Analysis of foreign capital inflows and stock market performance in Nigeria

The banking industry is one of the major industries in the Vietnamese stock market, so understanding how the industry index reacts to unusual events such as COVID-19’s impact is very important for the development of the Vietnamese stock market. This study examines the response of the banking sector index to three lockdown/blockage announcements to prevent the COVID-19 epidemic in Vietnam in 2020. Three times of lockdown/blockage: On February 13, 2020, blockade of Son Loi commune, Vinh Phuc province; on March 30, 2020, Vietnam announced the nationwide epidemic of COVID-19 and then nationwide lockdown, and on July 28, 2020, blockade in Da Nang. In the first case, the abnormal returns changed the sign around the notification date indicating that the stock price deviated from its fair value, but accumulating abnormal returns CAR (0;3] and CAR (0; 2] are both positive and statistically significant, which means that investors are more secure when the epidemic area is tightly controlled. The nationwide lockdown was the event that had the strongest impact on the stock price when both AR and CAR were negative and statistically significant before and after the date of the event’s announcement. Nationwide lockdown was the event that had the strongest impact on stock prices as both AR and CAR were negative in the days before and days after the event. This result supports the theory of imperfect substitution. Only AR [2] was positive and statistically significant, showing that the blockade event in Da Nang had a slight impact on the banking sector’s stock price.

The government bond (GB) has become the most attractive investment portfolio option, even though many macroeconomic factors affect the bond yield. This paper aims to investigate the determining factor of local currency government bond yield by considering the inflation rate, credit default swap, stock market index, exchange rate, and volatility index. This study used 240 data panel from the Bloomberg stock market in the form of data panel covering Southeast developing countries, namely Indonesia, Thailand, Malaysia, and the Philippines, for five years or sixty months from January 2015 to December 2019. Data analysis used recursive models and multivariate regression techniques using EViews software. The random effect model results revealed that change in the foreign exchange rate and volatility indexes affected, partially and simultaneously, the changes in the stock market index. The result also showed that changes in the stock market index, inflation rate, and credit default swap affected, partially and simultaneously, government bond yield changes. These results suggest that the government bond yield could be managed by controlling volatility index, foreign exchange rate, stock market index, inflation rates, and credit default swaps. This finding could provide an insight into the policymaker and fiscal authority on managing the risk of government bonds under control during high volatility or even making it reasonably lower. This result could contribute to the current research in the field of financial management.

Acknowledgment
It is the author’s pleasure to thank Muhammad Aulia SE MSc CSA® from the Ministry of Finance of Republic Indonesia, for his invaluable contribution to encourage this study and also to share the data required for this paper. He also delivers essential insights into improving the quality of this work. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Until recently, the statistical approach was the main technique in solving the prediction problem. In the framework of static models, the tasks of forecasting, the identification of hidden periodicity in data, analysis of dependencies, risk assessment in decision making, and others are solved. The general disadvantage of statistical models is the complexity of choosing the type of the model and selecting its parameters. Computing intelligence methods, among which artificial neural networks should be considered at first, can serve as alternative to statistical methods. The ability of the neural network to comprehensively process information follows from their ability to generalize and isolate hidden dependencies between input and output data. Significant advantage of neural networks is that they are capable of learning and generalizing the accumulated knowledge. The article proposes a method of neural networks training in solving the problem of prediction of the time series. Most of the predictive tasks of the time series are characterized by high levels of nonlinearity and non-stationary, noisiness, irregular trends, jumps, abnormal emissions. In these conditions, rigid statistical assumptions about the properties of the time series often limit the possibilities of classical forecasting methods. The alternative methods to statistical methods can be the methods of computational intelligence, which include artificial neural networks. The simulation results confirmed that the proposed method of training the neural network can significantly improve the prediction accuracy of the time series.