This Harriman House edition includes Charles Mackay's account of the three infamous financial manias - John Law's Mississipi Scheme, the South Sea Bubble, and Tulipomania.Between the three of them, these historic episodes confirm that greed and fear have always been the driving forces of financial markets, and, furthermore, that being sensible and clever is no defence against the mesmeric allure of a popular craze with the wind behind it.In writing the history of the great financial manias, Charles Mackay proved himself a master chronicler of social as well as financial history. Blessed with a cast of characters that covered all the vices, gifted a passage of events which was inevitably heading for disaster, and with the benefit of hindsight, he produced a record that is at once a riveting thriller and absorbing historical document. A century and a half later, it is as vibrant and lurid as the day it was written.For modern-day investors, still reeling from the dotcom crash, the moral of the popular manias scarcely needs spelling out. When the next stock market bubble comes along, as it surely will, you are advised to recall the plight of some of the unfortunates on these pages, and avoid getting dragged under the wheels of the careering bandwagon yourself.

    You're trading against other traders who care about only one thing: taking your money. That's the #1 hard reality of trading - and most traders either don't know it, or don't act as if they do. In this book, top trader and hedge fund manager Quint Tatro shows how to win consistently in the "zero sum" game of trading, where there's a loser for every winner. You'll learn how to reflect your trading competition in every facet of trading and investing: choosing companies to invest in, knowing when to jump in and out of the market, and mastering the psychology and gamesmanship of trading. Coverage includes: Understanding the "other side of the trade": the thousands of pros you're trading against. Finding a technical edge with technical analysis you can exploit over and over again. Understanding sentiment and overcoming the human emotions and biases that cost you dearly. Utilizing the most essential strategies of fundamental analysis. Playing positions and probabilities, not P+Ls. Recognizing and capturing huge opportunities in down markets.

    This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues.The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting general purpose nature of genetic algorithms as search methods that can be employed across disciplines.An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    The science of complexity studies how single elements, such as a species or a stock, spontaneously organize into complicated structures like ecosystems and economies; stars become galaxies, and snowflakes avalanches almost as if these systems were obeying a hidden yearning for order. Drawing from diverse fields, scientific luminaries such as Nobel Laureates Murray Gell-Mann and Kenneth Arrow are studying complexity at a think tank called The Santa Fe Institute. The revolutionary new discoveries researchers have made there could change the face of every science from biology to cosmology to economics. M. Mitchell Waldrop's groundbreaking bestseller takes readers into the hearts and minds of these scientists to tell the story behind this scientific revolution as it unfolds.

    Written by a world-renowned industry expert, the reference discusses how to forecast returns under different parameters. Expected returns of major asset classes, investment strategies, and the effects of underlying risk factors such as growth, inflation, liquidity, and different risk perspectives, are also explained. Judging expected returns requires balancing historical returns with both theoretical considerations and current market conditions. Expected Returns provides extensive empirical evidence, surveys of risk-based and behavioral theories, and practical insights.

