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THE EFFECTS OF EQUITY-FINANCED LONG-TERM ASSETS ON LIQUIDITY IN THE AGRICULTURE SECTOR OF TURKEY

Year 2019, Volume: 7 Issue: 1, 74 - 94, 15.07.2019
https://doi.org/10.18825/iremjournal.536367

Abstract

This study aims to conduct a typical regression
methodology on the long-term data of the agriculture sector in Turkey. The regressive
model represents current ratio as the dependent variable, and it uses the
ratios of short-term liabilities on total liabilities, bank credits payable in
the short-term on short-term liabilities, bank credits payable in the long-term
on total assets, and long-term assets on (shareholders’) equities as the
independent variables. The tests are executed by using the averages of
aggregate totals of the businesses from all scales in the sector in three years’
averages from 1998 until 2016. The findings statistically ensure and depict
that the framework indicator of liquidity or the famous current ratio depends
not only on the bank credit used or the level of short-term liabilities, which
is not surprising, but also on the ratio of long-term assets on equities. If
the businesses enrich their equities level in financing of long-term assets,
the liquidity favors. The independent variable of long-term assets to equities
ratio, which rather reflects the long-term movement of current ratio better
than the other variables, deeply affects the level of better liquidity as
significantly as other control variables of the study. As a conclusion, better
liquidity could profoundly be a lagging result of better equity-type financing
of the total assets. The outcomes of the study will expectedly signal the
decisions and policies of agriculture sector in Turkey by the long-term
evidence presented here.

