Does corporate R&D investment support to decrease of default probability of Asia firms?

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Does corporate R&D investment support to decrease of default probability of Asia firms? Victoria Cherkasova*, Alena Kurlyanova National Research University Higher School of Economics (NRU HSE), Faculty of Economic Sciences, School of Finance, 20 Myasnitskaya St., 101000, Moscow, Russia Received 8 January 2019; revised 26 May 2019; accepted 13 July 2019 Available online ▪ ▪ ▪

Abstract This paper examines the nature of the relationship between corporate R&D investment and the probability of default. Existing evidence on the topic is varied and often conflicting due to its complexity. In this paper, we investigated the non-linear relationship between R&D investment and the probability of default, and also detected several factors influencing the nature of the relationship. The research relies on the sample of Asian Tiger's countries (Hong Kong, Singapore, South Korea and Taiwan) for the period from 2012 to 2017. Results of the research reveal a Ushaped relationship between corporate R&D investment and default probability. Considering the relationship more precisely, we divide the sample into two parts based on the availability of financial resources, and test the significance of this factor. R&D investment is found to significantly decrease default probability for financially constrained firms. We also examine the investment efficiency factor by comparing R&D investment and default probability between underinvesting and overinvesting firms. The rise of R&D investment decreases default probability for underinvesting firms, and increases e for overinvesting ones. Studying separately high-tech firms, we reveal that R&D investment leads to decrease of default probability. _ Copyright © 2019, Borsa Istanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). JEL classification: G32; G33 Keywords: Innovation; Bankruptcy; Financial constraints; Overinvestment; Underinvestment; High-tech firms

1. Introduction Innovation plays a crucial role in economic growth and prosperity of nations. Today in the context of globalization innovation is considered as a determinant of competitiveness and success. Undoubtedly, a new stage of evolution development was achieved through growing innovation intensity. Sustainable economic and social growth, creating new industries, creating unified market space, decreasing costs, increasing quality of product and services and many other benefits are carried by innovation. * Corresponding author. E-mail addresses: [email protected] (V. Cherkasova), alena. [email protected] (A. Kurlyanova). _ Peer review under responsibility of Borsa Istanbul Anonim S¸irketi.

R&D investment is the main attribute of technological innovation. Year by year there is an increasing tendency of growing innovation activity worldwide and statistical facts confirm improvement of the innovation environment. During the last 12 years, total R&D spending for 1000 of the most innovative companies (according to the estimates of Strategy&, PWC)1 increased by 75%. By the end of 2017 R&D spending of top 1000 companies has reached more than 702 billion US dollars. During the last year 2017, R&D spending has increased by 3.2%. Studying various aspects of R&D intensity has become more and more actual as the amount of money that companies spend on R&D increases year by year.

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2017 Global Innovation 1000 Study (Strategy&, PWC).

https://doi.org/10.1016/j.bir.2019.07.009 _ 2214-8450/Copyright © 2019, Borsa Istanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article as: Cherkasova, V., & Kurlyanova, A., Does corporate R&D investment support to decrease of default probability of Asia firms?, Borsa _ Istanbul Review, https://doi.org/10.1016/j.bir.2019.07.009

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In terms of regions, Asia became a new engine of innovation in the 21st century, and today Asian countries complement existing innovation forces of previous leaders: Northern America and Europe. Since Krugman’s (1994) statement about the Asian high growth in the 1980's, a lot of attention has been paid to the technological development of the region. By now, the total share of Asia in worldwide corporate R&D spending exceeds 35%. Since the late 1970's Japan started to be an important player in global innovation activity. A few years later, so-called Asian Tigers, with Hong Kong, Singapore, South Korea and Taiwan, increased their innovation intensity rapidly. Today Japan and these four countries are still remaining leaders in innovation activity in Asia. However, if we look at the year by year growth rates of R&D spending in these countries, we can see that the level of R&D investment in Japan remains stable, but in the Tiger economy there are increasing dynamics of growth rates. That is why, as researchers, we are more interested in studying Asian Tigers, as we can investigate how the change in the level of R&D investment influences on some financial ratios. Moreover, we can study these countries as a whole group for at least two reasons. Firstly, Asian Tiger countries are close to each other in economic development. And secondly, they are close to each other in terms of innovation intensity.2 In the ranking of the Global Innovation Index 2017, Hong Kong, Singapore, South Korea and Taiwan are in the top-20 countries all over the world. There is one more reason why it is important to study this particular group of countries. Moving forward, there is novel potential in innovation development shown by up-and-coming Asian countries. New Asian Tigers e Indonesia, Malaysia, the Philippines, Thailand and Viet Nam e are going to join the group of most innovative Asian countries. These and other countries in Asia try to improve their innovation intensity, and they will use the experience of their more developed neighbors. Innovation growth allows emerging countries to declare themselves on the world stage. Rapidly developing countries are characterized by higher growth rates, and the difference between growth rates could be explained by diffusion of innovation. Successful innovation allows countries to achieve higher productivity, that leads to higher competitiveness. There is no doubt that all countries regardless of region or level of economic development gain benefits from the R&D spending growth. But what about the companies? There is a tradeoff between potential benefits and risks. On the one hand, R&D investment increases opportunities of long-term success. On the other hand, investing in innovation assumes high fixed costs which do not guarantee any success. In literature, investment in innovation is studied as a key driver of a firm's growth and long-term success. Comparatively little attention has been paid to the potential negative effect of

R&D spending. Some R&D projects may become unsuccessful and this fact increases risks, such as credit risk. R&D intensity grows year by year, but the question about its impact on firm risks remains unresolved. And particularly, there is no clear answer whether R&D spending increases or decreases the probability of default of the company. The main goal of this paper is to find the relationship between corporate R&D investment and the probability of default. Our research contributes existing theoretical and empirical studies in several dimensions. Firstly, consideration of developed R&D-intensive countries in Asia expands the set of existing countries studied. Most papers focused on the study of American and European countries, China and some other countries are considered as well. In contrast, Asian Tigers are not well studied, but they make a substantial contribution in R&D investment. Secondly, this paper reveals a non-linear (U-shaped) relationship between R&D investment and default probability. This finding resolves contradictions in previous studies of the topic. Thirdly, R&D investment and default probability relationship differs for financially constrained firms. Our research supposes negative relationship for financially constrained firms and an insignificant relationship for unconstrained ones. Furthermore, research makes a comparison between companies that underinvest and overinvest. For underinvesting firms, additional investment in R&D decreases the probability of default, and for overinvesting e increases. Finally, this research investigates features of the relationship between R&D investment and default probability for high technological companies. Higher R&D intensity indicates lower default probability. Hence, our research emphasized at least three factors influencing the character of the relationship between investment in R&D and default probability: financial constraints, deviation from an optimal level of investment, technological degree of the industry. Our research also has practical importance. As factors influencing on default probability are revealed, managers can estimate how the probability of default will change if the company varies these factors. Moreover, managers can develop optimal investment strategy in the field of R&D. The rest of the paper is organized as follows: Chapter 1 presents a review of relevant literature on the relationship between corporate R&D investment and default probability with the consequent main hypotheses of the research; Chapter 2 describes how these hypotheses can be tested and provides the methodology of the research; Chapter 3 describes the data used for analysis; Chapter 4 elucidates empirical results of the research and discusses limitations. 2. Literature review

