ARDL Inference for Some Macroeconomic Variables on Nigerian Stock Prices

Abstract

This study presents inferences about the effect of quarterly returns of some macroeconomic variables on Nigerian stock prices (NSP) by means of the autoregressive distributed lag (ARDL) model. Four models were studied using NSP, inflation rate (IFR), broad money supply (BMS), and exchange rate (EXR) data that spanned from 1999 to 2021. The rolling correlation was first carried out to test the significance of signals between each input variable and NSP. In testing for co-integration existence and to account for short- and long-term relationships between the study variables, the ARDL bounds test and error correction model (EMC) were utilized, respectively. Also, in determining the stability of the estimated parameters, the recursive cumulative sum (RCUSUM), RCUSUM of squares, and recursive moving sum (RMOSUM) charts were applied. The first model results revealed that at 5% IFR, it has no influence on NSP, both in the long run and short run. However, the second model revealed that BMS has a significant positive effect, both in the long run and short run, on NSP. The third model results showed that the EXR effect on NSP is not significant at 5% in the long run, but in the short run, it significantly negatively affects NSP in the second and third lags. Results from the fourth model revealed that IFR at lag one and BMS are positive and significant in influencing NSP in the long run. In contrast, EXR has a significant negative influence on NSP in the short run, but BMS has a positive and significant effect on NSP in the short run at 5%. The RCUSUM, RCSUMS, and RMOSUM charts revealed that the estimated parameters for the respective models are stable. The study concludes that NSP is influenced negatively by EXR and positively by IFR and BMS. The study recommends that the monetary policy rate should be controlled to lower the cost of borrowings, enhance liquidity levels in the stock market, and develop policies that can have a combined positive impact on stock prices.