Ai And Macroeconomic Modeling Deep Reinforcement Learning In An Rbc Model
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| Author | : Tohid Atashbar |
| Publisher | : International Monetary Fund |
| Total Pages | : 31 |
| Release | : 2023-02-24 |
| Genre | : Business & Economics |
| ISBN | : |
This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, one of which is deterministic without the technological shock and the other is stochastic. The objective of the deterministic environment is to compare the learning agent's behavior to a deterministic steady-state scenario. We demonstrate that in both deterministic and stochastic scenarios, the agent's choices are close to their optimal value. We also present cases of unstable learning behaviours. This AI-macro model may be enhanced in future research by adding additional variables or sectors to the model or by incorporating different DRL algorithms.
| Author | : Tohid Atashbar |
| Publisher | : International Monetary Fund |
| Total Pages | : 32 |
| Release | : 2022-12-16 |
| Genre | : Business & Economics |
| ISBN | : |
The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used to study a variety of economic problems, including optimal policy-making, game theory, and bounded rationality. In this paper, after a theoretical introduction to deep reinforcement learning and various DRL algorithms, we provide an overview of the literature on deep reinforcement learning in economics, with a focus on the main applications of deep reinforcement learning in macromodeling. Then, we analyze the potentials and limitations of deep reinforcement learning in macroeconomics and identify a number of issues that need to be addressed in order for deep reinforcement learning to be more widely used in macro modeling.
| Author | : Terisa Roberts |
| Publisher | : John Wiley & Sons |
| Total Pages | : 214 |
| Release | : 2022-09-20 |
| Genre | : Business & Economics |
| ISBN | : 111982494X |
A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume: Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques Covers the basic principles and nuances of feature engineering and common machine learning algorithms Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.
| Author | : Isaiah Hull |
| Publisher | : Apress |
| Total Pages | : 368 |
| Release | : 2020-11-26 |
| Genre | : Computers |
| ISBN | : 9781484263723 |
Work on economic problems and solutions with tools from machine learning. ML has taken time to move into the space of academic economics. This is because empirical work in economics is concentrated on the identification of causal relationships in parsimonious statistical models; whereas machine learning is oriented towards prediction and is generally uninterested in either causality or parsimony. That leaves a gap for both students and professionals in the economics industry without a standard reference. This book focuses on economic problems with an empirical dimension, where machine learning methods may offer something of value. This includes coverage of a variety of discriminative deep learning models (DNNs, CNNs, RNNs, LSTMs, the Transformer Model, etc.), generative machine learning models, random forests, gradient boosting, clustering, and feature extraction. You'll also learn about the intersection of empirical methods in economics and machine learning, including regression analysis, text analysis, and dimensionality reduction methods, such as principal components analysis. TensorFlow offers a toolset that can be used to setup and solve any mathematical model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. Otherwise complicated content is then distilled into accessible examples, so you can use TensorFlow to solve workhorse models in economics and finance. What You'll Learn Define, train, and evaluate machine learning models in TensorFlow 2 Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems Solve workhorse models in economics and finance Who This Book Is For Students and data scientists working in the economics industry. Academic economists and social scientists who have an interest in machine learning are also likely to find this book useful.
| Author | : Tshilidzi Marwala |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 271 |
| Release | : 2013-04-02 |
| Genre | : Computers |
| ISBN | : 1447150104 |
Economic Modeling Using Artificial Intelligence Methods examines the application of artificial intelligence methods to model economic data. Traditionally, economic modeling has been modeled in the linear domain where the principles of superposition are valid. The application of artificial intelligence for economic modeling allows for a flexible multi-order non-linear modeling. In addition, game theory has largely been applied in economic modeling. However, the inherent limitation of game theory when dealing with many player games encourages the use of multi-agent systems for modeling economic phenomena. The artificial intelligence techniques used to model economic data include: multi-layer perceptron neural networks radial basis functions support vector machines rough sets genetic algorithm particle swarm optimization simulated annealing multi-agent system incremental learning fuzzy networks Signal processing techniques are explored to analyze economic data, and these techniques are the time domain methods, time-frequency domain methods and fractals dimension approaches. Interesting economic problems such as causality versus correlation, simulating the stock market, modeling and controling inflation, option pricing, modeling economic growth as well as portfolio optimization are examined. The relationship between economic dependency and interstate conflict is explored, and knowledge on how economics is useful to foster peace – and vice versa – is investigated. Economic Modeling Using Artificial Intelligence Methods deals with the issue of causality in the non-linear domain and applies the automatic relevance determination, the evidence framework, Bayesian approach and Granger causality to understand causality and correlation. Economic Modeling Using Artificial Intelligence Methods makes an important contribution to the area of econometrics, and is a valuable source of reference for graduate students, researchers and financial practitioners.
| Author | : German Creamer |
| Publisher | : Routledge |
| Total Pages | : 206 |
| Release | : 2021-04-06 |
| Genre | : Business & Economics |
| ISBN | : 1000372049 |
The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.
| Author | : Mohammad Zoynul Abedin |
| Publisher | : Springer Nature |
| Total Pages | : 235 |
| Release | : 2023-03-01 |
| Genre | : Business & Economics |
| ISBN | : 3031185528 |
This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
| Author | : Matthew F. Dixon |
| Publisher | : Springer Nature |
| Total Pages | : 565 |
| Release | : 2020-07-01 |
| Genre | : Business & Economics |
| ISBN | : 3030410684 |
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
| Author | : Abdullah Karasan |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 334 |
| Release | : 2021-12-07 |
| Genre | : Computers |
| ISBN | : 1492085200 |
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models
| Author | : Jeffrey Ng |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 232 |
| Release | : 2020-07-10 |
| Genre | : Computers |
| ISBN | : 1788833961 |
Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with Python Key FeaturesUnderstand how to obtain financial data via Quandl or internal systemsAutomate commercial banking using artificial intelligence and Python programsImplement various artificial intelligence models to make personal banking easyBook Description Remodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI). Hands-On Artificial Intelligence for Banking is a practical guide that will help you advance in your career in the banking domain. The book will demonstrate AI implementation to make your banking services smoother, more cost-efficient, and accessible to clients, focusing on both the client- and server-side uses of AI. You’ll begin by understanding the importance of artificial intelligence, while also gaining insights into the recent AI revolution in the banking industry. Next, you’ll get hands-on machine learning experience, exploring how to use time series analysis and reinforcement learning to automate client procurements and banking and finance decisions. After this, you’ll progress to learning about mechanizing capital market decisions, using automated portfolio management systems and predicting the future of investment banking. In addition to this, you’ll explore concepts such as building personal wealth advisors and mass customization of client lifetime wealth. Finally, you’ll get to grips with some real-world AI considerations in the field of banking. By the end of this book, you’ll be equipped with the skills you need to navigate the finance domain by leveraging the power of AI. What you will learnAutomate commercial bank pricing with reinforcement learningPerform technical analysis using convolutional layers in KerasUse natural language processing (NLP) for predicting market responses and visualizing them using graph databasesDeploy a robot advisor to manage your personal finances via Open Bank APISense market needs using sentiment analysis for algorithmic marketingExplore AI adoption in banking using practical examplesUnderstand how to obtain financial data from commercial, open, and internal sourcesWho this book is for This is one of the most useful artificial intelligence books for machine learning engineers, data engineers, and data scientists working in the finance industry who are looking to implement AI in their business applications. The book will also help entrepreneurs, venture capitalists, investment bankers, and wealth managers who want to understand the importance of AI in finance and banking and how it can help them solve different problems related to these domains. Prior experience in the financial markets or banking domain, and working knowledge of the Python programming language are a must.