References

  • Acikgoz, A.F., Apak, S., & Demirkol, C. 2018a. Non-cash components of net working capital: A long-term outlook of the agriculture sector in Turkey. International Balkan and Near Eastern Social Sciences (IBANESS) Conference Series, March 24-25, 2018, Tekirdag, Turkey, Proceedings Book Volume I: 64-72.
  • Acikgoz, A.F., Demirkol, C., & Apak, S. 2018b. Net working capital versus marketing in agriculture sector of Turkey. Editor: Anna Haritonova. LAP Lambert Academic Publishing, International Book Market Service Ltd., OmniScriptum Publishing Group, Beau Bassin, Mauritius.
  • Acikgoz, A.F., Demirkol, C., & Apak, S. 2018c. The effects of liquidity on inventory: Evidence form forestry products subsector in Turkey, Eurasian Journal of Forest Science, 6: 98-110.
  • Ak, B.K., Dechow, P.M., Sun, Y., & Wang, A.Y. 2013. The use of financial ratio models to help investors predict and interpret significant corporate events. Australian Journal of Management, 38(3): 553–598.
  • Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. In: Petrov, B.N. and Csaki, F., Eds., Second International Symposium on Information Theory, Budapest. 267-281.
  • Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control. AC-19: 716–723.
  • Akaike, H. 1979. A bayesian extension of the minimum AIC procedure of autoregressive model fitting. Biometrika, 66 (2): 237-242.
  • Altman, E.I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4): 589-609.
  • Altman, E.I., & Narayan, P. 1997. An international survey of business failure classification models. Financial Markets, Institutions and Instruments, 6(2): 1-57.
  • Andrews, D.W.K. 1991. Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica, 59(3): 817-858.
  • Apak, S., Acikgoz, A.F., Erbay, E.R., & Tuncer, G. 2016. Cash vs. net working capital as strategic tools for the long-term relation between bank credits and liquidity: inequalities in Turkey. Procedia-Social and Behavioral Sciences, 235: 648-655.
  • Beaver, W.H. 1966. Financial ratios as predictors of failure. Journal of Accounting Research, 4 (Empirical Research in Accounting: Selected Studies) 71-111.
  • Breusch, T.S. 1978. Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17: 335-355.
  • Breusch, T.S., & Pagan, A.R. 1979. Simple test for heteroscedasticity and random coefficient variation. Econometrica (The Econometric Society), 47 (5): 1287-1294.
  • Brown, R.L., Durbin, J., & Evans, J.M. 1975. Techniques for Testing the Constancy of Regression Relationships over Time. Journal of the Royal Statistical Society, 37(2), 149-192.
  • Burkart, M., & Ellingsen, T. 2004. In-kind finance: a theory of trade credit, The American Economic Review, 94(3): 569-590.
  • CBRT (Central Bank of the Republic of Turkey, 2019): CBRT Real Sector Statistics 1999-2017, Real Sector Data and Archives for 1996–2016, the last sequence has been retrieved from http://www.tcmb.gov.tr on 10th of February 2019.
  • Chong, B., & Yi, H. 2011. Bank loans, trade credits, and borrower characteristics: Theory and empirical analysis. Asia-Pacific Journal of Financial Studies, 40: 37-68.
  • Coyle, B. 2000a. Corporate Credit Analysis. Glenlake Publishing Company Ltd, Chicago, London, New Delhi, AMACOM, American Management Association (AMA) Publications, The Chartered Institute of Bankers, New York.
  • Coyle, B. 2000b. Cash Flow Forecasting and Liquidity. Glenlake Publishing Company Ltd, Chicago, London, New Delhi, AMACOM, American Management Association (AMA), The Chartered Institute of Bankers, New York.
  • Demiroglu, C., & James, C.M. 2010. The information content of bank loan covenants. The Review of Financial Studies, 23(10): 3700-3737.
  • Dichev, I.D., & Skinner, D.J. 2002. Large-Sample Evidence on the Debt Covenant Hypothesis. Journal of Accounting Research, 40: 1091-1123.
  • Dickey, D.A., & Fuller, W.A. 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74: 427-431.
  • Durbin, J., & Watson, G.S. 1950. Testing for serial correlation in least squares regression. Biometrika, 37: 409-428.
  • Durbin, J. 1970. Testing for serial correlation in least squares regression when some of the regressors are lagged dependent variables. Econometrica, 38: 4410-4421.
  • Durbin, J., & Watson, G.S. 1971. Testing for serial correlation in least squares regression III. Biometrika, 58: 1-19.
  • Engle, R.F., & Granger, C.W.J. 1987. Co-integration and error correction: representation, estimation, and testing. Econometrica, 55 (2): 251-276.
  • Fisher, R.A. 1925. Statistical Methods for Research Workers, Oliver & Boyd., Edinburgh.
  • Fisher, R.A. 1932. Statistical Methods for Research Workers, 4th Edition, Edinburgh: Oliver & Boyd.
  • Godfrey, L. 1978a. Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46: 1293-1302.
  • Godfrey, L. G. 1978b. Testing for multiplicative heteroscedasticity. Journal of Econometrics, 8 227-236.
  • Granger, C.W.J. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37 (3): 424-438.
  • Granger, C.W.J., & Newbold, P. 1974. Spurious regressions in econometrics. Journal of Econometrics, 2: 111-120.
  • Im, K.S., Pesaran, M.H., & Shin, Y. 2003. Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115: 53-74.
  • Jarque, C. M., & Bera, A.K. 1980. Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6: 255-259.
  • Jarque, C. M., & Bera, A.K. 1987. A test for normality of observations and regression residuals. International Statistical Review, 55: 163-172.
  • Johansen, S. 1988. Statistical analysis of cointegration vectors. Journal of Econ. Dynamics and Control, 12: 231-254.
  • Johansen, S. 1995. Likelihood-based influence in cointegrated vector autoregressive models. Oxford University Press Oxford.
  • Johnson, W.B. 1979. The Cross-Sectional Stability of Financial Ratio Patterns. Journal of Financial and Quantitative Analysis, 14(5): 1035-1048.
  • Keefe, M. O., & Yaghoubi, M. 2016. The influence of cash flow volatility on capital structure and the use of debt of different maturities. Journal of Corporate Finance, 38: 18-36.
  • Kutner, M. H., Nachtsheim, C. J., & Neter, J., Li, W. 2005. Applied Linear Statistical Models. 5th Edition, McGraw-Hill-Irwin, New York.
  • Lev, B. 1969. Industry Averages as Targets for Financial Ratios. Journal of Accounting Research, 7(2): 290-299.
  • Levin, A., Lin, C.F., & Chu, C. 2002. Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108: 1-24.
  • Lutkepohl, H. 1991. Introduction to multiple time series analysis. Springer New York, USA.
  • Lutkepohl, H. 2004. Vector autoregressive and vector error correction models, in H. Lutkepohl and M. Kratzig (eds), Applied Time Series Econometrics, (86–158) Cambridge University Press, Cambridge, UK.
  • MacKinnon, J.G. 1996. Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11(6): 601-618.
  • MacKinnon, J. G., Haug, A., & Michelis, L. 1999. Numerical distribution functions of likelihood ratio tests for cointegration. Journal of Applied Econometrics, 14 (5): 563-577.
  • Michalski, G. 2008. Operational risk in current assets investment decisions: Portfolio management approach in accounts receivable. Agric. Econ. – Czech, 54(1): 12-19.
  • Newey, W.K., & West, K.D. 1987. A Simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3): 703-708.
  • Newey, W.K., & West, K.D. 1994. Automatic lag selection in covariance matrix estimation. Review of Economic Studies, 61: 631-653.
  • Pearson, K. 1920. Notes on the history of correlation. Biometrika, 13: 25-45.
  • Pesaran, M.H., & Shin, Y. 1998. An autoregressive distributed-lag modelling approach to cointegration analysis. Econometric Society Monographs, 31: 371-413.
  • Pesaran M.H., Shin Y., & Smith R.J. 2000. Structural analysis of vector error correction models with exogenous I(1) variables. Journal of Econometrics, 97: 293-343.
  • Phillips, P.C.B., & Perron, P. 1988. Testing for a unit root in time series regression, Biometrika, 75: 335-346.
  • Philips, T.K. 1999. Why do valuation ratios forecast long-run equity returns, Will the high returns of the last two decades persist? Journal of Portfolio Management, 25(3): 39-44.
  • Ponikvar, N., Tajnikar, M., & Pusnik, K. 2009. Performance ratios for managerial decision-making in a growing firm. Journal of Business Economics and Management, 10(2): 109-120.
  • Psillaki, M., & Eleftheriou, K. 2015. Trade credit, bank credit, and flight to quality: evidence from French SMEs. Journal of Small Business Management, 53(4): 1219-1240.
  • Schwarz, G. 1978. Estimating the dimension of a model, Annals of Statistics, 6: 461-464.
  • Sims C.A. 1980. Macroeconomics and reality. Econometrica, 48(1): 1-48.
  • Sun, L., Ford, J. L., & Dickinson, D. G. 2010. Bank loans and the effects of monetary policy in China: VAR/VECM approach. China Economic Review, 21: 65-97.
  • Wilcox, J. 1971. A Simple Theory of Financial Ratios as Predictors of Failure. Journal of Accounting Research, 9(2): 389-395.

