You didn't invest in Rental Properties to create another job for yourself, you did so to create a better Life! Stop the grind of daily rental operations and create the Life Output you truly deserve! The Time-Wealthy Investor 2.0: Your Real Estate Roadmap to Owning More, Working Less, and Creating the Life You Want is the book for the frustrated and Time-Weary rental property owner-operator. This updated version of the popular original teaches you steps to remove the aggravation felt by real estate investors and helps you get off the endless hamster wheel of the day-to-day rental property operations. Learn the Secret to setting up a rental operation using proven business tools, strategies, and concepts that are easy to understand and simple to implement. Most important, you will learn to put a framework in place that is scalable and won't cost a fortunate to operate. The 3 step, Vision-Infrastructure-Process (VIP) Paradigm outlined in this book has been taught to thousands seeking a better life by creating Time-Wealth through real estate; because you were meant for much more than driving around, collecting rent, and being frustrated by your tenants! You will learn to create a solid Vision for your future, to craft Infrastructure for your Real Estate business, and create Processes that when all working together, will help you rediscover time and spend it on purposeful Life Output that YOU decide. It's time to get serious about your real estate rental business; become a business OWNER and not just Self-Employed. This certainly isn't the only book on real estate investing, but it is definitely one needed in the library of the serious investor.
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.
Financial innovation has increased diversification opportunities and lowered investment costs, but has not reduced the relative cost of active (informed) investment strategies relative to passive (less informed) strategies. What are the consequences? I study an economy with linear production technologies, some more risky than others. Investors can use low quality public information or collect high quality, but costly, private information. Information helps avoiding excessively risky investments. Financial innovation lowers the incentives for private information collection and deteriorates public information: the economy invests more often in excessively risky technologies. This changes the business cycle properties and can reduce welfare by increasing the likelihood of "liquidation crises"
Derivatives Markets is a thorough and well-presented textbook that offers readers an introduction to derivatives instruments, with a gentle introduction to mathematical finance, and provides a working knowledge of derivatives to a wide area of market participants. This new and accessible book provides a lucid, down-to-earth, theoretically rigorous but applied introduction to derivatives. Many insights have been discovered since the seminal work in the 1970s and the text provides a bridge to and incorporates them. It develops the skill sets needed to both understand and to intelligently use derivatives. These skill sets are developed in part by using concept checks that test the reader's understanding of the material as it is presented. The text discusses some fairly sophisticated topics not usually discussed in introductory derivatives texts. For example, real-world electronic market trading platforms such as CME’s Globex. On the theory side, a much needed and detailed discussion of what risk-neutral valuation really means in the context of the dynamics of the hedge portfolio. The text is a balanced, logical presentation of the major derivatives classes including forward and futures contracts in Part I, swaps in Part II, and options in Part III. The material is unified by providing a modern conceptual framework and exploiting the no-arbitrage relationships between the different derivatives classes. Some of the elements explained in detail in the text are: Hedging, Basis Risk, Spreading, and Spread Basis Risk Financial Futures Contracts, their Underlying Instruments, Hedging and Speculating OTC Markets and Swaps Option Strategies: Hedging and Speculating Risk-Neutral Valuation and the Binomial Option Pricing Model Equivalent Martingale Measures: The Modern Approach to Option Pricing Option Pricing in Continuous Time: from Bachelier to Black-Scholes and Beyond. Professor Goldenberg’s clear and concise explanations and end-of-chapter problems, guide the reader through the derivatives markets, developing the reader’s skill sets needed in order to incorporate and manage derivatives in a corporate or risk management setting. This textbook is for students, both undergraduate and postgraduate, as well as for those with an interest in how and why these markets work and thrive.
A collection of solicited and refereed articles from distinguished researchers across the field of stochastic analysis and its application to finance. It covers the topics ranging from Markov processes, backward stochastic differential equations, stochastic partial differential equations, and stochastic control, to risk measure and risk theory.
