Here are 100 books that Escape from Model Land fans have personally recommended if you like
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A noted quantitative hedge fund manager and quant finance author, Ernie is the founder of QTS Capital Management and Predictnow.ai. Previously he has applied his expertise in machine learning at IBM T.J. Watson Research Center’s Human Language Technologies group, at Morgan Stanley’s Data Mining and Artificial Intelligence Group, and at Credit Suisse’s Horizon Trading Group. Ernie was quoted by Bloomberg, the Wall Street Journal, New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program. He is an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises student theses there. Ernie holds a Ph.D. in theoretical physics from Cornell University.
Finally, for those who are not afraid of math, they should read this book because there is a lot of heavy-duty math. The good news for the rest of us is you can ignore all the math and still get a lot out of it, especially knowledge about market microstructure and how to find the theoretically optimal trading strategies given some assumptions about the price dynamics. Even if you don’t want to or can’t solve those darn stochastic differential equations, you can still implement a numerical approximation. At the minimum, you will learn common trading lingo such as “walking the book” or “the ITCH feed”.
The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchanges work, whether the algorithm is trading with better informed traders (adverse selection), and the type of information available to market participants at both ultra-high and low frequency. Algorithmic and High-Frequency Trading is the first book that combines sophisticated mathematical modelling, empirical facts and…
Dr. Jeremy Kepner is head and founder of the MIT Lincoln Laboratory Supercomputing Center (LLSC), and also a Founder of the MIT-Air Force AI Accelerator. Lincoln Laboratory is a 4000-person National Laboratory whose mission is to create defensive technologies to protect our Nation and the freedoms enshrined in the Constitution of the United States. Dr. Kepner is one of five Lincoln Laboratory Fellows, a position that "recognizes the Laboratory's strongest technical talent for outstanding contributions to Laboratory programs over many years." Dr. Kepner is recognized as one of nine MIT Fellows of the Society of Industrial Applied Mathematics (SIAM), for "contributions to interactive parallel computing, matrix-based graph algorithms, green supercomputing, and big data."
What do pandemics, climate change, extreme weather, financial crises, wealth inequality, and social media all have in common? They are all well described by heavy-tail statistics, which you may have never heard about and were almost certainly never taught in your introductory statistics class. The Fundamentals of Heavy Tails is the first text that attempts to close this gap in undergraduate STEM education. This well-written text is a wonderful blend of intuition and rigorous results. The reader will be pleasantly surprised to learn that heavy-tail distributions are neither rare nor mysterious and are a natural result of multiplicative random processes.
Heavy tails -extreme events or values more common than expected -emerge everywhere: the economy, natural events, and social and information networks are just a few examples. Yet after decades of progress, they are still treated as mysterious, surprising, and even controversial, primarily because the necessary mathematical models and statistical methods are not widely known. This book, for the first time, provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability. It tackles and tames the zoo of terminology for models and properties, demystifying topics such as the generalized central limit theorem and regular variation. It tracks…
Economics isn't really a good starting point for financial market analysis for the simple reason that its models are wildly inaccurate. As behaviorial economists like Daniel Kahneman have been showing, irrationality and the inability to measure risk properly are a very big component of the investment and trading decisions. But statistical risk management is also sloppy when applied to human behavior because people are not objects that reliably behave the same way under similar circumstances. So when you read an economist about markets or an engineer about risk management, you're missing a lot of the story. In the end, technical analysis is fascinating because how and why humans behave is an enduring mystery.
The subtitle is The Untold Story of the Scientific Betting System that Beat the Casinos and Wall Street. This book is an easy-to-read narrative of the intersection of the grimy underbelly of betting--with high-minded math. It reminds you that trading is not conducted in a clean little bubble. Technical analysis can give you an edge, but trading is still engaging in battle with opposing forces; strategy and tactics can count as much as building an elegant technical system.
Your opponent on the trading battlefield will try to trick you, like a general in real warfare. He may keep selling and selling after you have bought, triggering a sell signal in your trading system. He is hunting for your sell signal. The mechanical response is to sell—your system says sell, and you should follow your system. To exit a position when the market goes against you is named a stop,…
In 1956, two Bell Labs scientists discovered the scientific formula for getting rich. One was mathematician Claude Shannon, neurotic father of our digital age, whose genius is ranked with Einstein's. The other was John L. Kelly Jr., a Texas-born, gun-toting physicist. Together they applied the science of information theory—the basis of computers and the Internet—to the problem of making as much money as possible, as fast as possible.
