Book Description
You have more information at hand about your business environment than ever before. But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool.
In Competing on Analytics: The New Science of Winning , Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling.
Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox—illuminate how to leverage the power of analytics.
Customer Reviews:
Covers the basics of both the what-is and the how-to of fact-based decision making.......2007-10-04
Mark Twain once said something to the effect that it isn't what you don't know that gets you into trouble, it's what you know for certain that isn't so that will get you. Too many businesses are run on assumptions, guesses, and inertia. What we are doing now worked in the past so lets keep doing it. Shareholders lose a lot of money when their businesses are run with that kind of thinking.
This book is about fact-based decision making. It is really more of an introduction to the subject than a detailed text, but it is still quite useful for those wanting to learn the basics of the subject. The first five chapters discuss what analytics are, how you compete using them, and the growth path from wondering what an analytic competitor is through the fives steps to becoming one. They also discuss what it means when using internal data that you completely control, and what it means when you do it using data you control and supplier or customer data that you do not control.
The last four chapters take on the practical side of implementing a road map to becoming an analytic competitor. I particularly enjoyed the chapter emphasizing that all your plans will fail if you don't have the right people. Systems alone won't do it. The next chapter discusses the kinds of systems you need. The last chapter discusses the future of analytics.
For the right audience, this is a fascinating book. The stories about businesses succeeding by using analytics or getting themselves into serious trouble by ignoring them are all good and entertaining. Be careful, though. Some of the stories talk about instances (such as the Red Sox losing the World Series by letting the pitcher go beyond his statistical maximum pitching range) rather than trends and large numbers of events. Statistics don't work on instances. That is, at any given moment a coin might come up heads or tails. Just because there have been ten heads flips in a row does not mean you should take less than 50-50 odds on the next flip. It is still 50-50. That pitcher might have won, might have lost that game and it would have become part of the statistical information. However, for the stats to become powerful, you would have to be able to make a strong prediction over a series of games that he pitched. That is, if he goes beyond X pitches in 10 games he will lose about 8 of them. That means he still wins two (or one or three) and you don't know when in the series the wins will come.
The idea that very small observations can be exploited for big advantage is very important in today's ever more competitive business climate. For example Harrah's learned that moving the odds on slot machines one-tenth of one percent in their favor did not affect customer play at all, but netted them at extra $80 million (company wide). Marriott's hotel management system improves hotel performance by a couple percent. Remember that these improvements incur little cost, so most of the improvement flows quickly to the bottom line.
I thought that might get your attention. Read it so you can learn and profit from it.
Reviewed by Craig Matteson, Ann Arbor, MI
A limited introduction to business analytics.......2007-09-21
MY RATING SYSTEM:
* - if you have to chose between torture and reading this book, then you might want to consider reading the book - although it depends on just how severe the torture would be.
** - if you've lost your job and have quite a bit of free time on your hands, and don't have anything else better to do, then you might want to consider reading this book; don't expect to learn much or really be entertained. It will however, help you pass the time until your death.
*** - meh...I'm indifferent. Reading this book will not alter your life in any significant way, yet it is not so horrendously dreadful that your taking the time to read it will be a complete waste of time.
**** - Good book to great book zone here. You should probably read this book if you have some spare time. This book could be interesting, entertaining, or informative.
***** - Outstanding book! Make time to read this book - you'll learn or be entertained or intrigued. The book might even be good enough to provide original or helpful insights into the world that we live in.
REVIEW:
Competing on Analytics serves as an interesting, albeit limited, introduction to the concept of using complex data collection, management, and analysis techniques to gain a competitive edge in business.
For me, the book served as a useful introduction, but fell far short of satisfying the objectives I had in mind when I first came across it. What I was expecting was a book that provide a detailed guide to developing and implementing an analytical approach to business decision making. While early on the authors acknowledge the limitations of the book, I found what followed to be less than satisfying.
The book contained a variety of examples of companies that were using analytical techniques to improve the quality of business decision making, and discussed a variety of business areas in which companies might want to adopt such analytical techniques but failed to present comprehensive case studies that would provide real guidance to readers. I would have liked to have been led through a few cases, from a diverse set of industries, where the authors describe what information was collected and why, how the information was manipulated, analyzed and presented, and how the entire analytics process was influenced by and/or influenced the company's strategy and performance. Instead, the book left me with the impression that I need to go out an hire a consulting firm to lead me through the development of an analytics program.
