The role of artificial intelligence and fintech in promoting eco-friendly investments and non-greenwashing practices in the US market.

This study explores the intricate connections among financial technology (FinTech), artificial intelligence (AI), and eco-friendly markets in the US, shedding light on their dynamic interplay and implications for sustainable investment and policy strategies. Specifically, our research delves into the transformative roles of FinTech and AI in broadening financial access, fostering green financing initiatives, and aligning financial practices with environmentally conscious objectives. We also investigate market reactions among the AI, FinTech, non-greenwashing, and eco-friendly markets during exogenous shocks, offering valuable insights into these markets' interconnectedness. An innovative connectedness approach, the R2 decomposed measures, is employed to capture the contemporaneous and lagged spillover effects using daily data from December 19, 2017, to November 1, 2023. We also focus on constructing a minimum connectedness portfolio using the time-varying parameter vector autoregressive approach. The findings reveal significant volatility connectivity within these intergroups, emphasizing the need for sustainable tech finance policies and real-time monitoring systems to address market fluctuations. Overall, this study contributes to an underexplored area by providing empirical evidence and valuable implications for scholars and policymakers, and can help in guiding sustainable investment and policy strategies aligned with zero-emissions agendas.

Assessment of Attentional Processes in Patients with Anxiety-Depressive Disorders Using Virtual Reality.

To characterize the attention deficits in one-hundred-fifteen participants, comprising two types of clinical profiles (affective and anxiety disorder), through a test of continuous VR execution. Method: Three tests (i.e., Nesplora Aquarium, BDI, and STAI) were used to obtain a standardized measure of attention, as well as the existence and severity of depression and anxiety, respectively. Results: Significant differences (CI = 95%) were found between the control group and the group with depression, in variables related to the speed of visual processing (p = 0.008) in the absence of distractors (p = 0.041) and during the first dual execution task (p = 0.011). For scores related to sustained attention, patients with depression and those with anxiety did not differ from controls. Our results suggest attentional deficits in both clinical populations when performing a continuous performance test that involved the participation of the central executive system of working memory.

Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach.

This paper provides an analysis of crude oil, diesel, and gasoline prices for the period from November 1, 2019 to December 31, 2020. We apply Log Periodic Power-Law Singularity (LPPLS) and Discrete Scale LPPLS bubble indicators to explore the dynamic bubbles of oil prices and predict their crash times. The results indicate that West Texas Light crude oil and North Sea Brent crude oil experienced a statistically significant negative financial bubble during the COVID-19 outbreak. In addition, gasoline and diesel prices are mainly driven by fundamentals. Our findings are expected to be useful to oil market investors, policymakers, and energy experts.