ISSN: 2056-3736 (Online Version) | 2056-3728 (Print Version)
Michael Koczyrkewycz, Taha Chaiechi and Rabiul Beg
Correspondence: Michael Koczyrkewycz, michael.koczyrkewycz@my.jcu.edu.au
College of Business, Law, and Governance, James Cook University, Australia
pdf (593.41 Kb) | doi: https://doi.org/10.47260/bae/8211
Productivity growth is an essential ingredient for achieving long-term economic growth and sustainable development. In the absence of such growth, economic growth is not achievable. Accordingly, this paper examines economic resilience through multiple productivity channels within the United States, Japan and South Korea. Adopting a Kaleckian post-Keynesian approach, productivity growth is constructed as a function of investment, capacity utilisation, indicators of financial development, and an indicator of fiscal policy. Utilising annual historical data from 1980-2019, this paper adopts Autoregressive Distributed Lag (ARDL) models, Vector Autoregressive-based Impulse Response Functions (IRF) and Variance Decompositions (VD) to examine the resilience of productivity growth through the speeds of adjustment after an external shock. Results show that long and short-run unidirectional causality between productivity growth and the explanatory variables exists amongst all economies through the error-correction terms (ECT) and ARDL models. When imposing a simulated one-time S.D. shock upon the explanatory variables, differing speeds of adjustment and recovery processes in the long-run are present. As such, the strength of causal relationships amongst productivity growth and the explanatory variables ultimately affects speeds of adjustment and hence recovery.
Economic Resilience, Productivity Growth, Kaleckian post-Keynesian, Autoregressive Distributed Lag (ARDL), Impulse Response Functions (IRF), Variance Decomposition (VD).
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Rafailidis Panagiotis and Katrakilidis Constantinos
Correspondence: Rafailidis Panagiotis, rafailidisp1@gmail.com
Department of Economics, Aristotle University of Thessaloniki, Greece
pdf (593.41 Kb) | doi: https://doi.org/10.47260/bae/8210
In this paper, we study the dynamics between US stock prices, exchange rates and oil prices. The data used are quarterly, covers the period from 1986 to 2016 and includes the Standard & Poor's 500 spot prices, the West Texas Intermediate spot prices and the effective exchange rate of US Dollar. We examine the presence of different sources of nonlinearities. The empirical analysis is based on the asymmetric ARDL cointegration methodology proposed by Shin et al (2011). The evidence implies that ignoring possible non-linearities lead to misleading results. The analysis reveals new evidence such as the existence of several structural brakes and asymmetries in both long-run and short-run relationships among the examined variables and that could be of major importance for researchers and other market participants.
stock prices; exchange rates; oil prices; asymmetric cointegration; ARDL; NARDL; Forecasting
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Νikolaos A. Kyriazis
Correspondence: Νikolaos A. Kyriazis, knikolaos@uth.gr
Department of Economics, University of Thessaly, Greece
pdf (593.41 Kb) | doi: https://doi.org/10.47260/bae/829
This paper sets under scrutiny whether the S&P500, oil, and Twitter-based uncertainty about financial markets affect the returns and volatility of three major cryptocurrencies. Estimations are conducted concerning Bitcoin, Bitcoin Cash, and Dogecoin during the first wave of the COVID-19 pandemic. Findings document that Twitter uncertainty exhibits a weaker impact on cryptocurrencies than the S&P500 and crude oil. S&P500 constitutes a positive and significant determinant while impacts of oil are weaker and mixed. The volatility of cryptocurrencies is found to display a non-linear character. Moreover, it is revealed that Dogecoin could be more useful to investors as a speculative tool than Bitcoin and Bitcoin Cash. These outcomes inform the interested reader that traditional investments are influential in a much larger degree towards modern financial assets than investor sentiment when economic conditions are stressed.
Twitter Sentiment, Stock, Oil, Cryptocurrency, COVID-19 pandemic.