Shannon and MIT mathematician Edward O. Thorp took the "Kelly formula" to Las Vegas. It worked. They realized that there was even more money to be made in the stock market. Thorp…
I am fascinated with the relationship between our individual behaviors and the social structures and institutions in which we live—and how these influence each over time. I think this sort of understanding is important if we want to consider the kind of world we want to live in, and how we might get there from where we are. I take insights from many disciplines, from physics and biology to the cognitive and social sciences, from philosophy and art to mathematics and engineering. I am currently a professor of cognitive and information sciences at the University of California, Merced, and an external professor at the Santa Fe Institute.
I have always been fascinated by how people join and leave groups.
What are the benefits of joining a particular group? Which group should I join? What happens if someone wants to join a group, but its current members don’t want them to? I once thought such questions were merely qualitative, and when I was a graduate student I thought I’d be the first to tackle them quantitatively.
I was humbled when I stumbled upon this book, written years earlier, in which two behavioral ecologists review game theoretic models that address questions of just this sort, starting simple, and building up models of increasing nuance and complexity. I think anyone interested in the dynamics of group formation in humans or other animals should read this book.
Although there is extensive literature in the field of behavioral ecology that attempts to explain foraging of individuals, social foraging--the ways in which animals search and compete for food in groups--has been relatively neglected. This book redresses that situation by providing both a synthesis of the existing literature and a new theory of social foraging. Giraldeau and Caraco develop models informed by game theory that offer a new framework for analysis. Social Foraging Theory contains the most comprehensive theoretical approach to its subject, coupled with quantitative methods that will underpin future work in the field. The new models and approaches…
It’s been fantastic to work in computer vision, especially when it is used to build biometric systems. I and my 80 odd PhD students have pioneered systems that recognise people by the way they walk, by their ears, and many other new things too. To build the systems, we needed computer vision techniques and architectures, both of which work with complex real-world imagery. That’s what computer vision gives you: a capability to ‘see’ using a computer. I think we can still go a lot further: to give blind people sight, to enable better invasive surgery, to autonomise more of our industrial society, and to give us capabilities we never knew we’d have.
David Marr shaped the field of computer vision in its early days. His seminal book laid the structure for interpreting images and one which is still largely followed. He popularised notions of the primal sketch and his work on edge detection led to one of the most sophisticated approaches. His work and influence continue to endure despite his early death: we missed and miss him a lot.
Available again, an influential book that offers a framework for understanding visual perception and considers fundamental questions about the brain and its functions.
David Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field. In Vision, Marr describes a general framework for understanding visual perception and touches on broader questions about how the brain and its functions can be studied and understood. Researchers from a range of brain and cognitive sciences have long valued Marr's creativity, intellectual power, and ability to integrate insights and data from neuroscience, psychology, and computation. This…
Having a master's degree in chemical engineering, I wasn't destined to work in the area of quantitative finance… the reason why I professionally moved to this discipline aren't worth exposing, but as a matter of fact, I've been quickly fascinated by this science, and encountered some of my favorites, such as maths and statistics, as used in the traditional activity of an engineer. And I had many opportunities of combining the knowledge and practice of financial markets with pragmatism, typically of the engineer’s education, i.e. oriented toward problem solving. In addition, I've always loved teaching, and writing books on financial markets & instruments, hence the importance I'm giving to pedagogy in professional books.
In the vast array of quantitative finance relative to financial markets instruments and related risks, the case of credit or counterparty risk remains by far the most complex one, and thus, unsurprisingly, the least mastered by financial markets professionals.
A lot has been done, but a lot remains to be done: covering this is precisely the goal of this book. In a nutshell, the main obstacle to succeed in developing grounded and useful models of default prediction is due to the fact that a default is (fortunately) a rare event, in other words, with a (very) low probability of occurrence, and statistical tools are uncomfortable with very low probability levels. Hence the need of this book, to help the practitioner to go ahead in this matter.
The book reveals to traders how to consistently outperform credit benchmarks, how to hedge the credit risk premium, and how to overcome pension liability deficits. In addition, several successful trading strategies are presented including debt versus equities, Co-Co bond trading and a quantitative analysis of the municipal bond market. Chapters include: Credit Models, Past Present and Future Predicting Annual Default Rates and Implications for Market Prices Risk and Relative Value in the Municipal Bond Market Contingent Collateral Bonds Model for Sovereign Default and Relative Value Beating Credit Benchmarks Analyzing and Hedging Systemic Liquidity Risk Building on the best-selling first edition,…
A noted quantitative hedge fund manager and quant finance author, Ernie is the founder of QTS Capital Management and Predictnow.ai. Previously he has applied his expertise in machine learning at IBM T.J. Watson Research Center’s Human Language Technologies group, at Morgan Stanley’s Data Mining and Artificial Intelligence Group, and at Credit Suisse’s Horizon Trading Group. Ernie was quoted by Bloomberg, the Wall Street Journal, New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program. He is an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises student theses there. Ernie holds a Ph.D. in theoretical physics from Cornell University.
