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We develop a dynamic model of corporate investment and financing, in which shocks to the value of collateralizable assets generate variation in firms' debt capacity. We show that the degree of similarity among firms' financial flexibility forecasts cross-sectional variation in return correlation. We test the implications of the model with firm-level data in two empirical analyses using i) an instrumental variable approach based on shocks to the value of collateralizable corporate assets and ii) the outbreak of the COVID-19 crisis as an event study. We find that firms in the same percentile of the cross-sectional distribution of financial flexibility have 62% higher correlation in stock-return residuals than firms 50 percentiles apart. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Going public by merging with a Special Purpose Acquisition Company (SPAC) is much more expensive than conducting a traditional IPO. We rationalize why some companies merge with a SPAC by listing the potential benefits. We analyze the agency problems that certain SPAC features address. SPAC IPO investors and deal sponsors have earned remarkably high annualized average returns, although we warn that recent deals are likely to disappoint. Public investors in the merged companies have earned very low market-adjusted returns on an equally weighted basis, although high redemptions on the worst deals have limited the amount of money that they lost. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

I examine the impact of the opioid epidemic on subprime auto lending. Using a difference-in-differences framework, I find that county-level increases in opioid abuse cause an increase in loan defaults. Moreover, I find that traditional credit scoring attributes (e.g., FICO score) fail to predict loan performance deterioration associated with opioid addiction. The weak predictive performance of traditional credit measures and the resulting higher default rates generate a negative externality for borrowers in opioid-afflicted areas, as evidenced by 5.7% higher loan costs for subprime borrowers. [ABSTRACT FROM AUTHOR]

Using novel merger valuation data, we show that firms selected by investment banks as "comparable peers" are more than twice as likely to later become takeover targets themselves compared to matched control firms. Peer firms not subsequently acquired attract more institutional ownership and analyst coverage, deliver strong operating performance, reduce investments, and increase payouts. Investors are inattentive, though, to peer identification at the time of merger filings' public disclosure. A portfolio that longs peers and shorts controls earns up to 15.6 |$\%$| alpha annually, which mainly comes from the long leg and is difficult to explain by short-sale constraints. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

We study how the U.S. government's anti-counterfeiting enforcement actions through Special 301 Reports influence U.S. businesses. We show that anti-counterfeiting enforcement in foreign countries improves U.S. firms' sales, profitability, and valuations. Firms significantly reduce capital and research and development investments when their brands and products are protected from counterfeiting activities. Anti-counterfeiting enforcement measures also improve brand asset value, brand profitability, brand inventiveness, market penetration, and customer loyalty. [ABSTRACT FROM AUTHOR]

We hypothesize that corporate takeover markets create significant constraints for short sellers. Both short sellers and corporate bidders often target firms with declining economic prospects. Yet, a target firm's stock price generally increases upon a takeover announcement, resulting in losses for short sellers. Therefore, short sellers should require higher rates of return when the takeover likelihood is higher. Consistent with this prediction, the return predictability of monthly short interest increases with industry-level takeover probability and decreases as takeover defenses are implemented. Our results suggest that efficient takeover markets create trading frictions for short sellers and can therefore inhibit overall market efficiency. [ABSTRACT FROM AUTHOR]

The fast warning for financial risk of enterprises has always been a realistic demand for their managers. Currently, this mainly relies on expert experience to make comprehensive analysis from massive business data. Benefitting from the strong computational performance of deep learning, this paper proposes a fuzzy neural network (FNN)-based intelligent warning method for financial risk of enterprises. An improved FNN structure with time-varying coefficients and time-varying time lags is established to extract features of enterprises from complex financial context. The algorithm of fuzzy C-means and fuzzy clustering based on sample data are studied. In this paper, the fuzzy C-means algorithm is used to cluster the samples, the input sample set is preprocessed, a new set of learning samples is formed, and then the neural network is trained. The enterprise financial risk sample and its modular FNN model are established, and the evaluation of the enterprise financial risk sample is simulated. Then, a decision part is added following the FNN part to output the warning results. After that, we have also conducted a case study as simulation experiments to evaluate the proposed technical framework. The obtained results show that it can perform well in the fast warning of financial risk for enterprises. [ABSTRACT FROM AUTHOR]

Researchers have long been interested in how owners affect firms' corporate social responsibility (CSR) performance. However, owners face diverging ethical preferences between funding and potentially benefiting from their firms' CSR performance. To better understand owners' influence on firms' CSR performance, we focus on ultimate controlling owners with the highest control rights over their firms. We theorize that ultimate controlling owners with more control rights have stronger motivations and greater decision-making power to promote firms' CSR performance to demonstrate that they are responsible owners and gain legitimacy and goodwill from their stakeholders. Moreover, we explore how this positive relationship is strengthened when ultimate controlling owners and their firms share similar corporate names and receive increased financial analyst coverage, as these conditions increase the likelihood of gaining legitimacy and goodwill through their firms' improved CSR performance. We test our theory using a sample of 852 publicly listed Chinese firms from 2008 to 2017. Our findings support our theoretical predictions and contribute to a more nuanced understanding of how differences in ownership structure and owner type associated with ultimate controlling owners shape their motives and power to affect CSR performance in their firms. [ABSTRACT FROM AUTHOR]