This volume provides the definitive treatment of fortune's formula or the Kelly capital growth criterion as it is often called. The strategy is to maximize long run wealth of the investor by maximizing the period by period expected utility of wealth with a logarithmic utility function. Mathematical theorems show that only the log utility function maximizes asymptotic long run wealth and minimizes the expected time to arbitrary large goals. In general, the strategy is risky in the short term but as the number of bets increase, the Kelly bettor's wealth tends to be much larger than those with essentially different strategies. So most of the time, the Kelly bettor will have much more wealth than these other bettors but the Kelly strategy can lead to considerable losses a small percent of the time. There are ways to reduce this risk at the cost of lower expected final wealth using fractional Kelly strategies that blend the Kelly suggested wager with cash. The various classic reprinted papers and the new ones written specifically for this volume cover various aspects of the theory and practice of dynamic investing. Good and bad properties are discussed, as are fixed-mix and volatility induced growth strategies. The relationships with utility theory and the use of these ideas by great investors are featured.Contents: "The Early Ideas and Contributions: "Introduction to the Early Ideas and ContributionsExposition of a New Theory on the Measurement of Risk (translated by Louise Sommer) "(D Bernoulli)"A New Interpretation of Information Rate "(J R Kelly, Jr)"Criteria for Choice among Risky Ventures "(H A Latan)"Optimal Gambling Systems for Favorable Games "(L Breiman)"Optimal Gambling Systems for Favorable Games "(E O Thorp)"Portfolio Choice and the Kelly Criterion "(E O Thorp)"Optimal Investment and Consumption Strategies under Risk for a Class of Utility Functions "(N H Hakansson)"On Optimal Myopic Portfolio Policies, with and without Serial Correlation of Yields "(N H Hakansson)"Evidence on the ?Growth-Optimum-Model? "(R Roll)""Classic Papers and Theories: "Introduction to the Classic Papers and TheoriesCompetitive Optimality of Logarithmic Investment "(R M Bell and T M Cover)"A Bound on the Financial Value of Information "(A R Barron and T M Cover)"Asymptotic Optimality and Asymptotic Equipartition Properties of Log-Optimum Investment "(P H Algoet and T M Cover)"Universal Portfolios "(T M Cover)"The Cost of Achieving the Best Portfolio in Hindsight "(E Ordentlich and T M Cover)"Optimal Strategies for Repeated Games "(M Finkelstein and R Whitley)"The Effect of Errors in Means, Variances and Co-Variances on Optimal Portfolio Choice "(V K Chopra and W T Ziemba)"Time to Wealth Goals in Capital Accumulation "(L C MacLean, W T Ziemba, and Y Li)"Survival and Evolutionary Stability of Rule the Kelly "(I V Evstigneev, T Hens, and K R Schenk-Hopp)"Application of the Kelly Criterion to Ornstein-Uhlenbeck Processes "(Y Lv and B K Meister)""The Relationship of Kelly Optimization to Asset Allocation: "Introduction to the Relationship of Kelly Optimization to Asset AllocationSurvival and Growth with a Liability: Optimal Portfolio Strategies in Continuous Time "(S Browne)"Growth versus Security in Dynamic Investment Analysis "(L C MacLean, W T Ziemba, and G Blazenko)"Capital Growth with Security "(L C MacLean, R Sanegre, Y Zhao, and W T Ziemba)"
Financial Mathematics: From Discrete to Continuous Time is a study of the mathematical ideas and techniques that are important to the two main arms of the area of financial mathematics: portfolio optimization and derivative valuation. The text is authored for courses taken by advanced undergraduates, MBA, or other students in quantitative finance programs. The approach will be mathematically correct but informal, sometimes omitting proofs of the more difficult results and stressing practical results and interpretation. The text will not be dependent on any particular technology, but it will be laced with examples requiring the numerical and graphical power of the machine. The text illustrates simulation techniques to stand in for analytical techniques when the latter are impractical. There will be an electronic version of the text that integrates Mathematica functionality into the development, making full use of the computational and simulation tools that this program provides. Prerequisites are good courses in mathematical probability, acquaintance with statistical estimation, and a grounding in matrix algebra. The highlights of the text are: A thorough presentation of the problem of portfolio optimization, leading in a natural way to the Capital Market Theory Dynamic programming and the optimal portfolio selection-consumption problem through time An intuitive approach to Brownian motion and stochastic integral models for continuous time problems The Black-Scholes equation for simple European option values, derived in several different ways A chapter on several types of exotic options Material on the management of risk in several contexts
