The CAPM suffers from several limitations, such as the use of a mean-variance framework and the fact that returns are captured by one risk factor—the market risk factor. In a well-diversified portfolio, the unsystematic risk of various stocks cancels out and is essentially eliminated.

The Arbitrage Pricing Theory (APT) model was put forward to address these shortcomings and offers a general approach of determining the asset prices other than the mean and variances.

The APT model assumes that the security returns are generated according to multiple factor models, which consist of a linear combination of several systematic risk factors. Such factors could be the inflation rate, GDP growth rate, real interest rates, or dividends.

The equilibrium asset pricing equation according to the APT model is as follows:

Here, is the expected rate of return on security , is the expected return on stock if all factors are negligible, is the sensitivity of the th...

Many Python packages such as SciPy come with several variants of regression functions. In particular, the statsmodels package is a complement to SciPy with descriptive statistics and estimation of statistical models. The official page for statsmodels is http://statsmodels.sourceforge.net/.

In this example, we will use the ols function of the statsmodels module to perform an ordinary least squares regression and view its summary.

Let's assume that you have implemented an APT model with seven factors that return the values of Y. Consider the following set of data collected over 9 time periods, to . X1 to X7 are independent variables observed at each period. The regression problem is therefore structured as:.

A simple ordinary least squares regression on values of X and Y can be performed with the following code:

""" Least squares regression with statsmodels """
import numpy as np
import statsmodels.api as sm
...

In the CAPM and APT pricing theories, we assumed linearity in the models and solved for expected security prices using regressions in Python.

As the number of securities in our portfolio increases, certain limitations are introduced as well. A portfolio manager would find himself constrained by these rules in pursing certain objectives mandated by investors.

Linear optimization helps you overcome the problem of portfolio allocation. Optimization focuses on minimizing or maximizing the value of the objective functions. The examples are maximizing returns and minimizing volatility. These objectives are usually governed by certain regulations, such as no short-selling rule, limits on the number of securities to be invested, and so on.

Unfortunately, in Python there is no single official package that supports this solution. However, there are third-party packages available with the implementation of the simplex algorithm for linear programming. For the purpose of this demonstration...

In the previous section, we looked at solving a system of linear equations with inequality constraints. If a set of systematic linear equations has constraints that are deterministic, we can represent the problem as matrices and apply matrix algebra. Matrix methods represent multiple linear equations in a compact manner while using the existing matrix library functions.

Suppose we would like to build a portfolio consisting of 3 securities, , and . The allocation of the portfolio must meet certain constraints: it must consist of 6 units of a long position in security a. With every 2 units of security a, 1 unit of security b, and 1 unit of security c invested, the net position must be 4 units long. With every 1 unit of security a, 3 units of security b, and 2 units of security c invested, the net position must be long 5 units.

To find out the number of securities to invest in, we can frame the problem mathematically as follows:

With all of the coefficients...