One of the most ironic components of the book was that while it touted the use of analytical techniques and objective analysis to motivate business decision making, it's argument was largely based on anecdotal evidence of a handful of companies that have adopted analytical approaches.
Good Overview of Business Analytics.......2007-09-20
Technology & the easy with which information spreads has rendered many products and services easily replicable. Companies need to compete on the basis of something their competitors can't recreate. What companies don't have ready access to is each other's data, i.e., on customers, suppliers, & processes. What companies do with this data is what can set them apart from competitors.
Davenport & Harris describe how data is transformed into competitive advantage by discussing the types of information used in analytics, the stages of becoming a more analytic corporation, and many examples of companies who have applied analytics to successful operations. Problems encountered down the road to becoming more analytical were similar to those described in another recent book on the criticality of enterprise data, Information Revolution by Davis, Miller, & Russell.
This book contains no numeric formulas or specific procedures for using analytics, but it is an excellent as an overall survey of business analytics as used today.
Who Is The Audience.......2007-08-30
This book is meant for those who make things happen and need to gain a fresh perspective. It is not meant for those who know a lot but can't make things happen yet keep looking for more information, while criticizing a good effort, which without doubt could have been better.
Great Subject/Weak Effort.......2007-08-26
Not a lot of meat to this topic other than the obvious. Not very exciting stuff.
Book Description
"This new edition of Active Portfolio Management continues the standard of excellence established in the first edition, with new and clear insights to help investment professionals."
-William E. Jacques, Partner and Chief Investment Officer, Martingale Asset Management.
"Active Portfolio Management offers investors an opportunity to better understand the balance between manager skill and portfolio risk. Both fundamental and quantitative investment managers will benefit from studying this updated edition by Grinold and Kahn."
-Scott Stewart, Portfolio Manager, Fidelity Select Equity ® Discipline
Co-Manager, Fidelity Freedom ® Funds.
"This Second edition will not remain on the shelf, but will be continually referenced by both novice and expert. There is a substantial expansion in both depth and breadth on the original. It clearly and concisely explains all aspects of the foundations and the latest thinking in active portfolio management."
-Eric N. Remole, Managing Director, Head of Global Structured Equity, Credit Suisse Asset Management.
Mathematically rigorous and meticulously organized, Active Portfolio Management broke new ground when it first became available to investment managers in 1994. By outlining an innovative process to uncover raw signals of asset returns, develop them into refined forecasts, then use those forecasts to construct portfolios of exceptional return and minimal risk, i.e., portfolios that consistently beat the market, this hallmark book helped thousands of investment managers. Active Portfolio Management, Second Edition, now sets the bar even higher. Like its predecessor, this volume details how to apply economics, econometrics, and operations research to solving practical investment problems, and uncovering superior profit opportunities. It outlines an active management framework that begins with a benchmark portfolio, then defines exceptional returns as they relate to that benchmark. Beyond the comprehensive treatment of the active management process covered previously, this new edition expands to cover asset allocation, long/short investing, information horizons, and other topics relevant today. It revisits a number of discussions from the first edition, shedding new light on some of today's most pressing issues, including risk, dispersion, market impact, and performance analysis, while providing empirical evidence where appropriate. The result is an updated, comprehensive set of strategic concepts and rules of thumb for guiding the process of-and increasing the profits from-active investment management.
Customer Reviews:
One to add to your reading list.......2007-06-30
I know many have this book and have never read it. Others read this book but never really understand it. However, if you can read it and understand it, it can offer a powerful tool for how to allocate capital. It actually is the basis for most indexing and quantitative methodologies. When applied to fundemental approaches to investment it can be quite powerful.
Sadly, though not enough money managers embrace what this book is trying to say with regards to risk and return.
Practical approach and mathematically rigorous at the same time.......2006-02-01
Excellent book for whom is looking for a practical approach that at the same time is presented through a rigorous mathematical methodology. The book is absolutely superior over the academic textbooks that usually limit themselves to CAPM and efficient market theory. Grinold and Kahn go much forward and at the same time had managed to clearly and meticulously show the CAPM model, its limitations and the more sophisticated tools developed from it. Beside of showing the active way of managing a portfolio, the serious mathematical presentations through which the different theories such as CAPM are described are very convincing of how difficult it could be to beat the market.
Theoretical framework with no practical examples........2005-01-20
There is important information in this book but most of us need to see numerical examples to reinforce theoretical concepts. This book really comes up short in this area. It provides some discussion with the formulas/equations it presents but is very incomplete in terms of worked out examples. Yes, including worked out examples might might mean a book three times as long, but the book would then be many, many times more useful to practitioners.