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Alexandros Koulis and Constantinos Kyriakopoulos
Correspondence: Alexandros Koulis , akoulis@ionio.gr
Department of Regional Development, Ionian University, Greece
pdf (593.41 Kb) | doi: https://doi.org/10.47260/bae/828
This paper investigates the hedging effectiveness of Bitcoin (BTC) future contract using daily settlement prices for the period of 1 January 2018 until 26 March 2021. Standard OLS regressions, Error Correction Model (ECM), as well as GARCH and EGARCH models are used to estimate the optimal hedge ratio which is necessary for trading and risk management. The findings indicate that the time varying hedge ratios, if estimated through the Error Correction Model (ECM), are more efficient than the fixed hedge ratios in terms of risk minimization.
Optimal hedge ratio, hedging models, bitcoin, futures market
Beneki, C., Koulis, A., Kyriazis, N. A., & Papadamou, S. (2019). Investigating volatility transmission and hedging properties between Bitcoin and Ethereum. Research in International Business and Finance, 48, 219-227.Bollerslev, T. (1986) ‘A generalized autoregressive conditional heteroscedasticity’, Journal of Econometrics, 31(3), 307–327.Butterworth, D. and Holmes, P. (2001) ‘The hedging effectiveness of stock index futures: evidence for the FTSE-100 and FTSE-Mid 250 indexes traded in the UK’, Applied Financial Economics, Vol. 11, pp.57–68.Chen, SS, CF Lee and K Shrestha (2001). On a mean-generalized semivariance approach to determining the hedge ratio. Journal of Futures Markets, 21, 581–598.Deng, J., Pan, H., Zhang, S., & Zou, B. (2020). Minimum-variance hedging of Bitcoin inverse futures. Applied Economics, 52(58), 6320-6337.Ederington, Louis H. (1979) The hedging performance of the new futures markets." The Journal of Finance 34.1: 157-170.Engle, R. and Granger, C. (1987). Cointegration and error correction: representation, estimation and testing, Econometrica, 251–276.Herbst, A. F., Kare, D. D., & Caples, S. C. (1989). Hedging effectiveness and minimum risk hedge ratios in the presence of autocorrelation: Foreign currency futures. Journal of Futures Markets, 9(3), 185-197.Johnson, L. L., (1960). The theory of hedging and speculation in commodity futures, Review of Economic Studies, 27, 139–151.Kenourgios, D., Samitas, A., & Drosos, P. (2008). Hedge ratio estimation and hedging effectiveness: the case of the S&P 500 stock index futures contract. International Journal of Risk assessment and management, 9(1-2), 121-134.Koulis, A., Beneki, C., & Kaimakamis, G. (2018). Hedging effectiveness for international index futures markets. Economics and Business, 32(1), 149-159.Lee, C. F., Wang, K., & Chen, Y. L. (2009). Hedging and optimal hedge ratios for international index futures markets. Review of Pacific Basin Financial Markets and Policies, 12(04), 593-610.Lypny, G. and Powalla, M. (1998) ‘The hedging effectiveness of DAX futures’, The European Journal of Finance, 4(4), 345–355.Nelson, D.B. (1991) ‘Conditional heteroskedasticity in asset returns: a new approach’, Econometrica, 347–370.Pal, D., & Mitra, S. K. (2019). Hedging bitcoin with other financial assets. Finance Research Letters, 30, 30-36.Park, H. Y., & Bera, A. K. (1987). Interest‐Rate Volatility, Basis Risk and Heteroscedasticity in Hedging Mortgages. Real Estate Economics, 15(2), 79-97.Sah, A. N., & Pandey, K. K. (2011). Hedging effectiveness of index futures contract: The case of S&P CNX Nifty. Global Journal of Finance and Management, 3(1), 77-89.Sebastião, H., & Godinho, P. (2020). Bitcoin futures: An effective tool for hedging cryptocurrencies. Finance Research Letters, 33, 101230.Switzer, LN and M El-Khoury (2007). Extreme volatility, speculative efficiency, and the hedging effectiveness of the oil futures markets. Journal of Futures Markets, 27, 61–84.Wang, J., & Hsu, H. (2010). Hedge Ratio Stability and Hedging Effectiveness of Time‐Varying Hedge Ratios in Volatile Index Futures Markets: Evidence from the Asian Financial Crisis. Asia‐Pacific Journal of Financial Studies, 39(5), 659-686.Zhou, J. (2016). Hedging performance of REIT index futures: A comparison of alternative hedge ratio estimation methods. Economic Modelling, 52, 690-698.
