Penelitian ini bertujuan untuk mengkaji pengaruh overconfidence terhadap keputusan investasi generasi Z di Indonesia serta peran literasi keuangan syariah sebagai variabel moderasi dalam hubungan tersebut. Metode yang digunakan adalah survei kuantitatif dengan melibatkan 100 responden investor generasi Z. Data dianalisis menggunakan regresi linear sederhana dan analisis regresi moderasi. Hasil penelitian menunjukkan bahwa overconfidence berpengaruh positif signifikan terhadap keputusan investasi. Selain itu, literasi keuangan syariah memperkuat pengaruh overconfidence terhadap keputusan investasi generasi Z. Temuan ini mengindikasikan bahwa literasi keuangan syariah tidak hanya meningkatkan pengetahuan dan kepercayaan diri investor muda, tetapi juga memperkuat pengaruh overconfidence dalam pengambilan keputusan investasi. Implikasi penelitian ini menekankan pentingnya edukasi literasi keuangan yang tidak hanya meningkatkan pengetahuan, tetapi juga mampu mengelola bias perilaku agar pengambilan keputusan investasi menjadi lebih rasional.
Vitor Correa da Silva, Nicolle Ferreira Mizzetti, Ântoni Izoton Bráz
et al.
Este estudo objetivou identificar como a liderança empoderadora influencia os controles gerenciais do tipo cibernéticos, a aprendizagem e o desempenho organizacional. Metodologicamente, fez-se um levantamento de campo (survey) junto a 100 (cem) profissionais de diferentes empresas contactados a partir da plataforma Linkedln. A técnica de modelagem de equações estruturais estimada por mínimos quadrados parciais foi utilizada para a análise dos dados. A teoria da visão baseada em recursos (VBR) foi utilizada para a construção do modelo teórico estrutural proposto. Os resultados indicaram que a liderança empoderadora influencia positivamente tanto na utilização dos controles gerenciais do tipo cibernéticos (orçamento e medidas de desempenho), como no nível de aprendizagem organizacional. Ademais, os controles cibernéticos também influenciam positivamente a aprendizagem organizacional. Por fim, identificou-se que a aprendizagem organizacional influencia positivamente o desempenho organizacional. Portanto, os resultados foram coerentes com as expectativas geradas pelas hipóteses da pesquisa e possuem implicações teóricas, metodológicas e práticas que contribuem para a academia e para a prática gerencial.
Thomas Sanchez, Pedro M. Gordaliza, Meritxell Bach Cuadra
Machine learning methods are increasingly applied in medical imaging, yet many reported improvements lack statistical robustness: recent works have highlighted that small but significant performance gains are highly likely to be false positives. However, these analyses do not take \emph{underspecification} into account -- the fact that models achieving similar validation scores may behave differently on unseen data due to random initialization or training dynamics. Here, we extend a recent statistical framework modeling false outperformance claims to include underspecification as an additional variance component. Our simulations demonstrate that even modest seed variability ($\sim1\%$) substantially increases the evidence required to support superiority claims. Our findings underscore the need for explicit modeling of training variance when validating medical imaging systems.
Modelling block maxima using the generalised extreme value (GEV) distribution is a classical and widely used method for studying univariate extremes. It allows for theoretically motivated estimation of return levels, including extrapolation beyond the range of observed data. A frequently overlooked challenge in applying this methodology comes from handling datasets containing missing values. In this case, one cannot be sure whether the true maximum has been recorded in each block, and simply ignoring the issue can lead to biased parameter estimators and, crucially, underestimated return levels. We propose an extension of the standard block maxima approach to overcome such missing data issues. This is achieved by explicitly accounting for the proportion of missing values in each block within the GEV model. Inference is carried out using likelihood-based techniques, and we propose an update to commonly used diagnostic plots to assess model fit. We assess the performance of our method via a simulation study, with results that are competitive with the "ideal" case of having no missing values. The practical use of our methodology is demonstrated on sea surge data from Brest, France, and air pollution data from Plymouth, U.K.
Carlos William Lima Ribeiro, Jorge Luiz De Santana Júnior, Renê Coppe Pimentel
et al.
Objective: This paper analyzes the initial impacts of IFRS 9 adoption on banks’ credit impairment level in an international perspective. Specifically, we analyze the impact on banks' financial position and performance, the bank- and country-specific determinants of the impairment magnitudes and the informational effects of the new standard.
Method: The study is based on a sample of 149 listed banks from 12 countries from G20 and bivariate and multivariate analyses are applied.
