The present study investigates a conceptual research framework on financial literacy in various investment planning and decision-making stages. The study comprises a review of 2182 articles published in peer-reviewed journals from 2001 to 2022 (January). The study employed bibliometric techniques such as citation network analysis, co-citation analysis, content analysis, publication trends, and keyword analysis to analyze the literature on financial literacy. The study aims to add to the literature on financial literacy by proposing ten clusters to improve research on financial literacy in order to help investors learn better. Financial literacy has evolved from a fledgling discipline to a significant teaching and research tool. Therefore, it is vital to investigate and identify current research trends in this field. The results are essential to the financial community, given that institutions and society are increasingly emphasizing financial literacy to strengthen individual citizens’ responsibilities in designing their investment strategies.

This paper investigates the nexus between CO₂ emissions (CO₂E), GDP, energy use (ENU), and population growth (PG) in India from 1980–2018 by comparing the “vector error correction” model (VECM) and “auto regressive distributed lag” (ARDL). We applied the unit root test, Johansen multi-variate cointegration, and performed a Variance decomposition analysis using the Cholesky approach. The VECM and ARDL-bound testing approaches to cointegration suggest a long-term equilibrium nexus between GDP, energy use, population growth and CO₂E. The empirical outcomes show the existence of a long-term equilibrium nexus between the variables. The Granger causality results show that short-term bi-directional causality exists between GDP and ENU, while a uni-directional causality between CO₂E and GDP, CO₂E and ENU, CO₂E and PG, and PG and ENU. Evidence from variance decomposition indicates that 58.4% of the future fluctuations in CO₂E are due to changes in ENU, 2.8% of the future fluctuations are due to changes in GDP, and 0.43% of the future fluctuations are due to changes in PG. Finally, the ARDL test results indicate that a 1% increase in PG will lead to a 1.4% increase in CO₂E. Our paper addresses some important policy implications.

Presently, the volatile and dynamic aspects of stock prices are significant research challenges for stock markets or any other financial sector to design accurate and profitable trading strategies in all market situations. To meet such challenges, the usage of computer-aided stock trading techniques has grown in prominence in recent decades owing to their ability to rapidly and accurately analyze stock market situations. In the recent past, deep reinforcement learning (DRL) methods and trading bots are commonly utilized for algorithmic trading. However, in the existing literature, the trading agents employ the historical and present trends of stock prices as an observing state to make trading decisions without taking into account the long-term market future pattern of stock prices. Therefore, in this study, we proposed a novel decision support system for automated stock trading based on deep reinforcement learning that observes both past and future trends of stock prices whether single and multi-step ahead as an observing state to make the optimal trading decisions of buying, selling, and holding the stocks. More specifically, at every time step, future trends are monitored concurrently using a forecasting network whose output is concatenated with past trends of stock prices. The concatenated vectors are subsequently supplied to the DRL agent as an observation state. In addition, the suggested forecasting network is built on a Gated Recurrent Unit (GRU). The GRU-based agent captures more informative and inherent aspects of time-series financial data. Furthermore, the suggested decision support system has been tested on several stock markets such as Tesla, IBM, Amazon, CSCO, and Chinese Stocks as well as equity markets i-e SSE Composite Index, NIFTY 50 Index, US Commodity Index Fund, and has achieved encouraging profit values while trading.

This study aims to investigate regional and periodic asymmetries in the impact of the outbreak of the Russia-Ukraine war on global equity markets. Employing the event study methodology, the current study examines global stock market reactions within a 61-day window centred around the event day, i.e., February 24, 2022. MSCI equity indices of 47 sample countries have been utilized to ensure uniformity in the index development methodology. They provide broader coverage of global equity markets by including large and mid-cap companies, representing approximately 85% of the free float-adjusted market capitalization for each sampled country. The study extends the event window to 61 days to assess the enduring effects of the war over a relatively longer period. The research delineates regional and periodic asymmetries and posits that the impact of the war on a market is contingent upon its geographical proximity and trade relations with Russia and Ukraine. Additionally, the impact is stronger during a shorter window surrounding the event date but diminishes over the extended period.

Demand forecasting is quite volatile and sensitive to several factors. These include firm-specific, i.e., endogenous as well as exogenous parameters. Endogenous factors are firm-specific, whereas exogenous factors are the macroeconomic indicators that significantly influence the demand forecasting of the firms involved in international trade. This research study investigates the significance of endogenous and exogenous indicators of demand forecasting. For this purpose, we use daily production data from a textile apparel firm for the period from May 2021 to January 2022. In the first step, we employ generalized least square and single-layer perceptron models for coefficient estimation to investigate the impact of each indicator. In the second step, we use linear regression (LR), support vector regression (SVR), and a long short-term memory (LSTM) model for demand forecasting. The forecasted results using SVR and LSTM reveal that errors are reduced when exogenous indicators (exchange and interest rates) are used as inputs.