Rafiqul Bhuyan, Mohammad Sogir Hossain Khandoker, Mahjuja Taznin, Md. Shanur Rahman and Lamia Akter
Correspondence: Mahjuja Taznin, mahjuja.taznin@aamu.edu
Department of Accounting and Finance, College of Business and Public Affairs, USA
pdf (593.41 Kb) | doi: https://doi.org/10.47260/bae/827
The objective of the study is to explore various micro and macro variables that affect stock returns of the Dhaka Stock Exchange surrounding the global financial crisis. We collect a sample of 30 listed banks covering the period of 9 years (2009 – 2017) for our study. The results indicate that debt to asset ratio (D/A), market capitalization (MKT CAP), interest rate, and foreign exchange rate (ForEx Rate) have positive and significant relationships with stock returns. On the other hand, inflation and leverage have negative and significant influence on stock returns among the eight micro variables and four macroeconomic variables that were used in our analyses.
Microeconomic & Macroeconomic Variables, DSE, OLS, Stock Returns and Panel data
Abdelkarim Almumani (2014). “Determinants of Equity Share Prices of the Listed Banks in Amman Stock Exchange: Quantitative Approach.” International Journal of Business and Social Science, 5(1), 91–104.Adam, A. M. (n.d.). Macroeconomic Factors and Stock Market Movement: Evidence from Ghana.Adam, A. M., & Tweneboah, G. (2008). Mp r a. (11256).Ahmmed, S. “Immediate Impact of Stock Market Fluctuations on GDP : The Case of Bangladesh.” Bangladesh Journal of Business, (April), 1–9, 2012.Alam, S., Miah, M. R., & Karim, M. A. “Analysis on Factors that Affect Stock Prices: A Study on Listed Cement Companies at Dhaka Stock Exchange.” Research Journal of Finance and Accounting, 7(18), 2222–2847, 2016.Ali, M. B. “Impact of Micro and Macroeconomic Variables on Emerging Stock Market Return : A Case on Dhaka Stock Exchange ( DSE ).” Interdisciplinary Journal of Research in Business, 1(May), 8–16, 2011. https://doi.org/10.1071/IS14017Arshad, Z., Arshaad, A. R., Yousaf, S., & Jamil, S.“Determinants of share prices of listed commercial banks in Pakistan.” IOSR Journal of Economics and Finance, 6(2), 56–64, 2015. https://doi.org/10.9790/5933-06235664Banks, C. “Factors affecting the share price: Evidence from Nepalese Commercial Banks.” (June). Nepal Business Journal, 2016. https://doi.org/10.13140/RG.2.1.2141.1440Chen, Nai-Fu, Richard Roll, and Stephen A. Ross. "Economic Forces and the Stock Market." The Journal of Business 59, no. 3 (1986): 383-403. Accessed August 15, 2021. http://www.jstor.org/stable/2352710Faruque, O. “Security Analysis of Banking Industry in Bangladesh.” Asian Business Review, 8(1), 21–24, 2018.Gupta, R., Reid, M., Study, E., Surprises, M., & Returns, S. “Macroeconomic Surprises and Stock Returns in South Africa.” Stellenbosch Economic Working Papers : 05 / 12, 2012.Ibrahim, M.H., Aziz, H. "Macroeconomic variables and the Malaysian equity market: A view through rolling subsamples", Journal of Economic Studies, Vol. 30 No. 1, pp. 2003. https://doi.org/10.1108/01443580310455241Khan, M. N. “Determinants of Share Prices at Karachi Stock Exchange.” Working paper (March), 2019.Khan, M. N., & Amanullah. “Determinants of Share Prices at Karachi Stock Exchange.” International Journal of Business and Management Studies, 4(1), 1309–8047, 2012.Malhotra, N., & Tandon, K. “Determinants of Stock Prices: Empirical Evidence from NSE 100 Companies.” Journal of Business Studies, 3(3), 86–95, 2013.Mukherjee, T.K. and Naka, A. “DYNAMIC RELATIONS BETWEEN MACROECONOMIC VARIABLES AND THE JAPANESE STOCK MARKET: AN APPLICATION OF A VECTOR ERROR CORRECTION MODEL”. Journal of Financial Research, 18: 223-237, (1995). https://doi.org/10.1111/j.1475-6803.1995.tb00563.xPradhan, R. S., & Dahal, S. “ Factors Affecting the Share Price: Evidence from Nepalese Commercial Banks.” 