Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH-LSTM based Approach

This study investigates the advantage of combining the forecasting abilities of multiple generalized autoregressive conditional heteroscedasticity (GARCH)-type models, such as the standard GARCH (GARCH), exponential GARCH (eGARCH), and threshold GARCH (tGARCH) models with advanced deep learning methods to predict the volatility of five important metals (nickel, copper, tin, lead, and gold) in the Indian commodity market. This paper proposes integrating the forecasts of one to three GARCH-type models into an ensemble learning-based hybrid long short-term memory (LSTM) model to forecast commodity price volatility. We further evaluate the forecasting performance of these models for standalone LSTM and GARCH-type models using the root mean squared error, mean absolute error, and mean fundamental percentage error. The results highlight that combining the information from the forecasts of multiple GARCH types into a hybrid LSTM model leads to superior volatility forecasting capability. The SET-LSTM, which represents the model that combines forecasts of the GARCH, eGARCH, and tGARCH into the LSTM hybrid, has shown the best overall results for all metals, barring a few exceptions. Moreover, the equivalence of forecasting accuracy is tested using the Diebold–Mariano and Wilcoxon signed-rank tests.     

Mergers and acquisitions research in finance and accounting: Past,present, and future

This study presents an analysis of publication patterns and major themes in research on mergers and acquisitions in finance and accounting. We find that takeovers as mechanisms of governance, drivers of mergers, mechanisms of mergers, bank mergers, cross-border mergers, shareholder wealth effects of mergers and related events, and the role of financial experts and ownership structure form major themes of research in the finance area, while in accounting area major themes are corporate governance and accounting outcomes, predicting takeovers and their outcomes, valuation, financial reporting and takeover decisions, and financial reporting and performance.     

How can banks and finance companies incorporate value chain factors in their risk management strategy? The case of agro-food firms

A value chain framework for guiding the financial firms in their credit decisions is urgent, as the current COVID-19 pandemic has highlighted, but missing in the extant literature, particularly for those that lend to industries sensitive to value and supply chain bottlenecks. This study creates knowledge in value chain finance, a big untapped and un-researched market. It constructs, confirms, and validates a value chain framework for assessing risks in lending to Agro and Food Processing firms in which value chain risks are major business concerns globally. To pursue the objectives of the study, we use a novel methodology that integrates the Modified Delphi technique, exploratory factor analysis, confirmatory factor analysis, and discriminant analysis. Based on testing and analysis of primary data, including loan data, a framework comprising six factors is proposed for use in conjunction with existing risk assessment models of finance companies to improve the quality of their credit decisions, contributing to their performance sustainability.     

Sovereign Bond Market Shock Spillover Over Different Maturities: A Journey from Normal to Covid-19 Period

Asia-Pacific Financial Markets - With application of Diebold and Yilmaz’s (Int J Forecast 28(1):57–66, 2012) spillover approach, we examine shock spillover in international sovereign...     

Benchmarking sustainable E-commerce enterprises based on evolving customer expectations amidst COVID-19 pandemic

The 2019 coronavirus disease (COVID-19) pandemic has seriously impacted the performance of all types of businesses. It has given a tremendous structural boost to e-commerce enterprises by forcing customers to online shopping over visiting physical stores. Moreover, customer expectations of the digital and operational capabilities of e-commerce firms are also increasing globally. Thus, it has become crucial for an e-commerce enterprise to reassess and realign its business practices to meet evolving customer needs and remain sustainable. This paper presents a comprehensive performance evaluation framework for e-commerce enterprises based on evolving customer expectations due to the COVID-19 pandemic. The framework comprises seven primary criteria, which are further divided into 25 sub-criteria, including two sustainability factors, namely, environmental sustainability and carbon emissions. The evaluation approach is then practically demonstrated by analyzing the case of three Indian e-commerce firms. The results are obtained using a multi-criteria decision-making (MCDM) method, namely, Fuzzy VIKOR, to capture the fuzziness of the inherent decision-making problem. Further, numerical analysis is conducted to evaluate and rank various e-commerce enterprises based on customer expectations and satisfaction benchmarks. The findings explain the most important criteria and sub-criteria for e-commerce businesses to ensure customer expectations along with their economic and environmental sustainability.     

