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Product details
File Size: 5603 KB
Print Length: 312 pages
Page Numbers Source ISBN: 946239069X
Publisher: Atlantis Press; 2014 edition (May 8, 2014)
Publication Date: May 8, 2014
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00K83T92E
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To begin with, I am unsure as to who the target audience for this book actually is. If its for computer scientists with little financial background, they will struggle with understanding some of the more abstract financial concepts in the book. If it's for people in the econ/finance fields, they will have trouble understanding some of the mathematics, not because the math is particularly difficult, but because this book is so poorly written. For example, the author will frequently change variable names from equation to equation without notice, leave some variables or methods completely unexplained, and give poor definitions for some more advanced statistical concepts and acronyms.Furthermore, this book must not have been edited. Just about every page I find a spelling, grammar, or syntax error. Plus, about 2/3 of the "R" code in the book simply doesn't work.
The R code is rather sparse...
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.
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.
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