Currency Hedging and Firm Value
Description
The data is taken from the Shenzhen stock exchange, one of the two main markets in China. As there have been increasing foreign exchange risks for Chinese firms in recent times following the liberalisation of the foreign exchange markets, firms have become more likely to use foreign currency derivatives, especially in the manufacturing industry, which are more likely to import and export goods, so require access to the foreign exchange markets. Thus, we have chosen listed firms from the manufacturing industry, classified by the CSRC (China Securities Regulatory Commission), and collected data related to forward contracts, swaps and futures whose underlying asset is foreign currency from each of the company’s annual reports. We consider only multinational corporations (MNCs), which is consistent with our focus on the use of foreign currency derivatives and an empirical strategy in line with Allayannis and Ofek (2001) and Pantzalis, Simkins and Laux (2001). MNCs have been selected by choosing firms with a ratio of foreign sales to total sales which is greater than 10%. After excluding the firms with incomplete information and firms with extremely limited data, there were 316 firms’ data running from 2012 to 2017. We begin in 2012 as this is after the main liberalisations of the Chinese financial markets. All the accounting data has been obtained from the Wind database while the use of derivatives has been collected by hand from the annual reports of each firm for each year.
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Steps to reproduce
. The specification used in this study is as follows: 〖FV〗_it=β_1 〖DER〗_it+β_2 〖EXP〗_it+β_3 〖INFORMATION〗_it+β_4 〖AGENCY〗_it+δ〖CONTROL〗_it+u_i+ε_it (1) i=1,2,3…N ,t=2,3,4…T〖FV〗_it refers to the current and past values of the firm. 〖DER〗_it represents the derivative usage of the firm and 〖EXP〗_it represents the level of foreign business of the firm. The information asymmetry and agency problems are represented by 〖INFORMATION〗_it and 〖AGENCY〗_it. 〖CONTROL〗_it refers to the control variables, which includes the firm’s characteristics, such as size, liquidity, profitability and growth opportunities. In a final robustness test, we also add the interaction terms between derivatives and agency costs as well as asymmetric information respectively. This gives: 〖FV〗_it=β_1 〖DER〗_it+β_2 〖EXP〗_it+β_3 〖INFORMATION〗_it+β_4 〖AGENCY〗_it+δ〖CONTROL〗_it+φ〖DER〗_it*〖INFORMATION〗_it+ϑ〖DER〗_it*〖AGENCY〗_it+u_i+ε_it The firm valuation effect of derivatives is dependent on the threshold level of the capital structure due to the effects of leverage on firm value, which is estimated using a threshold panel model. We follow the approach of Seo and Shin (2016) who suggest that the basic threshold effects model could be described as the following: y_it=(1,x_it^' ) ρ_l 1{q_it≤γ}+(1,x_it^' ) ρ_h 1{q_it>γ}+〖η_i+e〗_it (3) i=1,2,3…N; t=1,2,3…T Where y_it is the dependent variable while x_it^' refers to a vector of m_1×1 explanatory variables and control variables that we are interested in and may contain the lagged dependent variable. q_it represents the transition variable, γ is the threshold level and 1{∙} is an indicator function. ρ_l and ρ_h indicate the estimated coefficients on the regressors for the lower regime and for the upper regime, respectively.