Does oil price affect the value of firms? Evidence from Tunisian listed firms

doi:10.1007/s11708-016-0396-8

Front. Energy

2016, Vol. 10

Issue (1) : 1-13 https://doi.org/10.1007/s11708-016-0396-8

RESEARCH ARTICLE

Does oil price affect the value of firms? Evidence from Tunisian listed firms

Kaouther ZAABOUTI¹, Ezzeddine BEN MOHAMED²(

), Abdelfettah BOURI¹

¹. Faculty of Economics and Management of Sfax, University of Sfax, Monastir 5046, Tunisia
². Department of Accounting, College of Business & Economics, Qassim University, Buraidah 51431, Kingdom of Saudi Arabia

Download: PDF(165 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

A new debate on the potential impact of oil price changes on the value of firms was initiated in this paper. Using a stochastic frontier approach, an attempt was made to derive the optimal value Q* of firms and calculate the Q value observed. Then the shortfall (Q*–Q) which represents the inefficiency term was explained. Starting from 19 industrial Tunisian firms listed on the Tunis Stock Exchange between 2007 and 2011, the fact that variation of oil prices can largely explain distortions in the value of firms was empirically demonstrated.

Keywords oil price value of firm stochastic frontier approach industrial Tunisian firms

Corresponding Author(s): Ezzeddine BEN MOHAMED

Just Accepted Date: 25 December 2015 Online First Date: 01 February 2016 Issue Date: 29 February 2016

Cite this article:

Kaouther ZAABOUTI,Ezzeddine BEN MOHAMED,Abdelfettah BOURI. Does oil price affect the value of firms? Evidence from Tunisian listed firms[J]. Front. Energy, 2016, 10(1): 1-13.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-016-0396-8
https://academic.hep.com.cn/fie/EN/Y2016/V10/I1/1

