This book provides an investor-friendly presentation of the premises and applications of the quantitative finance models governing investment in one asset class of publicly traded stocks, specifically real estate investment trusts (REITs). The models provide highly advanced analytics for REIT investment, including: portfolio optimization using both historic and predictive return estimation; model backtesting; a complete spectrum of risk assessment and management tools with an emphasis on early warning systems, risk budgeting, estimating tail risk, and factor analysis; derivative valuation; and incorporating ESG ratings into REIT investment. These quantitative finance models are presented in a unified framework consistent with dynamic asset pricing (rational finance). Given its scope and practical orientation, this book will appeal to investors interested in portfolio optimization and innovative tools for investment risk assessment.

Real Estate Investment Trusts (REITs) have emerged as a popular and accessible avenue for individuals to participate in the real estate market without the burdens of direct property ownership. At its core, a Real Estate Investment Trust (REIT) is a company that owns, operates, or finances income-generating real estate. The concept of REITs was introduced in the United States in 1960 as a way to provide individuals with the opportunity to invest in large-scale, income-producing real estate without having to buy, manage, or finance properties directly. Topics Covered Chapter 1: Introduction to REIT Investing Chapter 2: Types of REITs Chapter 3: How REITs Work Chapter 4: Assessing Risks in REIT Investing Chapter 5: Getting Started with REIT Investing Chapter 6: Evaluating REITs Chapter 7: Creating a Passive Income Stream Chapter 8: Tax Considerations for REIT Investors Chapter 9: Monitoring and Adjusting Your REIT Portfolio Chapter 10: Advanced REIT Strategies Conclusion: The Future of REIT Investing

Recent Advances in Reliability and Maintenance Modeling contains the papers presented at the 11th Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM 2024, Nagoya, Japan, 26-30 August 2024). The contributions discuss and explore solutions to the various reliability challenges facing society. Reliability and maintenance is the technology required in various fields such as (but not limited to): - Power systems - Communication networks - Transportation - Cloud computing - Electronic systems - Buildings and infrastructure - Medical and healthcare - Aviation and railway systems. Recent Advances in Reliability and Maintenance Modeling is of interest to academics and professionals interested or involved in the above mentioned areas.

This interdisciplinary book argues that the economy has an underlying non-linear structure and that business cycles are endogenous, which allows a greater explanatory power with respect to the traditional assumption that dynamics are stochastic and shocks are exogenous. The first part of this work is formal-methodological and provides the mathematical background needed for the remainder, while the second part presents the view that signal processing involves construction and deconstruction of information and that the efficacy of this process can be measured. The third part focuses on economics and provides the related background and literature on economic dynamics and the fourth part is devoted to new perspectives in understanding nonlinearities in economic dynamics: growth and cycles. By pursuing this approach, the book seeks to (1) determine whether, and if so where, common features exist, (2) discover some hidden features of economic dynamics, and (3) highlight specific indicators of structural changes in time series. Accordingly, it is a must read for everyone interested in a better understanding of economic dynamics, business cycles, econometrics and complex systems, as well as non-linear dynamics and chaos theory.

This monograph collects in one place the basic definitions, a careful description of the model, and discussion of how convex optimization can be used in multi-period trading, all in a common notation and framework.

The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject. This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors—leading experts in financial modeling, machine learning, and quantitative research and analytics—employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book: Provides an integrated modeling approach to extract value from multiple types of datasets Treats the processes needed to make alternative data signals operational Helps investors and risk managers rethink how they engage with alternative datasets Features practical use case studies in many different financial markets and real-world techniques Describes how to avoid potential pitfalls and missteps in starting the alternative data journey Explains how to integrate information from different datasets to maximize informational value The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users.