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The Global Innovation Index 2017. Effective Innovation Policies for Development (Cornell University, INSTEAD, the World Intellectual Property Organization).

Our research is based on five major blocks of literature. The first block helps us to answer the main question of the research: if there is relationship between corporate R&D

Please cite this article as: Cherkasova, V., & Kurlyanova, A., Does corporate R&D investment support to decrease of default probability of Asia firms?, Borsa _ Istanbul Review, https://doi.org/10.1016/j.bir.2019.07.009

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investment and the probability of default? The second block of literature will make support to the first one as it considers different measures of default probability. Last three parts of literature will provide theoretical base for dipper analysis of the relationship between R&D investment and default probability. Third part indicates the difference between financially constrained and unconstrained firms, and how this difference can influence on studied relationship. The next part emphasizes how non-optimal investment policy of the firm influences on studied relationship. And the last part is related to the features of technological firms that have another nature of business and that is why another impact of R&D investment on default probability. 2.1. Theoretical background on the relationship between R&D investment and default probability 2.1.1. Studies found that investment in R&D decreases default probability There is large literature revealing negative influence of corporate R&D investment on the probability of default. In addition to different samples, researchers use contrasting approaches to estimate the relationship between innovation investment and risk, in terms of different models and different proxies for risk. Eberhart, Maxwell, and Siddique (2008) have found negative effect of increase in R&D intensity on default probability. Negative relationship was explained by bond ratings. Firms with high R&D intensity have lower required spreads and better bond rating, that is why default probability of such companies is lower. Moreover, authors also showed that despite on average there is negative relationship between R&D intensity and default probability, the tradeoff between risks and benefits of R&D investment exists. Firms with high initial default risk and with high level of debt have more benefits of increasing R&D investment in terms of lower default probability. Some researchers marked that all economies differ in terms of political and economic institutions and these differences influence on innovation activity of companies. Results of previous papers studied developed markets revealed negative relation between R&D investment and risk of bankruptcy. Zhou (2008) mentioned that emerging countries are characterized by higher risk of innovation activity comparatively to developed ones. It is caused by potentially more prevalent intellectual property theft, poorer institutional protection, less efficiency of market forces and less competitiveness. In transitioning economies risk related with innovation is not always associated with high payoffs. Fernandes and Paunov (2014) mentioned that multifunctional firms that produce various products can diversify their risks investing in innovation for different products. And contrary, firms that invest in one product increases their risks, as high payoffs in such case is less probable. Braga da Silva, Klotzle, Pinto, and Jacques da Motta (2018) emphasized that firms with high industry adjusted R&D intensity are less risky than those with low intensity, because of

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the lower volatility of future profits. R&D intensive companies tend to engage in multiple projects and diversify their projects’ risks. In addition, they show that companies with high R&D intensity also provide more information to the market about their innovation projects and can mitigate approximately 40% of their potential undervaluation. In paper of Hsu, Lee, Liu, and Zhang (2015) the relationship between innovation, default risk and bond pricing has been studied. Innovation inputs and outputs are considered as important factors of company's creditworthiness and that is why they influence on bonds and default likelihood. Innovation gives companies a competitive advantage because of increasing financial stability, and as a result innovation is associated with lower probability of default. Firms investing in R&D are more likely to earn first-mover advantages. Furthermore, entry costs of new firms will increase due to existing patents, and that is why firms having patents have a kind of monopoly power. 2.1.2. Studies found that investment in R&D increases default probability As it has been considered investment in innovation may decrease probability of default, at least with some conditions and restrictions. However, several papers refute this statement as they have found positive relationship between R&D intensity and default probability. Innovation is necessary condition for industrial changes, but it is necessary to consider innovation activity as a complex process. Any R&D investment connected with some probability of fail, and that is why increase of R&D expenditures may lead to bankruptcy. Ericson and Pakes (1995) confirmed positive relationship between R&D intensity and risk. R&D investments may increase efficiency of the firm and high efficiency will lead to higher profitability and sustainability. On the other hand, innovation output may be unsuccessful and the result will be in higher likelihood of bankruptcy. Authors notice that failures in innovation process occur often and eventually innovation activity increases the risk of default. There are arguments about volatility of equity in the studies. Grinblatt and Titman (1998) argue that high investment in R&D increases volatility of firm cash flows. It in turn increases stock price volatility. As a result, a company becomes riskier for potential and existing investors. Chan, Lakonishok, and Sougiannis (2001) have also found that stock return volatility increases with rising R&D intensity. This result was explained analytically by Fung (2006). Any R&D activity is accompanied by future uncertainty and information asymmetry. Moreover, there are high transaction fixed costs that are necessary for discovery and introduction of new product or service. But high costs cannot guarantee good future outcome. Even if return on R&D investments will be positive, long time is necessary to materialize benefits from innovation and period of positive returns may be short because of fast changing technology. Managers are often inclined to overestimate advantages of new product, and at the same time consumers may even underestimate them. That is why innovation products may stay

