7,448 research outputs found

    Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures

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    In this work we present a non-parametric online market regime detection method for multidimensional data structures using a path-wise two-sample test derived from a maximum mean discrepancy-based similarity metric on path space that uses rough path signatures as a feature map. The latter similarity metric has been developed and applied as a discriminator in recent generative models for small data environments, and has been optimised here to the setting where the size of new incoming data is particularly small, for faster reactivity. On the same principles, we also present a path-wise method for regime clustering which extends our previous work. The presented regime clustering techniques were designed as ex-ante market analysis tools that can identify periods of approximatively similar market activity, but the new results also apply to path-wise, high dimensional-, and to non-Markovian settings as well as to data structures that exhibit autocorrelation. We demonstrate our clustering tools on easily verifiable synthetic datasets of increasing complexity, and also show how the outlined regime detection techniques can be used as fast on-line automatic regime change detectors or as outlier detection tools, including a fully automated pipeline. Finally, we apply the fine-tuned algorithms to real-world historical data including high-dimensional baskets of equities and the recent price evolution of crypto assets, and we show that our methodology swiftly and accurately indicated historical periods of market turmoil.Comment: 65 pages, 52 figure

    Financial and Economic Review 22.

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    The combination of solar energy and buildings can greatly save energy, and a great deal of practical and theoretical research has been conducted on solar buildings around the world. Rural areas in southern Shaanxi, China, have wet and cold winters. The average room temperature is 4°C and below 2°C at night, which greatly exceeds the range of thermal comfort that the human body can tolerate. In response to a series of problems such as backward heating methods and low heating efficiency in southern Shaanxi, two fully passive heating methods are proposed for traditional houses in the region. They are rooftop solar heating storage systems and thermal storage wall heating systems (TSWHS), respectively. These two systems have been compared with the status quo heating system to confirm the practicality of the new system and to provide an idea for heating and energy saving in traditional houses in rural areas.挗äčć·žćž‚立性

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Land Use and Land Cover Mapping in a Changing World

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    It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classiïŹcation systems

    Blockchain Technology: Disruptor or Enhnancer to the Accounting and Auditing Profession

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    The unique features of blockchain technology (BCT) - peer-to-peer network, distribution ledger, consensus decision-making, transparency, immutability, auditability, and cryptographic security - coupled with the success enjoyed by Bitcoin and other cryptocurrencies have encouraged many to assume that the technology would revolutionise virtually all aspects of business. A growing body of scholarship suggests that BCT would disrupt the accounting and auditing fields by changing accounting practices, disintermediating auditors, and eliminating financial fraud. BCT disrupts audits (Lombard et al.,2021), reduces the role of audit firms (Yermack 2017), undermines accountants' roles with software developers and miners (Fortin & Pimentel 2022); eliminates many management functions, transforms businesses (Tapscott & Tapscott, 2017), facilitates a triple-entry accounting system (Cai, 2021), and prevents fraudulent transactions (Dai, et al., 2017; Rakshit et al., 2022). Despite these speculations, scholars have acknowledged that the application of BCT in the accounting and assurance industry is underexplored and many existing studies are said to lack engagement with practitioners (Dai & Vasarhelyi, 2017; Lombardi et al., 2021; Schmitz & Leoni, 2019). This study empirically explored whether BCT disrupts or enhances accounting and auditing fields. It also explored the relevance of audit in a BCT environment and the effectiveness of the BCT mechanism for fraud prevention and detection. The study further examined which technical skillsets accountants and auditors require in a BCT environment, and explored the incentives, barriers, and unintended consequences of the adoption of BCT in the accounting and auditing professions. The current COVID-19 environment was also investigated in terms of whether the pandemic has improved BCT adoption or not. A qualitative exploratory study used semi-structured interviews to engage practitioners from blockchain start-ups, IT experts, financial analysts, accountants, auditors, academics, organisational leaders, consultants, and editors who understood the technology. With the aid of NVIVO qualitative analysis software, the views of 44 participants from 13 countries: New Zealand, Australia, United States, United Kingdom, Canada, Germany, Italy, Ireland, Hong Kong, India, Pakistan, United Arab Emirates, and South Africa were analysed. The Technological, Organisational, and Environmental (TOE) framework with consequences of innovation context was adopted for this study. This expanded TOE framework was used as the theoretical lens to understand the disruption of BCT and its adoption in the accounting and auditing fields. Four clear patterns emerged. First, BCT is an emerging tool that accountants and auditors use mainly to analyse financial records because technology cannot disintermediate auditors from the financial system. Second, the technology can detect anomalies but cannot prevent financial fraud. Third, BCT has not been adopted by any organisation for financial reporting and accounting purposes, and accountants and auditors do not require new skillsets or an understanding of the BCT programming language to be able to operate in a BCT domain. Fourth, the advent of COVID-19 has not substantially enhanced the adoption of BCT. Additionally, this study highlights the incentives, barriers, and unintended consequences of adopting BCT as financial technology (FinTech). These findings shed light on important questions about BCT disrupting and disintermediating auditors, the extent of adoption in the accounting industry, preventing fraud and anomalies, and underscores the notion that blockchain, as an emerging technology, currently does not appear to be substantially disrupting the accounting and auditing profession. This study makes methodological, theoretical, and practical contributions. At the methodological level, the study adopted the social constructivist-interpretivism paradigm with an exploratory qualitative method to engage and understand BCT as a disruptive innovation in the accounting industry. The engagement with practitioners from diverse fields, professions, and different countries provides a distinctive and innovative contribution to methodological and practical knowledge. At the theoretical level, the findings contribute to the literature by offering an integrated conceptual TOE framework. The framework offers a reference for practitioners, academics and policymakers seeking to appraise comprehensive factors influencing BCT adoption and its likely unintended consequences. The findings suggest that, at present, no organisations are using BCT for financial reporting and accounting systems. This study contributes to practice by highlighting the differences between initial expectations and practical applications of what BCT can do in the accounting and auditing fields. The study could not find any empirical evidence that BCT will disrupt audits, eliminate the roles of auditors in a financial system, and prevent and detect financial fraud. Also, there was no significant evidence that accountants and auditors required higher-level skillsets and an understanding of BCT programming language to be able to use the technology. Future research should consider the implications of an external audit firm as a node in a BCT network on the internal audit functions. It is equally important to critically examine the relevance of including programming languages or codes in the curriculum of undergraduate accounting students. Future research could also empirically evaluate if a BCT-enabled triple-entry system could prevent financial statements and management fraud