Authors	Period	Context of the study	Objectives	Variables and variables definitions	Approach/Model	Data sources	Empirical findings
Haushalter et al. [7]	1992–1994	68 oil and gas producers	To examine the sensitivity of the value of firms of oil producers to changes in future oil price	Dependent variable: – firm debt ratio: the total debt scaled by the sum of the book value of debt and the market value of equity – Production cost: the cost of production per barrel of oil equivalent Independent variable: – the percentage change in oil price – the change in the implied volatility on future oil price – the return on the CRSP value weighted index	Multifactor market model	– The 1994 financial statements – COMPUSTAT – CRSP – New York Mercantile Exchange (NYMEX)	– The change in oil price is negatively correlated with a firm’s debt ratio and its production costs
Jin and Jorion [19]	1998–2001	119 US oil and gas producers	To test the difference in the value of firms between firms that hedge and those that do not hedge their oil and gas price risk	Dependent variable: – Tobin’s Q: the ratio of the MV of financial claims on the firm to the current replacement cost of the firm’s assets Independent variable: – Hedging variable: relative delta oil (gas) production and relative delta oil (gas) reserve – Control variables: • Firm size: the proxy is log of total assets. • Profitability: return on assets: ROA • Investment growth: capital expenditures to total assets • Access to financial markets: to proxy this variable ???they used a dividend dummy that equaled one if the ???firm paid dividends on common equity in the current ???year • Leverage: the BV of long-term debt over the MV of ???common equity • Production costs: the cost of extracting oil and natural ???gas and included both the lifting cost and production ???taxes • Industrial and geographic diversification credit rating ???(quality)	– Two–factor model – Univariate analysis – Multivariate analysis	– COMPUSTAT – The 1998 to 2001 annual reports	– The oil and gas prices have a significant positive effect on firm value of oil and gas producers – The hedging should increase the firm’s market value – Hedging reduces the firm’s stock price sensitivity to oil and gas prices
Boyer and Filion [8]	1995–1998 and 2000–2002	Oil and gas industry (105 Canadian oil and gas corporations)	To assess the financial determinants of Canadian oil and gas company stock returns	Dependent variable: – The return of Canadian energy stock Independent variable: – Market return – Interest rate – Exchange rate – Crude oil price – Natural gas price – Debt – Production – Cash flows – Proven reserves – Drilling success	The common multifactor model	– Bloomberg – Data stream – Canoil form – Woodside research	– The return of Canadian energy stock is positively associated with the Canadian stock market return and negatively with interest rates
Sadorsky [11]	1990–2006	The S&P 1500 (Consists of 600 small firms, 400 medium-sized firms, and 500 large firms)	To examine the relationship between oil prices and stock prices when the size of firms is allowed to vary	Dependent variable – Market returns: the returns on a value-weighted US stock market index Independent variable – Firm size: annual sales – The interest rate: the spread between the yield on a 10-year US government bond and the yield on a 3 month US T Bill – Oil price returns: the log difference of the yearly return on the West Texas Intermediate (WTI) nearest crude oil futures contract, which trades on the New York Mercantile Exchange (NYMEX) – Oil price volatility: The square root of the sum of squared daily returns for each calendar year	A multifactor market model	– COMPUSTAT – CRSP – Data stream – The Federal Reserve Board of St. Louis	The relationship between oil price movements and stock prices does vary with firm size and the relationship is strongest for medium-sized firms
Cameron and Schnusenberg [20]	March 20, 2001 to September 30, 2008	The three auto manufacturer indexes. (top six, SUV, and non-SUV)	To investigate the relationship between oil prices and stock prices of automobile manufacturers	Dependant variable: – The excess return on an automobile manufacturer stock price index) Independent variable: – The three Fama-French factors: the return on an average stock – Oil price factor: the first proxy for the oil price factor is the excess percentage change in the daily price of WTI crude oil over and above the one-month T-bill rate. The second proxy is the daily change in the energy ETF, in excess of the one-month T-bill rate	– The Fama-French model – Four–factor regression model	The American Exchange (AMEX) traded – Energy Select Sector SPDR (XLE) ETF – US Energy Information Administration, July 2006 – Ken French’s website (for the three Fama-French factors)	There is an inverse relationship between rising oil prices and an accompanying reduction in auto manufacturer stock price
