Dynamic analysis of the relationship between stock prices and macroeconomic variables

Abstract

Purpose The purpose of this paper is of twofold: first, to empirically examine the short-run and long-run impact of macroeconomic variables such as industrial production, foreign direct investment (FDI), trade balance (TB), exchange rate, interest rate (IR) and consumer price index (CPI) on stock prices (SP) of KSE-100 index; and second, to examine whether this relationship changes as a result of the financial crisis. Design/methodology/approach This study uses an autoregressive distributed lag model by using the full sample period data from 1997Q3 to 2018Q2 and the post-crisis period data from 2008Q3 to 2018Q2. Moreover, it uses variance decomposition analysis to examine the importance of each variable in explaining SP. Findings The findings of the full sample period indicate that in the long run, TB, exchange rate and IR negatively affect SP whereas CPI and industrial production positively affect SP. However, the post-crisis period data indicate that only CPI positively affects the SP in the long run. Finally, variance decomposition analysis indicates 30 percent variance in SP is explained by its own shock. Practical implications The study findings suggest that macroeconomic variables have a significant role and can be considered important for taking investment and/or policy decisions. Especially, Governments and other regulators may need to take measures to increase the TB since it can help to increase the performance of the Pakistani stock market. Furthermore, investors may consider that findings change when the financial crisis has been taken into consideration. Originality/value This study uses two additional variables, namely FDI and TB by using the robust technique in the context of emerging countries like Pakistan. Furthermore, it takes into account the impact of the financial crisis on the underlying variables.

Asymmetric effect of extreme changes in the exchange rate volatility on the US imports

“…This test is used to examine the joint impact of lagged-level variables. Upper and lower bounds critical are used to test the null hypothesis of no co-integration, whereas the null hypothesis of no co-integration is rejected, if the obtained test statistics fall above the upper bounds critical values (Anjum et al, 2017;Bhutto and Chang 2019;Chang et al, 2019a;Chang et al, 2019b).…”

Sectoral CO2 emissions in China: asymmetric and time-varying analysis

“…The non-linear ARDL model is an extension of the traditional time series ARDL approach that produces short-run and long-run relationships (Khan, Teng, and Khan 2019;Fareed et al 2018). Compared to any other cointegration-based approach, an important benefit of the NARDL model is that it can relax stationary constraints and be employed when the variables are I(0) or I(1) and I(0) or I(1) (Rasheed et al 2019;Chang et al 2019). The general form of the ARDL method we have used is, therefore:…”

Analisis Dinamis Hubungan Antara Harga Saham dan Variabel Makroekonomi

“…Dalam penelitian ini disertakan beberapa jurnal penelitian internasional sebelumnya yang berhubungan dengan harga saham dan variabel makro ekonomi. Chang et al (2019) di Pakistan menujukkan pengaruh variabel makroekonomi terhadap harga saham. Hasil kesimpulan dengan metode ARDL neraca perdagangan, nilai tukar dan suku bunga berpengaruh negatif terhadap harga saham, sedangkan inflasi dan indeks produksi industri berpengaruh positif terhadap harga saham.…”

“…Nilai beta CointEq yang negatif menunjukan bahwa model akan menuju keseimbangan dengan kecepatan 1.17% perbulan. Dalam jangka pendek nilai tukar berpengaruh negative signifikan terhadap harga saham sektor makanan dan minuman, hal ini sesuai dengan penelitian Chang et al (2019); Ajaz et al (2017). Sedangkan untuk variable indeks produksi industri minuman berpengaruh positif signifikan terhadap harga saham, sesuai dengan hasil penelitian Chang at all (2019); Humpe and Macmillan (2009).…”