3,595 research outputs found

    The influence of macroeconomic factors on the capital market of the Republic of Serbia

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    У овој дисертацији испитиван је утицај десет макроекономских фактора на тржиште капитала Републике Србије. Фокус је био на макроекономским факторима који су идентификовани као значајни за развој тржишта капитала, али у вези којих постоје различити налази у погледу правца њиховог утицаја на тржиште капитала. Конкретно, фокус је био на БДП-у, инфлацији, понуди новца, каматној стопи, девизном курсу, политичком окружењу и институционалној инфраструктури, степену економских слобода, незапослености, директним страним инвестицијама, друштвено одговорном понашању и потрошњи. За истраживање коришћени су месечни подаци одговарајућих индикатора макроекономских фактора. Подаци су прикупљати за период од јануара 2007. до септембра 2021. године, што обухвата и период Covid-19 пандемије. Избор индикатора у складу је са анализираном литературом. Резултати истраживања показују да постоји дугорочни коинтегишући однос између макроекономских фактора и тржишта капитала, што упућује на закључак о присуству дугорочне повезаности између варијабли, односно, да постоји заједничко дугорочно кретање. Налази истовремено показују да постоји краткорочна неусклађеност између фактора и тржишта капитала Републике Србије, односно, да након што поједини фактори изазову шок на тржиште капитала, његово избацивање из дугорочне равнотеже, оно настоји да се релативно брзо врати на дугорочни равнотежни ниво. Другим речима, налаз указује на постојање дугорочне каузалне везе између макроекономских варијабли и тржишта капитала Републике Србије, на коју одређени поремећаји у систему доводе до краткорочних одступања од дугорочног равнотежног кретања. Економетријским речником, налаз указује да постоји реверзибилна девијација од дугорочне равнотеже коjа се може исправити кроз време. Истовремено налази показују да се та девијација теже отклања како време више пролази, односно, са протоком времена, краткорочна неусклађеност између макроекономских фактора и тржишта капитала Републике Србије утиче на успоравање процеса прилагођавања система дугорочном равнотежном односу. Резултати истраживања такође указују на утицај Covid-19 пандемије на промену смера и интензитета утицаја код одређених фактора. Допринос дисертације огледа се у открићу да се на основу промена у макроекономским факторима може предвидети развој тржишта капитала. Другим речима, могуће је развити одговарајући модел који ће помоћи креаторима монетарне и уопште макроекономске политике и инвеститорима да лакше доносу одлуке везане за мере макроекономске политике, односно инвестиционе одлуке и формулишу стратегије улагања.U ovoj disertaciji ispitivan je uticaj deset makroekonomskih faktora na tržište kapitala Republike Srbije. Fokus je bio na makroekonomskim faktorima koji su identifikovani kao značajni za razvoj tržišta kapitala, ali u vezi kojih postoje različiti nalazi u pogledu pravca njihovog uticaja na tržište kapitala. Konkretno, fokus je bio na BDP-u, inflaciji, ponudi novca, kamatnoj stopi, deviznom kursu, političkom okruženju i institucionalnoj infrastrukturi, stepenu ekonomskih sloboda, nezaposlenosti, direktnim stranim investicijama, društveno odgovornom ponašanju i potrošnji. Za istraživanje korišćeni su mesečni podaci odgovarajućih indikatora makroekonomskih faktora. Podaci su prikupljati za period od januara 2007. do septembra 2021. godine, što obuhvata i period Covid-19 pandemije. Izbor indikatora u skladu je sa analiziranom literaturom. Rezultati istraživanja pokazuju da postoji dugoročni kointegišući odnos između makroekonomskih faktora i tržišta kapitala, što upućuje na zaključak o prisustvu dugoročne povezanosti između varijabli, odnosno, da postoji zajedničko dugoročno kretanje. Nalazi istovremeno pokazuju da postoji kratkoročna neusklađenost između faktora i tržišta kapitala Republike Srbije, odnosno, da nakon što pojedini faktori izazovu šok na tržište kapitala, njegovo izbacivanje iz dugoročne ravnoteže, ono nastoji da se