Elyasiani et al. [17]	From December 11, 1998 to December 29, 2006	Thirteen US industries	To examine the impact of changes in the oil returns and oil return volatility on excess stock returns and return volatilities of 13 US industries	Dependant variable: – The industry excess stock return (the Fama-French factors) Independent variable: – Daily return on one-month crude oil futures – Return on oil futures – Conditional volatility of oil futures returns – Excess market return	– The GARCH (1,1) technique – The Fama-French factors	– The Center for Research on Security Prices (CRSP) database. – The return on one–month crude oil futures (ROF), traded on the New York Mercantile Exchange (NYMEX)	The oil price fluctuations constitute a systematic asset price risk at the industry level as nine of the 13 sectors analyzed show statistically significant relationships between oil–futures return distribution and industry excess return
Dayanandan and Donker [10]	1990–2008	200 largest oil and gas companies listed on the US stock exchange	To investigate the relationship between commodity prices of crude oil, capital structure, firm size and accounting measures of firm performance using a sample of oil and gas firms	Dependent variable: – Return on equity: net income divided by equity Independent variable: – Total assets: log of total assets (US $) – Gearing: long-term liabilities divided by shareholder funds – Crude oil price: spot price – Average annual	The differenced GMM estimator	– The annual financial statements from OSIRIS database. – The crude oil price from the West Texas Intermediate oil and gas index	The crude oil prices positively and significantly impact the performance of oil and gas firms in North America using accounting measures of performance
Henriques and Sadorsky [34]	1990–2007	The company list is drawn from the S&P 1500 (consists of 500 large capitalization stocks, 600 midsize firms, and 400 small firms)	To investigate how oil price volatility affects the strategic investment decisions of a large panel of US firms	– Dependant variable: – Firm investment: is measured by capital expenditure on property, plant and equipment. – Independent variable: – The capital stock: is measured using total assets. – Tobin’s Q: is measured as market value of equity+ preferred. – Stock+ short-term liabilities net of short-term assets+ long-term – (Debt) / total assets – Cash flow: is measured as Income before extraordinary items+ depreciation and amortization)/ total assets.	Two OLS models: a GMM model, and five system GMM models	– COMPUSTAT data – The US Energy Information Agency	There is a U shaped relationship between oil price volatility and firm investment
Narayan and Sharma [24]	From 5 January 2000 to 31 December 2008	560 US firms listed on the NYSE	To examine the relationship between oil price and firm returns	Dependant variable: – The returns of firm Independent variable: – The growth rate in crude oil prices – The short-term US interest rate – The US-Euro nominal exchange rate – Firm size	– GARCH (1, 1) Models – Regression model	Energy Information Administration (www.eia.doe.gov).	– The oil price affects returns of firms differently depending on their sectoral location – There are a lagged effect of oil price on firm returns – The oil price affects firm returns based on different regimes – The oil price affects firm returns differently based on firm size
Ratti et al. [35]	From 1990 to 2006	1910 non-financial firms in a group of 15 European countries	To investigate the effect of relative energy price on firm-level investment in European countries	Dependant variable: – Investment/ Capital: investment scaled by the capital stock lagged one period Independent variable: – Total assets at the beginning of the period – Capital stock: net property, plant and equipment. – Total debt: debt in current liability+ long-term debt – Cost of goods sold: cost of goods sold scaled by the capital stock lagged one period – Leverage ratio: the ratio of total debt to total assets – Cash stock: cash and equivalents scaled by total assets – Cash flow: income before extraordinary items+ depreciation and amortization scaled by capital stock – Net sales/ capital: net sales at the end of period t?1. scaled by capital – Investment: net capital expenditure – Firm size: equal to the (log of) total assets in US dollars energy price	The dynamic model of investment	The COMPUSTAT Global database	– In manufacturing a 1% rise in real energy price reduces investment by a country's firms by 1.9% relative to that by firms in other countries with a smaller effect for non–manufacturing firms – The negative effect of a higher relative price of energy on firm–level investment is significantly less marked the larger the firm
Yoon and Ratti [36]	Between 1971 and 2006	2665 US firms manufacturing (NAICS 311 to 339)	To examines the effect of energy price uncertainty on firm-level investment	Dependant variable: I/K: ratio of investment to capital stock Independent variable: – Total assets: total assets at the beginning of the period – Capital stock: net property, plant and equipment – Capital expenditure: gross capital expenditure – Sale: net sales at the end of period t – Cash stock: cash and equivalents scaled by total assets – Cash flow: income before extraordinary items+ depreciation and amortization (MM$) – Total debt: debt in current liability+ long-term debt (Total) (MM$) – Investment: net capital expenditure scaled by capital (t–1) – Net sales: net sales at the end of period t scaled by capital – Cost of goods sold: cost of goods sold scaled by capital – Leverage: the ratio of total debt to total assets – Dividends payout: common dividends / (Operating income–interest expense–income taxes) – Producers Price Index	An error correction (ECM) model	The COMPUSTAT North-America (1970 – 2006): – The US Department of Energy: • Energy Information Administration (2002 index) (Btu= British thermal units) • The Bureau of Economic Analysis: – COMPUSTAT	A rise in uncertainty about energy price plays a role in firm–level investment decisions by reducing the positive effect of sales growth on investment at firms
