2,447 research outputs found
Explainable Artificial Intelligence Methods in FinTech Applications
The increasing amount of available data and access to high-performance computing allows companies to use complex Machine Learning (ML) models for their decision-making process, so-called âblack-boxâ models. These âblack-boxâ models typically show higher predictive accuracy than linear models on complex data sets. However, this improved predictive accuracy can only be achieved by deteriorating the explanatory power. âOpen the black boxâ and make the model predictions explainable is summarised under the research area of Explainable Artificial Intelligence (XAI). Using black-box models also raises practical and ethical issues, especially in critical industries such as finance. For this reason, the explainability of models is increasingly becoming a focus for regulators. Applying XAI methods to ML models makes their predictions explainable and hence, enables the application of ML models in the financial industries. The application of ML models increases predictive accuracy and supports the different stakeholders in the financial industries in their decision-making processes.
This thesis consists of five chapters: a general introduction, a chapter on conclusions and future research, and three separate chapters covering the underlying papers. Chapter 1 proposes an XAI method that can be used in credit risk management, in particular, in measuring the risks associated with borrowing through peer-to-peer lending platforms. The model applies correlation networks to Shapley values and thus the model predictions are grouped according to the similarity of the underlying explanations. Chapter 2 develops an alternative XAI method based on the Lorenz Zonoid approach. The new method is statistically normalised and can therefore be used as a standard for the application of Artificial Intelligence (AI) in credit risk management. The novel âShapley-Lorenzâ-approach can facilitate the validation of model results and supports the decision whether a model is sufficiently explained. In Chapter 3, an XAI method is applied to assess the impact of financial and non-financial factors on a firmâs ex-ante cost of capital, a measure that reflects investorsâ perceptions of a firmâs risk appetite. A combination of two explanatory
tools: the Shapley values and the Lorenz model selection approach, enabled the identification of the most important features and the reduction of the independent features. This allowed a substantial simplification of the model without a statistically significant decrease in predictive accuracy.The increasing amount of available data and access to high-performance computing allows companies to use complex Machine Learning (ML) models for their decision-making process, so-called âblack-boxâ models. These âblack-boxâ models typically show higher predictive accuracy than linear models on complex data sets. However, this improved predictive accuracy can only be achieved by deteriorating the explanatory power. âOpen the black boxâ and make the model predictions explainable is summarised under the research area of Explainable Artificial Intelligence (XAI). Using black-box models also raises practical and ethical issues, especially in critical industries such as finance. For this reason, the explainability of models is increasingly becoming a focus for regulators. Applying XAI methods to ML models makes their predictions explainable and hence, enables the application of ML models in the financial industries. The application of ML models increases predictive accuracy and supports the different stakeholders in the financial industries in their decision-making processes.
This thesis consists of five chapters: a general introduction, a chapter on conclusions and future research, and three separate chapters covering the underlying papers. Chapter 1 proposes an XAI method that can be used in credit risk management, in particular, in measuring the risks associated with borrowing through peer-to-peer lending platforms. The model applies correlation networks to Shapley values and thus the model predictions are grouped according to the similarity of the underlying explanations. Chapter 2 develops an alternative XAI method based on the Lorenz Zonoid approach. The new method is statistically normalised and can therefore be used as a standard for the application of Artificial Intelligence (AI) in credit risk management. The novel âShapley-Lorenzâ-approach can facilitate the validation of model results and supports the decision whether a model is sufficiently explained. In Chapter 3, an XAI method is applied to assess the impact of financial and non-financial factors on a firmâs ex-ante cost of capital, a measure that reflects investorsâ perceptions of a firmâs risk appetite. A combination of two explanatory
tools: the Shapley values and the Lorenz model selection approach, enabled the identification of the most important features and the reduction of the independent features. This allowed a substantial simplification of the model without a statistically significant decrease in predictive accuracy
Organizing sustainable development
The role and meaning of sustainable development have been recognized in the scientific literature for decades. However, there has recently been a dynamic increase in interest in the subject, which results in numerous, in-depth scientific research and publications with an interdisciplinary dimension. This edited volume is a compendium of theoretical knowledge on sustainable development. The context analysed in the publication includes a multi-level and multi-aspect analysis starting from the historical and legal conditions, through elements of the macro level and the micro level, inside the organization. Organizing Sustainable Development offers a systematic and comprehensive theoretical analysis of sustainable development supplemented with practical examples, which will allow obtaining comprehensive knowledge about the meaning and its multi-context application in practice. It shows the latest state of knowledge on the topic and will be of interest to students at an advanced level, academics and reflective practitioners in the fields of sustainable development, management studies, organizational studies and corporate social responsibility
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (âAIâ) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics â and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the CatĂłlica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Climate Change and Critical Agrarian Studies
Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution â as well as industrialisation under state-led socialism â the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people â in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation â are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change â and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial
Essays on Corporate Disclosure of Value Creation