relativno brzo vrati na dugoročni ravnotežni nivo. Drugim rečima, nalaz ukazuje na postojanje dugoročne kauzalne veze između makroekonomskih varijabli i tržišta kapitala Republike Srbije, na koju određeni poremećaji u sistemu dovode do kratkoročnih odstupanja od dugoročnog ravnotežnog kretanja. Ekonometrijskim rečnikom, nalaz ukazuje da postoji reverzibilna devijacija od dugoročne ravnoteže koja se može ispraviti kroz vreme. Istovremeno nalazi pokazuju da se ta devijacija teže otklanja kako vreme više prolazi, odnosno, sa protokom vremena, kratkoročna neusklađenost između makroekonomskih faktora i tržišta kapitala Republike Srbije utiče na usporavanje procesa prilagođavanja sistema dugoročnom ravnotežnom odnosu. Rezultati istraživanja takođe ukazuju na uticaj Covid-19 pandemije na promenu smera i intenziteta uticaja kod određenih faktora. Doprinos disertacije ogleda se u otkriću da se na osnovu promena u makroekonomskim faktorima može predvideti razvoj tržišta kapitala. Drugim rečima, moguće je razviti odgovarajući model koji će pomoći kreatorima monetarne i uopšte makroekonomske politike i investitorima da lakše donosu odluke vezane za mere makroekonomske politike, odnosno investicione odluke i formulišu strategije ulaganja.This dissertation examines the influence of ten macroeconomic factors on the capital market of the Republic of Serbia. The focus was on the macroeconomic factors that have been identified as important for the development of the capital market, as well as having different findings regarding the direction of their influence on the capital market. Specifically, the focus was on GDP, inflation, money supply, interest rate, exchange rate, political environment and institutional infrastructure, degree of economic freedom, unemployment, foreign direct investments, socially responsible behavior and consumption. Monthly data of the corresponding indicators of macroeconomic factors were used for the research. The data were collected for the period from January 2007 to September 2021, which includes the period of the Covid-19 pandemic. The selection of indicators is in accordance with the analyzed literature. The results of the research show that there is a long-run cointegrating relationship between macroeconomic factors and the capital market, leading to the conclusion of the presence of a long-term connection between the variables, i.e. that there is a common long-run movement trend. The findings also show that there is a short-term discrepancy between the factors and the capital market of the Republic of Serbia; i.e. that after certain factors cause a shock to the capital market, throwing it out of long-run equilibrium it relatively quickly tries to return to the long-run equilibrium level. Namely, the findings indicate the existence of a long-run causal relationship between macroeconomic variables and the capital market of the Republic of Serbia, regarding which certain disturbances in the system lead to short-term deviations from the long-run equilibrium movement trend. In econometric terms, the finding indicates that there is a reversible deviation from long-run equilibrium that can be corrected over time. At the same time, the findings show that this deviation is more difficult to eliminate as time passes, that is, with time the short-term discrepancy between macroeconomic factors and the capital market of the Republic of Serbia slows down the process of adjusting the system to a long-run equilibrium relationship. The research results also indicate the effect of the Covid-19 pandemic on the change in the direction and intensity of the impact of certain factors. The contribution of the dissertation is offered in discovering that the development of the capital market can be predicted based on the changes in macroeconomic factors. In other words, it is possible to develop an appropriate model that will help the creators of monetary and general macroeconomic policy and investors to make decisions related to macroeconomic policy measures more easily, namely investment decisions, and formulate investment strategies

