Disinvestment of Oil and Gas PSUs

Project Aim

Understanding the process and types of disinvestment, its benefits & drawbacks. Explored into the field of inferential statistics to determine the effectiveness of disinvestment on the financial ratios of Oil and Gas PSUs.

Members

• Arpitha Yoga
• Shreya Raghavendra
• Adithya LHS
• Annette Manoj Elsa
• Prakruti Vora
• Shubang Bhandarkar

Work Done

• Studied the basics of disinvestment and the types of disinvestments
• Collected financial data from PSU annual reports and performed financial ratio analysis on Excel to perform further statistical computation
• 4 PSUs considered in this study were: Oil and Natural Gas Corporation (ONGC), Indian Oil Corporation Ltd (IOCL), Engineers India Ltd (EIL) and Oil India Ltd (OIL)
• Used the ratios to evaluate the statistical performance of the PSU before and after disinvestment has taken place. Using the statistical package SPSS, inferential statistics techniques such as the paired t-test and One-Way ANOVA have been used to determine the performance of these PSUs.
• The paired t-test was used to evaluate whether there was a statistically significant difference (increase/decrease) in the financial ratios pre and post disinvestment.
• One-Way ANOVA was performed to see if there was a variation in performance between the 4 PSUs and to see which resulted in variations.
• Using the programming language R, time series forecasting was done on the data to visualise how the ratios would trend over time.The forecasted values from the ARIMA model was used back in the data to see if they made the data statistically significant.

Future Scope

The idea of this project can be taken further in terms of analysing the efficiency of the Oil and Gas sector PSUs (such as IOCL, HPCL, BPCL) against each other or possibly between private firms (such as Reliance Petroleum). Lot of efficiency analysis has been done to compare government firms to private ones. There are several techniques that can be implemented such as:

• DEA - Data Envelopment Analysis to calculate efficiency scores by feeding certain inputs and outputs.
• Total Factor Productivity (TFP) - multi-factor productivity efficiency measure
• Leontief Model - another input/output analysis, however, this may not be the most suitable model as it assumes that there are n industries producing N different products
• Qualitative methods

Detailed Report and Results

Introduction and Methods of Disinvestment

Public Sector Undertakings were set up as an integral part of our developmental plans and industrial policy. They were set up with the policies stressing on a minimum rate of return. Low returns, often running in the negatives have resulted in accumulation of losses, subsequently resulting in the Industrial Policy reform in 1991, thereby giving way to privatization and disinvestment of PSUs.

Disinvestment is the dilution of the stake of the government in a public enterprise, and need not mean complete privatization. If the liquidation is less than 50 percent the government retains control even though disinvestment takes place. It is not privatized. But if the dilution is more than 50 percent there is a transfer of ownership, which essentially is privatization.

The disinvestment process accelerated under the NDA 2 regime. Of the Rs 3.8 lakh crore disinvestment proceeds garnered over the ten-year period, from the financial year 2010 to 2019, Rs 2.8 lakh crore, accounting for 74% of the total amount, was raised during the aforementioned regime.

Total loss suffered by the 71 loss making Central Public Sector Undertakings (CPSUs) amounted to Rs 31,261 crore in FY18. Air India being a prime contemporary example of this with losses at Rs 5,338 crore.

In the current scenario, however, with the setting up of NITI Aayog and requisite targets in place, PSU’s bringing in money will have to face the heat, as loss making PSUs remain unsold.

Under the specifications set, and the possible profitable/advantageous ventures for the government, a plethora of disinvestment options can be seeked -