2016. SSRN, (June). https://doi.org/10.2139/ssrn.2793469Sharif, T., Purohit, H., & Pillai, R. “Analysis of Factors Affecting Share Prices: The Case of Bahrain Stock Exchange.” International Journal of Economics and Finance, 7(3), 34-46. 2015. https://doi.org/10.5539/ijef.v7n3p207Uddin, R. (2013). Determinants of Stock Prices in Financial Sector Companies in Bangladesh- A Study on Dhaka Stock Exchange (DSE). (2003), 471–480.Wadud, M. A., “Determinants of Share Prices of Listed Commercial Banks in Bangladesh.” 2018. SSRN. https://doi.org/10.2139/ssrn.3106243
Panagiotis Magdalinos and Ioannis Tsakalos
Correspondence: Ioannis Tsakalos, i.tsakalos@uth.gr
Department of Accounting & Finance, University of Thessaly, Greece
pdf (593.41 Kb) | doi: https://doi.org/10.47260/bae/826
The Eurozone Debt Crisis led to the outbreak of non-performing loans (NPLs). The purpose of this paper is to identify the macroeconomic and financial factors that enhanced the non-performing loans. We compare the impact of financial crisis to the following countries: Italy, Greece, Spain, Portugal, Ireland (PIIGS) and focus on specific parameters which affect the banking sector. We apply panel data analysis to highlight the relation among NPLs and specific parameters: Gross Domestic Product (GDP), unemployment rate, bank liquidity, and the real estate market. Findings confirm the negative correlation between NPLs and the GDP and the housing prices, as well as the positive one with the unemployment rate and the liquidity ratio (LDR).
Non-performing loans, panel data, macroeconomic factors, financial ratios
Abid, L., Ouertani, M.N. & Zouari- Ghorbel, S. (2014). Macroeconomic and Bank-Specific Determinants of Household’s Non-Performing Loans in Tunisia: a Dynamic Panel Data. In T. S. (TSFS) (Ed.), Procedia Economics and Finance 13. 13, pp. 58-68. Tunisia: Elsevier."Anastasiou, D., Louri & H., Tsionas, M. (2017). Non- Performing Loans in the Euro Area: Are Core-Peripher Banking Markets Fragmented?. Bank of Greece, Working Paper No 219Ari, A.Chen, S. & Ratnowski, L. (2019). The Dynamics of Non-Performing Loans During Banking Crises: A New Database. IMF Working Paper 19/272. Washington: International Monetary Fund.Arpa, M., Giulini, I., Ittner, A. & Pauer, F. (2001). The influence of macroeconomic developments on Austrian banks: implications for banking supervision. BIS Papers, 1, pp. 91- 116Baholli, F.,Dika,I. & Xhabija, G (2015). Non-Performing Loans with Econometric Model: Albanian Case. Mediteranian Jounal of Social Sciences,Vol 6 No 1.Bank of Greece (2019). Bank of Greece Monetary Policy Report 2019. Available at https://www.bankofgreece.gr/Publications/NomPol20182019.pdf (Accessed 14 May 2020)Beck, R., Jakubik, P. & Piloiu, A. (2013). Non-Performing Loans: What Matters in Addition to the Economic Cycle?. ECB Working Paper