Here are 100 books that Statistics and Data Analysis for Financial Engineering fans have personally recommended if you like
Statistics and Data Analysis for Financial Engineering.
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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.
As the book’s name suggests, it focuses on factor investing – i.e. long-term investments. Example: what do you think is the real (inflation-adjusted) return of the US stock vs bond markets over time? What is the best way to hedge inflation? (The answer may surprise you!)Nevertheless, a trader will also find inspiration in many of the market themes discussed. Example: Why is a mean-reverting strategy equivalent to shorting realized volatility?
This book has even less math than my 1st book pick, since Andrew Ang used it for his investment class for MBAs. Andrew was a well-known finance professor at Columbia University (where Warren Buffet got his Master’s). He is now Head of BlackRock (AUM=$9.5T!) Systematic Wealth Solutions. I have exchanged emails with him, and he is very friendly and patient with questions.
Stocks and bonds? Real estate? Hedge funds? Private equity? If you think those are the things to focus on in building an investment portfolio, Andrew Ang has accumulated a body of research that will prove otherwise. In his new book Asset Management: A Systematic Approach to Factor Investing, Ang upends the conventional wisdom about asset allocation by showing that what matters aren't asset class labels but the bundles of overlapping risks they represent. Making investments is like eating a healthy diet, Ang says: you've got to look through the foods you eat to focus on the nutrients they contain. Failing…
A moving story of love, betrayal, and the enduring power of hope in the face of darkness.
German pianist Hedda Schlagel's world collapsed when her fiancé, Fritz, vanished after being sent to an enemy alien camp in the United States during the Great War. Fifteen years later, in 1932, Hedda…
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
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…
Sine, a professor of creative writing, accompanies Sam, a neuroscientist, on a conference trip to a Hotel Castle. Sam wants to present a new device, the "monitor." Sine hopes to recover from tending to her mother who just passed away.
When they arrive, Sine is in a dream-like state. Real…
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…
I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.
One of my favorite professors, Gretchen Martinet, used this to teach a course called “Mathematical Statistics” when I was at the University of Virginia. It is an extremely profound course full of dense but fundamental mathematical proofs in classical statistics.
You will learn why the formula for the normal distribution is the way it is, why the sum of squares appears everywhere in statistics, and how to fit a linear regression by hand. In the same way calculus elevates our understanding of rates of changes, the book elevates your understanding of samples, averages, and distributions. Quant trading requires an intuitive sense of how data, models, and aggregates work, making this content essential for your success.
Modern Mathematical Statistics with Applications, Second Edition strikes a balance between mathematical foundations and statistical practice. In keeping with the recommendation that every math student should study statistics and probability with an emphasis on data analysis, accomplished authors Jay Devore and Kenneth Berk make statistical concepts and methods clear and relevant through careful explanations and a broad range of applications involving real data.
The main focus of the book is on presenting and illustrating methods of inferential statistics that are useful in research. It begins with a chapter on descriptive statistics that immediately exposes the reader to real data. The…
As a writer and historian, I’m all about rabbit holes. When something I’ve never heard about before catches my interest, I have to find out more—and sometimes I end up writing whole books on the subject! I have a head full of bizarre little nuggets of information, and I love reading books, like the ones here, that tell me something new and change my way of thinking.
A book on statistics that is interesting? Yes, actually. AndTheTigerthat Isn’tis more than just interesting, it’s useful. Maths was never my strong point at school, but even someone who never got the hang of quadratic equations can learn to ask useful questions when faced with bamboozlingly large numbers and dodgy ‘averages’.
This book offers a way to see through statistics that are used to conceal information as much as to reveal it. It’s worth reading just for the section on rice and random distribution. And the tiger in the title? It’s what happens when you think you see a pattern (in this case, stripes in the undergrowth), but there is no pattern at all.
Mathematics scares and depresses most of us, but politicians, journalists and everyone in power use numbers all the time to bamboozle us. Most maths is really simple - as easy as 2+2 in fact. Better still it can be understood without any jargon, any formulas - and in fact not even many numbers. Most of it is commonsense, and by using a few really simple principles one can quickly see when maths, statistics and numbers are being abused to play tricks - or create policies - which can waste millions of pounds. It is liberating to understand when numbers are…
In an age of splendor, a heretic king strips Egypt bare—forcing his queen to quell rebellion and plunging his children into a conspiracy against the crown.
