Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty

The Journal of Real Estate Finance and Economics - We analyze the role of macroeconomic uncertainty in predicting synchronization in housing price movements across all the United States (US) states...     

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.     

Effects of social distancing policy on labor market outcomes

US workers receive unemployment benefits if they lose their job, but not for reduced working hours. In alignment with the benefits incentives, we find that the labor market responded to COVID-19 and related closure-policies mostly on the extensive (12 pp outright job loss) margin. Exploiting timing variation in state closure-policies, difference-in-differences (DiD) estimates show, between March 12 and April 12, 2020, employment rate fell by 1.7 pp for every 10 extra days of state stay-at-home orders (SAH), with little effect on hours worked/earnings among those employed. Forty percentage of the unemployment was due to a nationwide shock, rest due to social-distancing policies, particularly among “non-essential” workers.     

Climate risks and U.S. stock-market tail risks: A forecasting experiment using over a century of data

We examine the predictive value of the uncertainty associated with growth in temperature for stock-market tail risk in the United States using monthly data that cover the sample period from 1895:02 to 2021:08. To this end, we measure stock-market tail risk by means of the popular Conditional Autoregressive Value at Risk (CAViaR) model. Our results show that accounting for the predictive value of the uncertainty associated with growth in temperature, as measured either by means of standard generalized autoregressive conditional heteroskedasticity (GARCH) models or a stochastic-volatility (SV) model, mainly is beneficial for a forecaster who suffers a sufficiently higher loss from an underestimation of tail risk than from a comparable overestimation.     

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.     

Modeling the key factors influencing the adoption of mobile financial services: an interpretive structural modeling approach

Mobile financial services are widely appreciated worldwide, but a considerable fragment of the population is resisting the technology. Therefore, the purpose of this paper is to identify and analyze the contextual relationships among a set of measures that influence the adoption of mobile financial services (MFSs). The paper employed the interpretive structural modeling technique to formulate a multilevel structural model with experts' knowledge and experience. Using MICMAC analysis, the factors were classified into four clusters: autonomous, linkage, dependent, and driving based on their dependence and driving power. The outcome shows that facilitating conditions is the most crucial factor in influencing the MFS adoption and demands special attention by authorities for better implementation of the technology. The findings will help the bank managers and telecom companies to direct their resources in significant areas.

     

Applying the theory of reasoned action to examine consumers' attitude and willingness to purchase organic foods

The organic food market has emerged as a growing trend among consumers. The present study examines the relationship between Health Consciousness (HC), Organic Food Knowledge (OFK), Subjective Norms (SN), Price Perception (PP), Environmental Concern (EC), Attitude (ATT), Willingness to Purchase (WP), and Actual Buying Behaviour (ABB) towards organic food. Furthermore, the study explores the mediating effects of ATT and WP in the relationship between the aforesaid variables and ABB. The data was collected from 240 respondents using convenience sampling approach. The Structure Equation Modelling (SEM) using ADANCO 2.2 is used to test the hypotheses of the conceptual model proposed in the present study. The study found that the variables HC, OFK, SN, EC, ATT, WP significantly affect the ABB. Also, it is observed that EC is the strongest predictor of ATT, WP, and ABB in organic food purchase, whereas PP is the least influencing factor. Further, HC positively influences ATT and WP but shows a negative association with ABB. The antecedents HC, OFK, SN, PP, EC has shown a significant indirect effect on ABB among respondents through ATT and WP. Finally, the theoretical and practical implications are discussed.     

Price sensitivity,government green interventions,and green product availability triggers intention toward buying green products

In the 21st century, environmental problems are wreaking havoc, and sustainability is now of primary importance. Several external factors like population growth, industrialization, development, and overexploitation of natural resources play a crucial role in environmental degradation. Thus, the present study endeavors to explore the impact of price sensitivity, governments green interventions and green product availability on green buying intention through the lenses of the theory of planned behavior and the theory of consumption values. It also intends to examine the moderating effect of demographic factors on green buying intention. A cross-sectional study was carried out. Responses were gathered through a self-administered questionnaire-based survey. The final data set of 708 respondents were subjected to structural equation modeling for hypothesis testing. Price sensitivity, government green interventions, and green product availability show negative and significant interaction effects. Perceived behavioral control shows a relatively more substantial impact on green buying intention. Indian consumers from the age group of 41–50 years relatively have higher intention toward green buying. Overall, gender does not reveal any different approaches to environmentally friendly products. Green marketers must focus on communicating the availability of green products to reduce perceived difficulty.