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unsuccessful and risk of bankruptcy for creator of such new products may increase (Gourville, 2005). Buddelmeyer, Jehsen, and Webster (2010) have found positive relationship between innovation and bankruptcy risk. They also consider market conditions and finance accessibility. In this study, radical innovation was also found as riskier activity. In long run period, average return on R&D investments is low, but probability of bankruptcy is high. Zhang (2015) noticed that besides volatility and uncertainty, investment in R&D increases risks due to adjustments costs and financial constraints. Author has found that R&D increases bankruptcy risk and effect is higher for financially constrained firms and during economic downturns. At the same time firms that previously were successful in innovation and ones that previously had high analyst coverage potentially may avoid risk. In paper, R&D intensity is considered as a tradeoff between benefits (in terms of improving technological performance and competitive advantages) and threats. Risk averse managers always prefer to mitigate risks and pay for lower risks by lower returns. 2.1.3. Non-linear relationship As it can be seen, results of previous studies are quite conflicting. While one part of empirical papers found that corporate R&D investment decreases the probability of default, another one demonstrated the opposite. Moreover, results of many studies make premises for assumptions about more complicated relationship. It has been mentioned, first studies on the topic were relied on the literature about the relationship between R&D investment and likelihood of survival. Study of Zhang and Mohnen (2013, p. 57) proposes to distinguish between innovation input measured as R&D expenditures and innovation output measured as new successfully introduced products. They have found an inverse U-shaped relationship between innovation and survival in long-run period. Moreover, R&D expenditures itself were found as more significant for likelihood of survival than success of new products. After a certain point, innovation input or output decreases, and decrease will be less for hightech companies as R&D intensity is riskier for such companies. Ugur, Trushin, and Solomon (2016) have also found inverse U-shaped relationship between R&D intensity and firm survival. It was found in studies of default probability that patent-toR&D ratio tends to fall as R&D intensity increases (Kortum, 1993). If there is sufficient growth of R&D intensity, the invention-to-R&D ratio will continuously decline as any industry will converge to a steady state. In this study, we will take into account both sides of tradeoff between benefits and threats of R&D, and assume non-linear relationship between investment in R&D and default probability. Increasing of R&D expenditures can reduce likelihood of default, but every additional increase in R&D is associated with less marginal decrease of default probability. Moreover, there exists some critical point at which risks of R&D investment overweight benefits and probability of default starts to increase.

Hypothesis 1. There is U-shaped relationship between corporate R&D investment and the probability of default. For the first time, default probability decreases with an increase of R&D investment, and it eventually rises after a certain level of R&D. 2.2. Default probability measurement Default risk measures the uncertainty about firm ability to service its obligations (Crosbie & Bohn, 2003). Probability of default is an indicator of firm stability. Investors use this information for uncertainty compensation, as spread over the interest rate for default-free company, and estimated default probability represents earning of investment. Average default probability among companies worldwide do not exceed 2% every year, but across companies, default probability distributes unevenly (Crosbie & Bohn, 2003). There are different approaches in the literature for estimation the likelihood of survival, bankruptcy and default of the firm. They can be divided into three groups: those that use information from financial statements (they are backward looking as they based on the past performance of the firm), those that use information from financial markets (they are forward looking, as they based on the market prices which take into account future prospects of the firm) and those that use appraisals of analysts (they may be subjective as they take into account willingness to buy or sell by one investor). 2.2.1. The Merton distance to default (Merton DD) model Since the beginning of 2000s several market-based bankruptcy models have been introduced. Researchers were motivated by Merton’s (1974) default model. Models with such structural form, at least in theory, should outperform accounting-based models as they are forward-looking and incorporate all relevant information. The Merton DD model implemented in papers of Vassalou and Xing (2004) and Bharath and Shumway (2008) is used for estimation expected default probability. This model allows to calculate distance to default, which is assumed to be normally distributed. By substituting calculated distance to default into cumulative density function, probability of firm default will be received. Received value is the probability of that the value of assets of the firm will fall below book value of liabilities of the firm over the next 12 months. Merton (1974) implies structural model for estimation the link between market value of equity and market value of assets, model for measure the distance between current value of firm and its bankruptcy threshold. In Merton DD model, equity of the firm is considered as European call option on the underlying value of its assets. Equity holders of the company are residual claimants after all other obligations have been met, and call option on assets has the same properties. That is why it is possible to examine equity as an option with strike price equal to book value of liabilities of the firm. If the value of assets is less than the

Please cite this article as: Cherkasova, V., & Kurlyanova, A., Does corporate R&D investment support to decrease of default probability of Asia firms?, Borsa _ Istanbul Review, https://doi.org/10.1016/j.bir.2019.07.009

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value of liabilities (strike price), then shareholders (holders of call option) will not exercise option. Classic Merton DD model has been modified several times, because simplifying assumptions implemented by Merton challenges predictive ability of the model and researchers tried to improve the model. Vassalou and Xing (2004) suggested iterative procedure using historical accounting information and daily market data. Bharath and Shumway (2008) propose alternative model for default probability estimation with the use of Merton model structural form. In another research, Campbell, Hilscher, and Szilagyi (2008) constructed reduced form of the model using also accounting and market data. They also found that lower profitability, higher leverage, lower and more volatile returns and little cash holdings increase the probability of firm default. All these studies established that market measures of default probability outperform accounting ones. Moreover, all these models are more or less robust to each other. Miao, Ramchander, Ryan, and Wang (2017) mentioned that discussed models have the similar drawback: several inputs that are used for estimation Merton DD themselves are not forward-looking at all. For example, those models use historical volatility and historical stock returns. Such backwardlooking inputs contradict with forward-looking nature of the model. Previous studies of Elton (1999), Pastor, Sinha, and Swaminathan (2008), Chava and Purnanandam (2010) have also noticed that past returns are noisy proxy for future expected returns. These authors and Miao et al. (2017) as well show that implied cost of capital is better proxy for expected returns under some assumptions about dividend growth and expected return processes. Implied cost of capital is such market rate of return that equates current market price of equity and discounted future dividend payout. Miao et al. (2017) also shew that implied cost of capital interconnected with bankruptcy risk. Miao et al. (2017) constructed different specifications with the use of implied volatility and implied cost of capital. Then authors used relative information content and prediction accuracy testing approaches to make comparison of different specifications performance. And they conclude that only one specification performs better that more naive models used in other studies. We weighted benefits (almost increasing predictive power) and drawbacks (complexity of calculation and necessity of additional assumptions) of more sophisticated models and decided to use naive Merton DD model. The specification was suggested by Bharath and Shumway (2008). Authors compare different methods of default probability calculation in the paper. The best specification of Merton's model is one proposed by Moody's KMV corporation. Moody's KMV uses its own large historical database to estimate the empirical distribution of changes in distances to default and it calculates default probabilities based on that distribution. As we don't have access to such database, we use specification that mostly correlated with Moody's KMV one: correlation is 0,786.