    Information Theory for Complex Systems Scientists

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    In the 21st century, many of the crucial scientific and technical issues facing humanity can be understood as problems associated with understanding, modelling, and ultimately controlling complex systems: systems comprised of a large number of non-trivially interacting components whose collective behaviour can be difficult to predict. Information theory, a branch of mathematics historically associated with questions about encoding and decoding messages, has emerged as something of a lingua franca for those studying complex systems, far exceeding its original narrow domain of communication systems engineering. In the context of complexity science, information theory provides a set of tools which allow researchers to uncover the statistical and effective dependencies between interacting components; relationships between systems and their environment; mereological whole-part relationships; and is sensitive to non-linearities missed by commonly parametric statistical models. In this review, we aim to provide an accessible introduction to the core of modern information theory, aimed specifically at aspiring (and established) complex systems scientists. This includes standard measures, such as Shannon entropy, relative entropy, and mutual information, before building to more advanced topics, including: information dynamics, measures of statistical complexity, information decomposition, and effective network inference. In addition to detailing the formal definitions, in this review we make an effort to discuss how information theory can be interpreted and develop the intuition behind abstract concepts like "entropy," in the hope that this will enable interested readers to understand what information is, and how it is used, at a more fundamental level

    Investor sentiment and statistical moments of the return distribution in the German stock market. A three stage empirical analysis.

