The Unsung Sectors in the Indian Manufacturing Industry: An Empirical Study on Influence of Accounting Variables with Share Price

1. Introduction: Manufacturing Dynamics & The Identification of Unsung Sectors

The enormous presence of the manufacturing sector in India has often invited many debatable research conclusions among researchers. It was Ahluwalia (1985) who observed that total factor productivity growth (TFPG) in the manufacturing sector in India was not robust during the post reform period of 1991. But the other research contributions have completely rejected the claim made by the former researcher. The research contributions of Balakrishnan & Pushpangadan (1994) claim that TFP growth in the Indian manufacturing sector stood strong and created a positive impact on the Indian economy. Several studies of such kind have disclosed that the manufacturing sectors that were liberalised in terms of FDI and Tariff, experienced 15 percent and 20 percent productivity growth during post reform period (Sivadasan, 2003).

This increase in productivity growth has led to a momentum of skill intensive industries in India which would gradually convert India to a manufacturing hub. In the study put forward by Sakhardande & Gaonkar (2021) twenty-five sectors were recognized to be under Make in India (MII). Apart from that, among these sectors, five sectors come under the category of superstar sectors (DPIIT, 2014). But there are many other performing sectors in the category of manufacturing sector in India, which does not find a place in above mentioned studies.

This has made the researcher think about such sectors and to study their growth prospect for the prospective investors to invest in them. The share prices are uncertain and there is all possibility of fluctuations. In this scenario, it would be useful for the investors in attaching the share price to a component that displays the strength of a company. The robust performance of a company is disclosed by its accounting variables, and this is made as a component in this research that establishes a relation with share price.

2. Theoretical Framework & Literature Review

There are numerous studies that were performed in finding out the relation between share price and accounting variables. All these studies emphasised on several accounting variables. This has formed the basis for the researcher to select the accounting variables for the study.

Among the six accounting variables considered for the study, Book value per share (BPS), Earnings per share (EPS), Dividend per share (DPS) and profit were also considered in the landmark studies of Ball & Brown (1968) and Ohlson (1995). The study conducted by the former was wrapped with the conclusion that profit is value relevant towards the stock price. Whereas the latter in his study proved that all financial information disclosed in balance sheet and income statement has an impact on share price.

The other two variables used in this study are Return on Total Assets (ROA) and Return on Capital Employed (ROCE). These variables were used in the study conducted by Lev (1989), Penman (1992), and Marx (2010). All these studies exposed the fact that these accounting variables have explanatory power on stock price.

3. Empirical Analysis, Model Estimation & Hypotheses

The hypothesis used in the study is based on the Ohlson Model, which describes the relation between accounting variables and share price. In this study, the accounting variables are independent variables and the share price is the dependent variable. The six primary hypotheses framed are as follows:

3.1 Application of Ohlson Model (Initial Regression)

The above table indicates that the accounting variables that have significant values less than .05, has a positive relation with share price. In this case, Book value per Share, Net profit margin and Dividend Per Share would only be taken ahead for establishing an association with share price. As the data collected for the study relates to time series, there could be two obstacles that would hinder the progress of this research. These two obstacles are: 1) Multi collinearity and 2) Auto Correlation.

If the VIF value lies between 1 to 10, then there exists no multicollinearity. At the same time if the VIF value is less than 1 or more than 10, then there is multicollinearity. In the above table, the three significant accounting variables so selected based on Ohlson have VIF values below 10 and not less than 1, hence they are not collinear.

3.2 Application of Exponential Smoothing & ARIMA Models

This table describes the time series model applied for each independent variable based on their respective nature of data:

3.3 Re-Applying Ohlson Model on Deseasonalized/Stationary Series

3.4 Pre and Post 2008 Financial Crisis Analysis

This analysis is done to understand the stability of these sectors. The stability is understood through sturdiness in the share price. After finding out fundamental strength of these sectors by establishing a relation with accounting variables and share price, an attempt is made to understand the reliability of these sectors. The reliability on these sectors depends on stability of share price. The share price fluctuates even with a minute change in the economy.

In this context, to establish the robustness of these sectors, an analysis is done on the share price and accounting variables of these sectors with a comparison of pre and post crisis that occurred in 2008 which named as sub-prime crisis. During this crisis, many established companies went bankrupt. In this context, the stability and reliability of these sectors are analysed based on pre and post crisis comparison:

4. Conclusion & Key Findings for Prospective Investors

The BPS, DPS, and Net Profit Margin have a positive relationship and explanatory power over the share price of unsung sectors. It is found that these three variables explain 45% of variations in the share price of these sectors. Hence the other 55% of variations in the share price could be due to factors like inflation, interest rate, exchange rate, industry life cycles, labour conditions, trend changes, and macroeconomic parameters.

References

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