With the aim of exploring the impact mechanism of scientific and technological financial efficiency on regional real economy growth in the context of ecological civilization construction, this study introduces environmental regulation as a mediating factor. By analyzing changes in science and financial efficiency of science and technology, we provide an effective basis for regional real economy development. To achieve this goal, we define concepts such as science and financial efficiency of science and technology and regional real economy, measure data from 2012 to 2021, analyze the impact of science and financial efficiency of science and technology on economic growth using intermediary models, test mediation effects with bootstrap methods, and identify significant differences between regions. It indicates that enhancing science and financial efficiency of sci-tech benefits China's regional real economy growth, but there's unbalanced development across regions. Additionally, environmental regulation serves as a crucial intermediary in the relationship between sci-tech finance and economic growth. There exist regional disparities in the mediation effects of environmental regulation, with eastern regions demonstrating stronger effects compared to central and western regions. [ABSTRACT FROM AUTHOR]

Long time series forecasting (LTSF), which involves modeling relationships within long time series to predict future values, has extensive applications in domains such as weather forecasting, financial analysis, and traffic prediction. Recently, numerous transformer-based models have been developed to address the challenges in LTSF. These models employ methods such as sparse attention to alleviate the inefficiencies associated with the attention mechanism and utilize decomposition architecture to enhance the predictability of the series. However, these complexity reduction methods necessitate additional calculations, and the series decomposition architecture overlooks the random components. To overcome these limitations, this paper proposes the Frequency Enhanced Decomposed Attention Free Transformer (FEDAF). FEDAF introduces two variants of the Frequency Enhanced Attention Free Mechanism (FEAFM), namely FEAFM-s and FEAFM-c, which seamlessly replace self-attention and cross-attention. Both variants perform calculations in the frequency domain without incurring additional costs, with the time and space complexity of FEAFM-s being O (L log L) . Additionally, FEDAF incorporates a time series decomposition architecture that considers random components. Unlike other models that solely decompose the series into trend and seasonal components, FEDAF also eliminates random terms by applying Fourier denoising. Our study quantifies data drift and validates that the proposed decomposition structure can mitigate the adverse effects caused by data shift. Overall, FEDAF demonstrates superior forecasting performance compared to state-of-the-art models across various domains, achieving a remarkable improvement of 19.49% for Traffic in particular. Furthermore, an efficiency analysis reveals that FEAFM enhances space efficiency by 12.8% compared to the vanilla attention mechanism and improves time efficiency by 43.63% compared to other attention mechanism variants. [ABSTRACT FROM AUTHOR]

Corporate green investment is the key driver for achieving low-carbon transition and sustainable marine economy. Existing literature has identified several influencing factors of corporate green investment, but there is still limited research on the relationship between climate policy uncertainty and corporate green investment. To fill this gap, this paper innovatively explores the nonlinear impact of climate policy uncertainty on corporate green investment, simultaneously focuses on the moderating role of corporate financing constraints and analyzes the coastal characteristic heterogeneity. Using the sample of listed companies in China from 2014-2020, we find that climate policy uncertainty has an inverted U-shaped impact on corporate green investment. From the perspective of heterogeneity, the inverted U-shaped effect is more significant in coastal areas. Moderating effect test indicates that corporate financing constraints strengthen the inverted U-shaped relationship and shift the turning point of curve to the left. Additionally, we further find that climate policy uncertainty has an inverted U-shaped impact on environmental performance of companies in coastal areas through green investment. Our research will provide some potential insights for China's climate policy making and corporate green transformation, so as to contribute to sustainable marine development. [ABSTRACT FROM AUTHOR]

The digital transformation of enterprises has become an important driving force in promoting the high-quality development of China's economy and in-depth implementation of an innovation-driven development strategy. To this end, this study uses the panel data of China's A-share listed enterprises from 2010 to 2019 to explore the relationship among enterprise digital transformation, government subsidies, and innovation. Results indicated that digital transformation can significantly stimulate enterprise innovation. The impact mechanism test shows that the digital transformation of enterprises can increase their R&D expenditure and alleviate their financing constraints, thereby jointly promoting enterprise innovation in terms of innovation resources and financing resources. Furthermore, heterogeneity analysis indicates that digital transformation has a greater incentive effect on private enterprises, non-technology enterprises, service industries, and growth stage enterprises. By introducing special financial subsidies for digital transformation, special financial subsidies for digital transformation are shown to have a positive impact on the sensitivity of digital transformation and enterprise innovation. This effect is also played out through innovation spending and corporate finance, and the role of long-term financing is greater than that of short-term financing. Therefore, the role of digital transformation in promoting enterprise innovation must be actively played to achieve a high-quality development of the regional economy. [ABSTRACT FROM AUTHOR]

There have been financial crises for as long as there have been economies; such crises have significant effects on the global economy. Their effects have become an attractive topic that produces much research every year. The goal of this paper is to use the bibliometrics method to explore research trends in the study of financial crises, with a focus on evaluating the caliber of research outputs by their mutual impacts and citation status. Data acquired from Scopus are used in the study. VOSViewer was used to visualize the networks and distribution of the data obtained from 5,011 articles in the financial crisis subject that were written between 1990 and 2022. The statistical findings offer a summary of citation patterns related to financial crises, including the most frequently cited publications and its network, keyword distribution, co-authorship status, bibliographic coupling analysis, and the most prestigious journals. This study aims to identify the developments and trends in financial crisis research. This can aid academics in developing a thorough grasp of this area and serve as a reference for the use of financial crisis visualization techniques. [ABSTRACT FROM AUTHOR]

