The study is about contributing to the ongoing discussion on the diversification opportunities for emerging markets with non-conventional asset class. The limited literature in the era of fourth industrial revolution motivates us to gauge diversification opportunities. This study is focusing on identifying diversification opportunities with a set of unique asset classes that are the proxies for Green Funds, FinTech and Artificial Intelligence-based index funds. The method and model applied in the study are time and frequency connectedness in a Wavelet Coherence, and for the robustness check—Network analysis has been applied. The originality of the study lies in identifying the impact of the outbreak of COVID-19. The results captured that FinTech-based asset was the most resilient asset class during the pre- and post-outbreak of COVID-19, followed by AI-based fund and finally by Green fund. Henceforth, FinTech provides superior diversification opportunities among all with MSCI Emerging Market. AI and Green funds are captured to be invested in the long term for diversification, whereas FinTech is suitable for both long- and short-term assets. The results are relevant for investors in emerging markets and for policymakers as well.

After the advent of a new economic policy, the stock market had shown exponential growth. The world’s financial markets have become a global financial village via the free flow of capital from one market to another. This provides breadth and depth in the stock market. On the other hand, with the dawn of globalization, the market has become cointegrated and thus more vulnerable to financial shocks. Thus, as a rational investor, catching early signs of financial distress and predicting stock prices is the challenge. This study considers the Altman Z-score to predict the financial distress and stock prices with special reference to the automotive sector in India. The study has been conducted in two parts: the first part focuses on analysing the financial distress of the automotive sector under the face of the financial crisis and GST regime. Thus, this study has been conducted in four window periods. The second part of the study deals with predicting the prices of auto stocks by panel data modelling for the period from 2000 to 2020. Using econometric-based growth curves, the study analyses that the automotive sector is affected by the financial crisis and the GST regime. Lastly, with the application of the panel data static-based fixed effects model, it has been analysed that EBITDA/TA and MV/TL are the significant ratios to predict the stock prices.

Purpose: Analysts expect reduced bank earnings as a result of the impact of the increase in bad loans. Banks have strategically created high provision coverage ratios allocating large funds for possible deterioration in asset quality. Given the expected faster growth and recovery in the bank lending sector, investors have always been interested in banking stocks, despite the waves of non-performing assets (NPAs) and recessionary influences. Historical references reiterate that the banking stocks have been better performers in their returns compared to the capital markets.Design/methodology/approach: The study aims to examine the impact of key accounting variables on the stock prices of Indian banks in the panel data framework.Findings: The current study explores the impact of accounting variables on the market prices of shares. After the study, it may be concluded that earning per share (EPS), return on equity (ROE), capital adequacy ratio (CAR) and net interest margin (NIM) have an incremental impact on the prices of banking stocks, and the current ratio (CR) and NPAs have a detrimental impact on them.Practical implications: Studying their impact on stock prices is the most convenient fundamental analysis that could be conducted on the stock prices of the banks.Originality/value: To provide insights into the association of the accounting and regulatory variables there is a severe limitation in the quantity of the literature available. This study has attempted to build a relationship between the accounting and regulatory variables and the stock prices of banking stocks, to help investors with some reliable methods to estimate the stock prices in the future.