THE EFFECTS OF EQUITY-FINANCED LONG-TERM ASSETS ON LIQUIDITY IN THE AGRICULTURE SECTOR OF TURKEY

Year 2019, Volume: 7 Issue: 1, 74 - 94, 15.07.2019
https://doi.org/10.18825/iremjournal.536367

Abstract

This study aims to conduct a typical regression
methodology on the long-term data of the agriculture sector in Turkey. The regressive
model represents current ratio as the dependent variable, and it uses the
ratios of short-term liabilities on total liabilities, bank credits payable in
the short-term on short-term liabilities, bank credits payable in the long-term
on total assets, and long-term assets on (shareholders’) equities as the
independent variables. The tests are executed by using the averages of
aggregate totals of the businesses from all scales in the sector in three years’
averages from 1998 until 2016. The findings statistically ensure and depict
that the framework indicator of liquidity or the famous current ratio depends
not only on the bank credit used or the level of short-term liabilities, which
is not surprising, but also on the ratio of long-term assets on equities. If
the businesses enrich their equities level in financing of long-term assets,
the liquidity favors. The independent variable of long-term assets to equities
ratio, which rather reflects the long-term movement of current ratio better
than the other variables, deeply affects the level of better liquidity as
significantly as other control variables of the study. As a conclusion, better
liquidity could profoundly be a lagging result of better equity-type financing
of the total assets. The outcomes of the study will expectedly signal the
decisions and policies of agriculture sector in Turkey by the long-term
evidence presented here.