Salvation in the Sun follows Nefertiti as she ascends the throne beside Pharaoh Amenhotep—soon to become Akhenaten—just as he declares war on Egypt’s ancient…
Tim Harford is the author of nine books, including The Undercover Economist and The Data Detective, and the host of the Cautionary Tales podcast. He presents the BBC Radio programs More or Less, Fifty Things That Made The Modern Economy, and How To Vaccinate The World. Tim is a senior columnist for the Financial Times, a member of Nuffield College, Oxford, and the only journalist to have been made an honorary fellow of the Royal Statistical Society.
I should declare an interest here: I present a BBC Radio show that Blastland and Dilnot created. This book was effectively my “how to” manual on the way into the studio that they had vacated. It’s a wise and varied guide to the power and the pitfalls of data, poetically written and full of subtle wisdoms.
The Strunk and White of statistics team up to help the average person navigate the numbers in the news
Drawing on their hugely popular BBC Radio 4 show More or Less, journalist Michael Blastland and internationally known economist Andrew Dilnot delight, amuse, and convert American mathphobes by showing how our everyday experiences make sense of numbers.
The radical premise of The Numbers Game is to show how much we already know and give practical ways to use our knowledge to become cannier consumers of the media. If you've ever wondered what "average" really means, whether the scare stories about cancer…
In my career as an academic librarian, I was often asked to teach students to think about the credibility of the information they incorporate into their academic, professional, personal, and civic lives. In my teaching and writing, I have struggled to make sense of the complex and nuanced factors that make some information more credible and other information less so. I don’t have all the answers for dealing with problematic information, but I try hard to convince people to think carefully about the information they encounter before accepting any of it as credible or dismissing any of it as non-credible.
I constantly recommend The Data Detective because it serves as an unmatched handbook for making sense of the statistical data to which we are constantly exposed.
What I like about it, besides its lively, readable style, is that the book convincingly and clearly explains 1) why we need statistical data to make informed decisions, 2) the factors that go into producing reliable statistics, 3) the factors that can produce unreliable statistics, and 4) how any statistics, reliable or not, can be misused to deceive us.
The author, Tim Harford, is an economist who writes for the Financial Times and hosts the brilliant podcast Cautionary Tales.
From “one of the great (greatest?) contemporary popular writers on economics” (Tyler Cowen) comes a smart, lively, and encouraging rethinking of how to use statistics.
Today we think statistics are the enemy, numbers used to mislead and confuse us. That’s a mistake, Tim Harford says in The Data Detective. We shouldn’t be suspicious of statistics—we need to understand what they mean and how they can improve our lives: they are, at heart, human behavior seen through the prism of numbers and are often “the only way of grasping much of what is going on around us.” If we can toss…
My professional life has been focused on teaching and research on chemical food safety as well as scientific applications of mathematics to animal and human health. The books on this list were riveting and eye-opening examples of how complex mathematical concepts, including zero and nothing, often get misused when applied to practical problems such as food safety and cancer. This misapplication is often a result of the unique properties and history of numbers like zero, which are hard to translate into practical endpoints. These books have given me a better understanding of this issue, as well as plunging me into the fascinating history of numbers through Eastern and Western civilizations.
This book was my first exposure to the practical application of many scientific principles to societal issues and their laws.
As a scientist who fully understands the limitations of certain statistical tests and chemical assays, I was shocked to see how these could be so misapplied. This book presents numerous examples of how statistics improperly conducted or interpreted can be simply wrong when they are taken out of context.
This foray into the popular literature transformed my thinking on the application of mathematical principles to everyday problems.
Here, by popular demand, is the updated edition to Joel Best's classic guide to understanding how numbers can confuse us. In his new afterword, Best uses examples from recent policy debates to reflect on the challenges to improving statistical literacy. Since its publication ten years ago, Damned Lies and Statistics has emerged as the go-to handbook for spotting bad statistics and learning to think critically about these influential numbers.
Born the heir of a master woodcutter in a queendom defined by guilds and matrilineal inheritance, nonbinary Sorin can’t quite seem to find their place. At seventeen, an opportunity to attend an alchemical guild fair and secure an apprenticeship with the…
I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.
What if you are faced with a problem for which a standard approach doesn’t yet exist? In such a case, you will need to be able to figure out the approach from the first principles. This book will help you learn how to derive insights starting from raw data.
'A statistical national treasure' Jeremy Vine, BBC Radio 2
'Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force' Popular Science
Do busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science.
Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way…