As it currently stands the book can only benefit the super-genius-theoretical types who do not need to see examples to understand OR someone who ALREADY really understands the concepts.
The book rather frequently presents variables or constants without explicitly defining them for the reader (it assumes we know what they mean from the accompanying discussion).
The book gives exercises, but without answers what good are these?
The one thing the book does is make you realize there is a lot you do not know. You can find ideas in portfolio management that exist by reading this book but if you are at all like me you are going to have to look elsewhere for the answers. I have had better luck with Google searches for stuff like Style Analysis.
The book shows how smart the authors are: they know stuff that must of us do not. Unfortunately this is the feeling I get as I read sections of their book. They intend to keep it this way. Bottom line: the book fails to bridge the gap between theory and practice.
This is the seminal text for Quantitative Finance.......2004-11-11
If you work for one of the top alpha quant shops (Barclays, Goldman, etc.), this text is a the proverbial must read. These are the guys that essentially invented quantitative finance in its modern form, building upon the [only somewhat applicable] concepts of Sharpe and Rosenberg and demonstrating how they can be harnassed to drive alpha. Anybody who has given this text a poor review obviously doesn't work in quantitative finance (chances are they're merely stock-pickers). If you want to understand how to drive alpha and beat the market, this text goes a lot further than explaining the simple concepts of information ratio and tracking error; instead, this book touches on the beauty of multi-factor models and covariance risk management.
Very boring and dry.......2004-10-05
This book is a funny phenomenon in itself: it seems that every portfolio manager keeps a copy on her desk, but nobody I've talked to likes the book, or has even really read it. I read it and had to struggle hard to go from one page to the next. It's one of the WORST books I've ever read in any field. The book attempts to give the reader a comprehensive overview of the portfolio management discipline. Unfortunately, it's extremely dry, to the point of boring the reader to death. A lot of pages are also wasted on topics of dubious value, while important subjects like global management is treated lightly. I highly recommend against this book. It's a waste of money.
Book Description
"This is a first-class book dealing with one of the most important areas of current research in applied statistics…the methods described are widely applicable…the standard of exposition is extremely high."
--Short Book Reviews from the International Statistical Institute
"The new chapters (10-14) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error---all vital topics in contemporary social statistics. In the tradition of the first edition, they are clearly written and make good use of interesting substantive examples to illustrate the methods. Advanced graduate students and social researchers will find the expanded edition immediately useful and pertinent to their research."
--TED GERBER, Sociology, University of Arizona
"Chapter 11 was also exciting reading and shows the versatility of the mixed model with the EM algorithm. There was a new revelation on practically every page. I found the exposition to be extremely clear. It was like being led from one treasure room to another, and all of the gems are inherently useful. These are problems that researchers face everyday, and this chapter gives us an excellent alternative to how we have traditionally handled these problems."
--PAUL SWANK, Houston School of Nursing, University of Texas, Houston
Popular in the
First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:
* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3
* New section on multivariate growth models in Chapter 6
* A discussion of research synthesis or meta-analysis applications in Chapter 7
* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators
While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcomes types in Part III:
* New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case
* New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model
* New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13)
The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level-1 errors, multivariate linear models, and hierarchical generalized linear models.
Customer Reviews:
pre-req: mid-level stats experience.......2006-07-12
I had taken a class in HLM before, and I bought this book to refresh myself on the details. It takes a good deal of attention to detail and concentration to really get the full measure from this book, although it's all in there. Despite the authors' best efforts, there is a good bit of stats jargon in the book, so a reader who is not familiar might have some difficulty. If you're at a point where learning HLM is a logical next step, you'll be fine and I recommend this book. However, if your over-eager faculty advisor told you to learn HLM, despite your minimal experience in stats, you're better off enrolling in a class or workshop.
Good but sometimes skipping ahead too fast.......2006-03-09
This book gives a detailed description of the use of an advanced method to deal with nested data sets.
At a general level the constructs and ideas are well written and can be followed reasonably easily.
However the mathematics is often written very dense, which makes reading and understanding complex.
My main problem with the book, is that in many of the examples they provide, the given formula's, and data skip rapidly to the solution. Thus it is often not insightfull at all, how the data led to the numerical outcome (and I and several of my colleagues could not reproduce all of the example outcomes). A more extensive discussion and a more step-by-step construction of the examples would have been helpful there.