The breadth of information about operations research and the overwhelming size of previous sources on the subject make it a difficult topic for non-specialists to grasp. Fortunately, Introduction to the Mathematics of Operations Research with Mathematica®, Second Edition delivers a concise analysis that benefits professionals in operations research and related fields in statistics, management, applied mathematics, and finance. The second edition retains the character of the earlier version, while incorporating developments in the sphere of operations research, technology, and mathematics pedagogy. Covering the topics crucial to applied mathematics, it examines graph theory, linear programming, stochastic processes, and dynamic programming. This self-contained text includes an accompanying electronic version and a package of useful commands. The electronic version is in the form of Mathematica notebooks, enabling you to devise, edit, and execute/reexecute commands, increasing your level of comprehension and problem-solving. Mathematica sharpens the impact of this book by allowing you to conveniently carry out graph algorithms, experiment with large powers of adjacency matrices in order to check the path counting theorem and Markov chains, construct feasible regions of linear programming problems, and use the "dictionary" method to solve these problems. You can also create simulators for Markov chains, Poisson processes, and Brownian motions in Mathematica, increasing your understanding of the defining conditions of these processes. Among many other benefits, Mathematica also promotes recursive solutions for problems related to first passage times and absorption probabilities.
This edited book aims to ignite both an academic and practitioner-oriented discussion regarding the question how the business and government sector can adapt to todays fast-changing climate. Specifically, the collection seeks to explore how businesses and policy makers can prepare for a world where freshwater is scarce, extreme weather events are common, floods and wildfires are frequent, and global sea levels rise by more than two meters. In addition to assessing incremental approaches, it explores strategies that employ interdisciplinary and innovative solutions to climate change adaptation. The chapters included in this book examine and propose business and policy solutions for climate-induced economic, technical, urban, and societal challenges. It draws on an international range of prominent authors and, therefore, will be of interest for academics and practitioners working in the field of sustainability management, sustainable finance, sustainable operations management, food management, strategy, and environmental management. It can also serve as a valuable guide for practitioners and policymakers in those fields. Thomas Walker is a Full Professor and Concordia University Research Chair in Emerging Risk Management at the John Molson School of Business, Concordia University, Canada. Stefan Wendt is a Full Professor and Dean of the Department of Business at Bifrost University, Iceland. Sherif Goubran is an Assistant Professor in the Department of Architecture (School of Sciences and Engineering) at the American University in Cairo, Egypt. Tyler Schwartz is an MSc candidate studying data science and business analytics at HEC Montreal, Canada.
The book is a collection of peer-reviewed scientific papers submitted by active researchers in the 37th National System Conference (NSC 2013). NSC is an annual event of the Systems Society of India (SSI), primarily oriented to strengthen the systems movement and its applications for the welfare of humanity. A galaxy of academicians, professionals, scientists, statesman and researchers from different parts of the country and abroad are invited to attend the conference. The book presents research articles in the areas of system’s modelling, complex network modelling, cyber security, sustainable systems design, health care systems, socio-economic systems, and clean and green technologies. The book can be used as a tool for further research.
Stochastic differential equations (SDEs) are a powerful tool in science, mathematics, economics and finance. This book will help the reader to master the basic theory and learn some applications of SDEs. In particular, the reader will be provided with the backward SDE technique for use in research when considering financial problems in the market, and with the reflecting SDE technique to enable study of optimal stochastic population control problems. These two techniques are powerful and efficient, and can also be applied to research in many other problems in nature, science and elsewhere.