No 1515, ECB.Berge, T.O. & Boye, K.G. (2007). An Analysis of Bank’s Problem Loans. Norges Bank Economic Bulletin 78, 65–76Berger, A.N & De Young R. (1997). Problem loans and cost efficiency in commercial banks. Journal of Banking and Finance, 21, 849-870Bonfondi, M. & Ropele, I. (2011). Makroeconomic determinants of bad loans: evidence from Italian banks. Occasional Papers, p 89Charalambakis, E., Dendramis, Y., Tzavalis, E. (2017). On The Determinants of NPLs: Lessons from Greece. Bank of Greece Working Paper 220, p 4ECB-Banking Supervision (2020). Non-Performing Loans. Available at https://www.bankingsupervision.europa.eu/banking/priorities/npl/html/index.el.htmlEspinoza, R. & Prasad, A. (2010). Nonperforming Loans in the GCC banking system and their macroeconomic effects. IMF Working Papers 10/224. Washington: International Monetary FundGarciya- Marco, T. & Robles- Fernandez, M.D. (2008). Risk- taking behaviour and ownership in the banking industry: the Spanish evidence. Journal of Economics and Banks, 60(4), 332-354Glen, J. & Mondragon- Velez, C. (2011). Business Cycle Effects on commercial bank loan portfolio performance in developing economies. International Finance Corporation, World Bank GroupGodlewski, C. (2004). Capital regulation and credit risk taking: empirical evidence from banks in emerging market economies. Finance 040903, EconWPALouzis, D.P, Vouldis, A.T & Metaxas, V.L. (2010). Makroeconomic and bank- specific determinants of non performing Loans in Greece: a comparative study of mortage, business and consumer loan portfolios. Working Paper, vol 118, Bank of GreeceMakri, V. , Tsagkanos, A. & Bellas, A. (2012). Determinants of Non Performing Loans: The case of Eurozone. PANOECONOMICUS, 2, pp. 193- 206Messai, A.S & Jouini, F. (2013). Micro and Macro Determinants of Non- performing Loans. International Journal of Economics and Financial Issues, ISSN: 2146-4138 ,Vol. 3, No. 4, pp.852-860.Monokroussos, P., Thomakos, D.D & Alexopoulos, T.A. (2016). Explaining Non- performing loans in Greece: a comparative study on the effects of recession and banking practices’. Hellenic Observatory Papers on Greece and Southeast Europe, Grease Paper No 101Nkusu, M. (2011). Nonperforming Loans and Macrofinancial Vulnerabilities in Advanced Economies. IMF Working Paper 11/161. Washington: International Monetary Fund.Podpiera, J. & Weill, L. (2008). Bad Luck or Management? Emerging Banking Market Experience. Journal of Financial Stability, 4 (2), 135-148Quagliarello, M. (2007). Banks’ Riskiness Over the Business Cycle: a Panel Analysis on Italian Intermediaries. Applied Financial Economics, 17(2), 119-138.Rinaldi, L. & Sanchis‐Arellano, A. (2006). Household debt sustainability: What explains household non‐performing loans? An empirical analysis. ECB Working Paper, n° 570.Salas, V. & Saurina, J. (2002). Credit risk in two institutional regimes: Spanish commercial and savings banks. Journal of Financial Services Research, 22: 3, pp 203-224Skarica, B. (2013). Determinants of Non- Performing Loans in Central and Eastern European Countries. Working Paper 13-07. Faculty of Economics and Business, ZagrebVerbeek, M. (2012) A Guide to Modern Econometrics (4th Edition). West Sussex, John Wiley & Sons Ltd.Wooldridge J. (2009). Introductory Econometrics. A Modern Approach. Fifth Edition,pp 448-676.