Information on a firmâs business model helps investors understand an entityâs resource requirements, priorities for action, and prospects (FASB, 2001, pp. 14-15; IASB, 2010, p. 12). Disclosures of strategy and business model (SBM) are therefore considered a central element of effective annual report commentary (Guillaume, 2018; IIRC, 2011). By applying natural language processing techniques, I explore what SBM disclosures look like when management are pressed to say something, analyse determinants of cross-sectional variation in SBM reporting properties, and assess whether and how managers respond to regulatory interventions seeking to promote SBM annual report commentary. This dissertation contains three main chapters. Chapter 2 presents a systematic review of the academic literature on non-financial reporting and the emerging literature on SBM reporting. Here, I also introduce my institutional setting. Chapter 3 and Chapter 4 form the empirical sections of this thesis. In Chapter 3, I construct the first large sample corpus of SBM annual report commentary and provide the first systematic analysis of the properties of such disclosures. My topic modelling analysis rejects the hypothesis that such disclosure is merely padding; instead finding themes align with popular strategy frameworks and management tailor the mix of SBM topics to reflect their unique approach to value creation. However, SBM commentary is less specific, less precise about time horizon (short- and long-term), and less balanced (more positive) in tone relative to general management commentary. My findings suggest symbolic compliance and legitimisation characterize the typical annual report discussion of SBM. Further analysis identifies proprietary cost considerations and obfuscation incentives as key determinants of symbolic reporting. In Chapter 4, I seek evidence on how managers respond to regulatory mandates by adapting the properties of disclosure and investigate whether the form of the mandate matters. Using a differences-in-differences research design, my results suggest a modest incremental response by treatment firms to the introduction of a comply or explain provision to provide disclosure on strategy and business model. In contrast, I find a substantial response to enacting the same requirements in law. My analysis provides clear and consistent evidence that treatment firms incrementally increase the volume of SBM disclosure, improve coverage across a broad range of topics as well as providing commentary with greater focus on the long term. My results point to substantial changes in SBM reporting properties following regulatory mandates, but the form of the mandate does matter. Overall, this dissertation contributes to the accounting literature by examining how firms discuss a central topic to economic decision making in annual reports and how firms respond to different forms of disclosure mandate. Furthermore, the results of my analysis are likely to be of value for regulators and policymakers currently reviewing or considering mandating disclosure requirements. By examining how companies adapt their reporting to different types of regulations, this study provides an empirical basis for recalibrating SBM disclosure mandates, thereby enhancing the information set of capital market participants and promoting stakeholder engagement in a landscape increasingly shaped by non-financial information
Recommended from our members
Essays on Subjective Expectations in Finance
In chapter one, I examine the predictive content of subjective return expectations derived from price targets issued by equity analysts. Equity price targets are an ubiquitous feature of the financial information landscape, but it is not clear how informative they actually are. In this chapter, I show that the cross-section of price-target implied subjective return expectations contains rich informational content for forecasting returns. In-sample, I find that expected returns correlate strongly with average cross-sectional returns to a large panel of portfolios formed on the basis of observable firm characteristics. In out-of-sample exercises, forecasting models using subjective expectations are shown to offer more accurate predictions for portfolio returns than several other commonly employed, cross-sectional predictors, including the book-to-market and dividend-price ratios, momentum, and forward-looking cash-flow measures. Furthermore, these differences are shown to be economically relevant, with conditional portfolios formed on the basis of subjective expectations offering substantially improved risk-adjusted returns compared to many of the other predictors considered. The relative informational content, as well as the production by analysts, of subjective return expectations is found, however, to peak during recessions, with negligible predictive advantage discernible in expansions.
In chapter two, my coauthors (Adam Rej, with CFM; David Thesmar, with MIT, CEPR, and NBER) and I empirically analyze a large panel of firm sales growth expectations. We find that the relationship between forecast errors and lagged revision is non-linear. Forecasters underreact to typical (positive or negative) news about future sales, but overreact to very significant news. To account for this non-linearity, we propose a simple framework, where (1) sales growth dynamics have a fat-tailed high frequency component and (2) forecasters use a simple linear rule. This framework qualitatively fits several additional features of data on sales growth dynamics, forecast errors, and stock returns.
In chapter three, my coauthor (Ken Teoh, with Columbia) and I construct a novel text-based measure of firm-level attention to macroeconomic conditions and document that stocks associated with higher macroeconomic attention earn lower returns. Moving from the bottom decile to top decile of macroeconomic attention decreases a stockâs average return by 11.6\% per year. We propose a risk-based explanation in which stocks with higher macroeconomic attention contribute less idiosyncratic cash flow risk to the investorâs portfolio, hence earn lower expected returns. Decomposing the unexpected returns of macroeconomic attention-sorted portfolios into cash flow and discount rate news, we find that portfolios with higher macroeconomic attention stocks have lower cash flow risk
NEMISA Digital Skills Conference (Colloquium) 2023
The purpose of the colloquium and events centred around the central role that data plays
today as a desirable commodity that must become an important part of massifying digital
skilling efforts. Governments amass even more critical data that, if leveraged, could
change the way public services are delivered, and even change the social and economic
fortunes of any country. Therefore, smart governments and organisations increasingly
require data skills to gain insights and foresight, to secure themselves, and for improved
decision making and efficiency. However, data skills are scarce, and even more
challenging is the inconsistency of the associated training programs with most curated for
the Science, Technology, Engineering, and Mathematics (STEM) disciplines.
Nonetheless, the interdisciplinary yet agnostic nature of data means that there is
opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog
- âŠ