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2.3. The relationship for financially constrained and unconstrained firms A firm is financially constrained if it is unable to obtain sufficient external funds and has to finance its investment mostly by internal funds. It is not necessary that financially constrained firm doesn't have access to external financing at all, but such firms face higher cost of external financing than unconstrained ones (Mulier, Schoors, & Merlevede, 2016). Campbell, Dhaliwal, and Schwartz (2012) found evidence that decrease of internal funds leads to decrease of corporate investment and this relationship is explained by the cost of capital. In earlier study of Duchin, Ozbas, and Sebsoy (2010) it was shown that during the financial crisis there was decreasing tendency of investment intensity, and the reason is the lack of supply of external finance. In literature, there is no direct measure of financial constraints, and moreover researchers do not have a consensus measure everyone can agree on. Researchers measure constraints indirectly through some variables or through estimation of investment cash flow sensitivity. In imperfect capital markets firms can face external financing constraints both on equity financing (Myers & Majluf, 1984) and debt financing (Stiglitz & Weiss, 1981) because of the information asymmetry. As mentioned by Zhang (2015), investment in R&D are linked with higher level of risk and uncertainty for financially constrained firms because they have insufficient resources to face possible failure of projects. Financially unconstrained firms, in contrast, have easy access for additional financing and they do not suffer much from risks of R&D investment. We suppose that risks of R&D investment can be mitigated for financially unconstrained firms, but for financially constrained firms, investment in R&D is risky. Hypothesis 2a. R&D investment has positive effect on default probability for financially constrained firms. Hypothesis 2b. Effect of R&D investment on default probability is mitigated for financially unconstrained firms.

2.4. The relationship for underinvesting and overinvesting firms Investment in innovation is not always efficient at the firm level due to high degree of information asymmetry and agency conflicts. There two sides of nonoptimal R&D investment problem. On the one hand, small and young innovative firms often face the challenge of the “funding gap” for investment and they tend to underinvest in R&D projects (Hall & Lerner, 2009). On the other hand, there are lot of examples of firm that invest huge amount of money in R&D, but results meet expectations rarely (Jensen, 1993). Overinvestment increases external financing dependence, that is more costly than internal financing, that is why

Please cite this article as: Cherkasova, V., & Kurlyanova, A., Does corporate R&D investment support to decrease of default probability of Asia firms?, Borsa _ Istanbul Review, https://doi.org/10.1016/j.bir.2019.07.009

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overinvesting increases risks. On the other hand, firms that underinvest may adhere such a policy due to poor opportunities to attract external financing, but growth opportunities may be good, and investment in R&D will not increase risks (Xiao, 2013). Overinvestment may also indicate a tendency of building empire and expropriating resources for private benefits (Jensen & Meckling, 1976; Jensen, 1986, 1993). These actions are nor beneficial for the company and risks will increase. As mentioned by Mauer and Sarkar (2005), overinvestment leads to decrease of firm value and increase of credit spread of debt. Pearce and Patel (2017) confirmed that there is an optimal performance trajectory of the company. Limiting both underinvestment and overinvestment will lead to minimal deviations from this trajectory, according to the results of the study. Relying on results of these studies we distinguish companies that underinvest and overinvest, and propose that the relationship between R&D investment and default probability will be different for these groups of firms. For overinvesting firms the relationship between R&D intensity and default risk should be positive due to high risks that already exists. For underinvesting firms the relationship should be the opposite, because such firms have enough growth opportunities and investment in R&D will be beneficial for them. This supposition is correlated with our hypothesis about U-shaped relationship for all firms. Hypothesis 3. R&D investment by firms that under(over) invest has negative(positive) effect on default probability.

2.5. Features of high technological firms There are wide range of economic benefits from innovative activity of the firm (Laeven, Levine, & Michalopoulos, 2015), however there is also high level of uncertainty in the outcome of R&D investment (Jalonen, 2012; Scherer, Harhoff, & Kukies, 2001, pp. 181e206). Firms face problems related to high fixed cost of financing projects and asymmetric information. For high-technological firms these problems looks even worse (Berger, Rosen, & Udell, 2007; Hall & Lerner, 2009). High-tech firms make significant contribution to the economic growth, but they always face difficulties in financing their further development (Zhang, He, & Zhou, 2013). Hightech firms have a lot of confidential information, and a condition of confidentiality inhibits the process of financing attraction (Xiong & Chen, 2007). At the same time, high-tech firms are characterized by large investment and high risk, and their risk is related with investment in capital (Zhang et al., 2013). As mentioned by Zhang et al. (2013), the default probability of high technological firms is explained mostly by current ratios than by forward strategic ones. That is why for high tech companies default probability is influenced by the current ratio, accounts receivable turnover, etc.

R&D investment also influences default probability, however relatively few studies have considered the relationship for high-tech companies separately. Nunes, Serrasqueiro, and Leitao (2012) compare performance of high-tech firms and firms from less technological industries. They mentioned that R&D intensity and financial constraints are more important factors for high-tech firms than for non-high-tech ones. Taking into account that high-tech firms are often financially constrained in financing R&D investment, moreover they are characterized by high intensity of R&D investment in comparison with firms from other industries having the same characteristics, we suppose the following hypothesis. Hypothesis 4. R&D investment has positive effect on default probability for high technological firms.

3. Methodology of research Step 1. To estimate the probability of default The probability of default in our research is the probability that the value of firm's total assets will fall below the book value of that firm's total liabilities over the next 12 months. In model, equity of the firm is considered as European call option on the underlying value of its assets. Equity holders of the company are residual claimants after all other obligations have been met, and call option on assets has the same properties. Strike price of option equals to book value of liabilities of the firm. If the value of assets is less than the value of liabilities (strike price), then shareholders (holders of call option) will not exercise option. Time to maturity of call option is equal to T, or 12 months in this study. This model is applicable for firms that financed both by equity and debt. And moreover, there are two critical assumptions, under which equity might be considered as call option. Firstly, market value of firm's total assets follows a stochastic process named geometric Brownian motion: dVA ¼ mVA dt þ sA VA dW

ð1Þ

where VA is value of assets of the firm; m is expected continuously compounded return on VA or drift rate of VA; sA is volatility of assets and dW is a standard Wiener process. According to the second assumption of the model, debt of the firm is expressed in one discount bond with time to maturity equal to T. Market value of debt in model can be approximated to the face value of debt: D ¼ F;

