This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories of technical analysis: Price, Volume, and Turnover. Various error metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root-Mean-Squared-Error (RMSE) have been used to check the performance of the models. Results show that the XGBoost algorithm performs best among the four ensemble models. The mean of absolute error and the root-mean-square -error vary around 3–5%. The feature importance plots generated by the models depict the importance of the variables in predicting the output. The proposed machine learning models help traders, investors, as well as portfolio managers, better predict the stock market trends and, in turn, the returns, particularly in banking stocks minimizing their sole dependency on macroeconomic factors. The techniques further assist the market participants in pre-empting any price-volume action across stocks irrespective of their size, liquidity, or past turnover. Finally, the techniques are incredibly robust and display a strong capability in predicting trend forecasts, particularly with any large deviations.

Anecdotal evidence indicates that the influence of crude oil shocks on US economic performance and, by extension, economic policy uncertainty is in decline. While existing studies present mixed evidence on the relationship between economic policy uncertainty and structural oil shocks, they fail to assess any time variation in this relationship. We examine the temporal nature of the influence of three structural oil shocks, oil supply shocks, global aggregate demand shocks, and shocks due to oil market-specific demand, on categorical economic policy uncertainty indexes from 1986 to 2023. We find that out of three oil shocks, only oil market-specific demand shocks negatively and significantly affect the US policy uncertainty over high-uncertainty regimes, with monetary policy uncertainty being the most influenced. At the same time, the impact of the other two shocks, oil supply shocks and aggregate demand shocks on EPU, is found to be fading. The oil shocks-EPU relationship exhibits regime dependencies with higher impact during the high volatile regime. Also, the role of partisan conflict in influencing the oil shocks-EPU relationship is established specifically in EPU subcategories relating to government expenditures. Hence, this paper uncovers interesting dynamics of the oil shocks-EPU relationship, such as regime dependencies, time-varying relationship, the importance of partisan conflict, and the fading influence of global oil supply shocks and oil shocks due to aggregate demand in the global economy.

The 2013 wto Bali package was to be discussed for adoption during the 2014 Geneva meeting. Members were particularly keen on the Trade Facilitation Agreement (tfa), which was supposed to boost world trade and benefit developing countries. However, India surprised all members by withholding its support for tfa and demanding further dialogue on the unrelated issue of food security, which was supposedly settled in Bali with a time-bound peace clause. This action posed an existential challenge to wto. This article explores India’s position through a game-theoretic negotiation framework with rational and indicative payoffs. We assert that India adopted a ‘suicide-bomber’ strategy and succeeded in ‘hostage-taking’ to extract the desired ‘ransom.’ Since India exploited the first-mover advantage, the rest of the world had no better choice than to pay the ransom. This is a Nash equilibrium of dominant strategies but with unequal bargaining power between the two players.

In this paper, we are motivated by the fast growing literature that investigates the performance of Divisia monetary aggregates. We construct Divisia monetary aggregates for India using monthly data form January 2001 to March 2020 and present a comprehensive comparison across the Indian Divisia monetary aggregates at four levels of monetary aggregation, M1, M2, M3, and M4. We do so in the context of three classes of empirical models. In particular, we compute correlations between the cyclical components of the Divisia monetary aggregates and the cyclical component of the industrial production index. We test for Granger causality running from the Divisia monetary aggregates to industrial production. We also test for time-varying Granger causality. We find that the levels of the Divisia monetary aggregates Granger cause economic activity in India during normal times, but the causal link broke during and in the aftermath of the extremely unusual circumstances of the Covid-19 crisis.

This paper uses neoclassical microeconomic theory to investigate the demand for energy and interfuel substitution in India at the sectoral level. It makes full use of the relevant economic theory and econometrics and generates inference in terms of Allen and Morishima elasticities of substitution that are internally consistent with the data and nonlinear models used. The results indicate that the interfuel substitution elasticities are consistently below unity in the household and power sectors, revealing the limited ability to substitute between major energy commodities in these two sectors. However, significant substitution relationships are found in the industrial and transportation sectors, suggesting that energy price changes in these sectors will significantly shift the demand for energy and consumption. Based on measured elasticities of substitution, we also discuss implications of energy price shocks on inflation and inflation targeting strategies by the central bank.