Aggarwal et al. [21]	From the beginning of January 1986 to the end of July 2008	all companies included in the S&P Transportation industry index	To examine the impact of oil price changes on transportation firms	Dependent variable: Abnormal firm returns Independent variables: • Oil price shocks • Profitability • Investment growth • Leverage • Size • Runup • Industry effect • Industry concentration Change in oil price	– The well-known event study methodology, – Cross-sectional regression model– Two-factor model	– West Texas Intermediate (WTI) – The New York Mercantile Exchange (NYMEX) – The CRSP database	– Transportation firm returns are influenced negatively by oil price increases, risks are increased more by oil price declines – in the S&P transportation sub-sector, industry concentration is negatively related to returns, oil price risk, and trading volume, and asymmetrically related to returns and market betas
Wattanatorn and Kanchanapoom [30]	From quarter 1, 2006 to quarter 4, 2010	11 sectors listed company on the Stock Exchange of Thailand (SET)	To investigate the impact of crude oil prices on the profitability performance of sector	Dependant variable: – ROA: quarterly return on asset Independent variables: – Oil price: average OPEC countries spot price FOB ($ per Barrel). – Interest rate: one day Bilateral repurchase rate. – Exchange rate: exchange rate baht/US – Firm size: log of total asset	The method of panel data regression	– SET Market Analysis and Reporting Tool (SETSMART) – The Bank of Thailand (interest rate and rate of exchange)	The oil prices have significant impact on profit of energy and food sectors
Tsai [22]	From March 1995 through October 2008	The 1500 firms that constituted the S&P 1500 index in 2008	To investigate whether a high oil price event that worsens the quality of a firm’s balance sheet in turn provides an additional transmission channel to the stock market, which then affects stock returns	Dependent variable: – Stock returns Independent variables: – Oil price shocks	An event-study methodology	– The COMPUSTAT data set – The FOMC announcement – The Trade and Quote (TAQ) database – The University of Chicago’s Center for Research in Security Prices (CRSP) database	More energy–intensive industries and durable–goods industries react more significantly to monetary shocks based on high oil price events than on those based on non–high oil price events
Ye et al. [16]	2011	All listed firms (768) from the seven regions of the CERTS of China in 2011	To study the impact of energy-saving efforts on firm value, using the carbon emission rights trading scheme (CERTS) of China as an exogenous shock	The logarithm of the market capitalization of firm at the end of the last fiscal year – The book-to-market ratio of firm at the end of the last fiscal year – The unexpected earnings to control the firms’ earnings	Event studies	– The CCER Database – The CSR index	The CERTS increases the market value of energy–related firms; moreover, the energy–saving efforts of firms further influence their market value and investor reaction
Aye et al. [23]	From February 1974 to December 2012	South African manufacturing production	To analyze the effects of oil price uncertainty on the manufacturing production of South Africa	Dependant variable: – Manufacturing production Independent variables: – Real oil price	– A bivariate GARCH-in-mean VAR simultaneously estimated	– The US Energy Information Administration – The International Monetary Fund’s International Financial Statistics (IFS).	The oil price volatility affects manufacturing production negatively given that decisions to invest in general and production decisions in particular are often influenced by potential returns
Mohanty et al. [15]	From September1983 to August 2011	6 sub-sectors: 1) Travel and Tourism, 2) Airlines, 3) Gambling, 4) Hotels, 5) Recreational Services, and 6) Restaurants	To explain the effect of oil price on US tourism and leisure sector by the cost pressures arising from higher energy prices	Dependant variable: – The excess monthly return on industry portfolio Independent variables: – The excess monthly return on the market portfolio – The difference in monthly return between a small cap portfolio and a large cap portfolio – The difference in monthly return between a portfolio of high book-to-market stocks and one of low book-to market stocks – The return on a zero investment portfolio long on winner and short on loser stocks – The monthly return on oil price	– The Fama–French (FF) model – Multifactor regression model	– Data stream – The West Texas Intermediate (WTI)	The oil price effects are significantly negative for U.S tourism and leisure sector