References

  • Acikgoz, A.F., Apak, S., & Demirkol, C. 2018a. Non-cash components of net working capital: A long-term outlook of the agriculture sector in Turkey. International Balkan and Near Eastern Social Sciences (IBANESS) Conference Series, March 24-25, 2018, Tekirdag, Turkey, Proceedings Book Volume I: 64-72.
  • Acikgoz, A.F., Demirkol, C., & Apak, S. 2018b. Net working capital versus marketing in agriculture sector of Turkey. Editor: Anna Haritonova. LAP Lambert Academic Publishing, International Book Market Service Ltd., OmniScriptum Publishing Group, Beau Bassin, Mauritius.
  • Acikgoz, A.F., Demirkol, C., & Apak, S. 2018c. The effects of liquidity on inventory: Evidence form forestry products subsector in Turkey, Eurasian Journal of Forest Science, 6: 98-110.
  • Ak, B.K., Dechow, P.M., Sun, Y., & Wang, A.Y. 2013. The use of financial ratio models to help investors predict and interpret significant corporate events. Australian Journal of Management, 38(3): 553–598.
  • Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. In: Petrov, B.N. and Csaki, F., Eds., Second International Symposium on Information Theory, Budapest. 267-281.
  • Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control. AC-19: 716–723.
  • Akaike, H. 1979. A bayesian extension of the minimum AIC procedure of autoregressive model fitting. Biometrika, 66 (2): 237-242.
  • Altman, E.I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4): 589-609.
  • Altman, E.I., & Narayan, P. 1997. An international survey of business failure classification models. Financial Markets, Institutions and Instruments, 6(2): 1-57.
  • Andrews, D.W.K. 1991. Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica, 59(3): 817-858.
  • Apak, S., Acikgoz, A.F., Erbay, E.R., & Tuncer, G. 2016. Cash vs. net working capital as strategic tools for the long-term relation between bank credits and liquidity: inequalities in Turkey. Procedia-Social and Behavioral Sciences, 235: 648-655.
  • Beaver, W.H. 1966. Financial ratios as predictors of failure. Journal of Accounting Research, 4 (Empirical Research in Accounting: Selected Studies) 71-111.
  • Breusch, T.S. 1978. Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17: 335-355.
  • Breusch, T.S., & Pagan, A.R. 1979. Simple test for heteroscedasticity and random coefficient variation. Econometrica (The Econometric Society), 47 (5): 1287-1294.
  • Brown, R.L., Durbin, J., & Evans, J.M. 1975. Techniques for Testing the Constancy of Regression Relationships over Time. Journal of the Royal Statistical Society, 37(2), 149-192.
  • Burkart, M., & Ellingsen, T. 2004. In-kind finance: a theory of trade credit, The American Economic Review, 94(3): 569-590.
  • CBRT (Central Bank of the Republic of Turkey, 2019): CBRT Real Sector Statistics 1999-2017, Real Sector Data and Archives for 1996–2016, the last sequence has been retrieved from http://www.tcmb.gov.tr on 10th of February 2019.
  • Chong, B., & Yi, H. 2011. Bank loans, trade credits, and borrower characteristics: Theory and empirical analysis. Asia-Pacific Journal of Financial Studies, 40: 37-68.
  • Coyle, B. 2000a. Corporate Credit Analysis. Glenlake Publishing Company Ltd, Chicago, London, New Delhi, AMACOM, American Management Association (AMA) Publications, The Chartered Institute of Bankers, New York.
  • Coyle, B. 2000b. Cash Flow Forecasting and Liquidity. Glenlake Publishing Company Ltd, Chicago, London, New Delhi, AMACOM, American Management Association (AMA), The Chartered Institute of Bankers, New York.
  • Demiroglu, C., & James, C.M. 2010. The information content of bank loan covenants. The Review of Financial Studies, 23(10): 3700-3737.
  • Dichev, I.D., & Skinner, D.J. 2002. Large-Sample Evidence on the Debt Covenant Hypothesis. Journal of Accounting Research, 40: 1091-1123.
  • Dickey, D.A., & Fuller, W.A. 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74: 427-431.
  • Durbin, J., & Watson, G.S. 1950. Testing for serial correlation in least squares regression. Biometrika, 37: 409-428.
  • Durbin, J. 1970. Testing for serial correlation in least squares regression when some of the regressors are lagged dependent variables. Econometrica, 38: 4410-4421.
  • Durbin, J., & Watson, G.S. 1971. Testing for serial correlation in least squares regression III. Biometrika, 58: 1-19.
  • Engle, R.F., & Granger, C.W.J. 1987. Co-integration and error correction: representation, estimation, and testing. Econometrica, 55 (2): 251-276.
  • Fisher, R.A. 1925. Statistical Methods for Research Workers, Oliver & Boyd., Edinburgh.
  • Fisher, R.A. 1932. Statistical Methods for Research Workers, 4th Edition, Edinburgh: Oliver & Boyd.
  • Godfrey, L. 1978a. Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46: 1293-1302.
