essentials of programming in mathematica

Section 7 describes robust equity portfolio optimization.

Section 6 applies bootstrapping to risk management and financial planning.

Section 5 describes how simulation can be used in capital budgeting.

Section 4 shows how to use built-in functions for introductory financial time-series analysis.

Section 3 discusses mean-conditional value-at-risk portfolio optimization.

Section 2 introduces linear programming and applies it to bond portfolio management.

Drag the handles at the far ends of the histogram (the thin vertical lines) to see the percentage of values that lie within two values. We chose returns of the Boeing Company for the histogram. (AAPL) and NVIDIA Corporation (NVDA) for the period May 1, 2000, to May 30, 2019, and computes descriptive statistics. You can drag these two thin vertical gray lines to vary the percentage of data within two values.įor example, this downloads monthly historical stock returns for Boeing Company (BA), Apple Inc.

The function takes a vector of numerical data and returns a histogram with a handle on each side. The function was taken from Stack Exchange Network ( ) and modified. The function computes 11 basic descriptive statistics given a list or a matrix of returns as its input argument. The function downloads historical stock returns given its four arguments: Mathematical background related to the models discussed here can be found in standard textbooks, including the ones cited.įirst, we define three supporting functions used in the rest of this article. Wellin gives a good introduction to programming in Mathematica. We do not explain financial concepts or any mathematical background related to the financial applications introduced in this article. Our main objective is to apply the Wolfram Language to solve financial models. The main topics covered are linear programming and its application in bond portfolio management, conditional value-at-risk minimization, introductory time-series analysis, simulation, bootstrapping, robust equity portfolio optimization and artificial intelligence. This article shows how to use some of Mathematica ’s built-in financial functions and define new functions useful for the practical analysis of real-world financial data.

Essentials of programming in mathematica