The purpose of this study is to analyse the impact of Chinese macroeconomic factors on Shanghai Stock Exchange (SSE) Composite returns and Indian macroeconomic factors on Nifty returns based on monthly data from January 1998 to December 2018. This study adopts quantile regression approach. The QR allows examining the conditional dependence of specific quantile of SSE and Nifty returns with respect to the conditioning factors. The authors present results for two sample periods that are prerecession and recession period from 1998 to 2008 and the post-recession period from 2009 to 2018. This paper also documents quite interesting and useful results for the entire period. From the results, It is concluded that Chinese consumer price index significantly affects the SSE returns only for lower quantiles. However, Indian consumer price index has a significant and positive impact on the Nifty returns for the upper quantiles. Further, Chinese interest rates and Indian interest rates have no impact on the SSE and Nifty returns respectively across the different quantiles. Moreover, the Chinese exchange rate influence the SSE returns at the extreme dataset. However, the Indian exchange rate is insignificant. It is important to note that the dependence structure of China shows a negligible change during the post-recession period. Conversely, the dependence structure has changed significantly for India postrecession. The implication of this paper would guide stock market participants. Contribution/ Originality:This research is the first application of Quantile Regression approach to address this issue. This study gives some concrete suggestions to investors. First, investors should carefully invest in the Chinese stock market using the Chinese Consumer Price Index and Exchange Rate. Second, avoid investing in the Indian stock market using Indian Interest Rate and Exchange Rate.

This paper analyses the impact of stock market reforms on the stock market performance in India using regression based event-study method. We consider nine stock market reforms introduced from 1998 to 2018. We find that the impact of stock market reforms on Nifty trading volume and Nifty return is different. This paper documents that the impact of the additional volatility measures, T+3 and T+2 settlement cycles, and margin provisions for intra-day crystallized losses reforms show a positive impact on trading volume post-reform. In contrast, internet trading, prohibition of fraudulent and unfair trade practices, delisting of equity shares, substantial acquisition of shares and takeovers listing obligations and disclosure requirements reforms decrease the trading volume post-reform. Our results of Nifty return reveal that the additional volatility measures, the T+2 settlement cycle, the prohibition of fraudulent and unfair trade practices, substantial acquisition of shares and takeovers, listing obligations and disclosure requirements have a significant and positive impact on return post-reform. It is evident that the impact of all nine stock market reforms is insignificant on Nifty return.

This paper examines the stock market linkages and interdependencies between China and India. We use the quantile regression approach as an alternative to Ordinary Least Squares estimation due to its flexibleness and robustness. Our results of the entire time period reveal the influence of Chinese CPI and ER on Nifty returns is not the same across the different quantiles. However, Chinese IR has no impact on Nifty returns. Further, Indian CPI has a negligible effect on SSE returns. In contrast, IR and ER do not affect SSE returns. This study also observes that the dependence structure between CPI and SSE returns indicates a negligible change post-recession period. However, the dependence structure between IR, ER, and SSE returns has not changed after the recession. Further, a significantly small change is found in the dependence structure between Chinese macroeconomic variables and Nifty returns post-recession.

Game-based learning is an exciting and interactive tool used by many teachers across the globe. This research aims to check whether any significant change is found in the learning of the student before and after introducing game-based learning in classroom teaching. MBA students were identified as the target group for this research. The production dice game was used for this experiment. The teacher engaged the first session traditionally and later with the production dice game. Student learning was captured through a Google form before and after the game. The Google form had questions ranging from understanding to analyzing to application-level to capture exactly the effectiveness of game-based learning, Paired sample t-test was applied to check the before and after test results, and it was found that there was a significant change in the learning among the identified target group. Through this study, the authors conclude that game-based learning provides better results in student learning as compared to regular classroom teaching.

No abstract