This research aims to determine the role of 2017-2021 general state tax amendments in terms of economic development and improvement of the business environment in Georgia. The following factors were used To evaluate the effectiveness of the tax burden: How the tax system is adjusted to the payer, whether it contributes to economic progress, and what the scope of taxes is. Tax policy experts were interviewed and asked to discuss tax policy reform objectives, options, and mechanisms. The interviews were semi-structured, supported by a briefing document and a topical guide, audio-recorded, transcribed, and analyzed. The efficiency of implemented changes for the EU Association Agreement was also evaluated. Compared to Georgian tax policy, EU tax policy is more liberal. The research revealed opportunities, future perspectives, and the significance of tax reforms and their positive effect on businesses. Based on in-depth interview findings, a detailed analysis of the financial sector is offered. [ABSTRACT FROM AUTHOR]

The interaction of climate change and financial fragility is set to challenge States' abilities to manage their economies. This article examines the configurations of industries for selected countries, revealing the vulnerabilities where climate change will have the severest impacts. The use of the foundational economy concept facilitates an analysis of financial fragility by isolating activities in national accounts data which contribute to human needs. Improvements in the satisfaction of human needs will require innovative State initiatives, including ways to lighten sovereign debt burdens to free funding for new programs. A Universal Basic Services scheme is a promising initiative. [ABSTRACT FROM AUTHOR]

Aspect-Based Sentiment Analysis (ABSA) for financial review still has some errors which impact the low accuracy of ABSA performance. One of the errors happens because there are implicit aspects and opinions, which are indicated as polysemy terms. For example, the term spread can refer to the aspect term for financial aspect categories and can also refer to the opinion term. Implicit opinion extraction in financial reviews requires in-depth attention because several opinion terms contained in reviews are nouns and not adjectives or verbs that describe explicit opinions (e.g., the review Copper market may get a 2003-style supply shock from Glencore closures). This research proposes ABSA for financial review with implicit aspects and opinions using Semantic similarity and a hybrid approach. We use the FiQA 2018 dataset, which has been classified into four aspect categories: Corporate, Economy, Stock, and Market. First, the dataset is pre-processed. Then, we extract aspect category keywords from Wikipedia using Word2vec. For the aspect categorization method, we use implicit aspect extraction, Semantic similarity, and hybrid BERTBiLSTM to calculate the similarity between extracted aspect terms and aspect category keywords for determining the aspect category. For the ABSA method, we use implicit opinion extraction and hybrid BERT-BiLSTM. The obtained performance result of aspect categorization reaches 91% and the obtained performance result of ABSA reaches 92%. [ABSTRACT FROM AUTHOR]

To find out how carbon performance and disclosure practices affects corporate's financial performance, this study investigates its non-linear influence on financial performance by considering non-financial 100 businesses that are listed on National Stock Exchange 200 Index in India for the consecutive 12 years, i.e., from 2010 to 2021. It employs two indicators of environmental-related information like carbon performance which is measured in terms of greenhouse gas reduction, and disclosure practices which is measured in terms of environmental disclosure score. It uses the dynamic panel data regression analysis technique to estimate the parameters. The empirical outcomes show an obvious non-linear impact of carbon performance on corporate financial performance, which is proxied by Tobin's Q. This indicates that at the initial stage, carbon performance decreases financial performance, but later on, further increase in the carbon performance is found to improve corporates financial performance in the long duration. But, in case of disclosure practices it shows no effect on market-based economic performance, i.e., Tobin's Q. Therefore, the study recommends the investors to be courageous and patience because carbon performance will decrease financial performance at the lower level, but can give benefits in the long run. This paper also suggests the regulators to incorporate environmental standards, and introduce severe forfeits for ecological wrongdoers with the aim of enhancing companies' environmental disclosure activities. [ABSTRACT FROM AUTHOR]

A cutting-edge Decision Support System (DSS) utilizing Deep Reinforcement Learning (DRL) for improved financial data analysis is the primary focus of the proposed research. In light of the prospering difficulties presented by large information in the monetary space, our creative methodology outfits the force of DRL to foster a powerful and versatile framework. By flawlessly incorporating DRL into the DSS structure, we mean to improve the framework's capacity to break down huge and complex monetary datasets. This DSS not only provides financial professionals with intelligent decision-making support but also real-time insights into market trends and patterns. The collaboration between enormous information investigation and DRL works with a dynamic and responsive framework equipped for adjusting to the quickly developing financial scene. Our exploration adds to the headway of choice by tending to the particular requests of monetary information, consequently enabling clients with ideal and informed dynamic abilities. The proposed DRL-based DSS addresses a change in perspective in monetary information examination, offering a versatile and effective answer for exploring the intricacies of enormous information in the financial area. This examination holds huge potential for changing dynamic cycles, advancing monetary security, and at last adding to the progression of the more extensive monetary industry. [ABSTRACT FROM AUTHOR]

Less-informed investors face greater costs of processing earnings news into actionable information. Our findings suggest financial analysis on social media reduces less-informed investors' disclosure processing costs. We document an attenuated spike in earnings announcement (EA) information asymmetry for quarters containing more financial analysis on social media in the weeks prior to the EA. Cross-sectional evidence suggests this finding is stronger when coverage from traditional intermediaries is lower, for financial analyses written by more credible authors, and for financial analyses that are more likely relevant to evaluating the EA. Further evidence suggests retail trades, but not institutional trades, at EAs are significantly more profitable in quarters with greater financial analysis on social media, consistent with financial analysis on social media benefitting traders who are otherwise less-informed. Overall, our evidence suggests that financial analysis on social media plays an important role in aiding less-informed investors by helping them better process EA news. [ABSTRACT FROM AUTHOR]

