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Computational Finance: An Introductory Course with R (Atlantis Studies in Computational Finance and Financial Engineering) von [Arratia, Argimiro]
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Computational Finance: An Introductory Course with R (Atlantis Studies in Computational Finance and Financial Engineering) Kindle Edition


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Produktbeschreibungen

Kurzbeschreibung

The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance. In that regard it is unique in its kind, for it touches upon the basic principles of all three main components of CF, with hands-on examples for programming models in R. Thus, the first chapter gives an introduction to the Principles of Corporate Finance: the markets of stock and options, valuation and economic theory, framed within Computation and Information Theory (e.g. the famous Efficient Market Hypothesis is stated in terms of computational complexity, a new perspective). Chapters 2 and 3 give the necessary tools of Statistics for analyzing financial time series, it also goes in depth into the concepts of correlation, causality and clustering. Chapters 4 and 5 review the most important discrete and continuous models for financial time series. Each model is provided with an example program in R. Chapter 6 covers the essentials of Technical Analysis (TA) and Fundamental Analysis. This chapter is suitable for people outside academics and into the world of financial investments, as a primer in the methods of charting and analysis of value for stocks, as it is done in the financial industry. Moreover, a mathematical foundation to the seemly ad-hoc methods of TA is given, and this is new in a presentation of TA. Chapter 7 reviews the most important heuristics for optimization: simulated annealing, genetic programming, and ant colonies (swarm intelligence) which is material to feed the computer savvy readers. Chapter 8 gives the basic principles of portfolio management, through the mean-variance model, and optimization under different constraints which is a topic of current research in computation, due to its complexity. One important aspect of this chapter is that it teaches how to use the powerful tools for portfolio analysis from  the  RMetrics R-package. Chapter 9 is a natural continuation of chapter 8 into the new area of research of online portfolio selection. The basic model of the universal portfolio of Cover and approximate methods to compute are also described.


Produktinformation

  • Format: Kindle Edition
  • Dateigröße: 5602 KB
  • Seitenzahl der Print-Ausgabe: 312 Seiten
  • ISBN-Quelle für Seitenzahl: 946239069X
  • Verlag: Atlantis Press; Auflage: 2014 (8. Mai 2014)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • ASIN: B00K83T92E
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Word Wise: Aktiviert
  • Verbesserter Schriftsatz: Nicht aktiviert
  • Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
  • Amazon Bestseller-Rang: #756.400 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)

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1 von 2 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen A unique textbook in Computational Finance 7. August 2015
Von ACN - Veröffentlicht auf Amazon.com
Format: Kindle Edition
I highly recommend the book Computational Finance, An Introductory Course with R,as a textbook for a course on computational aspects of finance. I have used as such with great success. The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance. In that regard it is unique in its kind, for it touches upon the basic principles of all three main components of CF, with hands-on examples for programming models in R.
The book has an accompanying web page computationalfinance.lsi.upc.edu where all the R programs in the book and more information are publicly available.

I will give some highlights of the book. The first chapter gives an introduction to the Principles of Corporate Finance: the markets of stocks and options, valuation and economic theory, framed within Computation and Information Theory (e.g. the famous Efficient Market Hypothesis is stated in terms of computational complexity, a new perspective). Chapters 2 and 3 give the necessary tools of Statistics for analyzing financial time series, it also goes in depth into the concepts of correlation, causality and clustering. Chapters 4 and 5 review the most important discrete and continuous models for financial time series. Each model is provided with an example program in R. Chapter 6 covers the essentials of Technical Analysis (TA) and Fundamental Analysis. This chapter is suitable for people outside academics and into the world of financial investments, as a primer in the methods of charting and analysis of value for stocks, as it is done in the financial industry. Moreover, this is the first textbook where I have seen an attempt to give a mathematical foundation to the seemly ad-hoc methods of TA, framed in terms of the theory of optimal stopping time of stochastic processes.
Chapter 7 reviews the most important heuristics for optimization: simulated annealing, genetic programming, and ant colonies (swarm intelligence) which is material to feed the computer savvy readers. Chapter 8 gives the basic principles of portfolio management, through the mean-variance model, and optimization under different constraints which is a topic of current research in computation, due to its complexity. One important aspect of this chapter is that it teaches how to use the tools from the RMetrics package for portfolio analysis, which is a powerful set of R functions whose only instructions manual is an approx. 400 euros textbook.
Chapter 9 is a natural continuation of chapter 8 into the new area of research of online portfolio selection, in it the universal portfolio model of Cover and approximate methods to compute it is explained.
0 von 1 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Highly informative and interesting to read 17. September 2015
Von Jan Quest - Veröffentlicht auf Amazon.com
Format: Kindle Edition
This book is highly informative and interesting to read. I found it is a good introduction to basic topics in corporate finance (definitions of financial securities, discounted cash flow models, arbitrage and risk neutral valuation), time series analisys (basic linear and nonlinear models), and it goes on to cover the more computational and algorithmic aspects of financial engineering, surveying optimization heuristics as simulated annealing, ant colony, genetic programming, each presented with applications to finance.