Results: Results indicate that credit impairment recognized under IFRS 9 is larger than under IAS 39, suggesting a more conservative accounting model and that the new standard affected banks’ performance and financial position. We show that IFRS 9 implementation was significantly different among countries, especially between high- and low-income countries and the information under IFRS 9 is more value-relevant than IAS39.
Contributions: We add to the previous literature by documenting the initial impact of IFRS 9 implementation on financial statements and on value relevance, as well as the bank- and country-specific determinants of the level of this impact.
The scaled Web 3.0 digital economy, represented by decentralized finance (DeFi), has sparked increasing interest in the past few years, which usually relies on blockchain for token transfer and diverse transaction logic. However, illegal behaviors, such as financial fraud, hacker attacks, and money laundering, are rampant in the blockchain ecosystem and seriously threaten its integrity and security. In this paper, we propose a novel double graph-based Ethereum account de-anonymization inference method, dubbed DBG4ETH, which aims to capture the behavioral patterns of accounts comprehensively and has more robust analytical and judgment capabilities for current complex and continuously generated transaction behaviors. Specifically, we first construct a global static graph to build complex interactions between the various account nodes for all transaction data. Then, we also construct a local dynamic graph to learn about the gradual evolution of transactions over different periods. Different graphs focus on information from different perspectives, and features of global and local, static and dynamic transaction graphs are available through DBG4ETH. In addition, we propose an adaptive confidence calibration method to predict the results by feeding the calibrated weighted prediction values into the classifier. Experimental results show that DBG4ETH achieves state-of-the-art results in the account identification task, improving the F1-score by at least 3.75% and up to 40.52% compared to processing each graph type individually and outperforming similar account identity inference methods by 5.23% to 12.91%.
To test the scenario that outflows accelerated by active galactic nuclei (AGN) have a major impact on galaxy-wide scales, we have analysed deep VLT/MUSE data for the type-2 quasar/ultraluminous infrared galaxy F13451+1232 - an object that represents the major mergers considered in models of galaxy evolution. After carefully accounting for the effects of atmospheric seeing that had smeared the emission from known compact nuclear outflows across the MUSE field of view, we find that the large-scale kinematics in F13451+1232 are consistent with gravitational motions that are expected in a galaxy merger. Therefore, the fast ($\mathrm{W_{80}}>500$ km s$^{-1}$) warm-ionised AGN-driven outflows in this object are limited to the central $\sim$100 pc of the galaxy, although we cannot rule out larger-scale, lower-velocity outflows. Moreover, we directly demonstrate that failure to account for the beam-smearing effects of atmospheric seeing would have led to the mass outflow rates and kinetic powers of spatially-extended emission being overestimated by orders of magnitude. We also show that beam-smeared compact-outflow emission can be significant beyond radial distances of 3.5 arcseconds (more than eight times the radius of the seeing disk), and support the argument that some previous claims of large-scale outflows in active galaxies were likely the result of this effect rather than genuine galaxy-wide ($r>5$ kpc) outflows. Our study therefore provides further evidence that warm-ionised AGN-driven outflows are limited to the central kiloparsecs of galaxies and highlights the critical importance of accounting for atmospheric seeing in ground-based observational studies of active galaxies.
Ana Paula Lemes da Silva, Renata Mendes de Oliveira, Thiago Alberto dos Reis Prado
et al.
Objetivo: O estudo teve por objeto analisar os indicadores de produção e distribuição do valor adicionado da Natura S.A, na busca por evidenciar o comportamento dos mesmos no período entre 2011 e 2020.
Metodologia: Este estudo caracteriza-se como uma pesquisa de natureza documental e descritiva. A coleta de dados foi realizada junto à base de dados da B3 e compreendeu o levantamento de informações da Demonstração do Valor Adicionado (DVA), além da consideração de indicadores de produção e distribuição do valor adicionado. Os dados foram analisados de forma analítica, com elaboração de tabelas para evidenciar as informações coletadas de forma estruturada.
Resultados: Os resultados demonstraram que o indicador de produção que mais se destaca é relacionado à riqueza própria (GPRP). Os valores adicionados apresentaram comportamento crescente durante o período observado, com destaque ao valor gerado pela empresa no ano de 2020. A distribuição dos valores gerado se dá principalmente entre Pessoal, Impostos, Taxas e Contribuições e Remuneração de Capitais de Terceiros, o que impacta nos indicadores de distribuição ao Pessoal (PDVAP), distribuição ao Governo (PDVAG) e distribuição ao Capital de Terceiros (PDVACT).