Panagiotis Liargovas, Vasilis Pilichos and Anastasia Angelopoulou
Correspondence: Panagiotis Liargovas, liargova@uop.gr
Department of Economics, University of Peloponnese, Greece
pdf (593.41 Kb) | doi: https://doi.org/10.47260/bae/825
In this paper, we examine the correlation between budget and growth forecast errors of the Greek Government, during the last decade. We explore if these budget forecast errors are the result of fiscal performance, economic conditions, or other qualitative characteristics of economic policy reform. We try to explain whether biased macroeconomic forecasts were responsible for biased fiscal forecasts. Besides, we investigate the role of business and consumers expectations, the election process and the financial aid disbursements following positive reviews of the Greek policy reform. We conclude that fiscal governance reform has improved fiscal forecasting framework, even though pessimistic forecasts prevail.
fiscal governance, fiscal planning, forecasting bias, Greek economic crisis
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London: Palgrave Macmillan UK.Beetsma, Roel, Xavier Debrun, Xiangming Fang, Young Kim, Victor Lledó, Samba Mbaye, and Xiaoxiao Zhang. (2019). "Independent fiscal councils: Recent trends and performance." European Journal of Political Economy 57:53-69. doi: https://doi.org/10.1016/j.ejpoleco.2018.07.004Beetsma, Roel, Massimo Giuliodori, Mark Walschot, and Peter Wierts. (2013). "Fifty years of fiscal planning and implementation in the Netherlands." European Journal of Political Economy 31:119-38. doi: https://doi.org/10.1016/j.ejpoleco.2013.04.001Beetsma, Roel, Massimo Giuliodori, and Peter Wierts. (2009). "Planning to cheat: EU fiscal policy in real time." Economic policy 24 (60):753-804.Blanchard, Olivier J., and Daniel Leigh. (2013). "Growth Forecast Errors and Fiscal Multipliers." American Economic Review 103 (3):117-20. doi: 10.1257/aer.103.3.117.Blöndal, Jón R. (2003). "Budget reform in OECD member countries: Common trends." OECD Journal on Budgeting 2 (4):7-25.Calmfors, Lars. 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"EMU and the Greek crisis: The political-economy perspective." European Journal of Political Economy 26 (4):568-76.Katsimi, Margarita, and Vassilis Sarantides. (2012). "Do elections affect the composition of fiscal policy in developed, established democracies?" Public Choice 151 (1-2):325-62.Kopits, G., and S.A. Symansky. (1998). Fiscal Policy Rules: INTERNATIONAL MONETARY FUND.Leal, Teresa, Javier J. Pérez, Mika Tujula, and Jean-Pierre Vidal. (2008). "Fiscal Forecasting: Lessons from the Literature and Challenges." Fiscal Studies 29 (3):347-86. doi: 10.1111/j.1475-5890.2008.00078.x.Menifield, C. (2010). Comparative Public Budgeting: A Global Perspective: Jones and Bartlett Publishers.Merola, Rossana, and Javier J. Pérez. (2013). "Fiscal forecast errors: Governments versus independent agencies?" European Journal of Political Economy 32:285-99. doi: https://doi.org/10.1016/j.ejpoleco.2013.09.002.Mody, Ashoka, and Stefania Fabrizio. (2014). "Can budget institutions counteract political indiscipline?" Economic policy 21 (48):690-739. doi: 10.1111/j.1468-0327.2006.00169.x.Obstfeld, Maurice, and Poul M Thomsen. (2016). "The IMF is not asking Greece for more austerity." Posted on IMF Direct, December 12.OECD. (2002). OECD best practices for budget transparency: OECD.———. (2019). Budgeting and Public Expenditures in OECD Countries 2019.Perotti, Roberto, and Yianos Kontopoulos. (2002). "Fragmented fiscal policy." Journal of public economics 86 (2):191-222. doi: https://doi.org/10.1016/S0047-2727(01)00146-3.Prichard, Wilson. (2014). Electoral Competitiveness, Political Budget Cycles and Taxation in Developing Countries.Sherwood, Monika. (2015). "Medium-Term Budgetary Frameworks in the EU Member States." In.: Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.Shi, Min, and Jakob Svensson. 2003. "Political Budget Cycles: A Review of Recent Developments." Nordic Journal of Political Economy 29:67-76.von Hagen, Jürgen, and Ian J. Harden. 1995. "Budget processes and commitment to fiscal discipline." European Economic Review 39 (3):771-9. doi: https://doi.org/10.1016/0014-2921(94)00084-DWorld Bank. 2005. "Public Financial Management Performance Measurement Framework." PEFA Secretariat. Washington, DC: World Bank.Wyplosz, Charles. 2015. "The centralization-decentralization issue." In.: Publications Office of the European Union.
