Model for Firm Performance

I examine whether the number of banking relationships has any impact on firm performance using the following model:

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The dependent variable, Firm Performance, refers to firm profitability measured with ROA or BEP. The variable of interest, Banking Relationship, is the number of banks that a firm has either cash and/or non-cash credit relationship with at the end of the year. It is measured with five banking relationship variables. NUMBERB indicates the total number of banking relationships and the others indicate the number of banking relationships depending on banks’ nationality, ownership structure and orientation.

I don’t have a priori expectation about the sign of the coefficient on all the banking relationship variables, except NUMBERF, because of the conflicting theoretical implications in the literature. Some studies indicates that there would be a positive relationship if multiple bank relationships reduce borrowing costs, incentives for strategic default, liquidity risks associated with the fragility of the banking system in a country or the probability of premature liquidation of a project. On the other hand, other studies suggest that there would also be a negative relationship between the number of banks and firm performance if multiple bank relationships increase monitoring, screening or renegotiation costs arising from information asymmetries between a bank and a firm or increase the risk of private information disclosure about a firm. Fok et al. (2004) show that there is a negative relationship between the number of domestic banks and firm performance but a positive relationship between the number of foreign banks and firm performance. Therefore, it is expected that firm performance increases with the number of foreign banking relationships, NUMBERF. However, this positive relationship may disappear during the crisis periods if foreign banks and firms are too sensitive to the economic and financial deteriorations during the crisis periods.

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As an additional control variable, only LIQUIDITY is included in the model in order to solve the identification problem in the 2SLS estimation model. To control for year and

industry effects, industry and calendar year dummy variables, respectively, are

included in the model. represents the disturbance term.

The age of a firm, AGE, is included in the model to capture the length of the firm achievement. Because of the nature of business life cycle, investment opportunities may be limited in latter stages and firm performance is expected to diminish as a firm gets older. I expect a negative relationship between firm age and firm performance.

SIZE and performance is expected to be negatively related if the size of a firm causes a decrease in firm performance because of diseconomies or an increase in agency problems. Conversely, this variable can also be positively related to firm performance if the size of a firm enhances it by raising the market power of a firm or making easy to find capital. Therefore, the sign of the coefficient of firm size is ambiguous.

The relationship between the innovativeness of a firm, INNOVA, and firm performance is also uncertain. If a firm invests in a project by increasing its R&D expenditures, its profitability can be low or even negative in early times of the investment. However, a firm may also enjoy high profit in the latter stages of investment, if the project brings higher returns. Therefore, the coefficient of this variable can be positive or negative depending on the stage in the investment project.

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The sign of the coefficient of LEVERAGE can be positive or negative because leverage may affect firm performance in different ways under the presence of market imperfections such as agency conflicts or informational asymmetries, etc. (Harris and Raviv, 1991). For example, using an agency conflict model, Chang (1992) predicts a negative relationship between leverage and firm performance, while several other studies argue a positive relationship between leverage and firm value (e.g., Hirshleifer and Thakor, 1992; Stulz, 1990).

In Turkey, almost all of the holding companies own a bank within their group. I expect that if a firm is a member of such an industrial group, then it may more easily obtain funding from that bank. This borrowing relation is called “related lending” in the literature. Therefore, I also add the dummy variable of BMEMBER into the performance model. However, the relationship between firm performance and related lending is ambiguous. On the one hand, according to “informational view” arguments, related banks can more easily access the true quality of the investment projects, and they may force firms to give up bad investments and invest only in good projects. Thus, they may improve firm performance (Rajan, 1992). On the other hand, according to “looting” (Akerlof and Romer, 1993) and “tunneling” (Johnson, La Porta, López-de-Silanes and Shleifer, 2000) arguments, a close relationship between a group bank and a firm may allow insiders to obtain resources from depositors even when a firm is in a bad financial condition. Therefore, the sign of the coefficient of BMEMBER in the firm performance equation is not known a priori.

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LIQUIDITY refers the liquidity ratio of a firm and is calculated as current assets divided by current liabilities. I include this variable in the model to solve the identification problem that occurs when firm performance and banking relationship equations are simultaneously estimated. Literature suggests that “rank condition” is the necessary and sufficient condition for identification in a two-equation simultaneous estimation model (Green, 2011). “Rank condition” states that the first model is identified if and only if the second model equation includes at least one exogenous variable with nonzero coefficient which is excluded from the first equation. To satisfy this “rank condition,” LIQUIDITY is added to the firm performance model and its coefficient is expected to be non-zero, since the relationship between firm liquidity and profitability is frequently emphasized in the literature. For example, Smith (1980) states that a large increase in profitability level would tend to reduce firm’s liquidity and similarly, a large increase in liquidity level would tend to negatively affect the profitability. Eljelly (2004), and Raheman and Nasr (2007) find a significant negative relationship for a sample of Saudi and Pakistani firms, respectively. On the other hand, Garcia-Teruel and Martinez-Solano (2007), and Gill, Biger and Mathur (2010) show that with a correct liquidity management strategy, managers can create profit for their firms in Spain and US, respectively. Thus, I do not have a priori expectation about the sign of the coefficient of LIQUIDITY.

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