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Simonian J: FOUNDATIONS OF FINANCIAL
DATA SCIENCE: METHODS, MODELS, AND APPL

FOUNDATIONS OF FINANCIAL DATA SCIENCE: METHODS, MODELS, AND APPLICATIONS

Econometric Society Monographs

A Framework for Economists with Policy Aspirations

Minimalist Market Design

This book explores how organizations can transform marketing and management by placing customer well-being at the center of value creation. As AI, data analytics, and digital technologies continue to reshape markets and society, success increasingly depends not only on customer satisfaction but also on richer customer experiences, human-centered innovation, and sustainable relationships.Moving beyond traditional marketing approaches focused on transactions, segmentation, and recommendation systems, the volume argues that customers should be viewed as active participants in value co-creation. The emphasis shifts from predicting purchasing behavior to understanding how products, services, and experiences contribute to personal development, quality of life, and overall well-being.Drawing on insights from service informatics, customer experience research, data science, and artificial intelligence, leading scholars and practitioners demonstrate how organizations can leverage digital technologies and explainable AI to design experiences that create both economic and societal value. The chapters further illustrate how customer experiences accumulate over time to shape consumer well-being and long-term organizational success.Offering a forward-looking perspective on the next generation of marketing, this volume provides valuable insights for researchers, graduate students, and professionals in marketing, management, information systems, service science, artificial intelligence, and consumer behavior who seek to understand the evolving relationship between technology, customer experience, and human well-being.

Based on Understanding Characteristics of Customer Well-being

The Creation of User Experience and Evolution of Management

A transparent prediction method that goes beyond regression and machine learning in sports In The Relevance Edge: A New Science of Sports Prediction, Mark P. Kritzman, MIT Sloan Senior Lecturer and founding partner of Cambridge Sports Analytics, together with data analytics colleagues and Cambridge Sports Analytics founding partners Megan Czasonis, Cel Kulasekaran, and David Turkington, introduce relevance-based prediction to the world of sports analytics. The method is designed to overcome the limitations of both linear regression and machine learning, and has been adopted in a wide range of fields ranging from finance and investing to health and medicine. Unlike regression's static assumptions or machine learning's opacity and overfitting, relevance-based prediction delivers transparent, adaptive forecasts and flags each prediction's reliability in advance. These benefits, and more, are available because relevance-based predictions are formed as carefully weighted averages of the most relevant historical experiences. The Relevance Edge builds from core concepts to full technical depth across seven chapters, grounding each idea in real case studies: NBA draft prospect evaluation, expected goals analysis of a Barcelona-Real Madrid match, NFL wide receiver prospect assessment with missing data, and World Series pitcher matchup confidence scoring. A dedicated final chapter provides rigorous mathematical treatment alongside profiles of intellectual forerunners who have helped pave the way to more transparent and effective predictions. Readers will also learn: <ul> <li>The fundamental building blocks that determine how relevant one athlete or experience is to another for the purpose of forming a prediction</li> <li>How to measure the task-specific importance of each predictive variable instead of relying on misleading averages</li> <li>How to handle missing data and extreme outliers that standard model-based approaches consistently struggle with</li> <li>Why linear regression and neural networks each fall short when applied to dynamic sports environments</li> <li>Real-world insights from player prediction and in-game decision case studies across basketball, baseball, soccer and American football</li> </ul> Written for professional sports organizations, sports technology companies, sportsbook analysts, and quantitative bettors, The Relevance Edge also serves students and faculty in sports analytics, data science, statistics, and predictive analytics. Professionals in business management, finance, risk analytics, healthcare research, and many other fields will find the methodology directly transferable to their own forecasting challenges.

A New Science of Sports Prediction

The Relevance Edge

This accessible and thought-provoking book offers a critical exploration of global health philanthropy, blending personal narrative with a rigorous examination of the field's challenges and opportunities. Grounded in the authors' decades of experience, Crossroads in Global Health reflects on the transformative power of medicine, the complexities of global health engagement, and the enduring human impulse to alleviate suffering. The authors examine the tension between short-term relief and durable system change, and show how reframing global health as a marketplace, powered by data, analytics, and AI, can better match resources to need and turn goodwill into measurable impact.Key Features:· Explores the history, evolution, and critiques of global health, with a focus on discrete philanthropic medical missions.· Examines the landscape of global health needs and resources, shaped by logistics, incentives, and institutional structures.· Reflects on the authors' personal journeys in global health, offering insights into the challenges and lessons learned from decades of experience.· Proposes innovative approaches to harnessing data and technology to create a more efficient and accountable marketplace for global health philanthropy.· Balances the importance of systemic solutions with the moral imperative of immediate care, emphasizing the enduring value of direct human connection in medicine.Building on the authors' previous works, including the World Compendium of Healthcare Facilities and Nonprofit Organizations, this book is an essential resource for medical practitioners, academics, and professionals in the non-profit, public, and private sectors. It offers a compelling call to action for those seeking to make a meaningful impact in global health, while honoring the profound responsibility and transformative potential of the medical profession.