Corporations face multifaceted environmental uncertainties (EU) in today's dynamic global economic environment. Such uncertainties profoundly affect corporate operations and pose significant challenges to their environmental, social, and governance (ESG) performance. Therefore, using data from Chinese A-share listed corporations from 2009 to 2021, we empirically analyze the impact of the EU on ESG performance. The results demonstrate that the EU significantly negatively impacts ESG performance, indicating that corporations frequently find it difficult to implement and maintain high-standard ESG policies and practices effectively. Additionally, they reveal that the EU inhibits the improvement of ESG performance by increasing corporate financing constraints (KZ). Lastly, this study explores the role of government subsidies (GOV), investor attention (IA), and geopolitical risks (GPR) as moderating variables. We discover that GOV can mitigate the negative impact of the EU on KZ because they provide additional resources that help corporations maintain their ESG in uncertain environments. Furthermore, IA can reduce the adverse impact of KZ on ESG. Positive moderating effects result from ESG issues; the capital they provide effectively reduces corporate KZ, thus enabling corporations to maintain good ESG performance despite operating in highly uncertain environments. However, due to the increased asymmetry of information in economic markets caused by geopolitical tensions, GPR exacerbates the negative impact of the EU on ESG performance, thus leading to an increase in KZ. These findings offer new perspectives on understanding how these moderating effects affect corporate ESG strategies. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the current literature on healthcare policies and cost analyses around international Voluntary Assisted Dying (VAD) laws. The study design is a mapping literature review following Preferred-Reporting-Items-for-Systematic-Reviews-and-Meta-Analyses (PRISMA) guidelines. Methods: Original research articles published between January 1990 to March 2023, investigating the financial cost and healthcare budget effect of VAD laws internationally. Citations were screened for relevance and eligibility, and any non-full-text research that did not explore cost analysis was excluded. The following data sources were screened: MEDLINE, PubMed, EMBASE, CINAHL and any relevant international health authority annual reports were also reviewed. Results: Of the 2790 screened articles, eight studies met the inclusion criteria and three were included in the mapping review. The reviewed studies included prospective studies, two Canadian and one US. Only one of the Canadian studies provided a cost analysis using data from current VAD laws. All three studies showed VAD laws would reduce healthcare spending, with the US approximating $627million in 1995. Canada approximating $17.1 to $77.1million in 2017 and $86.9 to $149.0million in 2021, overall, leading to an average percentage reduction in costs of approximately 87% compared to original costs of end-of-life care. Conclusion: This review identifies a scarcity in cost-analysis literature and provides a summary of the latest international VAD laws, from which a potential cost reduction is apparent. The absence of retrospectively collated financial VAD data highlights a need for future research to inform policymakers of the economic factors affecting current policies with a need for annual fiscal reports and to optimise future legislative frameworks internationally. Key points: - This study highlights the absence of cost analysis reports on the provision of VAD globally, at a time when VAD is becoming an evolving part of end-of-life care particularly in developed countries base on the notion of patient autonomy. - The impact of this study is its ability to inform future research and policymakers of the economic factors affecting current VAD policies with a need for annual fiscal reports and to optimise future legislative frameworks internationally. [ABSTRACT FROM AUTHOR]

This paper responds to Professor Paolo Giudici's call for papers in ‘Safe Machine Learning’ and explores the application of Bayesian inference within the SAFE model framework, aiming to enhance the accuracy and reliability of environmental, social, and governance (ESG) factor analysis in the financial sector. The paper begins by introducing the basic concept of the SAFE model, which integrates ESG factors into the assessment of corporate credit ratings to promote sustainable development in the financial field. Furthermore, this paper discusses forecast estimation, uncertainty quantification, Gaussian processes, iterative optimization, and model robustness within the SAFE model framework. It is important to note that the SAFE model is not limited to the aforementioned applications; it also has the capacity to understand the model's extensive utility in other financial sectors, which can reflect the model's comprehensive scope. [ABSTRACT FROM AUTHOR]

Robust optimization (RO) models have attracted a lot of interest in the area of portfolio selection. RO extends the framework of traditional portfolio optimization models, incorporating uncertainty through a formal and analytical approach into the modeling process. Although several RO models have been proposed in the literature, comprehensive empirical assessments of their performance are rather lacking. The objective of this study is to fill in this gap in the literature. To this end, we consider different types of RO models based on popular risk measures and conduct an extensive comparative analysis of their performance using data from the US market during the period 2005–2020. For the analysis, two different robust versions of the mean–variance model are considered, together with robust models for conditional value-at-risk and the Omega ratio. The robust versions are compared against the nominal ones through various portfolio performance metrics, focusing on out-of-sample results. [ABSTRACT FROM AUTHOR]

This study investigates the impact of digital finance on corporate leverage ratios. The study employed a large sample of China's Shanghai and Shenzhen A-share non-financial listed enterprises from 2011–2020. The study's results depict that the development of digital finance can significantly reduce the leverage ratio of enterprises. We empirically identified that digital finance affects the difference in the term structure of the corporate leverage ratio. It was found that the development of digital finance has a significant negative impact on enterprises' short-term and long-term leverage ratios. Moreover, our heterogeneity analysis shows that the negative effect of digital financial development on corporate leverage ratios is different in state-owned and non-state-owned enterprises, large-scale and small-scale enterprises, and high-leverage and low-leverage enterprises. Mechanism analysis shows that the development of digital finance can reduce corporate leverage by lowering financing costs, alleviating financing constraints, and weakening non-systemic risks. Therefore, policymakers should focus on developing and adopting digital finance by creating a supportive regulatory environment, improving access to digital financial services, and encouraging innovation in the digital finance sector. Finally, our results remain robust after addressing endogeneity issues and conducting robustness checks. [ABSTRACT FROM AUTHOR]