    There are more than 5000 fintech startups operating, and 50 of them have already reached a billion-dollar valuation. The scope of this market goes way beyond online payments. Financial technology promises to change the way we manage our money online, disrupting the landscape of the financial services industry is being disrupted. Understanding its many facets is the key to navigating the complex nuances of this global industry.Fintech in a Flash is your comprehensive guide to the future of banking and insurance. The book aims to break down the key concepts in a way that will help you understand every aspect so that you can take advantage of new technologies. Inside you’ll find an array of hot topics such as online payments, crowdfunding, challenger banks, online insurance, digital lending, big data, and digital commerce. It will make you rethink the way that you manage your money online, and even find new ways of making online payments. Comprehensive, organized, and detailed, this guide is your go-to source for everything you need to confidently navigate the ever-changing scene of this booming industry.If you decide to buy this book now, you'll get: Easy to understand explanations of the 14 main areas of fintech The author's view on the future of each of these areas Insight into the main fintech hubs in the world Insight into the so called Unicorns, the fintech firms that have made it past a $1 billion valuation More than 100 upcoming fintech companies to watch About the Author: Agustín Rubini is an argentinean-born economist, master in international business, and Director at Banking Innovations. Passionate about building the future of financial services, Agustín spends much of his time speaking and writing on financial technology and advising businesses on innovation and digital transformation. He is a specialist in driving changes in top class banks that want to lead in how customers manage their money online. Tags: fintech, financial technology, financial services technology, money online, online payment, online insurance, insurtech, investing online, wealth management online, wealthtech, regtech, cybercrime, digital lending, digital commerce, ecommerce, e-commerce. Get started immediately Download now and take the first step on your very own road to mastering fintech. Scroll to the top of the page and hit the buy button.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    The authors provide precise definitions and full proofs of results, sacrificing generalities and limiting the scope of the material in order to do so. The text is organized in four parts: strategic games, extensive games with perfect information, extensive games with imperfect information, and coalitional games. It includes over 100 exercises. Solution ManualTable of Contents, Errata, and more...

    The presentation stresses analytical methods, concrete examples, and geometric intuition. A unique feature of the book is its emphasis on applications. These include mechanical vibrations, lasers, biological rhythms, superconducting circuits, insect outbreaks, chemical oscillators, genetic control systems, chaotic waterwheels, and even a technique for using chaos to send secret messages. In each case, the scientific background is explained at an elementary level and closely integrated with mathematical theory.About the Author:Steven Strogatz is in the Center for Applied Mathematics and the Department of Theoretical and Applied Mathematics at Cornell University. Since receiving his Ph.D. from Harvard university in 1986, Professor Strogatz has been honored with several awards, including the E.M. Baker Award for Excellence, the highest teaching award given by MIT.

    Any ambitious proposal, however, inevitably runs into the buzz saw of how to find the money to pay for it, rooted in myths about deficits that are hobbling us as a country.Kelton busts through the myths that prevent us from taking action: that the federal government should budget like a household, that deficits will harm the next generation, crowd out private investment, and undermine long-term growth, and that entitlements are propelling us toward a grave fiscal crisis.MMT, as Kelton shows, shifts the terrain from narrow budgetary questions to one of broader economic and social benefits. With its important new ways of understanding money, taxes, and the critical role of deficit spending, MMT redefines how to responsibly use our resources so that we can maximize our potential as a society. MMT gives us the power to imagine a new politics and a new economy and move from a narrative of scarcity to one of opportunity.

    The author's conversational writing style presents the politics and science of economic theories to tomorrow's decision-makers.

    This work contains chapters, data, and research that reveal contemporary shenanigans that have been known to fool even veteran researchers.

    Exploring what makes a great trader a great trader, "Hedge Fund Market Wizards" breaks new ground, giving readers rare insight into the trading philosophy and successful methods employed by some of the most profitable individuals in the hedge fund business.Presents exclusive interviews with fifteen of the most successful hedge fund traders and what they've learned over the course of their careersIncludes interviews with Jamie Mai, Joel Greenblatt, Michael Platt, Ray Dalio, Colm O'Shea, Ed Thorp, and many moreExplains forty key lessons for tradersJoins "Stock Market Wizards, " "New Market Wizards, "and "Market Wizards" as the fourth installment of investment guru Jack Schwager's acclaimed bestselling series of interviews with stock market expertsA candid assessment of each trader's successes and failures, in their own words, the book shows readers what they can learn from each, and also outlines forty essential lessons--from finding a trading method that fits an investor's personality to learning to appreciate the value of diversification--that investment professionals everywhere can apply in their own careers.Bringing together the wisdom of the true masters of the markets, "Hedge Fund Market Wizards" is a collection of timeless insights into what it takes to trade in the hedge fund world.

    Today, ML algorithms accomplish tasks that - until recently - only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.In the book, readers will learn how to:Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting.Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.