ð2Þ

where D is market value of debt and F is face value of debt. Firms that have high probability of default have very risky debt, and the risk of the debt is correlated with the risk of the equity. That is why the volatility of debt can be expressed through the volatility of equity. The model assumes that 25%

Please cite this article as: Cherkasova, V., & Kurlyanova, A., Does corporate R&D investment support to decrease of default probability of Asia firms?, Borsa _ Istanbul Review, https://doi.org/10.1016/j.bir.2019.07.009

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of equity risk is reflected in debt risk. And moreover, there is term structure volatility. sD ¼ 0; 05 þ 0; 25sE ;

ð3Þ

where sD is volatility of debt and sE is volatility of equity. Then volatility of assets calculated with the use of equation: E D E sE þ sD ¼ sE EþD EþD EþF F ð0; 05 þ 0; 25sE Þ: þ EþF The distance to default is then:
   ln EþF þ ri;t1  0; 5s2A T F pffiffiffiffi DD ¼ ; sA T

sA ¼

ð4Þ

ð5Þ

where DD is distance to default, ri;t1 is firm's stock return over the previous year. Distance to default denotes a number of standard deviations that the firm is deviate from default. Theoretical probability of default (p) is expressed by a normal distribution: p ¼ NðDDÞ;

ð6Þ

where N(.) is cumulative density function of standard normal distribution. So, the most important inputs of the model are market value of equity, volatility of equity and book value of debt. Time to maturity is equal to 12 months. Volatility of assets is estimated within the model. And then distance to default and probability of default are calculated. Actually, the probability of default is not normally distributed, and it is a limitation of Merton DD model. It is more proper to use historical data for the construction the empirical distribution of default. But as it was shown in Bharath and Shumway (2008), for the purpose of forecasting defaults Merton DD model can also be used, as difference in prediction is not significant. Explained methodology of default probability calculation assumes the use of the same input information and the same functional form of classic Merton DD model. Step 2. To define whether corporate R&D investment has non-linear impact on default probability For the determination of non-linear relationship, we construct the following regression model: Defaulti;t ¼ b0 þ b1 R&Di;t þ b2 R&D2i;t þ b3 Detaulti;t1 þ b4 Market=Booki;t þ b5 Leveragei;t þ b6 ProfitMargini;t þ b7 Sizei;t þ b8 ln Agei;t þ b9 ROAi;t þ b10 Lossi;t þ b11 Cashi;t þ Industryj þ εi;t ð7Þ

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where Default is default probability, calculated at the previous step; R&D is R&D investment; Market=Book is market value assets divided by book value of assets; Leverage is debt to assets ratio; ProfitMargin is net income to revenue ratio; Size is natural logarithm of assets; lnAge is natural logarithm of age; Loss is dummy variable, that equals to 1 if firm exercises loss; Cash is cash to assets ratio; Industry denotes industry fixed effect. i and t are subscripts for firm and year, respectively. Estimates of coefficients b1 and b2 will explain the nature of the relationship between corporate R&D investment and default probability. At this step hypothesis 1 about U-shaped relationship between corporate R&D investment and default probability during the next 12 months is tested. Regression is estimated by the ordinary least square method. After an estimation of basic specification, a form of the model can be changed in order to obtain the best coefficient estimates. Based on literature review, we define factors influencing on the default probability, in addition to R&D intensity: R&D is the measure of R&D intensity, that is widely used in literature as indicator of innovation input. R&D investment is always scaled by assets or sales revenue in order to make investment of different firms comparable. Lagged default is included in the model, because default probability should be persistent over time, we expect to find positive relationship with the current value of Default (Hsu et al., 2015). Other variables are included based on studies of Blume, Lim, and Mackinlay (1998), Hsu et al. (2015), Kaplan and Urwitz (1979), Zhang (2015). Step 3. To define how the relationship between R&D investment and default probability differs for financially constrained and unconstrained firms At step 3, we define subsamples of financially constrained and unconstrained firms and estimate the relationship between R&D investment and default probability separately for both groups. To define financially constrained and unconstrained firms we use the ASCL-index (age e size e cash flow e leverage). As mentioned by Mulier et al. (2016), firm age, size, average cash flow level and the average indebtedness are the main drivers of external drivers supply. Age and size are widely used in different measures of financial constraints. Firm's cash flow is a proxy for the debt/repayment capacity of the firm, and leverage ratio is a proxy for solvency risk. In order to construct index, we firstly measure four variables:  Age e the difference between year of observation and year of firm incorporation;  Size e natural logarithm of total assets;  Cash flow e average cash flow to capital ratio of the previous two years;

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 Leverage e average total debt to total assets of the previous two years. Then we calculate industry median for these four variables for every year, and get scores in accordance with industry median. Firm gets a score of 1 for age, size and cash flow if the value of variable for company is lower than industry median, and 0 otherwise. As for the leverage ratio, firm gets a score 1 if value of leverage for firm is above industry median, and 0 otherwise. After summing four scores for each firm-year observation, we obtain composite score, that ranges between 0 (for absolutely unconstrained firms) and 4 (for absolutely constrained firms). And finally, we generate dummy variable for unconstrained firms, that takes value of 0 if ASCL-index is equal to 2 or higher (meaning that the firm is financially constrained), and value of 1 if ASCL-index is lower than 2 (meaning that the firm is financially unconstrained). ASCL-index was chosen as preferable because of two reasons. ASCL-index is based on data used in research. Moreover, ASCL-index can be calculated without assumptions about the comparative importance of four variables and also without assumptions about country differences, as the index is based on unweighted sum of scores. Then we add dummy variable for unconstrained firms in our model and estimate the following regression model: Defaulti;t ¼ b0 þ b1 R&Di;t þ b2 R&DxUnconstrainedi;t þ b3 Detaulti;t1 þ b4 Market=Booki;t þ b5 Leveragei;t þ b6 ProfitMargini;t þ b7 Sizei;t þ b8 ln Agei;t þ b9 ROAi;t þ b10 Lossi;t þ b11 Cashi;t þ Industryj þ εi;t ð8Þ At this step, hypotheses 2a and 2b are tested. Step 4. To define how the relationship between R&D investment and default probability differs for underinvesting and overinvesting firms At this step, we define two groups of firms: firms that tend to underinvest and those that are likely to overinvest; and determine the difference in relationship between R&D investment and default probability for these groups. Firstly, we need to define companies that may underinvest and overinvest. For this purpose, we use investment model, proposed by Biddle, Hilary, and Verdi (2009) and Gomariz and Ballesta (2014). According to those papers, investment can be explained by the firm's sales growth: Investmenti;t ¼ b0 þ b1 Sales growthi;t1 þ εi;t

ð9Þ

where Investmenti;t include capital and non-capital (R&D) investment. Regression is estimated for each industry-year. The residuals from the regression indicate the deviation from the expected investment level. A positive residual would indicate overinvestment, while negative residual e underinvestment.