So in short: Conceptually this book is fine, but for practical use mathematics are too dense, and examples are too hard to follow
Useful, but need solid background in stats.......2004-06-05
This book describes important advances in statistical analysis of social science data, circa 1992. Much of this data has a natural hierarchical grouping. But traditional statistical methods proved inadequate at coping. The biggest drawback was the failure of the assumption of independence. If at the lowest level, Items I1,...,In are grouped into sets J1,...,Jm, where m
To handle this, Hierarchical Linear Models were developed. The book gives a detailed treatment. A very comprehensive discussion. Including the handling of meta-analysis, where we wish to combine results across different studies. Which then involves using empirical Bayesian estimates. This method has also seen important usage in evaluating medical studies, conducted by different researchers on the same topic.
The book also illustrates the essential development of non-trivial computer programs to perform the gruntwork.
You will need a solid background in statistics to find this book useful. At a minimum, a year of statistics at the undergraduate level.
Book Description
Introduces the modern investment management techniques used by Goldman Sachs asset management to a broad range of institutional and sophisticated investors.
* Along with Fischer Black, Bob Litterman created the Black-Litterman asset allocation model, one of the most widely respected and used asset allocation models deployed by institutional investors.
* Litterman and his asset management group are often a driving force behind the asset allocation and investment decision-making of the world's largest 100 pension funds.
Download Description
Introduces the modern investment management techniques used by Goldman Sachs asset management to a broad range of institutional and sophisticated investors. * Along with Fischer Black, Bob Litterman created the Black-Litterman asset allocation model, one of the most widely respected and used asset allocation models deployed by institutional investors. * Litterman and his asset management group are often a driving force behind the asset allocation and investment decision-making of the world's largest 100 pension funds.
Customer Reviews:
Ignore the Bad Reviews Below.......2006-08-15
I am quite shocked by all of the poor reviews below. This text is actually very good, in that it address several topics that Grinold and Kahn do not, mainly utility theory (and its role in investor decision making), the international CAPM, and the Black-Litterman model. First, the presentation of the investment decision making process by Litterman from an economics (utility maximization) view point is right on target. Too often portfolio theory is simply presented in a pure mathematical finance format that, while teaching the mechanics, leaves the end user incapable of understanding the implications of the analysis they are performing. Additionally, Litterman's presentation of the international CAPM and universal hedge models are very well done and extremely important. Finally, the Black-Litterman model has become mainstream (it is incorporated into the Ibbotson software) and is completely ignored by Grinold!
I own both Litterman and Grinold, and if you can afford both I would buy both because Grinold does a nice job simply presenting the mathematics, but then so do so many other texts.
Crap.......2005-12-17
A couple of chps from here are reqd reading for the CFA Level III exam (last exam for CFA charter). I was expecting something MUCH better from GSAM who fancy themselves as the best on the street.
Thankfully, CFAI provided us with the chps and we did not have to purchase the book. Save your money and buy Grinold instead.
All Blather and No Substance.......2005-07-12
The boys at GSAM clearly wrote this book as an "alternative" to Grinold and Kahn and to help promote the group as the seek to raise assets.
Grinold and Kahn work at Barclays Global Investors, GSAM's biggest competitor, and they wrote a first-rate book on how to do quantitative management. Their book has become the standard, the must read, and is required by the CFA exam. This obviously bugged them to no end. It's no fun to see your biggest competitor getting tons of accolades. So they did what anyone with a big ego does: they wrote their own book, this book.
Only problem is this book STINKS. What's the matter with it you ask? It has no content. The boys at GSAM were so scared about divigulging anything that could help a competitor (or the market) that they didn't really want to SAY anything.
Now how do you not say anything but still write a book, you ask? Excellent question! The answer is you talk in infuriatingly broad generalities about very general topics.
For example, on the topic of how do you actually trade the portfolio, they come up with such gems of wisdom as:
"Tradomg is the process of executing the orders derived in the portfolio constrution step. To trade a list of stocks efficiently, investors must balance opportunity costs and execution price against market impact costs." [page 431]
This knowledge anyone who has ever thought for 2 seconds about trading knows. The real value might come if they gave you some cool way to think about measuring opportunity costs, ex-ante. Or a nice way of estimating market impact costs. Do they do either? Of course not! Just more and more banal talk.
The book is filled with millions of other examples. One should use a decay weight in estimating covariance matrices. How should we choose that decay weight is of course never mentioned or discussed!
They tell us when choosing between factors to predict returns, "the real challenge is to winnow down the list of factors to a parsimonious set." Okay, how might I do that you GSAM gods? They never ever tell you [see page 420]
You get the point, just lots of blather and really no content.