In this work, we explore the application of deep reinforcement learning (DRL) to algorithmic trading. While algorithmic trading is focused on using computer algorithms to automate a predefined trading strategy, in this work, we train a Double Deep Q-Network (DDQN) agent to learn its own optimal trading policy, with the goal of maximising returns whilst managing risk. In this study, we extended our approach by augmenting the Markov Decision Process (MDP) states with sentiment analysis of financial statements, through which the agent achieved up to a 70% increase in the cumulative reward over the testing period and an increase in the Calmar ratio from 0.9 to 1.3. The experimental results also showed that the DDQN agent's trading strategy was able to consistently outperform the benchmark set by the buy-and-hold strategy. Additionally, we further investigated the impact of the length of the window of past market data that the agent considers when deciding on the best trading action to take. The results of this study have validated DRL's ability to find effective solutions and its importance in studying the behaviour of agents in markets. This work serves to provide future researchers with a foundation to develop more advanced and adaptive DRL-based trading systems. [ABSTRACT FROM AUTHOR]

Copyright of REMAH Journal is the property of Research & Development of Human Recourses Center (REMAH) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Agroforestry practices play a vital role in improving rural income and livelihoods, yet quantifying their contributions in Ethiopia remains limited. This study investigated the contribution of agroforestry practices to rural income and livelihoods, along with factors influencing income derived from these practices. A two-stage sampling method was employed to select 160 rural households engaged in home garden and woodlot agroforestry practices. Data were collected through semi-structured questionnaires, key informant interviews, and focus group discussions. Descriptive statistics, content analysis, generalized linear model, Mann–Whitney U test, and financial analysis tools were used to analyze and interpret the data. The results demonstrated that agroforestry practices provide numerous benefits to rural households, including fuelwood, charcoal, poles, construction materials, fodder, perennial crops, food crops, and fruits for both consumption and income generation. Agroforestry practices significantly increased farm income, contributing 18.2% of the total household income. This income significantly supported annual household expenditures across various livelihood capitals. Income from woodlots was significantly higher than that from home gardens, while home gardens showed a better overall capability to improve livelihoods. Household size, agroforestry size, gender, crop income, access to seedlings, and farm elevation positively and significantly (p < 0.05) impacted agroforestry income. Overall, agroforestry practices significantly contribute to annual income and improve the human, social, natural, physical, and financial capital of rural households in the Tarmaber district. Policymakers and practitioners should consider the socioeconomic, ecological, and institutional factors influencing agroforestry income when developing policies and strategies to improve rural income and livelihoods. [ABSTRACT FROM AUTHOR]

Students reasonably assume that financial reporting provides useful insights into financial performance. Alaska Gold is an instructional case that is based on a disguised real-world company, where reported GAAP-based financial statements obfuscate the firm's underlying economics. The Company mined gold for its first two years of operations but reported zero gold revenue for each of these years because it did not sell any. In the third year, the Company sold all of the gold mined to date—and reported gold revenue and related cost of gold sold. Students use publicly available data to prepare an alternative analysis that unveils the economics of what really happened to Alaska Gold in each of the three years 2017–2019. The situation helps students appreciate that accounting rules, business models, managerial discretion, and economic context are important to understanding and evaluating financial performance. [ABSTRACT FROM AUTHOR]

Academic research should strive to increase our understanding of the world we live in, and as such open the door for improvements in this world. With this goal in mind, this piece represents a call for academics to jointly consider their research agenda and the audience for this research. This audience might include people working in industry or policy makers. Closer connections between academia and broader audiences contribute to research that informs debates on the salient issues of today, higher quality studies on these issues, and greater impact. To provide insight on the relation between academic research and the most salient current issues, I compare topics of focus within academic journals with topics of focus in industry articles and regulatory publications. I identify gaps and suggest areas for future research. [ABSTRACT FROM AUTHOR]

Mergers and acquisitions (M&As) are one of the most practised inorganic strategies to multiply economic profits, acquire new product capabilities, expand markets, diversify risk and bring synergistic gains. The purpose of the study is to review the literature related to companies' financial analysis, pre and post-M&A after the Patents (Amendment) Act, 2005. The study follows a systematic literature review (SLR) process where 128 research publications from 2005–2020 are examined after applying the required review protocols. SLR found a deficient practical contribution of the limited literature on the three perspectives, namely, from accounting, productivity and managerial perspectives at the international level. Furthermore, there is also a dearth of studies from the three perspectives for the Indian Pharmaceutical Industry (IPI) post the Patents (Amendment) Act, 2005. This review finds that there is a need for a unified approach for evaluating the success of M&A in IPI. Future studies can encourage research that ties these three perspectives to a common thread and furnishes a holistic approach for analysis. It will further bolster the research to provide practical insights to companies' rooting for a robust financial analysis. [ABSTRACT FROM AUTHOR]

