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008 160915s2014 fr | s |||| 0|eng d
020 _a9789462390706
_9978-94-6239-070-6
024 7 _a10.2991/978-94-6239-070-6
_2doi
035 _ato000546799
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQA76.9.C65
072 7 _aUGK
_2bicssc
072 7 _aCOM072000
_2bisacsh
082 0 4 _a003.3
_223
100 1 _aArratia, Argimiro.
_eauthor.
_9456080
245 1 0 _aComputational Finance
_helectronic resource
_bAn Introductory Course with R /
_cby Argimiro Arratia.
260 _aParis :
_bAtlantis Press :
_bImprint: Atlantis Press,
_c2014.
300 _aX, 301 p. 41 illus., 26 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aAtlantis Studies in Computational Finance and Financial Engineering,
_x2352-3255 ;
_v1
505 0 _aAn abridged introduction to finance -- Statistics of financial time series -- Correlations, causalities and similarities -- Time series models in finance -- Brownian motion, binomial trees and Monte Carlo simulation -- Trade on pattern mining or value estimation -- Optimization heuristics in finance -- Portfolio optimization -- Online finance -- Appendix: The R programming environment.
520 _aThe 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.
650 0 _aComputer Science.
_9155490
650 0 _aComputer simulation.
_9304569
650 0 _aFinance.
_9142509
650 0 _aMathematical statistics.
_9566264
650 0 _aEconomics
_xStatistics.
_9304057
650 1 4 _aComputer Science.
_9155490
650 2 4 _aSimulation and Modeling.
_9304570
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
_9304058
650 2 4 _aQuantitative Finance.
_9304891
650 2 4 _aFinancial Economics.
_9304566
650 2 4 _aStatistics and Computing/Statistics Programs.
_9303277
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aAtlantis Studies in Computational Finance and Financial Engineering,
_9456081
856 4 0 _uhttp://dx.doi.org/10.2991/978-94-6239-070-6
912 _aZDB-2-SCS
999 _c404470