Save your money and don't buy this book. They don't need your money they have enough already. And it's not like you are getting knowledge or anything valuable in return.
Oldschool.......2003-11-24
Nicely written from a journalistic perspective but rather old fashioned. Many mistakes and deliberate false claims in order to suit product interests of Goldman Sachs. Examples:
In the chapter on asset liability management there is always an analytical case for equities. However the only reason is that GS does not allow duration as a choice variable. Otherwise beta (in their formula) would become one and the optimal equity allocation is zero. Accidental? I doubt it.
They also claim to have found (earlier) a better method than Stambaugh on dealing with missing data. However either you publish or you shut up.
Waste of time for serious quants
The definitive equilibrium investing title.......2003-08-20
My highest commendations to the asset management team at Goldman Sachs. They have come together and created a highly comprehensive tome that covers all the bases within the realm of modern investment theory. Their solid equilibrium approach is applied to all areas, from traditional investments to alternative asset classes, from institutional funds to private wealth, using analysis and real world applications. Incredibly thorough, extremely recommended.
Book Description
THE APPROACH
"J. Scott Long’s approach is one that I highly commend. There is a decided emphasis on the application and interpretation of the specific statistical techniques. Long works from the premise that the major difficulty with the analysis of limited and categorical dependent variables (LCDVs) is the complexity of interpreting nonlinear models, and he provides tools for interpretation that can be widely applied across the different techniques."
--Robert L. Kaufman, Sociology, Ohio State University
"A thorough and comprehensive introduction to analyzing categorical and limited dependent variables from a traditional regression perspective that provides unusually clear discussions concerning estimation, identification, and the multiplicity of models available to the researcher to analyze such data."
--Scott Hershberger, Psychology, University of Kansas
THE ORGANIZATION
"The thing that impresses me the most about this book is how organized it is. The chapters are in excellent logical sequence. There is a useful repetition of important concepts (e.g., estimation, hypothesis testing) from chapter to chapter. J. Scott Long has done a terrific job of organizing like things from disparate literatures, such as the scaler measures of fit in Chapter 4."
--Herbert L. Smith, Sociology, University of Pennsylvania
"A major strength of the book is the way that it is organized. The chapter about each technique is written in a highly organized and parallel format. First the statistical basis and assumptions for the particular model are developed, then estimation issues are considered, then issues of testing and interpretation are considered, then variations and extensions are explored."
--Robert L. Kaufman, Sociology, Ohio State University
FOR THE COURSE
"I have been teaching a course on categorical data analysis to sociology graduate students for close to 20 years, but I have never found a book with which I was happy. J. Scott Long’s book, on the other hand, is nearly ideal for my objectives and preferences, and I expect that many other social scientists will feel the same way. I will definitely adopt it the next time I teach the course. It deals with the right topics in the most desirable sequence and it is clearly written."
--Paul D. Allison, Sociology, University of Pennsylvania
Class-tested at two major universities and written by an award-winning teacher, J. Scott Long’s book gives readers unified treatment of the most useful models for categorical and limited dependent variables (CLDVs). Throughout the book, the links among models are made explicit, and common methods of derivation, interpretation, and testing are applied. In addition, Long explains how models relate to linear regression models whenever possible. In order for the reader to see how these models can be applied, Long illustrates each model with data from a variety of applications, ranging from attitudes toward working mothers to scientific productivity.
The book begins with a review of the linear regression model and an introduction to maximum likelihood estimation. It then covers the logit and probit models for binary outcomes--providing details on each of the ways in which these models can be interpreted, reviews standard statistical tests associated with maximum likelihood estimation, and considers a variety of measures for assessing the fit of a model. Long extends the binary logit and probit models to ordered outcomes, presents the multinomial and conditioned logit models for nominal outcomes, and considers models with censored and truncated dependent variables with a focus on the tobit model. He also describes models for sample selection bias and presents models for count outcomes by beginning with the Poisson regression model and showing how this model leads to the negative binomial model and zero inflated count models. He concludes by comparing and contrasting the models from earlier chapters and discussing the links between these models and models not discussed in the book, such as loglinear and event history models. Helpful exercises are included in the book with brief answers included in the appendix so that readers can practice the techniques as they read about them.
Customer Reviews:
Very Very Readable Book.......2007-04-28
This book is very readable. The author correctly claims that this book has been designed following questions from his various students in understanding such regression models. There is clarity in each and every argument used together with alternative ways of interpretation and testing. Great book.