Pragmatic Solutions in a Complex World

Crossroads in Global Health

In 1977, The Economist coined the term "Dutch disease" to describe the damage to the real economy when wealth derives from resource exports rather than productive work. On April 1, 1978, the Second Amendment to the IMF's Articles of Agreement came into force, ending gold's monetary role within the IMF system and consolidating the transition to pure fiat money. With "money" no longer backed by anything real, the alchemists' dream of a "Philosopher's Stone"--the ability to create seemingly limitless wealth out of nothing--appeared to have been realized. But while real wealth produced by human labor is finite, fiat money creation is not subject to the same physical constraint. The result was a new and far more powerful variant of Dutch disease: the Philosopher's Stone Disease. The Money Powers' financialization of economies and imposition of neoliberalism increasingly privileged finance over production. Over the next four decades, the consequences extended beyond economics, eroding the cultural and civilizational heritage and traditions of large swaths of the global population. The socioeconomic impact of this pure fiat-money regime, globally and within the United States, has been an across-the-board deterioration, while its benefits accrued overwhelmingly to less than 0.01 percent of the population. Yet creating money cannot create the real goods it commands. The system therefore depends upon a steady external supply of real goods and the recycling of part of the resulting surpluses into U.S. financial assets. Energy revenues from West Asia have been key to sustaining it. A resurgent Iran thus posed an existential threat to the UK-established and U.S.-dominated oil order. Sanctions, assassinations, proxy wars, color revolutions, terrorist attacks and mass psychological manipulation followed, but failed to achieve their objective. If Iran emerges victorious from the war launched against it in 2026 and breaks U.S. control over the oil-rich Levant, the petrodollar recycling system will suffer a massive blow, accelerating withdrawal from U.S. Treasuries. The dilemma is stark: financial dominance ultimately requires real power to enforce it, while the Philosopher's Stone Disease has hollowed out the productive and military foundations of that power. Why the West Can't Recover: The Philosopher's Stone Disease explains why there may be no way back.

The Philosopoher's Stone Disease

Why the West Can't Recover

Chapman & Hall/CRC Texts in Statistical Science

From Alan Turing breaking the Enigma code to ChatGPT reshaping how we work and create, artificial intelligence has always been powered by a single unifying principle: learning from data under uncertainty. This book reveals the mathematical machinery behind modern AI, weaving together Bayesian probability, statistical learning, and deep neural networks into a coherent intellectual framework. Whether you seek to understand why large language models hallucinate, how recommendation systems predict your preferences, or what makes reinforcement learning agents master complex games, this book equips you with both the theoretical foundations and practical intuitions that define the modern AI playbook.Key Features:<ul> <li>Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breaking</li> <li>Bridges classical statistics and deep learning, connecting linear regression to transformers</li> <li>Covers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMs</li> <li>Emphasizes uncertainty quantification-building systems that know what they don't know</li> <li>Practical applications across finance, healthcare, operations, and autonomous systems</li> <li>Emerging topics: AI agents, RLHF, and retrieval-augmented generation</li> </ul>Written for graduate students, data scientists, and quantitatively-minded practitioners, this book assumes comfort with calculus and basic probability while building sophisticated intuitions progressively. Business analysts will appreciate the decision-theoretic framing; engineers will value the architectural insights; researchers will find rigorous foundations for further study. Whether used as a course textbook, professional reference, or intellectual companion for understanding the AI revolution transforming every industry, this book delivers the rare combination of mathematical depth and accessible exposition that makes complex ideas genuinely understandable.

Foundations of Data Science

Bayes, AI, and Deep Learning

Business Statistics of the United States

Business Statistics of the United States is a comprehensive and practical collection of data from as early as 1913 that reflects the nation's economic performance. It provides several years of annual, quarterly, and monthly data in industrial and demographic detail, including key indicators such as: gross domestic product, personal income, spending, saving, employment, unemployment, the capital stock, and more. Business Statistics of the United States is the best place to find historical perspectives on the U.S. economy. Of equal importance to the data are the introductory highlights, extensive notes, and figures for each chapter that help users to understand the data, use them appropriately, and, if desired, seek additional information from the source agencies. Business Statistics of the United States provides a rich and deep picture of the American economy and contains approximately 3,500 time series. The data are predominantly from federal government sources, including: ·Board of Governors of the Federal Reserve System ·Bureau of Economic Analysis ·Bureau of Labor Statistics ·Census Bureau ·Employment and Training Administration ·Energy Information Administration ·Federal Housing Finance Agency ·Department of the Treasury

FOUNDATIONS OF FINANCIAL DATA SCIENCE: METHODS, MODELS, AND APPLICATIONS

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