Pricing Via Artificial Intelligence
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Pricing Via Artificial Intelligence
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Author : Weipeng Zhang
language : en
Publisher:
Release Date : 2023
Pricing Via Artificial Intelligence written by Weipeng Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Classic artificial intelligence (Q-learning) algorithms have been capable of consistently learning supra-competitive pricing strategies in infinitely repeated Nash-Bertrand pricing games without human communication. Such algorithms have been able to converge due to the temporal correlation of consecutive states and actions in the learning process, which restores stationarity in an otherwise highly non-stationary setting. It is difficult for more realistic AI algorithms to converge, as the necessary training processes breaks the aforementioned temporal correlation, rendering the algorithms ineffective in learning reward-punishment strategies that result in collusive market outcomes. We adapt several widely used neural network architectures to the framework of model-free reinforcement learning and experimentally explore how the structure of AI algorithms affects market outcomes in a workhorse oligopolistic model of repeated price competition. While it is possible to train advance AI algorithms to always best respond in environments where the rival exercises a fixed strategy, it is unlikely that such algorithms can learn to coordinate in setting supra-competitive prices due to the non-stationarity of multi-agent learning processes, suggesting that algorithmic collusion may not be an immediate concern for antitrust authorities.
Artificial Intelligence For Automated Pricing Based On Product Descriptions
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Author : Nguyen Thi Ngoc Anh
language : en
Publisher: Springer Nature
Release Date : 2021-08-28
Artificial Intelligence For Automated Pricing Based On Product Descriptions written by Nguyen Thi Ngoc Anh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-28 with Technology & Engineering categories.
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.
Machine Learning In Asset Pricing
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Author : Stefan Nagel
language : en
Publisher: Princeton University Press
Release Date : 2021-05-11
Machine Learning In Asset Pricing written by Stefan Nagel and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-11 with Business & Economics categories.
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.
Artificial Intelligence In Asset Management
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Author : Söhnke M. Bartram
language : en
Publisher: CFA Institute Research Foundation
Release Date : 2020-08-28
Artificial Intelligence In Asset Management written by Söhnke M. Bartram and has been published by CFA Institute Research Foundation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-28 with Business & Economics categories.
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.
Ai In Digital Marketing Training Guide
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Author : Dwayne Anderson
language : en
Publisher: Estalontech
Release Date : 2022-08-19
Ai In Digital Marketing Training Guide written by Dwayne Anderson and has been published by Estalontech this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-19 with Business & Economics categories.
Are you looking to optimize Artificial Intelligence in Digital Marketing? Artificial Intelligence in Digital Marketing can be the golden ticket to creator success. Artificial intelligence is a hot topic for businesses. AI proficiencies are mounting the possibilities for how corporations approach real-time engagement with their customers, manage their processes, and make business continuity. As technology advances, corporations are finding new ways to innovate and expand. It is, therefore, no surprise that Artificial Intelligence in Digital Marketing is a growing trend in the online world. It is most definitely the future of digital marketing. It will bring human experiences closer to technology in the future. Creators and Marketers have built businesses and careers on and through optimizing AI in Digital marketing. Using Artificial Intelligence in Digital Marketing will assist brands in delivering a better-quality customer experience, marketing their brand well, and reaching the masses. As per the statistics, • 61% of marketers say artificial intelligence is the most crucial aspect of their data strategy. • 80% of business and tech innovators say AI already enhances efficiency. • Existing AI technology can improve business productivity by up to 40%. • 97% of mobile users are using AI-powered voice assistants • 83% of initial AI adopters have already attained substantial (30%) or moderate (53%) economic benefits AI would make digital advertising and marketing more targeted as well as accurate. It will assist in optimizing campaigns and deliver a better ROI than ever before. There are many reasons to believe that Artificial Intelligence in Digital Marketing is an integral part of today’s world. As a business owner, you must think of new and innovative ways to make people take notice of your products and services. With this awesome and up-to-date AI In Digital Marketing course ,it will enable you to develop a robust Artificial Intelligence Marketing strategy for your organization and create exclusive engagement to stand out, captivate your audience as well as raising profits exponentially. This guide will educate you about how Artificial intelligence is being used towards optimizing digital marketing campaigns by improvising almost all aspects, from understanding the customers well to analyzing the campaign performance. The most common uses of Artificial Intelligence in Digital Marketing are · Automation of the regular tasks and processes · Gain comprehensive customer insights · Understand the latest industry pattern as well as content creation trends · Personalization of marketing communication · Generate, nurture and convert leads Artificial Intelligence in Digital Marketing provides a robust and well-established platform with vast audiences and accessible intellectual machines and tools. It is time that your business should have practical Artificial Intelligence in your Digital Marketing strategy. Just use the tips and information we covered to get started. Using the strategy and information provided in our Mastery Guide, you will master the essentials of creating intelligent machines. So, consider getting our comprehensive and up-to-date guide jam-loaded with the latest and best-in-the-industry knowledge about Artificial Intelligence in Digital Marketing strategy.