Maximal clarity on the subject.......2005-05-31
If you have to do statistical analysis where your dependent variable is a count, a dichotomy, categorical, or ordinal, and if you are not a grad student in a statistics department, this is a book for you. Long clearly illustrates the need for the different models, covers the essentials of each, and provides further references. Obviously, no book on such a range of topics could be complete - there are entire long books written on each of the chapters in this one. But this is a good place to start, and it is nice to have it all 'tied together' - this makes it easier to see the relationships among the models.
Extremely good book on Logistic Regression.......2002-12-12
Since I do statistical modeling in industry, I was looking for a good book on Logistic regression that would give me a deep understanding of the subject; one that also had wide coverage (Poison regression, Tobit models, ..etc.). I decided on J. Scott Long's book, after considering Applied Logistic Regression by Hosmer and Lemeshow, and Limited Dependent and Qualitative Variables in Econometrics by Maddala. I must say I am very pleased with my choice. The topics are very clear, and the math is used as an aid to understanding, and you don't get bogged down in the math. It is a pleasure to read the book.
Most intuitive book on the subject.......2001-08-24
This book is especially useful to start understanding topics like ordered probit, multinomial logit, negative binomial regression and zero-inflated count models. Although it starts with a chapter on the linear regression model, it should not be mistaken for an introductory text. I would certainly advise readers with limited background in regression models to start with other books, like the one of Wooldridge (Introductory Econometrics). The quality of this book must be that I've yet to see a book that explains these topics more intuitively. That is not to say it is easy or without mathematics, it's not. It just looks like the mathematics is only used for better comprehension, not to give you the full proof. Furthermore, while reading it you get the feeling that the author understands what you, as a researcher, are interested in. This allows him to focus on the topics of interest, like model selection and testing and interpretation of output. So although this is not a cookbook, it may well be the closest thing to it, especially in combination with his new book on applying these models in Stata (only available at Stata). It is a pity that the author stops short of non-parametric models (next edition?).
Most intuitive book on the subject.......2001-08-24
This book is especially useful to start understanding topics like ordered probit, multinomial logit, negative binomial regression and zero-inflated count models. Although it starts with a chapter on the linear regression model, it should not be mistaken for an introductory text. I would certainly advise readers with limited background in regression models to start with other books, like the one of Wooldridge (Introductory Econometrics). The quality of this book must be that I've yet to see a book that explains these topics more intuitively. That is not to say it is easy or without mathematics, it's not. It just looks like the mathematics is only used for better comprehension, not to give you the full proof. Furthermore, while reading it you get the feeling that the author understands what you, as a researcher, are interested in. This allows him to focus on the topics of interest, like model selection and testing and interpretation of output. So although this is not a cookbook, it may well be the closest thing to it, especially in combination with his new book on applying these models in Stata. It is a pity that the author stops short of non-parametric models (next edition?).
Book Description
The implementation of sound quantitative risk models is a vital concern for all financial institutions, and this trend has accelerated in recent years with regulatory processes such as Basel II. This book provides a comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management and equips readers--whether financial risk analysts, actuaries, regulators, or students of quantitative finance--with practical tools to solve real-world problems. The authors cover methods for market, credit, and operational risk modelling; place standard industry approaches on a more formal footing; and describe recent developments that go beyond, and address main deficiencies of, current practice.
The book's methodology draws on diverse quantitative disciplines, from mathematical finance through statistics and econometrics to actuarial mathematics. Main concepts discussed include loss distributions, risk measures, and risk aggregation and allocation principles. A main theme is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. The techniques required derive from multivariate statistical analysis, financial time series modelling, copulas, and extreme value theory. A more technical chapter addresses credit derivatives. Based on courses taught to masters students and professionals, this book is a unique and fundamental reference that is set to become a standard in the field.
Customer Reviews:
Excellent for statisticians.......2007-08-03
This book is more like Mathematical Statistics for Risk Management. It covers some reviews of standard mathematical stat and some advanced and latest materials as well as applications in risk management. But as some other reviewers already mentioned, the focus is on the statistics and probability for risk management rather than the business context. And it is written in a rather formal theorem-proof format which, to some extent, could have been simplified for other audiences. It is excellent for someone with heavy stat background such as MS/PhD in statistics or PhD in Finance.
Another book that is a bit easier to read that covers Stat and Finance well with business context is: Statistics and Finance: An Introduction, which includes more than financial risk management.