• New Fund Offer :- It's a type of disinvestment in which units for funds are offered by investment companies like mutual funds. Money is pooled in a mutual fund by investors and that is used to buy securities through a strategy by the investment company. ETF (exchange traded funds) are investment funds traded on stock exchanges, much like stocks. It replicates the Nifty CPSE Index, is a concentrated portfolio of PSU stocks, whose objective is to help the government in disinvesting its stake in a few CPSEs via the ETF route.
• Warehousing :- Under this model, the government owned financial institutions were expected to buy the government’s share in select PSUs and hold them until a third buyer emerged. This method is very much suitable for those companies which have potential for growth in market prices and for listed Companies with adequate liquidity. The whole process takes one month and transaction cost is fixed return in warehouses less cost of funds for GOI. Pricing in this method is market determined after building in returns to the Warehouse.
• Golden Share :- In this model, the government retains a 26 percent share in the PSU. This 26 percent share will continue to give the Government the status of majority shareholder. During the year 1999-2000, a method of disinvestment was proposed where the Government will disinvest all to the private individual and retain only one share, known as the Golden Share. The idea of the golden share has been borrowed from the English Law. The Golden Share Concept was envisaged, by DoD as an instrument for the Government to have the right of veto over specified or significant changes in the constitution or Articles of Association of PSB.
• Strategic Sale :- Strategic sales are different from ordinary disinvestments. In the case of strategic sales, the transfer of ownership and control takes place where it is handed over to another entity, such as a private sector entity. In normal disinvestment, if the government is selling minority shares (less than 51%), they will continue being the legal owners. A strategic sale implies the sale of a major portion of upto 51% or higher percentage (as the authority may determine), along with the transfer of management control. This can be thought of as a sort of privatisation. At the same time, it is not necessary for more than 51% of the equity to be transferred to the strategic partner for them to have managerial control.
• Initial Public Offerings :- Initial Public Offer refers to an offer of shares by an unlisted CPSE or the Government out of its shareholding or a combination of both to the public for the first time. Issue of fresh equity in conjunction with the sale of Government’s stake is termed as piggy-back transaction. Under this method, the government simultaneously approaches the stock market along with the public enterprise for a public issue of shares. This method has the advantage of higher returns from the issue and is reported to be quicker and less expensive.
• Offer for Sale :- Offer for share is a transparent and simple method wherein promoters of a company can sell their shares to investors in the stock market. OFS is often carried out by an investment bank which is appointed as a selling agent for existing shareholders. It is a method to reduce and reverse stock ownership and make it more scattered. In an OFS, promoters dilute their stake by selling their shares on an exchange platform. Any investor registered with brokers of NSE or BSE, other than promoters or their group entities, can buy shares in OFS issues. The investor can place bids through his/her broker on the online trading platform. Once the bids are placed, shares are allocated to the different buyers.
• Buyback :- A buyback is when a company buys its own outstanding shares to reduce the number of shares available on the open market. Companies buy back shares for a number of reasons, such as to increase the value of remaining shares available by reducing the supply or to prevent other shareholders from taking a controlling stake.It allows companies to invest in themselves. A company may feel its shares are undervalued and do a buyback to provide investors with a return. And if the company is bullish on its current operations, a buyback also boosts the proportion of earnings that a share is allocated. This will raise the stock price if the same price-to-earnings (P/E) ratio is maintained.

Ratio Analysis

Ratio analysis is a quantitative method of gaining insight into a company's liquidity, operational efficiency, and profitability by studying its financial statements such as the balance sheet and income statement. Ratio analysis is a cornerstone of fundamental equity analysis. The following ratios were used to analyze a company’s functioning post and pre disinvestment in the last ten to fifteen years.

• EBT( Earnings before Tax): It measures a company's financial performance. Its calculation is revenue minus expenses, excluding taxes. It shows company earnings with the cost of goods sold (COGS), interest, depreciation, general and administrative expenses, and other operating expenses deducted from gross sales. Effectively EBT and EBITDA form a measure for the company’s annual taxable revenue. Below is the EBT of Steel Authority of India Limited over the last decade.

• Return on asset: The return on assets shows the percentage of how profitable a company's assets are in generating revenue. This number tells you what the company can do with what it has, i.e. how many dollars of earnings they derive from each dollar of assets they control.

• Total Asset Turnover: The asset turnover ratio measures the value of a company's sales or revenues relative to the value of its assets. The asset turnover ratio can be used as an indicator of the efficiency with which a company is using its assets to generate revenue. Total asset turnover can be excessively large when there’s an excess of capital but that capital is usually invested again.
• D/E Ratio: The ratio is used to evaluate a company's financial leverage. The D/E ratio is an important metric used in corporate finance, it reflects the ability of shareholder equity to cover all outstanding debts in the event of a business downturn.
• Current Ratio:The current ratio is a liquidity ratio that measures a company's ability to pay short-term obligations or those due within one year. It tells investors and analysts how a company can maximize the current assets on its balance sheet to satisfy its current debt and other payables.

• P/E Ratio: The price-to-earnings ratio (P/E ratio) is the ratio for valuing a company that measures its current share price relative to its per-share earnings (EPS).
• EPS: Earnings per share is calculated as a company's profit divided by the outstanding shares of its common stock. The resulting number serves as an indicator of a company's profitability.