Tab.1 Survey of the effect of oil prices on firm value

Tab.2 Variables’ notes

Variables	Parameters	Estimated values coefficients
Constant	b₀	2.135 (3.351)***
ln sales	b₁	?0.318 (–0.959)
(ln sales)²	b₂	–8.317 (1.737)**
I/K	b₃	1.110 (–0.512)
k/sales	b₄	73.523 (–0.659)
operMar	b₅	0.591 (3.947)***
LEV	b₆	–1.334 (–4.751)***
Constant	d₀	0.639 (0.281)
PP	d₁	1.295 (5.169)***
TE	d₂	–40.721 (0.125)
TCD	d₃	69.979 (1.723)**
Sigma-Squared	$ε i = v i − u i$	0.672 (6.972)***
Gamma		–83.595 (0.316)
Log likelihood function		–229.359

Tab.3 Estimation results

1	R D Huang, R W Masulis, H R Stoll. Energy shocks and financial markets. Journal of Futures Markets, 1996, 16(1): 1–27 https://doi.org/10.1002/(SICI)1096-9934(199602)16:1<1::AID-FUT1>3.0.CO;2-Q
2	C M Jones, G Kaul. Oil and the stock markets. Journal of Finance, 1996, 51(2): 463–491 https://doi.org/10.1111/j.1540-6261.1996.tb02691.x
3	J D Hamilton. Oil and the m+9878 acroeconomy since World War II. Journal of Political Economy, 1983, 91(2): 228–248 https://doi.org/10.1086/261140
4	R A Lizardo, A V Mollick. Oil price fluctuations and U.S. dollar exchange rates. Energy Economics, 2010, 32(2): 399–408 https://doi.org/10.1016/j.eneco.2009.10.005
5	P Sadorsky. Oil price shocks and stock market activity. Energy Economics, 1999, 21(5): 449–469 https://doi.org/10.1016/S0140-9883(99)00020-1
6	J S Chiou, Y H Lee. Jump dynamics and volatility: oil and the stock markets. Energy, 2009, 34(6): 788–796 https://doi.org/10.1016/j.energy.2009.02.011
7	G D Haushalter, R A Heron, E Lie. Price uncertainty and corporate value. Journal of Corporate Finance, 2002, 8(3): 271–286 https://doi.org/10.1016/S0929-1199(01)00043-8
8	M M Boyer, D Filion. Common and fundamental factors in stock returns of Canadian oil and gas companies. Energy Economics, 2007, 29(3): 428–453 https://doi.org/10.1016/j.eneco.2005.12.003
9	S B Ramos, H Veiga. Risk factors in oil and gas industry returns: international evidence. Energy Economics, 2011, 33(3): 525–542 https://doi.org/10.1016/j.eneco.2010.10.005
10	A Dayanandan, H Donker. Oil prices and accounting profits of oil and gas companies. International Review of Financial Analysis, 2011, 20(5): 252–257 https://doi.org/10.1016/j.irfa.2011.05.004
11	P Sadorsky. Assessing the impact of oil prices on firms of different sizes: its tough being in the middle. Energy Policy, 2008, 36(10): 3854–3861 https://doi.org/10.1016/j.enpol.2008.07.019
12	H G Doğrul, U Soytas. Relationship between oil prices, interest rate, and unemployment: evidence from an emerging market. Energy Economics, 2010, 32(6): 1523–1528 https://doi.org/10.1016/j.eneco.2010.09.005
13	R W Faff, T J Brailsford. Oil price risk and the Australian stock market. Journal of Energy Finance & Development, 1999, 4(1): 69–87 https://doi.org/10.1016/S1085-7443(99)00005-8
14	H U Buhl, S Strauβ, J Wiesent. The impact of commodity price risk management on the profits of a company. Resources Policy, 2011, 36(4): 346–353 https://doi.org/10.1016/j.resourpol.2011.07.003
15	S Mohanty, M Nandha, E Habis, E Juhabi. Oil price risk exposure: the case of the U.S. Travel and Leisure Industry. Energy Economics, 2014, 41: 117–124 https://doi.org/10.1016/j.eneco.2013.09.028