Distributional heterogeneity in climate change impacts and adaptation: Evidence from Indian agriculture

This study estimates the distributional heterogeneity in the effects of climate change on yields of three major cereal crops: rice, maize, and wheat in India using district-level information for the period 1966–2015. We distinguish between the effects of changes in growing season weather from those due to changes in long-term climate trends and the heterogeneity in these effects across the distribution of crop yields by estimating naïve and climate penalty inclusive models using fixed-effect quantile panel models. We observe an absence of adaptation against rising temperatures for rice and wheat. However, we find a statistically significant presence of adaptation for wheat and maize for changes in precipitation, though the magnitude is small. Moreover, we find that the effects are asymmetric, and are larger at the lower tail of productivity distribution and smaller at the upper tail of the distribution. A 1°C increase in temperature lowers rice and wheat productivity by 23% and 9%, respectively at the first quantile, but the damage is only 6% and 5% at the ninth quantile. Heterogeneity in impacts and adaptation estimates over the yield distribution curve and across crops suggests the importance of customizing strategies for adaptation to changing weather and climate conditions across regions, crops, and current productivity levels.     

Digital innovation: An essence for Industry 4.0

The goal of this paper is to explain why digital innovation is so important in business organizations in order to survive in Industry 4.0. The study helps to understand the new era of Industry 4.0 and the importance of introducing digital innovation into organizations. A systematic review of the literature and studies on Industry 4.0 and digital innovation were synthesized to find answers to the research questions. To improve their manufacturing industry, organizations have implemented digital technologies such as augmented reality, robotic sensing, artificial intelligence, cloud computing, cyber physical systems, and remote sensing technologies. These technologies focused on automating logistics and supply chain systems, improving manufacturing system performance, and simplifying automated production systems. Because digital innovations save time and energy, employees can devote more time and energy to creative and innovative activities. Organizations should plan to implement digital technology in order to keep the environment healthy and sustainable while meeting the demands of customers, consumers, and the Industry 4.0 dimension.     

Examining the Time Varying Spillover Dynamics of Indian Financial Indictors from Global and Local Economic Uncertainty

Journal of Quantitative Economics - The research aims to excavate the role of global (Fed Rate, Crude, Real Dollar Index) and endogenous economic variables (GDP and Consumer Price Index) in shaping...     

Twenty-five years of consumer vulnerability research: Critical insights and future directions

Consumer vulnerability is gaining increasing attention from researchers and practitioners as a social phenomenon in modern consumption-driven societies. In an attempt to conduct a comprehensive review using credible published research materials on the topic, this study applies a big data analytics approach to analyze scholarly research with a pool of 859 articles covering the last 25 years of post-modern marketing practices that have been a precursor to market-driven vulnerability. This paper identifies the most influential articles, as well as the top contributing journals and authors, along with their affiliations. In addition, this study reveals four major themes of research in consumer vulnerability, namely marketing, fraud and consumers, consumer vulnerability and well-being, ethics and vulnerable consumers, and consumption, disability and gender. These integrative perspectives will serve as a critical starting point to encourage focused theme-based exploration in the field.     

The impact of regional trade policy uncertainty on the productivity of Chinese export enterprises: The case of China–ASEAN Free Trade Area

Reduction of regional trade policy uncertainty is critical for promoting Chinese export enterprise productivity and will help the economy enter the high-quality development stage. We use the case of the China–ASEAN Free Trade Area to empirically tests the impact of the reduction of regional trade policy uncertainty on the productivity of Chinese export enterprises. We apply the difference-in-difference method to micro-enterprise level data from the “China industrial enterprise database” and “China Customs enterprise database” for 2007–2013. Our analysis and results show that establishing the China–ASEAN Free Trade Area has dramatically reduced regional trade policy uncertainty, significantly impacting the productivity of Chinese enterprises exporting to ASEAN. The results also show that enterprises with different ownership types, regions, factor density, and pollution emission intensity show significant heterogeneity in the level of productivity improvement. Applying a mechanism test, we found that the promotion of productivity from a reduction of regional trade policy uncertainty is achieved through the learning effect, the competition effect, and improving the trade environment of imported intermediate goods. The learning effect mechanism has the highest contribution, accounting for 39.01% of the net effect. The empirical results provide novel insight into promoting the productivity of Chinese enterprises exporting to ASEAN from the perspective of reducing regional trade policy uncertainty.