The book gives a basic intro to technical analysis proposing to the reader to built his/her own automatic trading system...however does not go into much detail on the rigmarole of constructing such system, as expecting from the reader some expertise in intelligent systems. Quite a challenge for computer scientists.
On the fundamental analysis (which is also covered in this book) it focus mainly on Benhamin Graham value investing philosophy.
So, all in all the book is introductory in many aspects of computational finance (not all possible themes) and so it can serve well for an advanced CS-Math-Finance undergradute course , or first to second year graduate course.
The book presents hands-on programming examples in R making use of several packages in this programming language geared for financial analysis.
It is suitable for use both as a textbook and for self-study for people with a degree in mathematics or computer science.
1 von 2 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Five Stars 30. Juni 2015
Von Kiba - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
Good and concise introduction to the field of computer science for finance.
1 von 5 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen [still] Disappointing in so many ways... 30. Januar 2015
Von MeanerReversion - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
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EDIT: I published my initial review for this book, when the book cost 60$ (Jan 2015). Although price has dropped significantly since, I do not change my rating. Back then the high price just added insult to injury.
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Rarely, I find a book that tries to accomplish so much and yet achieves so little. In the beginning, I went soft on the book, given that the author has little background in finance, but as I plowed through the book, my disappointment and anger grew with every page until I felt compelled to warn potential buyers to stay away from this outrageously overpriced piece of work. Maybe Springer realized that the book was of subpar quality and therefore decided to publish it under its Atlantis Press label. However, Atlantis Press (read Springer) did not shy away from charging 60$ for its ginger stepchild of a book that should have never exceeded the 20$ price mark. I will now elaborate on the flaws that led me to deduct stars for this book.

-1 star for not addressing the target audience appropriately
In the preface, the author states that this book is for "advanced undergraduate or graduate students in computer science, mathematics, business and non-academic financial investors". However, neither of the mentioned will gain anything from the book. While the non-business audience (computer scientists and mathematicians) will be confronted with a mix of concepts, they should already know and borderline-wrong concepts of finance, the financially literate crowd will be confronted with a shallow rehearsal of well-known concepts. I go as far as saying that this book misinforms the non-business audience about the importance of some concepts. While the chapter on Causality is nicely written, causality in finance is in most cases determined by common sense. Furthermore, a reader with a strong background in finance will cringe after seeing that both technical and fundamental analysis are addressed in a 27-page chapter that has the words "pattern mining" in its title. That is the equivalent of presenting the concepts of astronomy and astrology in a 50-page leaflet with the title "how to be a con-artist". The author should have definitely consulted a finance professional to avoid such poor writing.

-1 star for a sloppy and lazy implementation of R
Although the subtitle of the book promises "An Introductory Course in R" the book does far too little to live up to these expectations. Some chapters have no examples in R at all, although R is very capable of the tasks mentioned, while in most chapters the lines of R code appear half-hearted and cosmetic. It appears the author sprinkled R code onto his outpourings to lure the "R crowd" into buying his book. Furthermore, the R code is presented in a way that is incomprehensible to rookie R users (e.g. multiple lines of code are presented in one line). One should really expect that at least the code works, but, alas, the author seems to have skipped the opportunity to test his code lines before sending the manuscript out for printing.

- 1 star for massive spelling and grammar errors
At least one lesson is to be learned from this book. Publishers should not let authors be editors! This book is so poorly spell-checked that one wonders whether the author/editor found it worthwhile to run the basic Word spell-check on his manuscript.
Usually this would be the point where I give suggestions on how the author could improve his work in future editions, but I simply cannot do that. This book has no value as soon as one realizes that for the same prize one can almost get used editions of the excellent “Statistics and Data Analysis for Financial Engineering” by Ruppert AND “An Introduction to Statistical Learning” by James et al. Those two books are everything Computational Finance wants to be and even more. They also offer better R code.
1 von 5 Kunden fanden die folgende Rezension hilfreich
1.0 von 5 Sternen One Star 1. März 2015
Von Thomas Schlebusch - Veröffentlicht auf Amazon.com
Format: Kindle Edition Verifizierter Kauf
The R code is rather sparse...
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