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    This dissertation contributes to an increasing body of literature through a holistic perspective in research and model development to investigate the relationships between investor sentiment and the lower- and higher-order statistics of the return distribution in the German stock market utilizing the market-wide CDAX stock index as an exemplary sample. Since empirical studies on investor sentiment are conducted mainly with USmarket-based data, comparatively few academic contributions are made to the German investor sentiment literature. Moreover, previous findings for other countries cannot necessarily be generalized to Germany, especially as Germany appears to be mainly influenced by global trends and investor sentiments owing to its high dependence on foreign trade. Consequently, the empirical evidence for Germany in this research domain, which includes both cross-sectional and longitudinal perspectives, is sparse. As various approaches exist to measure and assess the links between investor sentiment and capital market movements, a proprietarily defined investor sentiment categorization system is established, in which each investor sentiment indicator is assigned. This dissertation's underlying investor sentiment sample consists of all three categories of the dedicated categorization system for investor sentiment indicators and covers up to 20 years to 2021. With regard to the thesis structure, a comprehensive overview of the literature and current research on market efficiency and investor sentiment is initially elaborated before the applied methodology and the evaluation results are analyzed. Of particular note is the three-stage empirical analysis conducted in this thesis: First, a principal component analysis-based investor sentiment risk factor is established to improve model performance in traditional cross-sectional multifactor models as measured by the corrected coefficient of determination and additional metrics. Second, the application of Long Short-Term Memory (LSTM) artificial recurrent neural network architecture models to account for time-varying investor sentiment risk premia explaining and predicting the return distribution's lower- and higherorder statistics leads to notable findings. A performant model for the German stock market results from fitting a deep neural network fed with 73 sentiment indicators without dimension reduction and performing out-of-sample tests. Third, an insightful exploratory Twitter study of social investor sentiment in the German stock market in times of COVID-19-induced market turmoil is elaborated. The study investigates the impact of incorporating unstructured data into investor sentiment analysis to improve discriminatory power and predictive accuracy and employs elaborate processing techniques. The exploratory study is based on a unique hand-curated dataset of almost two million tweets on the German stock market, exclusively collected for this study. In this context, the importance of investor sentiment in social media for volatility in the German stock market is investigated and highlighted. As a result, all three empirical studies address many vital matters, although new challenges worthy of investigation are as well raised and discussed in the final part of the thesis.AdministraciĂłn y DirecciĂłn de Empresa

    Knowledge-based Modelling of Additive Manufacturing for Sustainability Performance Analysis and Decision Making