Disclaimer: I like Euan’s books not because he is a friend and has endorsed my books. Long before we became friends, I have bought his book, and said to myself “Wow! This is the first book about options trading that is not just a bunch of trite statements about payouts from various straddles and spreads positions!” It talks about some unique arbitrage opportunities that only professionals knew about. On the other hand, the amount of mathematics is very manageable, and can largely be skipped without affecting the practical applications of the concepts.
An A to Z options trading guide for the new millennium and the new economy Written by professional trader and quantitative analyst Euan Sinclair, Option Trading is a comprehensive guide to this discipline covering everything from historical background, contract types, and market structure to volatility measurement, forecasting, and hedging techniques. This comprehensive guide presents the detail and practical information that professional option traders need, whether they're using options to hedge, manage money, arbitrage, or engage in structured finance deals. It contains information essential to anyone in this field, including option pricing and price forecasting, the Greeks, implied volatility, volatility measurement…
I’m an archaeologist, which means that I’ve been lucky enough to travel to many places to dig and survey ancient remains. What I’ve realized in handling those dusty old objects is that all over the world, in both past and present, people are defined by their stuff: what they made, used, broke, and threw away. Most compelling are the things that people cherished despite being worn or flawed, just like we have objects in our house that are broken or old but that we keep anyway.
This looks like it’s the sternest and most boring book ever, but I love Steedman’s cool-and-collected ability to address the implications of the obvious: You can only do one thing at a time. You only have two hands. And when you’re with one set of belongings, you’re neglecting all the other stuff you own.
Standard economic theory of consumer behaviour considers consumers' preferences, their incomes and commodity prices to be the determinants of consumption. However, consumption takes time and no consumer has more - or less - than 168 hours per week. This simple fact is almost invisible in standard theory, and takes the centre stage in this book.
I am fascinated with the relationship between our individual behaviors and the social structures and institutions in which we live—and how these influence each over time. I think this sort of understanding is important if we want to consider the kind of world we want to live in, and how we might get there from where we are. I take insights from many disciplines, from physics and biology to the cognitive and social sciences, from philosophy and art to mathematics and engineering. I am currently a professor of cognitive and information sciences at the University of California, Merced, and an external professor at the Santa Fe Institute.
Peter Turchin has gotten famous recently for predicting the US political upheaval of 2020 way back in 2012.
This book represents the first landmark of Turchin’s attempt to understand the ebbs and flows of history using dynamical models. The book’s centerpiece is a formalization of a theory about how empires rise and fall, first conceived by the 14th century (!) Arab scholar Ibn Khaldun.
The book inspired me to replicate the computational model it presents, and it was remarkably illuminating to watch empires grow, fight, and collapse on my computer screen.
Many historical processes are dynamic. Populations grow and decline. Empires expand and collapse. Religions spread and wither. Natural scientists have made great strides in understanding dynamical processes in the physical and biological worlds using a synthetic approach that combines mathematical modeling with statistical analyses. Taking up the problem of territorial dynamics--why some polities at certain times expand and at other times contract--this book shows that a similar research program can advance our understanding of dynamical processes in history. Peter Turchin develops hypotheses from a wide range of social, political, economic, and demographic factors: geopolitics, factors affecting collective solidarity, dynamics of…
A noted quantitative hedge fund manager and quant finance author, Ernie is the founder of QTS Capital Management and Predictnow.ai. Previously he has applied his expertise in machine learning at IBM T.J. Watson Research Center’s Human Language Technologies group, at Morgan Stanley’s Data Mining and Artificial Intelligence Group, and at Credit Suisse’s Horizon Trading Group. Ernie was quoted by Bloomberg, the Wall Street Journal, New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program. He is an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises student theses there. Ernie holds a Ph.D. in theoretical physics from Cornell University.
By now, you may notice that I like to recommend textbooks. I use this bestseller for my course in Financial Machine Learning at Northwestern University, but really, nobody interested in financial machine learning hasn’t read this book. The topics are highly relevant to every investor or trader – I read it at least 5 times to digest every nugget and have put them to very productive use in my trading as well as in my fintech firm predictnow.ai. It covers basic techniques such as random forest to advanced techniques such as Hierarchical Risk Parity, which is a big improvement over traditional portfolio optimization methods.
Marcos used to be Head of Machine Learning at AQR (AUM=$143B), and now is the Global Head of Quant Research at Abu Dhabi Investment Authority. He is also very approachable to his readers and students. There was seldom an email or message from me to which…
Learn to understand and implement the latest machine learning innovations to improve your investment performance
Machine learning (ML) is changing virtually every aspect of our lives. 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
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