Computational Finance : An Introductory Course with R / by Argimiro Arratia.

Format:

Book

Author/Creator:

Arratia, Argimiro, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Atlantis studies in computational finance and financial engineering 2352-3255 ; 1.

Atlantis Studies in Computational Finance and Financial Engineering, 2352-3255 ; 1

Language:

English

Subjects (All):

Computer simulation.

Statistics.

Economics, Mathematical.

Macroeconomics.

Simulation and Modeling.

Statistics for Business, Management, Economics, Finance, Insurance.

Quantitative Finance.

Macroeconomics/Monetary Economics//Financial Economics.

Statistics and Computing/Statistics Programs.

Local Subjects:

Simulation and Modeling.

Statistics for Business, Management, Economics, Finance, Insurance.

Quantitative Finance.

Macroeconomics/Monetary Economics//Financial Economics.

Statistics and Computing/Statistics Programs.

Physical Description:

1 online resource (X, 301 pages) : 41 illustrations, 26 illustrations in color.

Edition:

First edition 2014.

Contained In:

Springer eBooks

Place of Publication:

Paris : Atlantis Press : Imprint: Atlantis Press, 2014.

System Details:

text file PDF

Summary:

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.

Contents:

An 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.

Other Format:

Printed edition:

ISBN:

978-94-6239-070-6

9789462390706

Access Restriction:

Restricted for use by site license.