Samuel Antwi, Mohammed Issah and Richard Kpodo
Correspondence: Mohammed Issah, mohammed.issah@upsamail.edu.gh
Department of Accounting, University of Professional Studies, Ghana
pdf (593.41 Kb) | doi: https://doi.org/10.47260/bae/824
The aim of this study is to determine the role of monetary policy on the stock market and economic growth nexus in Ghana. The study used annual time series macroeconomic and stock market data from the year 1990 to 2019. Secondary data was collected on Gross Domestic Product (GDP), market capitalization (MC), Commercial bank (CB), inflation (INF), labour (L), capital stock (K), and trade openness (TO). Inflation was measured with consumer price index (CPI), and broad money (M2) were examined. The ARDL cointegration bounce test approach was used. It was revealed that stock market development has a significant positive effect on economic growth both in the short and long run. The study also found a support for a positive and significant nexus between monetary policy and economic growth. Based on the results it is suggested that efforts must be mounted to increase the number of firms to be listed to promote liquidity and raise the size of the market via capitalization ratio.
Stock Market, Economic Growth, Monetary Policy, Ghana
Adusei, M. (2014). Does stock market development promote economic growth in Ghana? International journal of economics and finance, 6(6), 119-126Antonios, A. (2010). Stock Market and Economic Growth: An Empirical Analysis for Nigeria. Business and Economics Journal, 1–12. https://doi.org/10.2139/ssrn.3402423Asante, S., Agyapong, D., & Adam, A. M. (2011). Bank Competition, Stock Market and Economic Growth in Ghana. International Journal of Business Administration, 2(4), 33–41. https://doi.org/10.5430/ijba.v2n4p33Asamoah, G. (2018). Impact of Ghana Stock Exchange Development on Economic Growth: A Robust Time Series Econometric Analysis. Interdisciplinary Research Journal of Theology, Apologetics, Natural and Social Sciences, Vol.1, No.1&2.Akinlo, O. O. (2014). Oil price and stock market: Empirical evidence from Nigeria. European Journal of Sustainable Development, 3(2), 33-33.Alajekwu, U. B., Ezeabasili, V. N., & Nzotta, S. M. (2013). Trade openness, stock market development and economic growth of Nigeria: Empirical evidence. Research Journal of Finance and Accounting, 4(3), 120-127.Bayar, Y., Kaya, A., & Yildirim, M. (2014). Effects of stock market development on economic growth: Evidence from Turkey. International Journal of Financial Research, 5(1), 93.Bjørnland, H. C., & Leitemo, K. (2009). Identifying the interdependence between US monetary policy and the stock market. Journal of Monetary Economics, 56(2), 275-282.Christiano, L., Ilut, C. L., Motto, R., & Rostagno, M. (2010). Monetary policy and stock market booms (No. w16402). National Bureau of Economic Research.Derk, N. (2020). Stock Market and Economic Performance in Malaysian Context. Finance & Accounting Research Journal, 2(2), 82-90.Enisan, A. A., & Olufisayo, A. O. (2009). Stock market development and economic growth: Evidence from seven sub-Sahara African countries. Journal of economics and business, 61(2), 162-171.Hoque, M. E., & Yakob, N. A. (2017). Revisiting stock market development and economic growth nexus: The moderating role of foreign capital inflows and exchange rates. Cogent Economics & Finance, 5(1), 1329975.Kapaya, S. M. (2020). Stock market development and economic growth in Tanzania: an ARDL and bound testing approach. Review of Economics and Political Science.Ngare, E., Nyamongo, E. M., & Misati, R. N. (2014). Stock market development and economic growth in Africa. Journal of Economics and Business, 74, 24-39.Nzomoi, J. N., & Ikikii, S. M. (2013). An analysis of the effects of stock market development on economic growth in Kenya.Onyeiwu, C. (2012). Monetary policy and economic growth of Nigeria. Journal