Then we sort firms into quartiles based on residuals. Firm-year observations from the bottom quartile (the most negative residuals) are classified as underinvesting; observations from the top quartile (the most positive) e as overinvesting. And observations from two middle quartiles are used as benchmark. Then, in order to test hypothesis 3, we estimate following regression separately for underinvestment and overinvestment sample: Defaulti;t ¼ b0 þ b1 R&Di;t þ b2 Detaulti;t1 þ b3 Market=Booki;t þ b4 Leveragei;t þ b5 ProfitMargini;t þ b8 Sizei;t þ b9 ln Agei;t þ b10 ROAi;t þ b11 Lossi;t þ b12 Cashi;t þ εi;t ð10Þ Step 5. To estimate the relationship between R&D investment and default probability for high technological companies At this step, we separate firms from high technological industries and find the difference in the relationship between R&D investment and default probability for companies from these industries. OECD provides classification of manufacturing industries based on R&D intensity and R&D embodied in intermediate goods and investment goods. They distinguish four categories: high, medium-high, medium-low and low. High technological category contains:     

Aircraft and spacecraft; Pharmaceuticals; Office, accounting and computing machinery; Radio, TV and communication equipment; Medical, precision and optical instruments.

We define high-tech sample, that consists of companies from these five industries. And then we estimate regression, considering that for default probability of high-tech companies, current ratio and capex are also relevant. Defaulti;t ¼ b0 þ b1 R&Di;t þ b2 Detaulti;t1 þ b3 Market=Booki;t þ b4 Leveragei;t þ b5 ProfitMargini;t þ b6 Sizei;t þ b7 ln Agei;t þ b8 ROAi;t þ b9 Lossi;t þ b10 Cashi;t þ b11 CurrentRatioi;t þ b12 Capexi;t þ εi;t ð11Þ At this step, hypothesis 4 is tested. 4. Data description For the empirical study, firm level data is obtained from S&P Capital IQ Platform and Bloomberg Terminal. Capital IQ provides fundamental yearly data; from Bloomberg database, we collect daily data on market stock prices that is necessary

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for default probability calculation. Data covers the period from 2012 to 2017. Sample contains four Asian Tigers countries: Hong Kong, Republic of Korea, Singapore and Taiwan. There are several criteria of sample selection. Firstly, we consider only publicly traded companies, as we need market information about the firms. We exclude firms that are belong to the Financial and Utility industries, because of the differences in regulatory oversight for these industries. We also exclude firms that have revenue less than $10 million, because the presence of very small firms in the sample could lead to increasing errors of the regression estimates. And finally, we delete firm-year observations with missing data and delete several outliers. The final sample includes 19 926 firm-year observations. Table 2 provides the summary statistics of all the variables used in our analysis. There is information about mean and median values, standard deviation and 1% and 99% cutoff points. Median company in our sample has default probability during the next year that equals 0.61%, while the mean value of default probability is about 2%. There are no companies that became bankrupts during the studying period, however there are companies that have default probability close to 100% (share of such observations is less than 1% of the sample). Presumably, survivorship bias will not lead to worse accuracy of estimates. R&D expenditures of mean company is 1,7% of its assets, that is not high level of R&D intensity. Table 3 shows the correlation between variables. Correlation coefficients are not very high, but signs of them are quite expected. Default probability and R&D intensity are negatively correlated, it means that R&D intensive firms in our sample are less risky that those spending on R&D less amount of money. Default probability has positive correlation with market to book ratio, leverage, coverage ratio, age and loss. At the same time, there is negative correlation with profit margin, size, return on assets and cash ratio. It is important to note that correlation reflects one-dimensional relationship between variables and do not take into account the effects of presence of other variables. That is why it would be more appropriate to rely on the results of the regression analysis rather than the correlation one.

Table 1 Determinants of default probability. Variable

Calculation

R&D Default (lagged) Market/Book

R&D Expenditures/Total Assets Default probability in previous year [Market Capitalization þ (Total Assets e Total Equity)]/Total Assets Total debt/Total Assets Net Income/Revenue ln (Total Assets) ln (T e Year of Incorporation) Net Income/Total Assets Dummy: 1 e net loss; 0 e net income Cash and Cash Equivalents/Total Assets

Leverage Profit Margin Size lnAge ROA Loss Cash

9

Table 2 Firm-year characteristics for full sample. Variable

Mean

Basic firm-year characteristics Default 2.056 R&D 0.017 Market/Book 1.426 Leverage 0.203 Profit Margin 0.122 Size 5.335 lnAge 3.321 ROA 0.007 Loss 0.270 Cash 0.153 Additional characteristics HI 0.053 ASCL-index 1.872 Unconstrained 0.362 Underinvestment 0.175 Overinvestment 0.217 High-tech 0.313

Std. Dev.

1%

Median

99%

5.012 0.039 2.067 0.241 4.051 1.630 0.583 0.297 0.444 0.139

0.000 0.000 0.396 0.000 5.281 2.283 1.609 0.559 0.000 0.003

0.610 0.001 1.040 0.173 0.035 5.079 3.376 0.027 0.000 0.114

19.075 0.183 6.751 0.672 1.681 10.036 4.477 0.226 1.000 0.678

0.025 0.979 0.480 0.366 0.499 0.464

0.020 0.000 0.000 0.000 0.000 0.000

0.048 2.000 0.000 0.000 1.000 0.000

0.263 4.000 1.000 1.000 1.000 1.000

Source: S&P Capital IQ. Authors' computations.

