Data Driven Perspective on Stock Price - Macroeconomic Variables: Indonesia Economy 2016-2020

Agung Nusantara; Sri Nawatmi; Agus Budi Santosa

doi:10.24856/mem.v37i2.2818

Data Driven Perspective on Stock Price - Macroeconomic Variables: Indonesia Economy 2016-2020

Agung Nusantara, Sri Nawatmi, Agus Budi Santosa

Abstract

Abstract

The use of a theory-driven perspective is very common, especially in economics research, and even becomes an inevitable approach. Problems arise when data, as a form of reality, does not synergize with theory. The resulting conclusion is very likely to be different from the theoretical statement. One method that refers to data-driven is the Vector Auto-Regressive (VAR) model, which puts all the variables involved in a position as endogenous variables. This study seeks to identify a statistically more accurate relationship in the relationship between variables, stock prices, consumer price index, Jakarta Inter-Bank Over rate, exchange rate, and Net Balance Trade. Observations were made from January 2016 to December 2020. This study found evidence that there is a recursive relationship between stock price variables and macroeconomic variables. The VAR model identifies the Net Balance Trade variable as an endogenous variable in 3 types of sectoral stocks and only manufacturing sector stocks that resemble it. These results have two theoretical consequences: first, setting stock prices without differentiating sectors carries the risk of generalization errors. Second, setting stock prices as the endogenous variable means assuming that the market is perfect, and efficient and market participants have rational behavior.

Keywords

Stock Prices; Macroeconomics Variables,; Data Driven; Vector Auto-Regressive.

Full Text:

PDF

References

Aboura, S., & Lepinette, E. (2017). New Developments on the Modigliani-Miller Theorem. Theory Robability Application, 61(1), 3–14.

Adiwibowo, P., & Sihombing, P. (2019). Determinant of Government Bond Yielding. DIJBM, 1(1), 86–99.

Agung, I. G. N. (2009). Time Series Data Analysis Using EViews. John Wiley & Sons (Asia) Pte. Ltd.

Ali, M., Sun, G., & Chowdhury, M. A. A. (2018). Dynamic Interaction between Macroeconomic Fundamentals and Stock Prices in Bangladesh. Indonesia Journal of Managemen and Business Economics, 1(1), 66–84.

Anastasiou, D., & Kapopolous, P. (2021). Dynamic Linkages among Financial Stability, House Price and Residential Investment in Greece. Munich Personal RePEc, 107833. http://mpra.ub.uni-muenchen.de

Benakovic, D., & Posedel, P. (2010). Do Macroeconomic Factors Matter for Stock Returns? Evidence from Estimating a Multifactor Model on the Croatian Market. Business Systems Reserach, 01(1–2), 1–50.

Camilleri, S. J., Scicluna, N., & Bai, Y. (2019). Do Stock Markets Lead or Lag Macroeconomic Variables? Evidence from Select European Countries. American Journal of Economics and Finance, 48, 170–186.

Caruso, A. (2018). Macroeconomic News and Market Reaction: Surprise Indexes Meet Nowcasting. ECARES, Universite Libre de Bruxelles.

Chang, B. H., Meo, M. S., Syed, Q. R., & Abro, Z. (2019). Dynamic Analysis of the Relationship between Stock Prices and Macroeconomic Variables: An Empirical Study of Pakistan Stock Exchange. Soth Asian Journal of Business Studies. https://doi.org/DOI: 10.1108/SAJBS-06-2018-0062

Chen, N.-F., Roll, R., & Ross, S. A. (1986). Economic Forces and the Stock Market. The Journal of Business, 59(3), 383–403.

Cheung, Y.-W., & Ng, L. K. (1998). International Evidence on Stock Market and Aggregate Economic Activity. Journal of Empirical Finance, 5, 181–196.

Cieslak, A., & Pang, H. (2020). Common Shocks in Stocks and Bonds (Working Paper No. W28184; NBER Working Paper Series). NBER.

Cline, W. R. (2015). Testing the Modigliani-Miller Theorem of Capital Structure Irrelevance for Banks (Working Paper WP 15-8). Peterson Institute for International Economics.

Demir, C. (2019). Macroeconomic Determinants of Stock Market Fluctuations: The Case of BIST-100. Economies, 7(8), 1–14.

Dubravka, B., & Posedel, P. (2010). Do macroeconomic factors matter for stock returns? Evidence from estimating a multifactor model on the Croatian market. Business Systems Research, 01(1–2), 1–50.

Enders, W. (2015). Applied Econometric Time Series (fourth). John Wiley & Sons, Inc.

Fama, E. F. (1990). Stock Returns, Expected Returns, and Real Activity. The Journal of Finance, 45(4), 1089–1108.

Fama, E. F., & French, K. R. (1990). Business Conditions and Expected Returns on Stocks and Bonds. Journal of Financial Economics, 25, 23–49.

