Advances In Artificial Intelligence In Economics Finance And Management
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| Author | : Tankiso Moloi |
| Publisher | : Springer Nature |
| Total Pages | : 131 |
| Release | : 2020-05-07 |
| Genre | : Computers |
| ISBN | : 3030429628 |
As Artificial Intelligence (AI) seizes all aspects of human life, there is a fundamental shift in the way in which humans are thinking of and doing things. Ordinarily, humans have relied on economics and finance theories to make sense of, and predict concepts such as comparative advantage, long run economic growth, lack or distortion of information and failures, role of labour as a factor of production and the decision making process for the purpose of allocating resources among other theories. Of interest though is that literature has not attempted to utilize these advances in technology in order to modernize economic and finance theories that are fundamental in the decision making process for the purpose of allocating scarce resources among other things. With the simulated intelligence in machines, which allows machines to act like humans and to some extent even anticipate events better than humans, thanks to their ability to handle massive data sets, this book will use artificial intelligence to explain what these economic and finance theories mean in the context of the agent wanting to make a decision. The main feature of finance and economic theories is that they try to eliminate the effects of uncertainties by attempting to bring the future to the present. The fundamentals of this statement is deeply rooted in risk and risk management. In behavioural sciences, economics as a discipline has always provided a well-established foundation for understanding uncertainties and what this means for decision making. Finance and economics have done this through different models which attempt to predict the future. On its part, risk management attempts to hedge or mitigate these uncertainties in order for “the planner” to reach the favourable outcome. This book focuses on how AI is to redefine certain important economic and financial theories that are specifically used for the purpose of eliminating uncertainties so as to allow agents to make informed decisions. In effect, certain aspects of finance and economic theories cannot be understood in their entirety without the incorporation of AI.
| Author | : El Bachir Boukherouaa |
| Publisher | : International Monetary Fund |
| Total Pages | : 35 |
| Release | : 2021-10-22 |
| Genre | : Business & Economics |
| ISBN | : 1589063953 |
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
| Author | : Marcos Lopez de Prado |
| Publisher | : John Wiley & Sons |
| Total Pages | : 395 |
| Release | : 2018-01-23 |
| Genre | : Business & Economics |
| ISBN | : 1119482119 |
Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
| Author | : Ajay Agrawal |
| Publisher | : University of Chicago Press |
| Total Pages | : 172 |
| Release | : 2024-03-05 |
| Genre | : Business & Economics |
| ISBN | : 0226833127 |
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
| Author | : John D. Johnson |
| Publisher | : |
| Total Pages | : |
| Release | : 1996-03 |
| Genre | : |
| ISBN | : 9781559386579 |
Part of a series providing a forum for research in applied artificial intelligence. Applications are focused toward both the academician and practitioner in the business community. Short theoretical pieces as well as longer pieces outlining major AI projects are included.
| Author | : Shu-Heng Chen |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 476 |
| Release | : 2002-05-27 |
| Genre | : Computers |
| ISBN | : 9783790814767 |
After a decade's development, evolutionary computation (EC) proves to be a powerful tool kit for economic analysis. While the demand for this equipment is increasing, there is no volume exclusively written for economists. This volume for the first time helps economists to get a quick grasp on how EC may support their research. A comprehensive coverage of the subject is given, that includes the following three areas: game theory, agent-based economic modelling and financial engineering. Twenty leading scholars from each of these areas contribute a chapter to the volume. The reader will find himself treading the path of the history of this research area, from the fledgling stage to the burgeoning era. The results on games, labour markets, pollution control, institution and productivity, financial markets, trading systems design and derivative pricing, are new and interesting for different target groups. The book also includes informations on web sites, conferences, and computer software.
| Author | : Marcos M. López de Prado |
| Publisher | : Cambridge University Press |
| Total Pages | : 152 |
| Release | : 2020-04-22 |
| Genre | : Business & Economics |
| ISBN | : 1108879721 |
Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to "learn" complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.
| Author | : Yves Hilpisch |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 478 |
| Release | : 2020-10-14 |
| Genre | : Business & Economics |
| ISBN | : 1492055387 |
The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about
| Author | : William W. Cooper |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 236 |
| Release | : 2012-12-06 |
| Genre | : Business & Economics |
| ISBN | : 940110770X |
New Directions in Computational Economics brings together for the first time a diverse selection of papers, sharing the underlying theme of application of computing technology as a tool for achieving solutions to realistic problems in computational economics and related areas in the environmental, ecological and energy fields. Part I of the volume addresses experimental and computational issues in auction mechanisms, including a survey of recent results for sealed bid auctions. The second contribution uses neural networks as the basis for estimating bid functions for first price sealed bid auctions. Also presented is the `smart market' computational mechanism which better matches bids and offers for natural gas. Part II consists of papers that formulate and solve models of economics systems. Amman and Kendrick's paper deals with control models and the computational difficulties that result from nonconvexities. Using goal programming, Nagurney, Thore and Pan formulate spatial resource allocation models to analyze various policy issues. Thompson and Thrall next present a rigorous mathematical analysis of the relationship between efficiency and profitability. The problem of matching uncertain streams of assets and liabilities is solved using stochastic optimization techniques in the following paper in this section. Finally, Part III applies economic concepts to issues in computer science in addition to using computational techniques to solve economic models.
| Author | : Emilio Corchado |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 785 |
| Release | : 2008-09-10 |
| Genre | : Computers |
| ISBN | : 3540876553 |
This volume constitutes the proceedings of the Third International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2008, held in Burgos, Spain, during September 24-26, 2008. The 93 papers presented, together with 4 invited talks, were carefully reviewed and selected from 280 submissions. The topics covered are agents and multi-agent systems; evolutionary computation; connectionist models; optimization sysetms; fuzzy logic systems; classification and classifiers; cluster analysis; video and image analysis; learning systems, algorithms and applications; hybrid systems based on negotiation and social network modelling; real world applications of HAIS under uncertainty; hybrid intelligent systems for multi-robot and multi-agent systems; applications of hybrid artificial intelligence in bioinformatics; and novel approaches to genetic fuzzy systems.