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    Additiivista valmistusta on pidetty kÀyttökelpoisena monimutkaisissa geometrioissa, topologisesti optimoiduissa kappaleissa ja kappaleissa joita on muuten vaikea valmistaa perinteisillÀ valmistusprosesseilla. Eduista huolimatta, yksi additiivisen valmistuksen vallitsevista haasteista on ollut heikko kyky tuottaa toimivia osia kilpailukykyisillÀ tuotantomÀÀrillÀ perinteisen valmistuksen kanssa. Mallintaminen ja simulointi ovat tehokkaita työkaluja, jotka voivat auttaa lyhentÀmÀÀn suunnittelun, rakentamisen ja testauksen sykliÀ mahdollistamalla erilaisten tuotesuunnitelmien ja prosessiskenaarioiden nopean analyysin. Perinteisten ja edistyneiden valmistusteknologioiden mahdollisuudet ja rajoitukset mÀÀrittelevÀt kuitenkin rajat uusille tuotekehityksille. Siksi on tÀrkeÀÀ, ettÀ suunnittelijoilla on kÀytettÀvissÀÀn menetelmÀt ja työkalut, joiden avulla he voivat mallintaa ja simuloida tuotteen suorituskykyÀ ja siihen liittyvÀn valmistusprosessin suorituskykyÀ, toimivien korkea arvoisten tuotteiden toteuttamiseksi. Motivaation tÀmÀn vÀitöstutkimuksen tekemiselle on, meneillÀÀn oleva kehitystyö uudenlaisen korkean lÀmpötilan suprajohtavan (high temperature superconducting (HTS)) magneettikokoonpanon kehittÀmisessÀ, joka toimii kryogeenisissÀ lÀmpötiloissa. Sen monimutkaisuus edellyttÀÀ monitieteisen asiantuntemuksen lÀhentymistÀ suunnittelun ja prototyyppien valmistuksen aikana. Tutkimus hyödyntÀÀ tietopohjaista mallinnusta valmistusprosessin analysoinnin ja pÀÀtöksenteon apuna HTS-magneettien mekaanisten komponenttien suunnittelussa. TÀmÀn lisÀksi, tutkimus etsii mahdollisuuksia additiivisen valmistuksen toteutettavuuteen HTS-magneettikokoonpanon tuotannossa. Kehitetty lÀhestymistapa kÀyttÀÀ fysikaalisiin kokeisiin perustuvaa tuote-prosessi-integroitua mallinnusta tuottamaan kvantitatiivista ja laadullista tietoa, joka mÀÀrittelee prosessi-rakenne-ominaisuus-suorituskyky-vuorovaikutuksia tietyille materiaali-prosessi-yhdistelmille. Tuloksina saadut vuorovaikutukset integroidaan kaaviopohjaiseen malliin, joka voi auttaa suunnittelutilan tutkimisessa ja tÀten auttaa varhaisessa suunnittelu- ja valmistuspÀÀtöksenteossa. TÀtÀ varten testikomponentit valmistetaan kÀyttÀmÀllÀ kahta metallin additiivista valmistus prosessia: lankakaarihitsaus additiivista valmistusta (wire arc additive manufacturing) ja selektiivistÀ lasersulatusta (selective laser melting). Rakenteellisissa sovelluksissa yleisesti kÀytetyistÀ metalliseoksista (ruostumaton terÀs, pehmeÀ terÀs, luja niukkaseosteinen terÀs, alumiini ja kupariseokset) testataan niiden mekaaniset, lÀmpö- ja sÀhköiset ominaisuudet. LisÀksi tehdÀÀn metalliseosten mikrorakenteen karakterisointi, jotta voidaan ymmÀrtÀÀ paremmin valmistusprosessin parametrien vaikutusta materiaalin ominaisuuksiin. Integroitu mallinnustapa yhdistÀÀ kerÀtyn kokeellisen tiedon, olemassa olevat analyyttiset ja empiiriset vuorovaikutus suhteet, sekÀ muut tietopohjaiset mallit (esim. elementtimallit, koneoppimismallit) pÀÀtöksenteon tukijÀrjestelmÀn muodossa, joka mahdollistaa optimaalisen materiaalin, valmistustekniikan, prosessiparametrien ja muitten ohjausmuuttujien valinnan, lopullisen 3d-tulosteun komponentin halutun rakenteen, ominaisuuksien ja suorituskyvyn saavuttamiseksi. ValmistuspÀÀtöksenteko tapahtuu todennÀköisyysmallin, eli Bayesin verkkomallin toteuttamisen kautta, joka on vankka, modulaarinen ja sovellettavissa muihin valmistusjÀrjestelmiin ja tuotesuunnitelmiin. VÀitöstyössÀ esitetyn mallin kyky parantaa additiivisien valmistusprosessien suorituskykyÀ ja laatua, tÀten edistÀÀ kestÀvÀn tuotannon tavoitteita.Additive manufacturing (AM) has been considered viable for complex geometries, topology optimized parts, and parts that are otherwise difficult to produce using conventional manufacturing processes. Despite the advantages, one of the prevalent challenges in AM has been the poor capability of producing functional parts at production volumes that are competitive with traditional manufacturing. Modelling and simulation are powerful tools that can help shorten the design-build-test cycle by enabling rapid analysis of various product designs and process scenarios. Nevertheless, the capabilities and limitations of traditional and advanced manufacturing technologies do define the bounds for new product development. Thus, it is important that the designers have access to methods and tools that enable them to model and simulate product performance and associated manufacturing process performance to realize functional high value products. The motivation for this dissertation research stems from ongoing development of a novel high temperature superconducting (HTS) magnet assembly, which operates in cryogenic environment. Its complexity requires the convergence of multidisciplinary expertise during design and prototyping. The research applies knowledge-based modelling to aid manufacturing process analysis and decision making in the design of mechanical components of the HTS magnet. Further, it explores the feasibility of using AM in the production of the HTS magnet assembly. The developed approach uses product-process integrated modelling based on physical experiments to generate quantitative and qualitative information that define process-structure-property-performance interactions for given material-process combinations. The resulting interactions are then integrated into a graph-based model that can aid in design space exploration to assist early design and manufacturing decision-making. To do so, test components are fabricated using two metal AM processes: wire and arc additive manufacturing and selective laser melting. Metal alloys (stainless steel, mild steel, high-strength low-alloyed steel, aluminium, and copper alloys) commonly used in structural applications are tested for their mechanical-, thermal-, and electrical properties. In addition, microstructural characterization of the alloys is performed to further understand the impact of manufacturing process parameters on material properties. The integrated modelling approach combines the collected experimental data, existing analytical and empirical relationships, and other data-driven models (e.g., finite element models, machine learning models) in the form of a decision support system that enables optimal selection of material, manufacturing technology, process parameters, and other control variables for attaining desired structure, property, and performance characteristics of the final printed component. The manufacturing decision making is performed through implementation of a probabilistic model i.e., a Bayesian network model, which is robust, modular, and can be adapted for other manufacturing systems and product designs. The ability of the model to improve throughput and quality of additive manufacturing processes will boost sustainable manufacturing goals
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