16	D Ye, S Liu, D Kong. Do efforts on energy saving enhance firm values? Evidence from China’s stock market. Energy Economics, 2013, 40: 360–369 https://doi.org/10.1016/j.eneco.2013.07.017
17	E Elyasiani, I Mansur, B Odusami. Oil price shocks and industry stock returns. Energy Economics, 2011, 33(5): 966–974 https://doi.org/10.1016/j.eneco.2011.03.013
18	D Phan, H Nguyen, R Faff. Uncovering the asymmetric linkage between financial derivatives and firm value: the case of oil and gas exploration and production companies. Energy Economics, 2014, 45: 340–352 https://doi.org/10.1016/j.eneco.2014.07.018
19	Y Jin, P Jorion. Firm value and hedging: evidence from U.S. oil and gas producers. Journal of Finance, 2006, 61(2): 893–919 https://doi.org/10.1111/j.1540-6261.2006.00858.x
20	K Cameron, O Schnusenberg. Oil prices, SUVs, and Iraq: an investigation of automobile manufacturer oil price sensitivity. Energy Economics, 2009, 31(3): 375–381 https://doi.org/10.1016/j.eneco.2008.12.003
21	R Aggarwal, A Akhigbe, S K Mohanty. Oil price shocks and transportation firm asset prices. Energy Economics, 2012, 34(5): 1370–1379 https://doi.org/10.1016/j.eneco.2012.05.001
22	C L Tsai. The high-frequency asymmetric response of stock returns to monetary policy for high oil price events. Energy Economics, 2013, 36: 166–176 https://doi.org/10.1016/j.eneco.2012.12.009
23	G C Aye, V Dadam, R Gupta, B Mamba. Oil price uncertainty and manufacturing production. Energy Economics, 2014, 43: 41–47 https://doi.org/10.1016/j.eneco.2014.02.004
24	P K Narayan, S S Sharma. New evidence on oil price and firm returns. Journal of Banking & Finance, 2011, 35(12): 3253–3262 https://doi.org/10.1016/j.jbankfin.2011.05.010
25	R Jiménez-Rodríguez. The impact of oil price shocks: evidence from the industries of six OECD countries. Energy Economics, 2008, 30(6): 3095–3108 https://doi.org/10.1016/j.eneco.2008.06.002
26	S A Basher, P Sadorsky. Oil price risk and emerging stock markets. Global Finance Journal, 2006, 17(2): 224–251 https://doi.org/10.1016/j.gfj.2006.04.001
27	S Dossogne. Valorisation and Cession of Firms from Intension to Finalization: From Value to Price. 2nd ed. Wallonia, Belgium, 2006, 227
28	M A Habib, A Ljungqvist. Firm value and managerial incentives: a stochastic frontier approach michael. 2015–01–12
29	L Oxelheim, T Randoy. The impact of foreign board membership on firm value. Journal of Banking & Finance, 2003, 27(12): 2369–2392 https://doi.org/10.1016/S0378-4266(02)00395-3
30	W Wattanatorn, T Kanchanapoom. Oil prices and profitability performance: sector analysis. Procedia: Social and Behavioral Sciences, 2012, 40: 763–767 https://doi.org/10.1016/j.sbspro.2012.03.263
31	B Lee, C W Yang, B Huang. Oil price movements and stock markets revisited: a case of sector stock price indexes in the G-7 countries. Energy Economics, 2012, 34(5): 1284–1300 https://doi.org/10.1016/j.eneco.2012.06.004
32	J Vickery. How and why do small firms manage interest rate risk? Journal of Financial Economics, 2008, 87(2): 446–470 https://doi.org/10.1016/j.jfineco.2006.09.011
33	M Y Chen. Adjustments in managerial ownership and changes in firm value. International Review of Economics & Finance, 2013, 25: 1–12 https://doi.org/10.1016/j.iref.2012.04.008