Contribuições do Estudo: Esta pesquisa contribuem para teoria e para a prática no campo da contabilidade. De forma geral, os resultados apresentados reforçam o exposto por outras pesquisas e oferece novas evidências em relação a ampliação do período de observação. Ademais, auxiliam na compreensão em relação ao processo de produção e distribuição do valor adicionado, bem como ao demonstrar a relevância da DVA.
In this commentary article to 'The Puzzle of Ideography' by Morin, we put forth a new cognitive account of the puzzle of ideography, that complements the standardization account of Morin. Efficient standardization of spoken language is phenomenologically attributed to a modality effect coupled with chunking of cognitive representations, further aided by multi-sensory integration and the serialized nature of attention. These cognitive mechanisms are crucial for explaining why languages dominate graphic codes for general-purpose human communication.
Acoustic traps use forces exerted by sound waves to confine and transport small objects. The dynamics of an object moving in the force landscape of an acoustic trap can be significantly influenced by the inertia of the surrounding fluid medium. These inertial effects can be observed by setting a trapped object in oscillation and tracking it as it relaxes back to mechanical equilibrium in its trap. Large deviations from Stokesian dynamics during this process can be explained quantitatively by accounting for boundary-layer effects in the fluid. The measured oscillations of a perturbed particle then can be used not only to calibrate the trap but also to characterize the particle.
Academic and policy proposals on algorithmic accountability often seek to understand algorithmic systems in their socio-technical context, recognising that they are produced by 'many hands'. Increasingly, however, algorithmic systems are also produced, deployed, and used within a supply chain comprising multiple actors tied together by flows of data between them. In such cases, it is the working together of an algorithmic supply chain of different actors who contribute to the production, deployment, use, and functionality that drives systems and produces particular outcomes. We argue that algorithmic accountability discussions must consider supply chains and the difficult implications they raise for the governance and accountability of algorithmic systems. In doing so, we explore algorithmic supply chains, locating them in their broader technical and political economic context and identifying some key features that should be understood in future work on algorithmic governance and accountability (particularly regarding general purpose AI services). To highlight ways forward and areas warranting attention, we further discuss some implications raised by supply chains: challenges for allocating accountability stemming from distributed responsibility for systems between actors, limited visibility due to the accountability horizon, service models of use and liability, and cross-border supply chains and regulatory arbitrage
Online manipulation is a pressing concern for democracies, but the actions and strategies of coordinated inauthentic accounts, which have been used to interfere in elections, are not well understood. We analyze a five million-tweet multilingual dataset related to the 2017 French presidential election, when a major information campaign led by Russia called "#MacronLeaks" took place. We utilize heuristics to identify coordinated inauthentic accounts and detect attitudes, concerns and emotions within their tweets, collectively known as socio-linguistic characteristics. We find that coordinated accounts retweet other coordinated accounts far more than expected by chance, while being exceptionally active just before the second round of voting. Concurrently, socio-linguistic characteristics reveal that coordinated accounts share tweets promoting a candidate at three times the rate of non-coordinated accounts. Coordinated account tactics also varied in time to reflect news events and rounds of voting. Our analysis highlights the utility of socio-linguistic characteristics to inform researchers about tactics of coordinated accounts and how these may feed into online social manipulation.
“Parallel bookkeeping” is a key technical arrangement to achieve the goal of moderately separating and connecting the financial accounting system and budget accounting system established by the government accounting system. It is still a new thing for the majority of financial personnel in the government accounting subject. A deep neural network is the basis of deep learning. Up to now, the neural network has been applied in many fields, and its application in the financial field is more in-depth. The neural network is of great help to financial accounting. Integrating it into parallel bookkeeping in accounting can improve the work efficiency and accuracy of financial personnel. Through experimental analysis, it is found that its efficiency and accuracy are improved by 45% and 21.34% compared with the previous parallel bookkeeping path. The accounting parallel bookkeeping path based on the deep neural network studied in this paper not only has great practical significance for the work of financial personnel but also has far-reaching significance for the research of accounting paths in the future.
