Capital Structure and Industry Financing

Khan, A. G. (2012). The relationship of capital structure decisions with firm performance: A study of the engineering sector of Pakistan. International

Journal of Accounting and Financial Reporting, 2(1), Pages-245-262.

http://www.macrothink.org/journal/index.php/ijafr/article/view/1825/1516

This paper aims to find the relationship of capital structure decision with the performance of firms in developing market economies like Pakistan. The authors apply Pooled Ordinary Least Square regression to 36 engineering sector firms in Pakistani market listed on the Karachi Stock Exchange (KSE) during the period 2003-09. The results show that financial leverage measured by short-term debt to total assets (STDTA) and total debt to total assets (TDTA) has a significantly negative relationship with the firm performance measured by Return on Assets (ROA), Gross Profit Margin (GM) and Tobin’s Q. The relationship between financial leverage and firm performance measured by the return on equity (ROE) is negative but insignificant. Asset size has an insignificant relationship with the firm performance measured by Return on Assets (ROA) but negative and significant relationship exists with Tobin’s Q. Firms in the engineering sector of Pakistan are largely dependent on short term debt but debts are attached with strong covenants which affect the performance of the firm. Loopholes in the implementation of accounting standards can be the basis for evading taxes and flow of dividends in an illegal way can be the reason for lower equity positions and increased leverage levels.

Umar, M., Tanveer, Z., Aslam, S., & Sajid, M. (2012). Impact of capital structure on firms’ financial performance: Evidence from Pakistan.

Research Journal of Finance and Accounting, 3(9), 1-12.

http://iiste.org/Journals/index.php/RJFA/article/view/3145

This research examines the impact of capital structure on firms’ financial performance in Pakistan of top 100 consecutive companies in Karachi Stock Exchange for a period of four years from 2006 to 2009. Exponential generalized least square regression is used to test the relationship between capital structure and firms’ financial performance. The results show that all the three variables of capital structure negatively impacts the Earnings before interest and tax (EBIT), Return on Assets (RoA), Earnings per share (EPS) and Net Profit Margin (NPM). Moreover, PE ratio shows a negative relationship with Current Liabilities (CL) to Total Assets (TA) and a positive relationship between Long Term Liabilities (LTL) and Total Assets (TA) where the relationship is insignificant with, Total Liabilities to Total Assets. These results, in general, lead to the conclusion that capital structure choice is an important determinant of financial performance of firms. The authors suggest that an increase in leverage negativity affects the performance of firms’. The study has limitations because it only focuses on only one emerging market.

Iyer, R., Khwaja, A. I., Luttmer, E. F., & Shue, K. (2009). Screening peers softly: Inferring the quality of small borrowers (No. w15242). National Bureau of Economic Research

http://www.nber.org/papers/w15242.pdf

The recent banking crisis highlights the challenges faced in credit intermediation. New online peer-to-peer lending markets offer opportunities to examine lending models and generate more types of information on which to screen. This paper evaluates screening in a peer-to-peer market where lenders observe both standard financial information and soft, or nonstandard, information about borrower quality. The authors observe a borrower’s exact credit score and find that lenders are able to predict default with 45{5fae829be7f2ac2fe240ef3893666b371329fe804754bc76382cc5045395a053} greater accuracy than what is achievable based on just the borrower’s credit score, the traditional measure of creditworthiness used by banks. The authors also find that lenders effectively use nonstandard or soft information and that such information is relatively more important when screening borrowers of lower credit quality. In addition to estimating the overall inference of creditworthiness, they also find that lenders infer a third of the variation in the dimension of creditworthiness that is captured by the credit score. This credit-score inference relies primarily upon standard hard information, but still draws relatively more from softer or less standard information when screening lower-quality borrowers. The results highlight the importance of screening mechanisms that rely on soft information, especially in settings targeted at smaller borrowers.

Ansari, M. S., & Riazuddin, R. (2006). An empirical investigation of cost efficiency in the banking sector of Pakistan. State Bank of Pakistan.

http://www.sbp.org.pk/publications/wpapers/2008/wp12.pdf

This study uses the distribution free approach to estimate levels of cost efficiency of individual banks operating in Pakistan. Furthermore, these levels of efficiency are analyzed under CAMELS indicators to provide micro insights of their financial standings to justify their prevailing positions. The results show that banks are significantly distinct at different efficiency levels ranging from 87 percent to 49 percent. Technology has played a significant role in reducing the cost of banking industry. However, the banking industry is still operating under diseconomies of scale. Moreover, non-performing loans have adversely impacted the cost structure of banking industry. CAMELS ratios indicate that the most efficient banks are those with lesser amount of non-performing loans, high capital adequacy, and lesser non-interest expenditure which leads to high profitability. Overall, there is great room in the banking industry to minimize cost by eliminating the inefficiency elements.

Khwaja, A. I., & Mian, A. (2005). Do lenders favor politically connected firms? Rent provision in an emerging financial market. The Quarterly Journal of Economics, 1371-1411.

http://www.jstor.org/stable/25098774?seq=1#page_scan_tab_contents

Authors use a loan level data set of more than 90,000 firms that represents the universe of corporate lending in Pakistan between 1996-2002, they investigate rents to politically connected firms in banking. The authors have classified a firm as “political” if its director participates in an election, they examine the extent, nature and economic costs of political rent provision. The findings show that political firms borrow 45 percent more and have 50 percent higher default rates. Such preferential treatment occurs extensively in government banks- private banks provide no political favors. The study uses firm fixed effects and exploits variation for the same firm across lenders or over time allows for cleaner identification of the political preference result. They also find that political rents increase with the strength of the firm’s politician and whether he or his party is in power and fall with the degree of electoral participation in his constituency. They also provide direct evidence against alternative explanations such as socially motivated lending by government banks to politicians. The economy wide costs of the rents identified are estimated to be 0.3 to 1.9 percent of GDP every year.