5. Results of research We separately investigate the results of testing different hypotheses. Firstly, we estimate the basic regression for the full sample, and then we consider additional information in order to test other hypotheses. Regressions were estimated using pooled OLS model as well as fixed-effect and random-effect models. Then we compared model with the use of Wald test, Breusch-Pagan test and Hausman test. It was found that pooled model describes the data better than other models. We also found heteroscedasticity problem that was solved with the use of White robust standard errors. There is no multicollinearity problem in our model. 5.1. Full sample results First, we present the estimation of U-shaped relationship between R&D investment and default probability for the full sample. Results are presented in the column (1) of Table 4. Hypothesis 1 about U-shaped relationship is not rejected. As coefficient for R&D is negative and significant, and coefficient for R&D squared is positive and significant, we are able to conclude that firstly, with an increase of R&D intensity default probability decreases, then after a point of minimal default probability, it starts to increase with an increase of R&D intensity. This result resolves the contradiction in the existing evidence on the relationship between corporate R&D investment and default probability. The effect of R&D investment on default probability is the subject to diminishing returns. For the first time, the probability of default decreases at diminishing rates indicating that advantages of investment overweight risks, and it eventually rises up as R&D intensity exceeds an optimal level, risk of default becomes higher than potential benefits.

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10 Table 3 Correlation matrix.

Default R&D Market/Book Leverage PR. Margin Size lnAge ROA Loss Cash

Default

R&D

Market/Book

Leverage

Profit margin

Size

lnAge

ROA

Loss

Cash

1.0000 0.0838 0.0008 0.1117 0.0079 0.0047 0.0486 0.0875 0.1706 0.0942

1.0000 0.2078 0.0354 0.0078 0.2261 0.2152 0.0518 0.0605 0.2651

1.0000 0.0844 0.0023 0.2011 0.1568 0.1331 0.0737 0.2435

1.0000 0.0053 0.1429 0.0735 0.1416 0.1420 0.2343

1.0000 0.0136 0.0065 0.0288 0.0277 0.0089

1.0000 0.2600 0.1158 0.2321 0.2610

1.0000 0.0471 0.1112 0.2202

1.0000 0.3025 0.0006

1.0000 0.0214

1.0000

Source: S&P Capital IQ. Authors' computations.

Estimated coefficients for the control variables are largely consistent with those reflected in literature. We conclude that default probability is persistent over time and other things being equal it increases every year by 0.58%. Leverage and loss have significant positive impact on default probability and it fits theory of finance. Firms with large debt are closer to bankruptcy and rise of leverage by one standard deviation will increase default probability by 2%. Firms reported net loss increase default probability by 0.91%. Market to book ratio, age, return on assets and cash ratio negatively related to default probability. High market to book ratio indicates high growth opportunities, and increase of this ratio by one standard deviation indicates decrease of default probability by 0.13%. Old firms are more stable and that is why they have lower default probability. Increase of ROA and cash ratio by one standard deviation leads to decrease of default probability by 0.73% and 1.31% respectively. Industry fixed effects are also found significant. Turning point for R&D for the full sample is equal to 0.317. We cannot say that this level of R&D intensity is optimal in terms of minimal default probability. However, we are able to observe an interval that is related to lower default probability. R&D intensity from 0.285 to 0.349 assumes lower default likelihood. 5.2. Results for financially constrained and unconstrained firms

and prudent strategy of investment in R&D, they choose safe project and well-thought sources of financing, that is why risk decreases. For financially unconstrained firms, investment in R&D do not have significant effect on default probability. In case of problems, such companies can easily attract additional financing at good conditions. For unconstrained firms, the uncertainty of random outcomes of R&D investment does not evoke existential risk of default. 5.3. Results for underinvesting and overinvesting firms We construct two sub-samples to differentiate the relationship between R&D investment and default probability for underinvesting and overinvesting firms. Results are provided in columns (5) and (6) of Table 4. Hypothesis 3 is not rejected. Rise of R&D investment by one standard deviation decreases default probability of firms that underinvest by 8.19% and increases default probability of firms that overinvest by 4.48%. These results mean that underinvesting firms are on the left side of U-shaped relationship, and overinvesting firms are on the right side. These results are related to the first hypothesis and help to identify the trajectory for the minimal default probability. Limiting both underinvestment and overinvestment may reduce firm's deviation from its optimal performance trajectory and minimize the probability of default. 5.4. Results for high-tech firms

We construct dummy variable that indicate companies with and without financial constraints. We estimate the regression equation (8) firstly, that consider R&D investment of financially unconstrained firms within the model. And secondly, we separately estimate regression for financially constrained and unconstrained sub-samples. Results are provided in columns (2)e(4) of Table 4. We reject both hypotheses 2a and 2b, but results nevertheless are interesting. We have found that investment in R&D made by financially constrained firms decreases their default probability, and effect is higher than for the full sample. Increase of R&D investment by one standard deviation decreases default probability of financially constrained firm by 4.35%. It means that financially constrained firms adhere to a cautious

We separately estimate the regression equation (11) for sub-sample of high technological firms and results of this model are the best in comparison with all previous models. Results are provided in the column (7) of Table 4. Hypothesis 4 is rejected, R&D investment decreases default probability of high-tech firms. Increase of R&D investment by one standard deviation leads to 2% lower default probability. Firms from high technological sector that spend more on R&D investment than their peers have more predictable future cash flows. Moreover, such companies position themselves better and they have higher probability to win the technology race. That is why they have lower probability of default during the next year.

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(1) Non-linear

(2) Linear

(3) Con

(4) Uncon

(5) Under

(6) Over

(7) High

5.118*** (1.046) 8.068*** (2.873)

3.511*** (1.015)

4.353*** (1.017)

1.715 (1.940)

8.185* (7.521)

4.480*** (1.059)

2.091*** (0.489)

0.905 (0.954) 0.579*** (0.057) 0.117*** (0.031) 2.077*** (0.434) 0.000 (0.001) 0.027 (0.023) 0.137** (0.060) 0.760*** (0.248) 0.911*** (0.113) 1.321*** (0.311)

0.385*** (0.010) 0.127*** (0.020) 2.376*** (0.229) 0.002 (0.002) 0.034 (0.031) 0.078 (0.078) 1.122*** (0.195) 1.022*** (0.097) 1.563*** (0.347)

0.763*** (0.010) 0.127*** (0.058) 2.653*** (0.399) 0.001 (0.002) 0.038 (0.037) 0.281** (0.129) 1.065*** (0.718) 1.096*** (0.177) 1.180*** (0.438)

0.830*** (0.012) 0.075* (0.038) 0.718 (0.481) 0.000 (0.002) 0.006 (0.058) 0.080 (0.151) 0.562** (0.248) 0.526*** (0.248) 1.296** (0.645)

0.392*** (0.018) 0.125*** (0.029) 2.213*** (0.272) 0.003 (0.003) 0.082** (0.035) 0.013 (0.093) 1.237*** (0.379) 1.335*** (0.138) 1.671*** (0.416)