Software implementation available for S-Plus and R.......2007-07-24
Although not obvious, there is software available to implement the functionality described mathematically in the book. Alexander McNeil provides S-Plus code on his personal website, and there is an R port of that code on CRAN called QRMlib. Most of the provided software is on fitting fat-tailed distributions. This is all very useful in practice, if you care to be statistically precise. Unfortunately, many practitioners would clearly prefer rules of thumb to quantitative methods only usable with statistical software that doesn't run in Excel. Excellent theoretical text with solid backing software.
Good for academia, bad for practice.......2007-06-08
This is a typical theoretical book. With all pros and cons around that statement. As a mathematician I found it well written in terms of math introduction to the subject. BUT I would never recommend that book for the practical learning. It is SO FAR away from the practical quants everyday job, that one would never use that book. 3 stars= 5/2(theory)+1/2(practice)
Very good.......2007-02-13
I found this book very interesting and well written, but not for all readers. It covers all quantitative risk management topics with a good mathematical approach: this could be a great textbook for general risk management courses, and it give importance to EVT technics, mulivariate models and copula-modelling. The treatment of market risk is complete and exhaustive, and a good actuarial approach for credit risk can be found too. The operational risk chapter is poor, it gives only an introductive section for it and for actuarial models.
It requires a good mathematical background to be well understand, but it is a great book to introduce the whole quantitative risk management theory.
very well written.......2006-02-16
It's a very well written self-contained yet compact "QUANTITATIVE" risk management book. Just as the title, it gives a comprehensive and in-depth coverage of the concepts, techniques and tools, applicable in modeling equity, credit, or operational risk. The writing style is clear and rigorous; it serves a good reference book.
Customer Reviews:
Buy It!.......2007-09-10
This is an excellent resoure for anyone conducting survey research. It's complete, provides easy to understand examples, and is structured in a way that guides the reader to a grasp of the material.
Book Description
Quantitative equity portfolio management combines theories and advanced techniques from several disciplines, including financial economics, accounting, mathematics, and operational research. While many texts are devoted to these disciplines, few deal with quantitative equity investing in a systematic and mathematical framework that is suitable for quantitative investment students. Providing a solid foundation in the subject, Quantitative Equity Portfolio Management: Modern Techniques and Applications presents a self-contained overview and a detailed mathematical treatment of various topics. From the theoretical basis of behavior finance to recently developed techniques, the authors review quantitative investment strategies and factors that are commonly used in practice, including value, momentum, and quality, accompanied by their academic origins. They present advanced techniques and applications in return forecasting models, risk management, portfolio construction, and portfolio implementation that include examples such as optimal multi-factor models, contextual and nonlinear models, factor timing techniques, portfolio turnover control, Monte Carlo valuation of firm values, and optimal trading. In many cases, the text frames related problems in mathematical terms and illustrates the mathematical concepts and solutions with numerical and empirical examples. Ideal for students in computational and quantitative finance programs, Quantitative Equity Portfolio Management serves as a guide to combat many common modeling issues and provides a rich understanding of portfolio management using mathematical analysis.
Book Description
Harnessing the Power of Quantitative Techniques to Create a Winning Trading ProgramLars Kestner Quantitative Trading Strategies takes readers through the development and evaluation stages of today's most popular and market-proven technical trading strategies. Quantifying every subjective decision in the trading process, this analytical book evaluates the work of well-known "quants" from John Henry to Monroe Trout and introduces 12 all-new trading strategies. It debunks numerous popular misconceptions, and is certain to make waves--and change minds--in the world of technical analysis and trading.
Download Description
Harnessing the Power of Quantitative Techniques to Create a Winning Trading ProgramLars Kestner Quantitative Trading Strategies takes readers through the development and evaluation stages of today's most popular and market-proven technical trading strategies. Quantifying every subjective decision in the trading process, this analytical book evaluates the work of well-known "quants" from John Henry to Monroe Trout and introduces 12 all-new trading strategies. It debunks numerous popular misconceptions, and is certain to make waves--and change minds--in the world of technical analysis and trading.
Customer Reviews:
Not substantive enough.......2005-10-19
I must agree with reviewer Ira Balli from London below. This book lacks substantive information regarding the quantitative methods and therefore is merely an introduction to quant ideas that have been discussed in the marketplace. Of course, anyone who might have proprietary and successful quant methods would be foolhardly to disclose them, so one should not expect that from any public writer.
As an alternative, some of the chapters covering quant strategies in "Trade Like a Hedge Fund : 20 Successful Uncorrelated Strategies & Techniques to Winning Profits" by James Altucher may be easier to read as introductory work.