This study employs a robust quantitative ex post facto research design to investigate the complex relationship between audit fees and earnings management. The financial information of 164 firms admitted to the Tehran Stock Exchange (TSE) was used from 2010 to 2019 (pre-COVID period) to achieve the research goal. Analysing data from the Tehran Stock Exchange firms, the study uncovers an inverted U-shaped relationship between audit fees and earnings management. This suggests that moderate audit fees can lead to higher earnings management. Key contributions of this paper include highlighting the role of audit fees in influencing financial reporting quality and risk management, providing empirical evidence on the asymmetric effects of normal and abnormal audit fees on earnings management, and emphasising the need for balanced audit fee structures to ensure financial transparency and mitigate risk. The findings offer valuable insights for academics, practitioners, and policymakers in understanding the nuances of audit fees and their impact on corporate financial practices. This study advances the literature on financial risk management and corporate finance. It emphasises the importance of balanced audit fee structures for management teams, auditors, and policymakers to ensure transparent financial reporting practices. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the impact of digital finance on the breakthrough innovation of enterprises with a sample of A-share listed companies in Shanghai and Shenzhen from 2011 to 2022. It is found that digital finance can promote corporate breakthrough innovation, and presents certain structural heterogeneity characteristics. The mechanism test shows that digital finance has the dual attributes of a financing platform and a social platform, which can promote breakthrough innovation by alleviating corporate financing constraints and expanding corporate social networks. Heterogeneity analysis reveals that the role of digital finance in promoting breakthrough innovation is characterized by regional heterogeneity, with digital finance playing a greater role in promoting breakthrough innovation in provinces with a low level of development of the banking sector, provinces with a high level of development of the capital market sector, and the central region. In addition, the degree of firms' external financing dependence and the degree of product market competition can strengthen the positive effect of digital finance on firms' breakthrough innovation. This paper enriches the related research on the impact of digital finance on enterprise innovation, and provides theoretical basis and policy insights on how digital finance can better assist the innovation-driven development strategy. [ABSTRACT FROM AUTHOR]

From the perspective of annual report text information, we study the relationship between the annual report text's positive tone and corporate green innovation. Taking listed companies from 2010 to 2022 as a sample, we found that the positive tone of the annual report text significantly improves the company's green innovation while improving the quantity and quality of green innovation. The mechanism test shows that the main channels are easing corporate financing constraints and enhancing external attention. Regarding heterogeneity analysis, we found that the positive annual report text has a more significant effect on corporate green innovation in companies with high economic policy uncertainty and non-heavily polluting industries. Finally, we found that the positive tone of the annual report text can ultimately improve the company's long-term value through green innovation. Our study has enriched the theoretical research on the annual report text tone and provided empirical evidence for promoting enterprise green innovation. [ABSTRACT FROM AUTHOR]

Analysis of the criticality in finance and economics is an important topic for scholars. The crises are conditions in which unpredictable behavior have been emerged. On the other hand, the contagion from one market to another one due to the complex nature of them is a crucial concept. Therefore, we should consider economies and financial markets as entangled systems that interact with each other, and studying one’s behavior without considering its interacting structures will not provide us with adequate information. In this paper, we have considered the critical states of some emerging and mature markets’ indices. S&P500 and Nikkei 225 as representatives of mature markets, and SSE Composite and TEDPIX as representatives of emerging markets have been analyzed. Coupled criticality emerges when two interacting systems reach their critical state due to their interactions. One of the efficient methods to study the coupled criticality is the bi-variate multifractal random walk model. This method is useful to assess the correlation of rare events and coupled criticality in entangled systems such as financial markets. The observations in this paper show that TEDPIX and S&P500 have higher critical states among others in most time lags by assessing criticality parameter explained in the model, and in longer time lags criticality parameter decreases in all indices. Coupled criticality parameter explained in the model is assessed between each index mentioned and TEDPIX. Results show that TEDPIX and S&P500 have the highest level of coupled criticality. To figure out how long-range time correlation between rare events in each index affect its critical behavior, the data of each index are shuffled, and the criticality parameter is calculated for the shuffled data. From the observations, it can be concluded that this matter has more substantial effect on mature markets. As it is known, shuffling removes the effect of the long-range time correlations. Therefore, the coupled criticality parameter is calculated for the shuffled S&P500 data and TEDPIX. Results show that the long-range correlation of S&P500 data has a significant effect on the coupled criticality behavior of S&P500 and TEDPIX. [ABSTRACT FROM AUTHOR]

Weibo sentiment and R&D innovation are important signals that influence the financing processes of venture corporations. We collect relevant data between 1 January 2011, and 31 December 2020, to analyse the effect of the signalling mechanism of Weibo sentiment and R&D innovation on venture corporate financing. The conclusions are as follows. The positive effects of Weibo sentiment on venture valuation and investment success are weaker than the effects of R&D innovation, and the positive effects of Weibo sentiment and R&D innovation on venture valuations are stronger for young than older venture firms. The positive effects of Weibo sentiment and R&D innovation on venture valuations are stronger for less experienced ventures than for more experienced ventures. These conclusions show that the two signals of Weibo sentiment and R&D innovation have different strengths in terms of venture valuation and investment success, with the effect of the Weibo sentiment signal being relatively less strong. Moreover, venture corporate age and venture corporate experience play important negative moderating roles in the relationship between the two signals and venture valuation. [ABSTRACT FROM AUTHOR]

This research is important for both the academic and business environments due to the extraordinary results obtained. Additionally, the significance of the study is also attributed to the addressed topic, which is intensively studied in the world of corporate finance. The primary aim of this research is to scrutinize a cohort of 66 information and technology (IT) companies, all of which are constituents of the American Standard and Poor's 500 Index (S&P 500). The study period spans two decades, covering the years 2003–2022. To summarize the outcomes, the analytical framework incorporated linear models with both fixed (fe) and random effects (re), as well as quantile regression models. This study's key outcomes highlight that firm size, sales growth, current ratio, long-term debt to capital, free cash flow, asset turnover and receivable turnover, board meeting frequency, female board representation, chief executive officer age, audit committee independence, and the presence of compensation and nomination committees, alongside a pandemic indicator, positively impact firm performance. Conversely, firm age, dividend payout ratio, effective tax rate, board size, chief executive officer duality, and corporate social responsibility committee presence have negative effects on performance. Also, regarding quantile regressions, CEO duality significantly influences companies with high profitability rates, and companies with low to medium profitability rates are more strongly and negatively influenced by board size. The implications of the core policy in this research focusing on corporate governance will consider certain rules and guidelines regarding financial transparency and protecting shareholders' interests. Additionally, it will take into account the independence of the board of directors and the presence of its committees, as well as ethical leadership practices. [ABSTRACT FROM AUTHOR]