34	I Henriques, P Sadorsky. The effect of oil price volatility on strategic investment. Energy Economics, 2011, 33(1): 79–87 https://doi.org/10.1016/j.eneco.2010.09.001
35	R A Ratti, Y Seol, K H Yoon. Relative energy price and investment by European firms. Energy Economics, 2011, 33(5): 721–731 https://doi.org/10.1016/j.eneco.2010.12.010
36	K H Yoon, R A Ratti. Energy price uncertainty, energy intensity and firm investment. Energy Economics, 2011, 33(1): 67–78 https://doi.org/10.1016/j.eneco.2010.04.011
37	Y T Mak, Y Kusnadi. Size really matters: further evidence on the negative relationship between board size and firm value. Pacific-Basin Finance Journal, 2005, 13(3): 301–318 https://doi.org/10.1016/j.pacfin.2004.09.002
38	T Eisenberg, S Sundgren, M T Wells. Larger board size and decreasing firm value in small firms. Journal of Financial Economics, 1998, 48(1): 35–54 https://doi.org/10.1016/S0304-405X(98)00003-8
39	J L Coles, N D Daniel, L Naveen. Boards: Does one size fit all? Journal of Financial Economics, 2008, 87(2): 329–356 https://doi.org/10.1016/j.jfineco.2006.08.008
40	A Baccar, E Ben Mohamed, A Bouri. Managerial optimism, overconfidence and board characteristics: towards a new role of corporate governance. Australian Journal of Basic and Applied Sciences, 2013, 7(7): 287–301
41	E Ben Mohamed, A Baccar, R Fairchild, A Bouri. Does corporate governance affect managerial optimism: evidence from NYSE panel data firms. International Journal of Euro-Mediterranean Studies, 2012, 5(1): 41–56 https://doi.org/10.1007/s40321-012-0004-6
42	E Ben Mohamed, R Fairchild, A Bouri. Investment cash flow sensitivity under managerial optimism: new evidence from NYSE panel data firms. Journal of Economics Finance and Administrative Sciences, 2014, 19(36): 11–18 https://doi.org/10.1016/j.jefas.2014.04.001
43	S C Kumbhakar, C A K Lovell. Stochastic frontier analysis. Journal of Banking & Finance, 2000, 36: 1311–1319
44	S K Bhaumik, P K Das, S C Kumbhakar. A stochastic frontier approach to modelling financial constraints in firms: an application to India. Journal of Banking & Finance, 2012, 36(5): 1311–1319 https://doi.org/10.1016/j.jbankfin.2011.11.026
45	D Aigner, C A K Lovell, P Schmidt. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 1977, 6(1): 21–37 https://doi.org/10.1016/0304-4076(77)90052-5
46	G Pawlina, L Renneboog. Is investment-cash flow sensitivity caused by the agency costs or asymmetric information? Evidence from the UK. European Financial Management, 2005, 11(4): 483–513 https://doi.org/10.1111/j.1354-7798.2005.00294.x
47	E Ben Mohamed, S Jarboui, A Baccar, A Bouri. On the effect of CEOs’ personal characteristics in transport firm value? A stochastic frontier model. Case Studies on Transport Policy, 2015, 3(2): 1–6
48	L Oxelheim, T Randøy. The impact of foreign board membership on firm value. Journal of Banking & Finance, 2003, 27(12): 2369–2392 https://doi.org/10.1016/S0378-4266(02)00395-3
49	S Bhagat, B Bolton. Corporate governance and firm performance. Journal of Corporate Finance, 2008, 14(3): 257–273 https://doi.org/10.1016/j.jcorpfin.2008.03.006
50	R Kouwenberg. Does voluntary corporate governance code adoption increase firm value in emerging markets? Evidence from Thailand. 2006–11–30

Viewed

Full text

Abstract

Cited

Shared

Discussed