0.255*** (0.015) 0.140*** (0.017) 1.286*** (0.134) 0.002*** (0.000) 0.063*** (0.019) 0.264*** (0.056) 0.711** (0.117) 0.566*** (0.062) 0.630** (0.193) 1.234** (0.491) 0.028*** (0.007)

Yes 2.411*** (0.325) 0.4615 14 644

Yes 2.349*** (0.333) 0.3550 14644

Yes 2.825*** (0.326) 0.3277 9677

Yes 2.203*** (0.524) 0.5892 4967

0.312 (0.628) 0.6810 2574

2.158*** (0.366) 0.3600 3190

2.169*** (0.212) 0.3073 4799

In column (1) regression equation (7) is estimated for the full sample. In column (2) regression equation (8) is estimated for the full sample. In columns (3) and (4) regression equations for financially constrained and unconstrained firms is estimated separately. In columns (5) and (6) regression equation (10) is estimated, for firms that underinvest and overinvest. In column (7) regression equation (11) is estimated, for firms from high-tech industries. For estimation, pooled OLS model is used. Default probability is the dependent variable. List of regressors is defined in the Table 1. Robust standard deviations are in parentheses. *, ** and *** denote significance levels at 10%, 5% and 1% respectively. Source: Authors' computations.

MODEL

0.579*** (0.057) 0.127*** (0.029) 2.007*** (0.465) 0.001 (0.001) 0.028 (0.023) 0.142** (0.059) 0.725*** (0.247) 0.910*** (0.114) 1.312*** (0.326)

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R&D R&D squared R&D x Unconstrained Default (lagged) Market/Book Leverage Profit Margin Size lnAge ROA Loss Cash Capex Current ratio Industry fixed effect Constant R2 Observations

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Table 4 Results.

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All the explanatory variables are found significant, signs are the same as expected. Additional capital expenditures increase default probability of high technological firms. It was mentioned in the literature that investment in capital is risky for high-tech companies. Current ratio, contrariwise, have negative relationship with default probability. High technological firms need liquidity to face eventual problems and decrease the probability of default. 6. Conclusion and discussion Undoubtedly, innovation plays one of the crucial roles in the economic development of nations. Investments in innovation provide conditions for smooth economic growth and allow to gain technological advantage. At the same time, on a firm level, investment in innovation is important as well. High R&D intensity increases the chances of a company to win in the technological race. However, from the scientific perspective, there is no clear agreement about the relationship between R&D investment and performance of the firm, and particularly whether investment in R&D increases or decreases the probability of default during the next 12 months. Asia became a new engine of innovation in the 21st century. So-called Asian Tigers, with Hong Kong, Singapore, South Korea and Taiwan, are some of the leaders in innovation intensity in the region. They increase the level of R&D spending year by year. These countries are not well studied, but their experience may be relevant not only for future development for this group of countries, but also for their neighbors. We have tested the relationship between R&D investment and default risk using the data of Asian Tiger countries for the period from 2012 to 2017. A build model considers the nonlinear relationship between corporate R&D investment and default probability. The results of the research contribute to the existing literature in several ways. The U-shaped relationship between R&D investment and default probability have been found. Initially, an increase in R&D investment decreases the default probability of the firm, but after reaching the minimal default probability point, it begins to increase following an increase of R&D investment. Furthermore, it was found that an increase in R&D intensity is beneficial for financially constrained firms, in terms of decreasing default probability. Firms that face difficulties of external financing, invest in less risky R&D projects, because in difficult situations they cannot easily attract additional financing. They increase R&D intensity by investing in wellthought-out projects where the advantages overweight the risks by the benefits, and default probability decreases. For financially unconstrained firms, investment in R&D was found irrelevant for determination of default probability, the reason is that default risk is mitigated by high opportunities of refinancing debt. Moreover, additional investment in R&D increases default probability of firms that tend to overinvest with relation to the expected and optimal level of investment, and decreases

default probability of firms that conversely tend to underinvest. Correlating this result with the found U-shaped relationship, we can estimate the trajectory to minimal level of default probability for underinvesting and overinvesting firms. In addition to R&D investment there are several financial characteristics of firms that determine default probability. Market to book ratio, size, age, return on assets and cash ratio are negatively related with default probability, means that increase of these variables leads to lower default probability. High leverage and existence of financial loss on the contrary, increase default probability. Default probability is also persistent over time and tends to increase year by year ceteris paribus. We also separately estimate the relationship for high technological companies and found that additional investment in R&D decreases default probability of them. High-tech firms spending on R&D more than their peers have more predictable future cash flows and position themselves better. Investment policy of high-tech firms should differ from optimal practices of firms from other industries. Despite the fact that results of the study are significant and robust, there are several limitations of our research. Firstly, it should be noted that we have used simple methods of dividing firms into the financially constrained and unconstrained, and for defining underinvesting and overinvesting firms. It was decided not to use complicated methodologies as our aim was restricted by finding the existence of different relationships for firms with special characteristics. Coefficients may be estimated more precisely in future studies, with use of more sophisticated methods. Secondly, a problem with specification of the model may also exist. Although we include in the model different financial characteristics, influencing default probability in theory, there may be some country or industry features that we did not take into account and that may be considered later. Our research could also be expanded in future by considering not only inputs, but also outputs of innovation in determining default probability. It was found that companies with previously successful R&D investments may have different risks, and that is why the relationship between R&D investment and default probability will not be the same. It is necessary to include the patents in the model, but we do not have information about patents today. Conflict of interest The authors declare no conflict of interest. References Berger, A. N., Rosen, R. J., & Udell, G. F. (2007). Does market size structure affect competition? The case of small business lending. Journal of Banking and Finance, 31(1), 11e33. https://doi.org/10.1016/j.jbankfin.2005.10. 010. Bharath, S. T., & Shumway, T. (2008). Forecasting default with the Merton distance to default model. Review of Financial Studies, 21, 1339e1369. https://doi.org/10.1093/rfs/hhn044. Biddle, G. C., Hilary, G., & Verdi, R. S. (2009). How does financial reporting quality relate to investment efficiency? Journal of Accounting and Economics, 48, 112e131. https://doi.org/10.1016/j.jacceco.2009.09.001.

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Please cite this article as: Cherkasova, V., & Kurlyanova, A., Does corporate R&D investment support to decrease of default probability of Asia firms?, Borsa _ Istanbul Review, https://doi.org/10.1016/j.bir.2019.07.009