of Economics and Sustainable development, 3(7), 62-70.Olweny, T. O., & Kimani, D. (2011). Stock market performance and economic growth Empirical Evidence from Kenya using Causality Test Approach. Advances in Management and Applied Economics, 1(3), 177.Osamwonyi, I. O., & Kasimu, A. (2013). Stock market and economic growth in Ghana, Kenya and Nigeria. International Journal of Financial Research, 4(2), 83.Qamruzzaman, M., & Wei, J. (2018). Financial innovation, stock market development, and economic growth: An application of ARDL model. International Journal of Financial Studies, 6(3), 69.Rajabi, E., & Muhammad, J. (2014). The stock markets, banks and growth nexus: Asian Islamic countries. Economic Notes: Review of Banking, Finance and Monetary Economics, 43(2), 137-165.Rehman, M. Z. (2018). Banking sector development, stock market development and economic growth: Evidence from Saudi Arabia. Academy of Accounting and Financial Studies Journal, 22(4), 1-15.Saibu, M. O., Bowale, K. E. I., & Akinlo, A. E. (2009). Financial Structure and economic growth: empirical evidence from Nigeria. International Journal of Business and Emerging Markets, 1(3), 264-281.Thorbecke, W. (1997). On stock market returns and monetary policy. The Journal of Finance, 52(2), 635-654.Ioannidis, C., & Kontonikas, A. (2008). The impact of monetary policy on stock prices. Journal of policy modeling, 30(1), 33-53.Zivengwa, T., Mashika, J., Bokosi, F. K., & Makova, T. (2011). Stock market development and economic growth in Zimbabwe. International Journal of economics and Finance, 3(5), 140-150.
Ioannis Dokas, Christos Leontidis, Nicolaos Eriotis and Konstantinos Hazakis
Correspondence: Ioannis Dokas, intokas@econ.duth.gr
Department of Economics, Democritus University of Thrace, Greece
pdf (593.41 Kb) | doi: https://doi.org/10.47260/bae/823
This article aims to present a critical overview of the traditional studies in earnings management, focusing on the impact on the making decision process. This overview in the literature provides numerous aspects of this topic, in line with the firms’ motivations. Earnings management procedure includes smoothing and opportunistic practices and illustrating accounting rules with a significant effect on accounting information quality. Several researchers have shifted their attention to real activities as a primary method or supplementary to accrual-based methods to obtain a complete view of the earnings management levels. Earnings management is considered an opportunistic instrument, and it can be part of the aggregate long-term business strategy. This review study provides some guidelines, to academics and professionals, in line with the models and the motivations that lead managers to engage in this procedure. This overview creates new research avenues enhancing the existing knowledge.
Earnings management, accounting information quality, manipulation, financial reports
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Chu-Hsiung Lin, Tzu-Chuan Kao, Chang-Cheng Changchien and Chien-Hui Wu
Correspondence: Chang-Cheng Changchien, changchien@mail.cjcu.edu.tw
Chang Jung Christian University, Taiwan
pdf (593.41 Kb) | doi: https://doi.org/10.47260/bae/822
This study reexamines the effects of corporate social responsibility (CSR) on credit ratings. On examining a sample of listed firms in Taiwan from 2013 to 2015, we find that our results do not support that CSR activities can enjoy more favorable credit ratings. However, firm capabilities can improve credit ratings, and the relationship between CSR and credit ratings is significant for firms with high capability. Our results indicate that CSR activities are beneficial to credit ratings only for firms with high capabilities.
Corporate Social Responsibility, Credit Ratings, Firm Capability, Corporate Financial Performance.
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