a way so as to attract customers' attention. The power of advertising in building strong brands has been proposed by both marketing practitioners (e.g. Martin 1989) and academics (e.g. Aaker 1991Aaker , 1996)).

Though advertising is one of the most potent and effective marketing tools available to marketers for informing and persuading buyers, the efficiency and effectiveness of advertisement spending is of considerable interest both to academicians and practitioners (Xueming and Donthu, 2002).

In general, sales or market response research has made it more difficult to answer a long-standing question: "Is advertising an investment or an expense?" (Mergy and Lade 2001). Many academic researchers have argued that advertising should be treated as an investment because of its role in improving the longterm market performance of a firm (Chauvin and Hirschey 1993; Dean 1966; Dekimpe and Hanssens 1995; Graham and Frankenberger 2000; Hirschey and Weygandt 1985; Hula 1988).

Further, the firms that allocate large amounts of their resources to value advertising expect their expenditures to contribute, ultimately, to the financial performance of the firm. Several studies have focused on the relationship between advertising expenditures and financial performance measures such as stock returns and ROI on advertising, while mainstream advertising effectiveness research in marketing has probed the relationship between advertising and market performance measures in relatively shorter time periods (Hanssens, Parsons, and Shultz, 1990).

There is a strong reason behind companies adopting advertising expenditures to escalate their sales and market share assuming a direct relationship between the two. Companies with a higher amount of sales revenue can afford to spend more on advertisements when compared to the ones with lower sales revenue. Therefore, it can be assumed that the businesses with higher sales in period 1 lead to higher advertising spending in period 2. While some of the researches reveal the presence of long-term equilibrium relationship between advertising and consumption (Guo,2003and Phillip, 2007), some others view that advertising expenditure causes sales but sales do not simultaneously cause advertising (Leach and Reekie, 1996).

The present study attempts to establish the linkages between advertisement spending, sales and profit in the case of Indian manufacturing companies.

The study is organized as follows. The present section introduces the concept of the study and outlines the need for it; the second section presents the objectives of the study; the third section reviews the literature available; the fourth section describes the methodology for the research; the fifth section presents the results of the study and the sixth section concludes.

The study aims to achieve the following objectives: a. To understand the change patterns in the advertisement, sales and profit in Indian manufacturing companies; b. To study the inter-relationship between advertisement, sales and profit; c. To draw policy implications for marketers as to whether increase in advertisements leads to increase in sales.

In the past, researchers have attempted to explain some of the confusion regarding the impact and effectiveness of marketing communications, most often focusing on advertising and promotional expenditures. Farris and Buzzell (1979) explained in their study how and why differences in marketing communication intensity (as measured by advertising and promotion expenditures to sales) were related to some basic variables. Therefore, an attempt was made to identify the factors that empirically explain the variations in advertising and promotion to sales. Their study indicated that advertising and promotional expenditures expressed as a proportion of sales vary across industries, across firms within an industry and across time for a given firm.

Balasubramanian and Kumar (1990)also confirmed the same finding. Zinkhan and Cheng (1992) again used the ratio of advertising and promotional expenditures to sales as a proxy for marketing communication intensity. They investigated the variation of communication intensity due to the type of offering (productor service) and the type of market (consumer or manufacturing). They found that, both, the type of offering and the type of market affect the variation of communication intensity. Their results indicated that consumer product firms spend more on advertising than manufacturing product firms.

Simultaneously though, under pressure to produce immediate profits, managers still tend to view advertising as an expense and reduce advertising budgets in times of downturn, even though they recognize that advertising can be treated as an investment (Dean 1966;Hirschey and Weygandt 1985).

Even though this research stream has shed some light on how advertising works or should work, its contributions to our understanding of the role of advertising in a competitive, complicated, and everchanging market environment have been limited. For example, a group of marketing researchers in this area (Bass and Leone 1983; Clarke 1976; Srinivasan and Weir 1988) who employed market-level data to explore the long-termor carryover effects of advertising found that the duration of advertising effects depended on the data interval under study. Clarke (1976) and Assmus, Farley, and Lehmann (1984) suggested that 90 percent of advertising effects dissipate after three to fifteen months. Leone (1995) argued that the range of advertising effects should be2narrowed to six to nine months based on his study. However, Dekimpe and Hanssens (1995) concluded that the effects of advertising did not disperse within a year. These contradictory findings could be partially attributed to the different sources of data used in the studies (Vakratsas and Ambler 1999).