The tax system of any country as a tool to fill the budget is never static, constantly subject to development and improvement, so it is relevant to study and apply its progressive elements used by different states. The subject of the study is the current tax transformation in Brazil, Argentina, Colombia, and its task is to study and apply its results, since the pandemic particularly affected the economies of developing countries. Countries of South America have been actively pursuing tax reforms since the middle of the last century, but localizing the effects of COVID‑19 requires tax changes in this area at the present time. The tax transformation in Brazil has involved a tax on dividends, capitalization of accumulated profits and dividends paid in kind, corporate income tax and corporate reorganizations. The tax transformation in Argentina also takes place in terms of income tax (restrictions on depreciation payments, permanent establishment), but there are also areas, such as tax haven policy, capital gains tax on non-residents of Argentina, and taxation of controlled foreign companies. The author carried the research on using general scientific methods of analysis, comparison, and deduction. The results of the study of tax transformation in South American countries can, considering the specifics of our economy, find practical implementation in the tax’s improvement system in the Russian Federation. Thus, it is proposed to adapt the following tax instruments in order to localize the effects of COVID‑19: tax loss carryforwards, deduction of R&D expenses.
Linguistic specificity effectively reduces barriers to information cognition, increasing the efficiency of information acquisition, integration and processing. Combining the psycholinguistics theory of the concreteness effect with asset-pricing theory, we determine that linguistic specificity in the management discussion and analysis section of a firm’s annual reports is negatively associated with stock price synchronicity, particularly in firms with strong external information demand or insufficient information supply. Furthermore, only specificity of the review section leads to a reduction in stock price synchronicity. Mechanism tests show that specificity reduces information processing costs and enhances information credibility. Additionally, proprietary costs are an essential determinant of linguistic specificity adoption. Our findings suggest that linguistic specificity plays an essential role in improving market pricing efficiency.
Scoring the creditworthiness of accounts that interact with decentralized financial (DeFi) protocols remains an important yet unsolved problem. In this paper, we propose a credit scoring system for those accounts that have interacted with the Aave v2 liquidity protocol. The key component of this system is a tree-based binary classifier that predicts "position delinquency." To the community, we provide our method, results, and the (abridged) dataset on which this system is built.
The increasing uptake of electrical vehicles (EVs) has increased the awareness of battery degradation costs and how they can be minimized. However, from a planning perspective it is difficult to integrate battery degradation models into existing route planning models and to assess how policies that aim at reducing battery degradation affect route planning costs and degradation across the fleet. In this paper, a simple transportation vehicle routing problem (VRP) is formulated as a mixed-integer nonlinear problem (MINLP), with a modification that allows monitoring the maximum and minimum depth-of-discharge (DoD) of the entire fleet. This allows us to measure the battery health degradation during the online optimization process. The results show that accounting for the impact of different route characteristics on battery degradation can have an impact on the route planning of the entire fleet as well as the battery degradation for all vehicles. The latter is achieved by forcing vehicles to adapt to certain DoD boundaries in the long term.
Abstrak: Tujuan Kegiatan untuk memberikan pengetahuan akuntansi dan pendampingan penggunaan Sheet Accounting dalam pembuaatn Book Keeping agar mempermudah pembuatan laporan keuangan Pada Usaha Industri Rumah dengan objek PKM usaha Camilan Inaq Yuli dihadiri 5 pserta dilaksanakan di Rumah peserta di Kelurahan Babakan, Kota Mataram Nusa Tenggara Barat mengunakan 4 langkah pelaksanan kegiatan, yaitu langkah pertama analisis situasi dengan metode observasi, wawancara, langkah kedua perancangan kegiatan dengan metode pembagian tugas tim serta merancang teknologi yang digunakan, langkah ketiga pelaksanaan kegiatan dengan metode edukasi, presentasi, diskusi, dan pendampingan, langkah keempat evaluasi dan monitoring kegiatan. Dengan hasil 33% peserta PKM lebih memahami akuntansi, 17 % mampu melakukan pencatatan akuntansi sederhana/Book Keeping dan 50% mampu menggunakan Sheet Accounting dalam pembuatan pencatan transaksi usaha menggunakan media laptop.Abstract: The purpose of the activity is to provide accounting knowledge and assistance in the use of Sheet Accounting in the creation of BookKeeping in order to facilitate the preparation of financial reports for the Home Industry Business with the object of PKM business Snack Inaq Yuli attended by 5 participants and carried out at the participant's house in Babakan Village, Mataram City, West Nusa Tenggara. Using 4 steps of implementing activities, namely the first step of situation analysis with the method of observation, interviews, the second step is to design activities with the method of dividing team tasks and designing the technology used, the third step is the implementation of activities with the methods of education, presentation, discussion, and mentoring, the fourth step is to evaluate and monitor activities. With the result that 33% of PKM participants understand more about accounting, 17% are able to do simple accounting records/book keeping. And 50% are able to use Sheet Accounting in recording business transactions using laptop media.