The need for novel antibiotics to combat emerging multi-drug resistant bacterial strains is widely acknowledged. The development of new therapeutic agents relies on small and medium-sized biotechnology enterprises (SMEs), representing 75% of the late-stage pipeline. However, most SME sponsors of an antibacterial approved by the FDA since 2010 have gone bankrupt, or exited at a loss, below investment cost. Uncovering financial flows related to the development and commercialisation of a single drug is complex and typically untransparent. There is therefore a lack of empirical research on the financial vulnerabilities of these critical SMEs. The development of plazomicin by Achaogen (2004–2019) entailed financial disclosures as a public company enabling application of financial analysis methods to: determine quantum and timing of public and private investments; quantify development costs; and provide a deeper understanding of the role of capital market dependency in exacerbating pipeline fragility. Achaogen's widely cited bankruptcy, and plazomicin's commercialisation failure, created a perception that novel antibiotics have zero market value, causing investors to question the SME developer business model. Our analysis of Achaogen's inability to fund commercialisation suggests three key implications for the antibiotic investment ecosystem: (1) novel antibiotics with narrow approval for small patient populations affected by severe resistant infections cannot be successfully commercialised in the current US antibiotic market; (2) SMEs need incentive payments structured to enable them to survive the commercialisation cashflow drought, and (3) these changes are necessary to restore industry and financial investor confidence in the antibiotic SME development model. Achaogen's demise demonstrates that proposals to incentivise innovation, e.g. by providing one-off payments at registration, may be insufficient to ensure access to novel antibiotics developed by SMEs. In plazomicin's case, moreover, US government biosecurity investments have not resulted in access, as the Indian and Chinese companies which bought post-bankruptcy rights have not widely commercialised the drug. This study is timely as new market-based incentives are currently being proposed by the US, EU, Canada and Japan. In order to make further government funding effective, ensuring access, not only innovation, these must support sustainable financial models for the SMEs critical to novel antibiotic development. [ABSTRACT FROM AUTHOR]

The existing research on the impact of digital inclusive finance on corporate behaviour is relatively extensive, but the research on the impact of digital inclusive finance on corporate value is ignored. To fill in this gap, this study examines the impact of digital inclusive finance on firm value from the perspective of financing constraints, utilizing data on A-share listed companies in China from 2012 to 2019. We found that digital inclusive finance helps increase corporate value. And digital inclusive finance uses digital technology to alleviate financing constraints and improve the credit supply, which in turn increases firm value. Moreover, the impact of digital inclusive finance on firm value demonstrates heterogeneity. Compared with other firms, digital inclusive finance has a greater value-enhancing effect on small-scale and private firms. Our findings suggest that increasing the level of digital inclusive finance in developing countries can help enhance firm value. [ABSTRACT FROM AUTHOR]

Remittances have become a significant component of international capital flows, with millions of migrants sending billions of dollars back to their home countries annually. However, the way these outflows affect macroeconomic variables has not received sufficient attention in the literature, especially in the context of varying levels of financial development. Using time series data from 1987 to 2022 for the United Kingdom, this study examines the macroeconomic effects of remittance outflows and financial development. Our baseline estimation using the Autoregressive Distributed Lag model reveals heterogeneous impacts, as remittance outflows adversely affect economic growth but improve exchange rates. We find remittances do not have a significant effect on inflation or bank rates. The moderating effect of financial development analysis reveals a similar outcome. Our results suggest governments should consider stimulus policies that support investment in productive sectors to improve macroeconomic indicators and facilitate financial inclusion to enhance the adoption of growth strategies that promote remittances. [ABSTRACT FROM AUTHOR]

Delays in legal proceedings significantly impact both corporate finances and individual livelihoods. Traditional methods for managing these delays typically rely on subjective assessments of what constitutes a reasonable process duration. This study explores a more precise approach by integrating machine learning and process mining techniques to enhance prediction of legal proceedings' overall time. Diverging from previous works that either utilized machine learning analysis or process mining in isolation, this research synergizes these approaches. We applied process mining clustering techniques to over 60,000 cases from Brazilian labor courts to segment cases based on their procedural movements, creating clusters. These clusters, along with other procedural characteristics, such as case subject, class, and digital status, were then incorporated into a feature set for regression modelling. We employed linear regression, support vector regressor, and gradient boosting based methods to develop models that predicted case duration. The gradient boosting model demonstrated the best performance with an R 2 -score of 0.87. Furthermore, our analysis identifies time bands where the model performs better and employs explainable AI techniques to elucidate key features influencing case durations. The clustering features emerged among the most significant for the task. The proposed combined approach offers a comprehensive method for analyzing and forecasting legal case timelines and also shows the potential of process mining clustering techniques to improve the analysis. [ABSTRACT FROM AUTHOR]

In this study, we examine the green innovation effect of investor preference on Chinese listed companies from 2008 to 2020. We observe that investors with environmental protection preferences can significantly increase enterprise green innovation. The baseline conclusions remain robust after we conduct multiple sensitivity tests, such as establishing a shift-share instrumental variable (Bartik IV), using the Heckman's two-step method, changing the regression methods, substituting the core variables, adopting the DID method, and considering investors' ownership. Our analysis shows that the green innovation effect of environmental investors is highly pronounced in companies with a high level of indebtedness and low managerial ability. We verify that environmental investors can improve the green innovation level of enterprises through two potential mechanisms, namely, easing corporate financing constraints and incentivising managers. In addition, political and public attention can strengthen the positive relationship between environmental investors and green innovation. [ABSTRACT FROM AUTHOR]