Don't buy this book.......2005-05-26
I am trading since 10 years, lead two hedge funds and I am about to finish my study in Msc of Mathematical trading and Finance.
This is the first review I wrote, because I always found it helpfull if somebody prevented me from buying a useless book.
This is definitely useless book!
Lars Kestners 11 new trading strategies described as "new" are at least 20 years old (moving average crossover, MACD, stochastic crossover, momentum, 3 in a row to mention some). The author has the nerves even to document how negative this straegies performed. Hence his stragies are old and were already at that time useless. The infomation content is appart from the title, the name of theauthor and the price = zero. Any novice that is able to spell "technical analysis" knows more about trading systems than this book teaches you. I don't even to mention that the this book has nothing to do with quantitative except you declare a moving average, a log function or the ADX calculation as a quantitative method.
professional.......2005-04-02
This book is written by a market professional. And it is obvious from the start. There is no hype, and the whole approach is based on maths and probability. The book is concise, and it would be very difficult to make the text shorter than it is.
Strategies presented in the book are well discussed. There is also a valuable and interesting discussion on the system's cycle. Author also presents his method of money management. And there are some ideas on quantitative investing, as opposed to trading. This may be a good way to diversify.
In general, this book is good value for money. It contains eighteen trading systems, tested on variety of instruments, fully disclosed. Some (more often useless than not) trading systems are sold for several times the price of this book. It is fun to read and gives many ideas for developing your own systems.
Nice introduction to quantitative trading.......2004-04-26
Very easy reading. You will find this book quite useful if you are trading using a mechanized approach via a platform such as TradeStation. You will also find it useful if you are developing software like TradeStation that backtests trading strategies. This is why I read the book.
In part one, he describes his testing methodology and discusses the building blocks that make up the strategies that he discusses later. For example, moving averages, channel breakouts, momentum, etc. are discussed under trend following techniques and relative strength index stochastics, and MACD under price oscillators. Most importantly, he describes how to use statistical measurements to analyze the performance of a strategy.
In Part 2, he presents his results of testing the following strategies:
Channel Breakout
Dual Moving Average Crossover
Momentum
Volatility Breakout
Stochastics
Relative Strength Index
MACD
followed by some of his ideas and innovations that improve upon them. He uses 12 years of daily price data (1990 - 2001) and each strategy tests 29 different futures contracts along with 34 different stocks. He also discusses money management, which is must reading.
Although he does not provide any code (which I would have liked to have seen), he does give enough information so that you can implement any of these strategies in TradeStation or any other strategy back testing software, assuming that you have some knowledge of basic programming.
I would have liked to have seen some strategies dealing with pure price patterns. Other than that, a very well organized and thought out book. My rating for this book is 4.5 stars.
it's ok.......2004-04-23
I agree with the reviewer below. This book did not give many pure mathematical ideas. He gave some good examples that may be helpful to some but not to people who are looking for good solid mathematical strategies.
If you are just getting interested in quantitative strategy development I would suggest the book.
Book Description
This best-selling volume has long been considered one of the most user accessible books for management science. The new edition retains and updates the traditional, comprehensive coverage of past editions but now adds spreadsheet-based problem solutions to most chapters. In addition, the book is now packaged with free software.
The authors provide a comprehensive introduction to quantitative analysis, probability concepts and applications and decision theory models, as well as forecasting, control models, linear programming models and applications, transportation and assignment models, integer programming, goal programming, nonlinear programming, and branch and bound models, network models, project management, lines and queuing theory models and statistical quality control.
For those interested in a comprehensive, yet accessible presentation of QM for management.
Customer Reviews:
A book for graduates--Not undergrads.......2003-10-28
After the first week of our class falling behind, my professor mentioned to our class that this book was the wrong format for undergrads. So this book is only good as the teacher and the students. It's easy to read, but hard to understand. And, the CD-ROM was not user-friendly enough, but had an awesome integration into MS Excel. I aced all of my math classes in college (undergrad), but this class...I'm not gonna to ace. But if you aced all math classes and you're using this book for a grad class, then go for it. Otherwise, find another book.
Great Book.......2001-12-12
I had one of the worst instructors on the planet this semester. I literally had to teach myself this entire course.
This book made it so much easier. Great diagrams, simple explanations. I ended up with an A.
Easy to follow.......2000-09-17
This book is easy to follow and the included cd makes solving the problems easy. It also has online links for each chapter.
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