Empirical researches used different tools to analyze the data about relationship between advertising and sales. Guo(2003) and Leong et al. (1996) applied the cointegration to analyse and evaluate the data. Taylor and Weiserbs (1972)put to use the Houtakker-Taylor model in their research for evaluation purpose. Leach and Reekie (1996) Guo (2003) implemented the unit root test for evaluation. Metwally (1997) implemented the correlation test for the evaluation of the data. Telser (1964), Rundfelt (1973) utilized the correlation test to examine the data.

Leach and Reekie (1996) concluded that advertising expenditure causes sales but sales do not simultaneously cause advertising. Another point to note is that marketing is defined widely in the literature. As outlined by Webster (1992) there are four different aspects of marketing practice:

(1) transactional marketing involves managing the marketing mix to attract and satisfy customers; (2) database marketing uses technology to target and retain customers; (3) interaction marketing involves developing interpersonal relationships between buyers and sellers; and (4) network marketing develops interfirm relationships for mutual benefit. This thesis specially focuses on the relationship between MC (which comes under transactional marketing) and shareholder value.

In the present study the inter-relationship between advertisement, sales and profit has been studied. The study focuses on the manufacturing sector. The impact of advertisement on sales can be calculated for such companies because unlike the services sector, the sales in units are available for manufacturing companies. Hence, in order to establish the relationship between advertisement and sales, the study selects the sample from manufacturing companies. The paper draws its sample from the NSE's NIFTY index. Twenty manufacturing companies indexed in NIFTY are used as sample for the study. These include Tata Motors, Maruti Suzuki, Reliance, ONGC, Hindustan Uniliver, ITC, Cipla, Sunpharma, Mahindra & Mahindra, Hero Motors, Dr. Reddy, Tata Steel, BHEL, NHPC, Coal India, Lupin, Gail, Bajaj, Asian paints and L&T. These companies are among the most renowned in their respective industries.

The sample period for the study is ten years ranging from 2005-06 to 2014-15.To analyse the cause and effect relationship between sales and advertisement, the ten years data of profit, net sales and selling expenses are taken. The study uses descriptive statistics, correlation and regression for analysing the data.

Following tools are used for data analysis.

The mean is a particularly informative measure of the "central tendency" of the variable if it is reported along with its confidence intervals.

Usually we are interested in statistics (such as the mean) from our sample only to the extent to which they can infer information about the population. The confidence intervals for the mean give us a range of values around the mean where we expect the "true" (population) mean is located (with a given level of certainty). s = ( )

where µ is the population mean and N is the population size

The sample estimate of the population standard deviation is computed as: A line in a two-dimensional or two-variable space is defined by the equation Y=a+bX; in full text, the Y variable can be expressed in terms of a constant (a) and a slope (b) times the X variable. The constant is also referred to as the intercept, and the slope as the regression coefficient or B coefficient. Multiple regression procedures will estimate a linear equation of the form: Y=a+b 1 X 1 +b 2 X 2 +...+b p X p (1.6) The regression line expresses the best prediction of the dependent variable (Y), given the independent variables (X). However, nature is rarely (if ever) perfectly predictable, and usually there is substantial variation of the observed points around the fitted regression line.

The paper presents the analysis in three parts as discussed in the methodology section above. These include descriptive statistics, correlation and regression. The descriptive statistics present an insight into the variables of advertisement expenses, sales, and profits of the twenty companies under reference. Correlation presents the coefficient of correlation between sales-advertisement expenses, sales-profit and advertisement expenses-profit. The regression part is further divided into two sub-parts. One, sales are regressed by taking advertisement expenses as the independent variable. Two, profit is regressed by taking advertisement expenses and sales as two independent variables.