  • Godfrey, L. G. 1978b. Testing for multiplicative heteroscedasticity. Journal of Econometrics, 8 227-236.
  • Granger, C.W.J. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37 (3): 424-438.
  • Granger, C.W.J., & Newbold, P. 1974. Spurious regressions in econometrics. Journal of Econometrics, 2: 111-120.
  • Im, K.S., Pesaran, M.H., & Shin, Y. 2003. Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115: 53-74.
  • Jarque, C. M., & Bera, A.K. 1980. Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6: 255-259.
  • Jarque, C. M., & Bera, A.K. 1987. A test for normality of observations and regression residuals. International Statistical Review, 55: 163-172.
  • Johansen, S. 1988. Statistical analysis of cointegration vectors. Journal of Econ. Dynamics and Control, 12: 231-254.
  • Johansen, S. 1995. Likelihood-based influence in cointegrated vector autoregressive models. Oxford University Press Oxford.
  • Johnson, W.B. 1979. The Cross-Sectional Stability of Financial Ratio Patterns. Journal of Financial and Quantitative Analysis, 14(5): 1035-1048.
  • Keefe, M. O., & Yaghoubi, M. 2016. The influence of cash flow volatility on capital structure and the use of debt of different maturities. Journal of Corporate Finance, 38: 18-36.
  • Kutner, M. H., Nachtsheim, C. J., & Neter, J., Li, W. 2005. Applied Linear Statistical Models. 5th Edition, McGraw-Hill-Irwin, New York.
  • Lev, B. 1969. Industry Averages as Targets for Financial Ratios. Journal of Accounting Research, 7(2): 290-299.
  • Levin, A., Lin, C.F., & Chu, C. 2002. Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108: 1-24.
  • Lutkepohl, H. 1991. Introduction to multiple time series analysis. Springer New York, USA.
  • Lutkepohl, H. 2004. Vector autoregressive and vector error correction models, in H. Lutkepohl and M. Kratzig (eds), Applied Time Series Econometrics, (86–158) Cambridge University Press, Cambridge, UK.
  • MacKinnon, J.G. 1996. Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11(6): 601-618.
  • MacKinnon, J. G., Haug, A., & Michelis, L. 1999. Numerical distribution functions of likelihood ratio tests for cointegration. Journal of Applied Econometrics, 14 (5): 563-577.
  • Michalski, G. 2008. Operational risk in current assets investment decisions: Portfolio management approach in accounts receivable. Agric. Econ. – Czech, 54(1): 12-19.
  • Newey, W.K., & West, K.D. 1987. A Simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3): 703-708.
  • Newey, W.K., & West, K.D. 1994. Automatic lag selection in covariance matrix estimation. Review of Economic Studies, 61: 631-653.
  • Pearson, K. 1920. Notes on the history of correlation. Biometrika, 13: 25-45.
  • Pesaran, M.H., & Shin, Y. 1998. An autoregressive distributed-lag modelling approach to cointegration analysis. Econometric Society Monographs, 31: 371-413.
  • Pesaran M.H., Shin Y., & Smith R.J. 2000. Structural analysis of vector error correction models with exogenous I(1) variables. Journal of Econometrics, 97: 293-343.
  • Phillips, P.C.B., & Perron, P. 1988. Testing for a unit root in time series regression, Biometrika, 75: 335-346.
  • Philips, T.K. 1999. Why do valuation ratios forecast long-run equity returns, Will the high returns of the last two decades persist? Journal of Portfolio Management, 25(3): 39-44.
  • Ponikvar, N., Tajnikar, M., & Pusnik, K. 2009. Performance ratios for managerial decision-making in a growing firm. Journal of Business Economics and Management, 10(2): 109-120.
  • Psillaki, M., & Eleftheriou, K. 2015. Trade credit, bank credit, and flight to quality: evidence from French SMEs. Journal of Small Business Management, 53(4): 1219-1240.
  • Schwarz, G. 1978. Estimating the dimension of a model, Annals of Statistics, 6: 461-464.
  • Sims C.A. 1980. Macroeconomics and reality. Econometrica, 48(1): 1-48.
  • Sun, L., Ford, J. L., & Dickinson, D. G. 2010. Bank loans and the effects of monetary policy in China: VAR/VECM approach. China Economic Review, 21: 65-97.
  • Wilcox, J. 1971. A Simple Theory of Financial Ratios as Predictors of Failure. Journal of Accounting Research, 9(2): 389-395.
There are 61 citations in total.

Details

Primary Language English
Journal Section ARTICLES
Authors

Ali Faruk Açıkgöz 0000-0002-6426-983X

Celal Demirkol This is me 0000-0002-8598-3557

Umut Taç 0000-0002-8452-2708

Publication Date July 15, 2019
Submission Date March 6, 2019
Acceptance Date July 13, 2019
Published in Issue Year 2019 Volume: 7 Issue: 1

Cite

APA Açıkgöz, A. F., Demirkol, C., & Taç, U. (2019). THE EFFECTS OF EQUITY-FINANCED LONG-TERM ASSETS ON LIQUIDITY IN THE AGRICULTURE SECTOR OF TURKEY. International Review of Economics and Management, 7(1), 74-94. https://doi.org/10.18825/iremjournal.536367