    Advances in machine learning algorithms for financial risk management

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    In this thesis, three novel machine learning techniques are introduced to address distinct yet interrelated challenges involved in financial risk management tasks. These approaches collectively offer a comprehensive strategy, beginning with the precise classification of credit risks, advancing through the nuanced forecasting of financial asset volatility, and ending with the strategic optimisation of financial asset portfolios. Firstly, a Hybrid Dual-Resampling and Cost-Sensitive technique has been proposed to combat the prevalent issue of class imbalance in financial datasets, particularly in credit risk assessment. The key process involves the creation of heuristically balanced datasets to effectively address the problem. It uses a resampling technique based on Gaussian mixture modelling to generate a synthetic minority class from the minority class data and concurrently uses k-means clustering on the majority class. Feature selection is then performed using the Extra Tree Ensemble technique. Subsequently, a cost-sensitive logistic regression model is then applied to predict the probability of default using the heuristically balanced datasets. The results underscore the effectiveness of our proposed technique, with superior performance observed in comparison to other imbalanced preprocessing approaches. This advancement in credit risk classification lays a solid foundation for understanding individual financial behaviours, a crucial first step in the broader context of financial risk management. Building on this foundation, the thesis then explores the forecasting of financial asset volatility, a critical aspect of understanding market dynamics. A novel model that combines a Triple Discriminator Generative Adversarial Network with a continuous wavelet transform is proposed. The proposed model has the ability to decompose volatility time series into signal-like and noise-like frequency components, to allow the separate detection and monitoring of non-stationary volatility data. The network comprises of a wavelet transform component consisting of continuous wavelet transforms and inverse wavelet transform components, an auto-encoder component made up of encoder and decoder networks, and a Generative Adversarial Network consisting of triple Discriminator and Generator networks. The proposed Generative Adversarial Network employs an ensemble of unsupervised loss derived from the Generative Adversarial Network component during training, supervised loss and reconstruction loss as part of its framework. Data from nine financial assets are employed to demonstrate the effectiveness of the proposed model. This approach not only enhances our understanding of market fluctuations but also bridges the gap between individual credit risk assessment and macro-level market analysis. Finally the thesis ends with a novel proposal of a novel technique or Portfolio optimisation. This involves the use of a model-free reinforcement learning strategy for portfolio optimisation using historical Low, High, and Close prices of assets as input with weights of assets as output. A deep Capsules Network is employed to simulate the investment strategy, which involves the reallocation of the different assets to maximise the expected return on investment based on deep reinforcement learning. To provide more learning stability in an online training process, a Markov Differential Sharpe Ratio reward function has been proposed as the reinforcement learning objective function. Additionally, a Multi-Memory Weight Reservoir has also been introduced to facilitate the learning process and optimisation of computed asset weights, helping to sequentially re-balance the portfolio throughout a specified trading period. The use of the insights gained from volatility forecasting into this strategy shows the interconnected nature of the financial markets. Comparative experiments with other models demonstrated that our proposed technique is capable of achieving superior results based on risk-adjusted reward performance measures. In a nut-shell, this thesis not only addresses individual challenges in financial risk management but it also incorporates them into a comprehensive framework; from enhancing the accuracy of credit risk classification, through the improvement and understanding of market volatility, to optimisation of investment strategies. These methodologies collectively show the potential of the use of machine learning to improve financial risk management

    Bayesian Forecasting in Economics and Finance: A Modern Review

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    The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem -- model, parameters, latent states -- is able to be quantified explicitly, and factored into the forecast distribution via the process of integration or averaging. Allied with the elegance of the method, Bayesian forecasting is now underpinned by the burgeoning field of Bayesian computation, which enables Bayesian forecasts to be produced for virtually any problem, no matter how large, or complex. The current state of play in Bayesian forecasting in economics and finance is the subject of this review. The aim is to provide the reader with an overview of modern approaches to the field, set in some historical context; and with sufficient computational detail given to assist the reader with implementation.Comment: The paper is now published online at: https://doi.org/10.1016/j.ijforecast.2023.05.00

    Limit Theory under Network Dependence and Nonstationarity

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    These lecture notes represent supplementary material for a short course on time series econometrics and network econometrics. We give emphasis on limit theory for time series regression models as well as the use of the local-to-unity parametrization when modeling time series nonstationarity. Moreover, we present various non-asymptotic theory results for moderate deviation principles when considering the eigenvalues of covariance matrices as well as asymptotics for unit root moderate deviations in nonstationary autoregressive processes. Although not all applications from the literature are covered we also discuss some open problems in the time series and network econometrics literature.Comment: arXiv admin note: text overlap with arXiv:1705.08413 by other author