Algorithms Artificial Intelligence And Simple Rule Based Pricing
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Author : Qiaochu Wang
language : en
Publisher:
Release Date : 2022
Algorithms Artificial Intelligence And Simple Rule Based Pricing written by Qiaochu Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
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.
Economic Modeling Using Artificial Intelligence Methods
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Author : Tshilidzi Marwala
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-02
Economic Modeling Using Artificial Intelligence Methods written by Tshilidzi Marwala and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-02 with Computers categories.
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.
Ai Powered Commerce
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Author : Andy Pandharikar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-01-28
Ai Powered Commerce written by Andy Pandharikar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-28 with Computers categories.
Learn how to use artificial intelligence for product and service innovation, including the diverse use cases of Commerce.AI Key FeaturesLearn how to integrate data and AI in your innovation workflowsUnlock insights into how various industries are using AI for innovationApply your knowledge to real innovation use cases like product strategy and market intelligenceBook Description Commerce.AI is a suite of artificial intelligence (AI) tools, trained on over a trillion data points, to help businesses build next-gen products and services. If you want to be the best business on the block, using AI is a must. Developers and analysts working with AI will be able to put their knowledge to work with this practical guide. You'll begin by learning the core themes of new product and service innovation, including how to identify market opportunities, come up with ideas, and predict trends. With plenty of use cases as reference, you'll learn how to apply AI for innovation, both programmatically and with Commerce.AI. You'll also find out how to analyze product and service data with tools such as GPT-J, Python pandas, Prophet, and TextBlob. As you progress, you'll explore the evolution of commerce in AI, including how top businesses today are using AI. You'll learn how Commerce.AI merges machine learning, product expertise, and big data to help businesses make more accurate decisions. Finally, you'll use the Commerce.AI suite for product ideation and analyzing market trends. By the end of this artificial intelligence book, you'll be able to strategize new product opportunities by using AI, and also have an understanding of how to use Commerce.AI for product ideation, trend analysis, and predictions. What you will learnFind out how machine learning can help you identify new market opportunitiesUnderstand how to use consumer data to create new products and servicesUse state-of-the-art AI frameworks and tools for data analysisLaunch, track, and improve products and services with AIRise above the competition with unparalleled insights from AITurn customer touchpoints into business winsGenerate high-conversion product and service copyWho this book is for This AI book is for AI developers, data scientists, data product managers, analysts, and consumer insights professionals. The book will guide you through the process of product and service innovation, no matter your pre-existing skillset.
Empirical Asset Pricing
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Author : Wayne Ferson
language : en
Publisher: MIT Press
Release Date : 2019-03-12
Empirical Asset Pricing written by Wayne Ferson and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-12 with Business & Economics categories.
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.
Artificial Intelligence In Practice
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Author : Bernard Marr
language : en
Publisher: John Wiley & Sons
Release Date : 2019-04-15
Artificial Intelligence In Practice written by Bernard Marr and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-15 with Business & Economics categories.
Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce.