Download or read book Pricing and Market Modeling with Artificial Intelligence written by Antal Ratku and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Economic Modeling Using Artificial Intelligence Methods written by Tshilidzi Marwala and published by Springer Science & Business Media. This book was released on 2013-04-02 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Machine Learning in Asset Pricing written by Stefan Nagel and published by Princeton University Press. This book was released on 2021-05-11 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

Download or read book Computational Intelligence Applications to Option Pricing Volatility Forecasting and Value at Risk written by Fahed Mostafa and published by Springer. This book was released on 2017-02-28 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling. These features mean that they can be applied to market-risk problems to overcome classic problems associated with statistical models.

Download or read book Algorithms Artificial Intelligence and Simple Rule Based Pricing written by Qiaochu Wang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automated pricing comes in two forms - rule-based (e.g., targeting or undercutting the lowest price, etc) and artificial intelligence (AI) powered algorithms (e.g., reinforcement learning (RL) based). While rule-based pricing is the most widely used automated pricing strategy today, many retailers have increasingly adopting pricing algorithms powered by AI. Q-learning algorithm (a specific type of RL algorithm) is particularly appealing for pricing because it autonomously learns an optimal pricing policy and can adapt to any evolution in competitors' pricing strategy and market environment. It is commonly believed that the Q-learning algorithm has a significant advantage over simple rule-based pricing algorithms; therefore, in a competitive environment, most firms should adopt Q-learning based pricing algorithms if their competitors are using such algorithms. However, through extensive pricing experiments in a workhorse oligopoly model of repeated price competition, we show that a firm's best response to its competitor's Q-learning based algorithms is to use simple rule-based pricing algorithms. We find that when a Q-learning algorithm competes against a rule-based pricing algorithm, higher prices are sustained in the market in comparison to when multiple Q-learning algorithms compete against each other. The high prices are sustained because the rule-based algorithm introduces stationarity into the repeated price competition, which allows the Q-learning algorithm to more effectively search for the optimal policy benefiting both sellers. Further, the experimental phase where the Q-learning algorithm learns the optimal pricing policy is significantly shorter when it competes against a rule-based pricing algorithm in comparison to when it competes against another Q-learning algorithm. Our results are robust to alternative modeling assumptions on market structure, algorithm type, number of players, etc.

Download or read book Artificial Intelligence for Automated Pricing Based on Product Descriptions written by Nguyen Thi Ngoc Anh and published by Springer Nature. This book was released on 2021-08-28 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights artificial intelligence algorithms used in implementation of automated pricing. It presents the process for building automated pricing models from crawl data, preprocessed data to implement models, and their applications. The book also focuses on machine learning and deep learning methods for pricing, including from regression methods to hybrid and ensemble methods. The computational experiments are presented to illustrate the pricing processes and models.

Download or read book AI for Marketing and Product Innovation written by A. K. Pradeep and published by John Wiley & Sons. This book was released on 2018-12-06 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get on board the next massive marketing revolution AI for Marketing and Product Innovation offers creatives and marketing professionals a non-tech guide to artificial intelligence (AI) and machine learning (ML)—twin technologies that stand poised to revolutionize the way we sell. The future is here, and we are in the thick of it; AI and ML are already in our lives every day, whether we know it or not. The technology continues to evolve and grow, but the capabilities that make these tools world-changing for marketers are already here—whether we use them or not. This book helps you lean into the curve and take advantage of AI’s unparalleled and rapidly expanding power. More than a simple primer on the technology, this book goes beyond the “what” to show you the “how”: How do we use AI and ML in ways that speak to the human spirit? How to we translate cold technological innovation into creative tools that forge deep human connections? Written by a team of experts at the intersection of neuroscience, technology, and marketing, this book shows you the ins and outs of these groundbreaking technological tools. Understand AI and ML technology in layman’s terms Harness the twin technologies unparalleled power to transform marketing Learn which skills and resources you need to use AI and ML effectively Employ AI and ML in ways that resonate meaningfully with customers Learn practical examples of how to reinvest product innovation, brand building, targeted marketing and media measurement to connect with people and enhance ROI Discover the true impact of AI and ML from real-world examples, and learn the thinking, best practices, and metrics you need to capture this lightning and take the next massive leap in the evolution of customer connection. AI for Marketing and Product Innovation shows you everything you need to know to get on board.

Download or read book Powering the Digital Economy Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.

Download or read book Imitation Market Modeling in Digital Economy Game Theoretic Approaches written by Elena G. Popkova and published by Springer Nature. This book was released on 2022-01-21 with total page 850 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes the best studies on the results of the International Scientific and Practical Conference “New behaviors of market players in the digital economy,” which was held by the Institute of Scientific Communications on July 8, 2021, online, in YouTube format. This book is devoted to the study of digital economy markets from the standpoint of various market players—society (consumers), entrepreneurship, and the state—from the standpoint of various sciences—economic, managerial, social, and legal—which ensures the multidisciplinarity of the book. The uniqueness of the book lies in the application of a new scientific and methodological approach to the study of digital economy markets—simulation modeling. The advantages of a game-based scientific and methodological approach to reducing the uncertainty of economic processes and systems—a combination of quantitative and qualitative analytical methods, a systematic consideration of economic processes and systems from a socio-economic point of view—make it especially suitable for studying digital economy markets. The book identifies the impact of globalization and digitalization on the modern economy and industry markets. The trends and features of the use of advanced technologies in the digital economy markets are studied. The modern practices of business management and business integration in the digital economy are considered. The foundations of economic security and sustainable development of markets and enterprises in the digital economy are revealed. The book is suitable for scientists studying the markets of the digital economy, who will find in it scientific and methodological recommendations and developments on the application of game theory, as well as ready simulation models of the digital economy markets.

Download or read book Modeling the Market written by Sergio M. Focardi and published by John Wiley & Sons. This book was released on 1997-01-15 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authors have done an admirable job...This book is a revealing and fascinating glimpse of the technologies which may rule the financial world in the years to come. --The Financial Times, February 1997 [This] new book looks at the progress made, both in practice and in theory, toward producing a usable model of the market. Some of the theoretical foundations of efficient market theory are being demolished.

Download or read book Artificial Intelligence for Financial Markets written by Thomas Barrau and published by Springer Nature. This book was released on 2022-05-31 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the novel artificial intelligence technique of polymodels and applies it to the prediction of stock returns. The idea of polymodels is to describe a system by its sensitivities to an environment, and to monitor it, imitating what a natural brain does spontaneously. In practice this involves running a collection of non-linear univariate models. This very powerful standalone technique has several advantages over traditional multivariate regressions. With its easy to interpret results, this method provides an ideal preliminary step towards the traditional neural network approach. The first two chapters compare the technique with other regression alternatives and introduces an estimation method which regularizes a polynomial regression using cross-validation. The rest of the book applies these ideas to financial markets. Certain equity return components are predicted using polymodels in very different ways, and a genetic algorithm is described which combines these different predictions into a single portfolio, aiming to optimize the portfolio returns net of transaction costs. Addressed to investors at all levels of experience this book will also be of interest to both seasoned and non-seasoned statisticians.

Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.

Download or read book Artificial Intelligence and Pricing written by John Asker and published by . This book was released on 2021 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: The behavior of artificial intelligences algorithms (AIAs) is shaped by how they learn about their environment. We compare the prices generated by AIAs that use different learning protocols when there is market interaction. Asynchronous learning occurs when the AIA only learns about the return from the action it took. Synchronous learning occurs when the AIA conducts counterfactuals to learn about the returns it would have earned had it taken an alternative action. The two lead to markedly different market prices. When future profits are not given positive weight by the AIA, synchronous updating leads to competitive pricing, while asynchronous can lead to pricing close to monopoly levels. We investigate how this result varies when either counterfactuals can only be calculated imperfectly and/or when the AIA places a weight on future profits.

Download or read book Artificial Intelligence Marketing and Predicting Consumer Choice written by Steven Struhl and published by Kogan Page Publishers. This book was released on 2017-04-03 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ability to predict consumer choice is a fundamental aspect to success for any business. In the context of artificial intelligence marketing, there are a wide array of predictive analytic techniques available to achieve this purpose, each with its own unique advantages and disadvantages. Artificial Intelligence Marketing and Predicting Consumer Choice serves to integrate these widely disparate approaches, and show the strengths, weaknesses, and best applications of each. It provides a bridge between the person who must apply or learn these problem-solving methods and the community of experts who do the actual analysis. It is also a practical and accessible guide to the many remarkable advances that have been recently made in this fascinating field. Online resources: bonus chapters on AI, ensembles and neural nets, and finishing experiments, plus single and multiple product simulators.

Download or read book Advanced Option Pricing Models written by Jeffrey Owen Katz and published by McGraw Hill Professional. This book was released on 2005-02-04 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Option Pricing Models details specific conditions under which current option pricing models fail to provide accurate price estimates and then shows option traders how to construct improved models for better pricing in a wider range of market conditions. Model-building steps cover options pricing under conditional or marginal distributions, using polynomial approximations and curve fitting, and compensating for mean reversion. The authors also develop effective prototype models that can be put to immediate use, with real-time examples of the models in action.

Download or read book The Strategy and Tactics of Pricing written by Thomas T. Nagle and published by Taylor & Francis. This book was released on 2023-07-31 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Strategy and Tactics of Pricing is the most well-established and influential strategic pricing text available, relied on by practitioners and students globally as a core guide for value-based pricing. The book explains how to balance the ability to create and extract value through from markets by managing pricing decisions in a more strategic and profitable manner. Rather than calculating prices to cover costs or to achieve sales goals, readers will learn to frame more strategic choices that proactively influence customer perceptions of value, manage internal costs, and profitably shift demand curves. This edition features new discussions on harnessing concepts from behavioral economics as well as a refined "value cascade" to help organize the topics covered in this book. Readers will also benefit from: Major revisions to more than a third of the chapters, including an expanded discussion of the role of artificial intelligence and machine learning analytics tools to assist in the evaluation of new pricing opportunities Discussion of many of the new pricing and revenue-recognition models such as consumption-based pricing, outcomes-based pricing, and others An expanded discussion on "Special Topics in Pricing" that cover many of the transformative pricing moves successful companies have made in the past few years in response to major disruptive forces such as the pandemic as well as re-emergent inflation In-chapter textboxes and call-out to highlight different "pricing concepts in action" using actual examples of companies addressing market challenges Chapter summaries and visual aids to help the reader better understand the ideas and concepts presented throughout this book This comprehensive, managerially-focused text is a must-read for students and professionals with an interest in strategic price management and achieving commercial excellence for their organizations. Additional online resources include PowerPoint slides and an instructor’s manual, including exercises, mini-cases, and examination questions.