    Making Connections: A Handbook for Effective Formal Mentoring Programs in Academia

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    This book, Making Connections: A Handbook for Effective Formal Mentoring Programs in Academia, makes a unique and needed contribution to the mentoring field as it focuses solely on mentoring in academia. This handbook is a collaborative institutional effort between Utah State University’s (USU) Empowering Teaching Open Access Book Series and the Mentoring Institute at the University of New Mexico (UNM). This book is available through (a) an e-book through Pressbooks, (b) a downloadable PDF version on USU’s Open Access Book Series website), and (c) a print version available for purchase on the USU Empower Teaching Open Access page, and on Amazon

    2023-2024 academic bulletin & course catalog

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    University of South Carolina Aiken publishes a catalog with information about the university, student life, undergraduate and graduate academic programs, and faculty and staff listings

    Standardized Exclusion: A Theory of Barrier Lock-In

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    The United States has relaxed antitrust scrutiny of private standard-setting organizations in recognition of their potential procompetitive benefits. In the meantime, however, the growing importance of network industries—and the coinciding move toward vendor-led standards consortia—has welcomed new, insidious anticompetitive risks. This Note proffers one such risk: barrier lock-in. A theory of barrier lock-in recognizes that dominant vendors can capture and control standards consortia to keep standardized equipment complex and costly. These practices are exclusionary. This Note situates barrier lock-in within the existing antitrust literature and jurisprudence, provides a potential example of barrier lock-in in the 5G network equipment standardization process, and proposes two solutions for future legislative, executive, and judicial action against misbehaving standard-setters

    Humans in the Loop

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    From lethal drones to cancer diagnostics, humans are increasingly working with complex and artificially intelligent algorithms to make decisions which affect human lives, raising questions about how best to regulate these “human-in-the-loop” systems. We make four contributions to the discourse. First, contrary to the popular narrative, law is already profoundly and often problematically involved in governing human-in-the-loop systems: it regularly affects whether humans are retained in or removed from the loop. Second, we identify “the MABA-MABA trap,” which occurs when policymakers attempt to address concerns about algorithmic incapacities by inserting a human into a decisionmaking process. Regardless of whether the law governing these systems is old or new, inadvertent or intentional, it rarely accounts for the fact that human-machine systems are more than the sum of their parts: they raise their own problems and require their own distinct regulatory interventions. But how to regulate for success? Our third contribution is to highlight the panoply of roles humans might be expected to play, to assist regulators in understanding and choosing among the options. For our fourth contribution, we draw on legal case studies and synthesize lessons from human factors engineering to suggest regulatory alternatives to the MABA-MABA approach. Namely, rather than carelessly placing a human in the loop, policymakers should regulate the human-in-the-loop system

    Humans in the Loop

    Get PDF
    From lethal drones to cancer diagnostics, humans are increasingly working with complex and artificially intelligent algorithms to make decisions which affect human lives, raising questions about how best to regulate these “human-in-the-loop” systems. We make four contributions to the discourse. First, contrary to the popular narrative, law is already profoundly and often problematically involved in governing human-in-the-loop systems: it regularly affects whether humans are retained in or removed from the loop. Second, we identify “the MABA-MABA trap,” which occurs when policymakers attempt to address concerns about algorithmic incapacities by inserting a human into a decisionmaking process. Regardless of whether the law governing these systems is old or new, inadvertent or intentional, it rarely accounts for the fact that human-machine systems are more than the sum of their parts: they raise their own problems and require their own distinct regulatory interventions. But how to regulate for success? Our third contribution is to highlight the panoply of roles humans might be expected to play, to assist regulators in understanding and choosing among the options. For our fourth contribution, we draw on legal case studies and synthesize lessons from human factors engineering to suggest regulatory alternatives to the MABA-MABA approach. Namely, rather than carelessly placing a human in the loop, policymakers should regulate the human-in-the-loop system
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