Computational Finance: An Introductory Course with R (Atlantis Studies in Computational Finance and Financial Engineering Book 1)

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. This book covers a wide range of topics 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. This book first 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. It then gives the necessary tools of Statistics for analyzing financial time series, it also goes in depth into the concepts of correlation, causality and clustering. This is follows by a review of the most important discrete and continuous models for financial time series. Each model is provided with an example program in R. This book also covers the essentials of Technical Analysis (TA) and Fundamental Analysis. This text reviews the most important heuristics for optimization: simulated annealing, genetic programming, and ant colonies (swarm intelligence). It also 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. This book teaches how to use the powerful tools for portfolio analysis from the RMetrics R-package. It also covers 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 Front Matter....Pages i-x An Abridged Introduction to Finance....Pages 1-36 Statistics of Financial Time Series....Pages 37-70 Correlations, Causalities and Similarities....Pages 71-107 Time Series Models in Finance....Pages 109-143 Brownian Motion, Binomial Trees and Monte Carlo Simulation....Pages 145-175 Trade on Pattern Mining or Value Estimation....Pages 177-206 Optimization Heuristics in Finance....Pages 207-237 Portfolio Optimization....Pages 239-265 Online Finance....Pages 267-282 Back Matter....Pages 283-301