Paired t-test and its Interpretations

The paired sample t-test, sometimes called the dependent sample t-test, is a statistical procedure used to determine whether the mean difference between two sets of observations is zero. In a paired sample t-test, each subject or entity is measured twice, resulting in pairs of observations.

The paired t-tests were conducted for ROA, Asset Turnover, Net Profit Margin, D/E, Current Ratio, Quick Ratio, Basic EPS, Price/ Net Operating Revenue and P/E on the companies ONGC, IOCL, Oil and EIL. Throughout the conduction of the tests on Excel, a p value of 0.05 had been chosen to determine the statistical significance of the data but none of the tests resulted in a value less than p indicating that there is statistically no difference between pre and post.

• Null Hypothesis (H0): The mean difference between the P/E ratios pre and post disinvestment is zero
• Alternative Hypothesis (H1): The mean difference between the P/E ratio pre and post disinvestment is not zero (two-tailed hypothesis)

The above data is an Excel paired t-test output for the P/E ratios of ONGC, IOCL, EIL and OIL and the p value in the bottom comes out to be 0.76388.. (>0.05). Hence, the null hypothesis is not rejected. Similar tests were run for the other ratios as well.

All of them only showed a positive correlation for pre and post disinvestment with minimal change in the mean, however the P/E ratio does indicate a negative ratio, especially the sharp drop in that of IOCL due to the government lowering the stock price by more than a third, reports also show that the company incurred a major loss of Rs.7047 during its disinvestment in the year 2008, add on the economic depression in the year of 2008 proved to cause a major fall back to IOCL.

Another interesting ratio which saw a major fall is the Basic EPS with a p of 0.014, not to mention that ONGC is quite alarming and indicative of a poorly performing company. The years post disinvestment of ONGC there was a shocking fall in crude oil prices in addition by 2012 the company had carried a hefty amount of India’s oil subsidies and seen a 38% loss to top it off another major blunder caused by the company auctioning off a Rs. 13,000 core worth 5% stake for mere Rs. 2000 crore.

Besides these bumps faced by certain companies at a certain period of time, it can be concluded based on the t-tests that disinvestments did not have that major an impact on the companies under study.

Similar paired t-tests and extensive inferences were done on other ratios as well.

One-Way ANOVA and Interpretations of the Line Graphs

Interpretations of the Line Graphs

The current ratio is a critical liquidity ratio utilized extensively by banks and other financing institutions while extending loans to the businesses. The current ratio is a figure resulting from dividing current assets by current liabilities of a firm.

In 2012-2014, the short term borrowings by OIL were the main contributors for the spike in the total current liabilities. For 2015-2016, the fall in current liabilities was greater than the dip in current assets which gave a maximum current ratio in 2016. But the further decrease in total assets gave a minima in current ratio for FY 2017.

For FY 18-19 OIL witnessed an increase in current liabilities and current assets by 86.8% and 39% therefore a decrease in current ratio.

EIL saw a gradual increment 2011-2014 due to disinvestment. Another disinvestment in the FY 2015-2016 further increased the ratio (max).

Even though the P/E ratio for the FY after 2017 didn't decline, they don't account for the debt or other liabilities and hence there is a decrease in the current ratio.

IOCL didn't show a very satisfying growth in terms of current ratio as compared to others by disinvestment in FY 2017

From 2010-2014, ONGC there was a substantial increase in equity involved and reserves and the unsecured loans were also paid off thereby decreasing the liability.

A more extensive inference was done on other ratios similarly.

One Way ANOVA for Current Ratio

The one-way analysis of variance (ANOVA) is used to determine whether there are any statistically significant differences between the means of two or more independent (unrelated) groups. It is only seen to be used when we have a minimum 3 groups.The ANOVA results are derived through the F-Statistic, whereas the paired t-test comes from the t-statistic.

• Null Hypothesis (H0): The means of the PSU ratios are equal
• Alternative Hypothesis (H1): The means of the PSU ratios are unequal

From One Way ANOVA results from SPSS, we can understand that there's a statistically significant difference between the means of the current ratios of the 4 PSUs as significance value (p value) is 0.000 which is below 0.05.