Flannery, M. J., & Protopapadakis, A. A. (2002). Macroeconomic Factors Do Influence Aggregate Stock Returns. The Review of Financial Studies, 15(3), 751–782.

Forrester, J. W. (1958, August). Industrial Dynamics: A Major Breakthrough for Decision Makers. Harvard Business Review, 37–66.

Gabaix, X. (2019). Behavioral New Keynesian Model. National Bureau of Economic Research, Working Paper 22954. http://www.nber.org/paper/w22954

Gersbach, H., Haller, H., & Muller, J. (2015). The Macroeconomics of Modigliani-Miller. Journal of Economic Theory, 157, 1081–1113.

Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica, 37(3), 424–438.

Haldane, A. G., & Turrel, A. E. (2017). An Interdisciplinary Model for Macroeconomics. Bank of England, Staff Working Paper No. 696.

Hsing, Y. (2011). The Stock Market and Macroeconomic Variables in a BRICS Country and Policy Implications. International Journal of Economics and Financial Issues, 1(1), 12–18.

Hsing, Y., & Hsieh, W. (2012). Impacts of Macroeconomic Variables on the Stock Market Index in Poland: New Evidence. Journal of Business Economics and Management, 13(2), 334–343.

Jagadish, H. V. (2015a). Bid Data and Science: Myths and Reality. Big Data Research, 2, 49–52.

Jagadish, H. V. (2015b). Big Data and Science: Myths and Reality. Big Data Reserach, 2, 49–52.

Jansen, E. P. (2018). Bridging the Gap between Theory and Practice in management Accounting. Accounting, Auditing & Accountibility Journal, 31(5), 1486–1509.

Juselius, K. (2006). The Cointegrated VAR Model: Methodologu and Applications (1st ed.). Oxford University Press.

Kitchin, R. (2014). Big Data, New Epistemologies and Paradigm Shifts. Big Data & Society, 1–12. https://doi.org/DOI: 10.1177/2053951714528481

Kling, A. (2022, Winter). How Effective is Economic Theory? National Affairs, No. 55. https://www.nationalaffairs.com/publications/detail/how-effective-is-economic-theory

Lucas Jr., R. E., & Sargent, T. J. (1979). After Keynesian Macroeconomics. Federal Reserve of Bank Minneapolis Quaterly Review, 3(2), 1–16.

Lutkepohl, H., & Kratzig, M. (2004). Applied Time Series Econometrics. Cambridge University Press.

Machmuddah, Z., Utomo, St. D., Suhartono, E., Ali, S., & Ghulam, W. A. (2020). Stock Market Reaction to COVID-19: Evidence in Customer Goods Sector with the Implication for Open Innovation. Journal of Open Innovation, 6(99), 1–13.

Megaravalli, A. V., & Sampagnaro, G. (2018). Macroeconomic Indicators and Their Impact on Stock Markets in Asian-3: A Pooled Mean Group Approach. Cogent Economics & Finance, 6, 1–14.

Moelands, B. (2017). The Impact of Fluctuations in the Crude Oil Price on the Stock Price of Renewable Energy Companies, an Impulse Response Analysis [Thesis]. Erasmus University - Rotterdam Erasmus School of Economics.

Mohamed, I. A., & Ahmed, S. (2018). The Effects of Macroeconomic Variables on Stock Returns in the Jordanian Stock Market. Global Journal of Management and Business, 5(2), 87–93.

Rapach, D. E., Wohar, M. E., & Rangvid, J. (2005). Macro Variables and International Stock Return Predictability. International Journal of Forecasting, 21, 137–166.

Sembel, R. H. M. (2015). Bridging the Gap between Theory and Practice: Lesson Learned from the Academic Literature and Experiences of Living a Hybrid Life. DeReMa Jurnal Manajemen, 10(1), 1–21.

Shmueli, G., & Koppius, O. (2010, July). Predictive Analytics in Information Systems Research.

Shmueli, G., & Koppius, O. R. (2011). Predictive Analytics in Information System Research. Management Information Systems Research Center, 35(3), 553–572.

Singhal, S., Choudhary, S., & Biswal, P. C. (2019). Return and Volatility Linkages among International Crude Oil Price, Gold Price, Exchange Rate and Stock Markets: Evidence from Mexico. Resources Policy, 60, 255–261.

Smyth, R., & Narayan, P. K. (2018). What Do We Know about Oil and Stock Returns? International Review of Financial Analysis, 57, 148–156.

Wu, G. G.-R., Hou, T. C.-T., & Lin, J.-L. (2018). Can Economic News Predict Taiwan Stock Market Returns? Asian Pacific Management Review, 30, 1–6.

DOI: http://dx.doi.org/10.24856/mem.v37i2.2818