Electric vehicles (EVs) are increasingly becoming popular as a viable means of transportation for the future. The use of EVs may help in providing better climatic conditions in urban areas with a pocket friendly cost for transportation to the consumers throughout its life. EVs enact as a boon to the society by providing zero tailpipe emissions, better comfort, low lifecycle cost and higher connectivity. The article aims to provide scientific information throughout the literature across various aspects of EVs in their lifetime and thus, assist the scholarly community and various organisations to understand the impact of EVs. In this study we have gathered information from the articles published in SCOPUS database and through grey literature with the focus of information post 2009. We have also used a hybrid methodology using Best-Worst Method (BWM) and technique for order preference by similarity to ideal solution (TOPSIS) for comparing EVs, internal combustion engine vehicles (ICEVs) and hybrid vehicles in various price segments. The study has helped us conclude that EVs should be preferred over ICEVs and hybrids by the users.

This study introduces novel concepts in the analysis of limit order books (LOBs) with a focus on unveiling strategic insights into spread prediction and understanding the global mid-price (GMP) phenomenon. We define and analyze the total market order book bid--ask spread (TMOBBAS) and GMP, showcasing their significance in providing a deeper understanding of market dynamics beyond traditional LOB models. Employing high-frequency data, we comprehensively examine these concepts through various methodological lenses, including tail behavior analysis, dynamics of log-returns, and risk--return performance evaluation. Our findings reveal the intricate behavior of TMOBBAS and GMP under different market conditions, offering new perspectives on the liquidity, volatility, and efficiency of markets. This paper not only contributes to the academic discourse on financial markets but also presents practical implications for traders, risk managers, and policymakers seeking to navigate the complexities of modern financial systems.

Total value locked (TVL) is widely used to measure the size and popularity of protocols and the broader ecosystem in decentralized finance (DeFi). However, the prevalent TVL calculation framework suffers from a "double counting" issue that results in an inflated metric. We find existing methodologies addressing double counting either inconsistent or flawed. To mitigate the double counting issue, we formalize the TVL framework and propose a new framework, total value redeemable (TVR), designed to accurately assess the true value within individual DeFi protocol and DeFi systems. The formalization of TVL indicates that decentralized financial contagion propagates through derivative tokens across the complex network of DeFi protocols and escalates liquidations and stablecoin depegging during market turmoil. By mirroring the concept of money multiplier in traditional finance (TradFi), we construct the DeFi multiplier to quantify the double counting in TVL. Our empirical analysis demonstrates a notable enhancement in the performance of TVR relative to TVL. Specifically, during the peak of DeFi activity on December 2, 2021, the discrepancy between TVL and TVR widened to \$139.87 billion, resulting in a TVL-to-TVR ratio of approximately 2. We further show that TVR is a more stable metric than TVL, especially during market turmoil. For instance, a 25% decrease in the price of Ether (ETH) results in an overestimation of the DeFi market value by more than \$1 billion when measuring using TVL as opposed to TVR. Overall, our findings suggest that TVR provides a more reliable and stable metric compared to the traditional TVL calculation.

We present an approach for automatically generating and testing, in silico, social scientific hypotheses. This automation is made possible by recent advances in large language models (LLM), but the key feature of the approach is the use of structural causal models. Structural causal models provide a language to state hypotheses, a blueprint for constructing LLM-based agents, an experimental design, and a plan for data analysis. The fitted structural causal model becomes an object available for prediction or the planning of follow-on experiments. We demonstrate the approach with several scenarios: a negotiation, a bail hearing, a job interview, and an auction. In each case, causal relationships are both proposed and tested by the system, finding evidence for some and not others. We provide evidence that the insights from these simulations of social interactions are not available to the LLM purely through direct elicitation. When given its proposed structural causal model for each scenario, the LLM is good at predicting the signs of estimated effects, but it cannot reliably predict the magnitudes of those estimates. In the auction experiment, the in silico simulation results closely match the predictions of auction theory, but elicited predictions of the clearing prices from the LLM are inaccurate. However, the LLM's predictions are dramatically improved if the model can condition on the fitted structural causal model. In short, the LLM knows more than it can (immediately) tell.

Market fluctuations caused by overtrading are important components of systemic market risk. This study examines the effect of investor sentiment on intraday overtrading activities in the Chinese A-share market. Employing high-frequency sentiment indices inferred from social media posts on the Eastmoney forum Guba, the research focuses on constituents of the CSI 300 and CSI 500 indices over a period from 01/01/2018, to 12/30/2022. The empirical analysis indicates that investor sentiment exerts a significantly positive impact on intraday overtrading, with the influence being more pronounced among institutional investors relative to individual traders. Moreover, sentiment-driven overtrading is found to be more prevalent during bull markets as opposed to bear markets. Additionally, the effect of sentiment on overtrading is observed to be more pronounced among individual investors in large-cap stocks compared to small- and mid-cap stocks.

This article reviews the economics literature of, primarily, the last 20 years, that studies the link between income shocks and consumption fluctuations at the household level. We identify three broad approaches through which researchers estimate the consumption response to income shocks: 1.) structural methods in which a fully or partially specified model helps identify the consumption response to income shocks from the data; 2.) natural experiments in which the consumption response of one group who receives an income shock is compared to another group who does not; 3.) elicitation surveys in which consumers are asked how they expect to react to various hypothetical events.

In this paper we compare Skill-Relatedness Networks (SRNs) for selected countries, that is to say statistically significant inter-industrial interactions representing latent skills exchanges derived from observed labor flows, a kind of industry spaces. Using data from Argentina (ARG), Germany (DEU) and Sweden (SWE), we compare their SRNs utilizing an information-theoretic method that permits to compare networks of "non-aligned" nodes, which is the case of interest. For each SRN we extract its portrait, a fingerprint of structural measures of the distributions of their shortest paths, and calculate their pairwise divergences. This allows us also to contrast differences in structural (binary) connectivity with differences in the information provided by the (weighted) skill relatedness indicator (SR). We find that, in the case of ARG, structural connectivity is very different from their counterpart in DEU and SWE, but through the glass of SR the distances analyzed are all substantially smaller and more alike. These results qualify the role of the SR indicator as revealing some hidden dimension different from connectivity alone, providing empirical support to the suggestion that industry spaces may differ across countries.