This research intends to demonstrate the potential integration of large language models (LLMs) in accounting practices in Jordan. It is a mixed methods study that employs semi-structured interviews and content analysis of published financial reports. A total of 13 semi-structured interviews are conducted with various accounting professionals, such as accountant, financial analyst, financial controller, auditor, tax consultant, and finance manager. In addition, the study includes a thorough content analysis of financial reports, which reveals a compelling pattern highlighting the distinct narrative content richness prevalent across financial, industrial, and service sectors. The results emphasize the need for sector-specific adaptation, suggesting a paradigm shift in financial reporting practices. This study represents the initial empirical exploration in Jordan. It serves as a bridge between theory and application, offering both theoretical insights and practical guidance for accounting professionals. Ultimately, the study envisions a future where LLMs significantly enrich financial reporting practices across diverse sectors. [ABSTRACT FROM AUTHOR]

With the deepening of enterprise financialization, the trend of "moving away from reality to emptiness" has increased the difficulty of financial management in the manufacturing industry. This paper selects the data of A-share main board listed companies from 2012 to 2021 to study the motivation of financial investment in the manufacturing industry and its impact on financial risk. The research results show that the main motivation of listed companies' financial investment in the manufacturing industry is "substitution" motivation. With the purpose of maximizing profits, the excessive allocation of monetary assets, especially long-term financial assets, increases financial risks of enterprises. Furthermore, the financial risk caused by the financial investment of state-owned enterprises is greater. [ABSTRACT FROM AUTHOR]

This study investigates the intangible assets related to the circular economy, defined as 'circular patents', that are classified and measured to be bundled into the innovation capabilities of a firm. The impact of a business's capabilities on its level of circular patent activism is empirically analysed in this study in a theoretical framework of dynamic capabilities. To this end, a model of the cause-and-effect relationship between the circular patents held by firms and their capabilities is designed using partial least squares structural equation modelling (PLS-SEM) and is tested using a sample of 120,406 patents in 2216 Spanish companies. In this analysis, patents that can foster the sharing economy and the circular economy in sensu latu are also considered circular patents, in addition to waste patents and other green patents, offering an enhanced measurement of the intangible assets related to the circular economy. Based on these results, this study provides new insight of how accounting can enable or constrain the transition to a circular economy business model through the measurement and valuation of related intangible assets and the specific business' capabilities in an environmental management framework. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of financial risk management using a new expected shortfall regression. The latter is based on the expectile model for financial risk-threshold. Unlike the VaR model, the expectile threshold is constructed by an asymmetric least square loss function. We construct an estimator of this new model using the k-nearest neighbors (kNN) smoothing approach. The mathematical properties of the constructed estimator are stated through the establishment of the pointwise complete convergence. Additionally, we prove that the constructed estimator is uniformly consistent over the nearest neighbors (UCNN). Such asymptotic results constitute a good mathematical support of the proposed financial risk process. Thus, we examine the easy implantation of this process through an artificial and real data. Our empirical analysis confirms the superiority of the kNN-approach over the kernel method as well as the superiority of the expectile over the quantile in financial risk analysis. [ABSTRACT FROM AUTHOR]

In this paper, the STIRPAT model was developed to study the role of financial development in urban low-carbon economic transformation. The statistical analysis used data from 281 prefecture-level cities in China from 2009 to 2019. The results show that: (1) there is a positive U-shaped nonlinear relationship between financial development and urban low-carbon economic transformation. (2) Financial development not only directly affects the transformation of the low-carbon economy, but also indirectly affects it through three transmission channels: environmental regulation, industrial structure, and urbanization. (3) The financial development itself and the level of urbanization have a threshold effect on the transformation of the low-carbon economy. Under the threshold effect of environmental regulation and industrial structure, the role of financial development in the transformation of low-carbon economies presents a process of 'first decrease and then increase'. Therefore, improving the intensity of environmental regulation, implementing differentiated financial and industrial development policies, and controlling the urbanization process are effective measures to promote the transformation of low-carbon economies. [ABSTRACT FROM AUTHOR]

Standard financial theories assume that investment and financing decisions are intertwined. Investment decisions involve selecting assets that generate positive cash flows and maximize shareholder value, while financing decisions involve identifying the optimal mix of debt and equity financing to fund these investments. The main objective of this paper is to analyze the interdependence of assets and liabilities in a sample of Colombian manufacturing firms with varying degrees of roundaboutness (capital-intensive nature). This study employs canonical correlation analysis to determine the interaction between balance sheet components. Subsequently, a threshold regression model is employed to evaluate the influence of financial indicators on the financial performance of firms with heterogeneous capital intensity. The findings reveal that low-capital intensity firms match the maturity structure of assets and liabilities. In firms with low roundaboutness there is a significant relation between throughput, working capital turnover and the proportion of capital expenditure to operating cash flow with ROIC. However, this relationship becomes non-significant at high levels of indebtedness. Conversely, capital-intensive firms exhibit a mismatch between asset and liability maturities, relying on shareholders' funds to finance longterm assets and inventories. In this group of companies, throughput is positively associated with ROIC regardless of the level of debt, whereas the relationship between working capital turnover and ROIC is not significant until a certain debt threshold is exceeded. The impact of the capital expenditure to operating cash flow ratio on ROIC varies depending on debt levels. Companies with high roundaboutness that exceeds a debt threshold experience a negative impact. [ABSTRACT FROM AUTHOR]