Smart beta portfolio investment strategy during the COVID-19 pandemic in Indonesia

The purpose of the paper is to select the right market proxy for calculating the expected return, since critically evaluating proxies or selecting the correct proxy market portfolio is essential for portfolio management because the change in the market portfolio proxy affects returns. In this study, monthly data of equity indices are evaluated to find out the better market proxy. The indices taken are BSE 30 (Sensex), Nifty 50, BSE 100, BSE 200, and BSE 500. The macroeconomic variables used in the study are industrial production index (IIP), consumer price index (CPI), money supply (M1), and exchange rate in India. To avoid the influence of COVID-19, the research period was from January 2013 to December 2019 to critically evaluate these proxies in order to find the most appropriate market proxy. This paper reveals a noteworthy relationship between stock market returns and macroeconomic factors, while suggesting that the BSE 500 is a better choice for all equity indices, as the index also shows a significant relationship with all macroeconomic variables. BSE500 is a composite index comprising all sectors with low, mid and large cap securities, therefore it reflects the impact of macroeconomic factors most efficiently, taking it as a market proxy. This study was carried out in the context of India and can be replicated for other countries.

The current empirical study attempts to analyze the impact of COVID-19 on the performance of the Indian stock market concerning two composite indices (BSE 500 and BSE Sensex) and eight sectoral indices of Bombay Stock Exchange (BSE) (Auto, Bankex, Consumer Durables, Capital Goods, Fast Moving Consumer Goods, Health Care, Information Technology, and Realty) of India, and compare the composite indices of India with three global indexes S&P 500, Nikkei 225, and FTSE 100. The daily data from January 2019 to May 2020 have been considered in this study. GLS regression has been applied to assess the impact of COVID-19 on the multiple measures of volatility, namely standard deviation, skewness, and kurtosis of all indices. All indices’ key findings show lower mean daily return than specific, negative returns in the crisis period compared to the pre-crisis period. The standard deviation of all the indices has gone up, the skewness has become negative, and the kurtosis values are exceptionally large. The relation between indices has increased during the crisis period. The Indian stock market depicts roughly the same standard deviation as the global markets but has higher negative skewness and higher positive kurtosis of returns, making the market seem more volatile.

This study aims to investigate the role of Fintech in predicting the spread of COVID-19 based on consumers’ Fintech perceptions and behavior before and after the outbreak of COVID-19. The study used a questionnaire-based survey distributed in different countries of the world using the LinkedIn platform for this purpose to reach the targeted population. The snowball sampling technique was used. The study targeted consumers with Fintech experience, especially in digital payments services. 507 samples were retrieved. For the analysis, the Structural Equation Modeling (SEM) was used. The study revealed novel results in predicting COVID-19 spread; these three variables (Fintech Behavior before COVID-19, Fintech Behavior after COVID-19, and Fintech Perception after COVID-19) could predict 52.5% of the variance in the dependent variable (COVID-19 Spread) (R² = 0.525, p < 0.05). The findings show that Higher Fintech perception and behavior among Fintech users will help in reducing the spread of COVID-19 by avoiding the use of contact payment methods. Contactless payment methods are the main tools in Fintech that might help in avoiding the probability of COVID-19 spread. Consumers’ Fintech perceptions and behavior are the most influencing factors that could predict the spread of COVID-19 in this study, where digital payments are the main concern. It is recommended that consumers adopt digital payment methods and tools, especially contactless payment methods, to fulfill their financial services. Other researchers are also encouraged to use the same model to predict the spread of this virus in the Fintech context.