6. Conclusion

comparing it with two values which indicate no autocorrelation. Since test statistics are smaller than 2, it can be said that it is autocorrelation. Pesaran test was performed to examine the crosssectional dependence in the model. The null hypothesis of no cross-sectional dependent is rejected at 1% significance level. For this reason, resistance fixed effect panel data model results were obtained by using in [37] estimator, which provided consistent estimates in the case of

Coef. Std. error t stat. Prob. [95% conf. interval]

PDP 0.0004057 0.0001086 3.73\* 0.000 (0.0006193, 0.0001922) ICP 0.000696 0.0002185 3.19\* 0.002 (0.0002665, 0.0011255) CCC 0.0004332 0.0001124 3.85\* 0.000 (0.0006542, 0.0002121) SG 0.0847359 0.0126799 6.68\* 0.000 (0.0598066, 0.1096653) FDSD 0.0595096 0.0168587 3.53\* 0.000 (0.0926546, 0.0263646) FATA 0.2220324 0.0346947 6.40\* 0.000 (0.2902438, 0.1538209) Constant 0.2240622 0.0218885 10.24\* 0.000 (0.1810283, 0.2670962)

When the resistive fixed effects model presented in Table 6 is examined, it is seen that the coefficients do not change, but t statistics and confidence intervals calculated by using Driscoll and Kraay standard errors change. These estimates give consistent results in the case of

PDP 0.0004057 0.0000774 5.24\* 0.000 (0.0005761, 0.0002354) ICP 0.000696 0.0002746 2.53\*\* 0.028 (0.0000916, 0.0013003) CCC 0.0004332 0.000068 6.37\* 0.000 (0.0005828, 0.0002835) SG 0.0847359 0.0173669 4.88\* 0.000 (0.0465118, 0.1229601) FDSD 0.0595096 0.0172452 3.45\* 0.005 (0.0974661, 0.0215532) FATA 0.2220324 0.0302661 7.34\* 0.000 (0.2886475, 0.155417) Constant 0.2240622 0.0127534 17.57\* 0.000 (0.1959921, 0.2521324)

Coef. Driscoll and Kraay Std. Error t stat. Prob. [95% conf. interval]

heteroskedasticity, autocorrelation, and cross-sectional dependent [35].

Modified Wald test for groupwise heteroskedasticity: 918.72 (prob. = 0.000).

Pesaran test of cross sectional independence = 6.814 (prob. = 0.000).

F test stat. = 16.92 (prob. = 0.000).

\*indicates significance at the level 1%.

F test stat. = 329.63 (prob. = 0.000).

Table 6. Resistance fixed effect panel data model.

Significance at the level 1%. \*\*Significance at the level 5%.

\*

Baltagi-Wu LBI = 1.7238703.

Modified Bhargava et al. Durbin-Watson = 1.3899562.

214 Financial Management from an Emerging Market Perspective

Table 5. The fixed effects panel data model results.

heteroskedasticity, autocorrelation, and cross-sectional dependent.

In emerging countries like Turkey, the development of the industrial sector plays a key role in the development of the country's economy. Firms in this sector need to solve the financing problem, which is one of the most important problems to survive in markets based on competition. Industrial firms need to become greater in their profitability by effectively managing their working capital in order to reduce the need for external financing due to scarce resources. In this context, this study aims to reveal the tradeoff between WCC and firm's profitability by using the data of the firms listed on BIST Industry Index in Turkey.

In the study, panel regression analysis was used to investigate the tradeoff between WCC and the profitability of the 41 firms listed on BIST Industrial Index. Dependent variable is defined as ROA; independent variables are CCC, ICP, and PDP; and control variables are SG, FDSD, and FATA. For the model estimation in the study, it was determined that the model had a cross section effect by performing the LR test. The Hausman test defined that the fixed effects panel data model should be applied for analysis. In the fixed effect model, the coefficients and the model were determined to be statistically significant at the 1% significance level.

The results of the study show the existence of a meaningful relationship between firms' profitability and WCC. In the industrial firms in the study, the decrease in CCC contributed to the increase of ROA. While the other variables remain constant, the increase in ICP raises the firm's profitability. This situation may be expressed as the fact that the benefit provided by meeting the customers' demands on time by keeping stocks is more than the cost of holding stocks. Another consequence of the study is that industrial firms can become greater ROA by reducing the duration of PDP. It can be said that the discounts provided by the suppliers for timely payments may contribute to the firm's profitability. According to the results of the study, a negative relationship exists between FDSD and FATA variables and ROA, while a positive relationship exists between SG and ROA. While an increase in sales volume of the firms may positively affect ROA, the increase in short-term financial liabilities may raise the financial risk of the firms and decrease the firm's profitability.

Both the findings obtained in the study and the studies in the literature reveal that there is an impact of WCM on the industrial firm's profitability in emerging countries such as Turkey. In this context, decreasing the cash return period of the firms will reduce the funds used for the financing of the current assets and contribute to increase their asset profitability. In addition to this, the firms should benefit from discounting by reducing the payables deferral period, which will help increase the firm's profitability. Besides, industrial firms can contribute to raise the firm's profitability by increasing Inventory conversion period and sales.