We can only find out which company is causing the variation from the post hoc tests. The results from ANOVA only show whether there is a change in the means of the groups. The post hoc test compares the means between all the groups individually as seen in the table. We can see that there's no statically significant difference in variance between the current ratios of ONGC and IOCL as p (sig) >0.05. In this case, we do not reject our null hypothesis. And you can see that in the line graph as well. The graphs of ONGC and IOCL are very similar, hence supporting the results of the ANOVA. For all other groups, there's a statistical difference in variances since p≤0.05.

Time Series Forecasting

The ratios for the PSUs were forecasted using the ARIMA model on the programming language

R. ARIMA is short for Auto-Regressive Integrated Moving Average. The ARIMA model is a type of time series modeling where it predicts the future values using the historical values. Its own lags and lagged errors are used.

Time series forecasting can be done if the dataset is not constant and if the dataset does not follow a known mathematical function (sine/cos). The ARIMA model is implemented by ensuring that the data is stationary. The stationarity of the data is tested using the Augmented Dickey Fuller test (ADF). Since there were trends and irregularities in the data, the data had to be made stationary using the differencing function. Once it passed the ADF test, the time series data was ready to use. The functions of an ARIMA model are p d q where d is found from the number of differences taken and p and q are found from the PACF and ACF graphs respectively.

The 5 years forecasted data were then used back into the paired t-test to see if the forecasts created any change results and made the p value statistically significant.

Forecasted Results of Return on Assets Ratio:

ONGC ARIMA Forecast IOCL ARIMA Forecast

EIL ARIMA Forecast OIL ARIMA Forecast

The forecasts for all the PSUs except EIL are showing a constant average forecasted value (Naive Forecast) whereas there is some drift and non-zero mean with the EIL forecasts.

The forecasts were put back into the paired t-test and the new means after disinvestment were calculated and the results of the paired t-test are shown below:

Paired t-test without forecasts Paired t-test with forecasts

• Null Hypothesis (H0): The mean difference between the ROA ratios pre and post disinvestment is zero
• Alternative Hypothesis (H1): The mean difference between the ROA ratio pre and post disinvestment is not zero (two-tailed hypothesis)

The change in the two-tailed p-value can be seen as p= 0.1175… without the forecasts and p=0.0995.. after adding the forecasts. This shows that even after adding the forecasts, the null hypothesis is not rejected. Hence, there is no statistically significant difference pre and post merger even with the 5 year forecasts for the Return on Assets ratio.

These sort of forecasts were similarly done with the other ratios.

While ARIMA deals with univariate data, a multivariate forecasting method, appropriate for our dataset, is VAR (Vector Autoregression). This was done on R as well, however, it’s accuracy is not quite clear as compared to the ARIMA due to factors such as handling unequal data etc.

Conclusion

• The paired t-test is important in the analysis as it indicates whether there was a statistically significant different in the ratios, pre and post disinvestment.
• There was a general trend in the data pre and post (an increase or decrease depending on the ratio), however all the ratios except EPS showed no significant change in the mean after disinvestment.
• Although there has been an increase in some of the ratios (indicating improvement in the financial performance), it has not been statistically significant to reject the Null Hypothesis.
• Hence, it can be concluded that disinvestment on an overall scale has had no statistically significant effect on the financial ratios
• From the ANOVA test, we see that there's a statistically significant difference between the means of the current ratios of the 4 PSUs as the p-value<0.05. But, on conducting the post HOC tests, it can be inferred that for ONGC and IOCL, there is no statically significant difference in variance between the current ratios as p (sig) >0.05. For OIL and EIL, however, the opposite is true.
• Further, for all ratios except for EPS, there were variations in the means of the PSUs post hoc results.
• Hence, it can be concluded that for the 4 PSUs, there is variation in performance even though they run in the same industry.

Limitations

• A source of error could be from the past values of the financial ratios. Initially the data peaks and later is subsidised over a couple of years. While it may seem that the financial ratios are decreasing over time, it could also mean the growth in the numerator is lesser than the denominator. Hence this could lead to a smaller financial ratio. Using older values may mean that the t-test values don’t find a significant difference in the means.
• EIL is a PSU from the Oil and Gas Sector, however, unlike the others it is a consultancy PSU. Therefore, due to the nature of the PSU, it can result in variation in the results of paired t-test and ANOVA.
• For all 4 companies, ratios were calculated using the company balance sheets, and referred to a website with pre-calculated ratios; some of these ratios didn’t match. Further, some parameters weren’t available online or in the balance sheets that were necessary for our calculations. It may be possible that there are slight discrepancies in the ratios, and hence the values obtained in the statistical inference tests.