Financial Theory With Python

Synopsis

Nowadays, finance, mathematics, and programming are intrinsically linked. This book provides the relevant foundations of each discipline to give you the major tools you need to get started in the world of computational finance.

Using an approach where mathematical concepts provide the common background against which financial ideas and programming techniques are learned, this practical guide teaches you the basics of financial economics. Written by the best-selling author of Python for Finance, Yves Hilpisch, Financial Theory with Python explains financial, mathematical, and Python programming concepts in an integrative manner so that the interdisciplinary concepts reinforce each other.

• Draw upon mathematics to learn the foundations of financial theory and Python programming
• Learn about financial theory, financial data modeling, and the use of Python for computational finance
• Leverage simple economic models to better understand basic notions of finance and Python programming concepts
• Use both static and dynamic financial modeling to address fundamental problems in finance, such as pricing, decision-making, equilibrium, and asset allocation
• Learn the basics of Python packages useful for financial modeling, such as NumPy, pandas, Matplotlib, and SymPy

Summary

Chapter 1: Introduction to Financial Theory

* Summary:
* Definition of finance: Management of money and resources over time.
* Scope of financial theory: Investment, risk management, corporate finance, and financial institutions.
* Importance of python in financial theory: Data analysis, modeling, and optimization.
* Example: Calculating the time value of money using Python:

```python
import numpy as np

present_value = 100
interest_rate = 0.10
years = 5

future_value = present_value * (1 + interest_rate)years
print(f"Future value after {years} years: {future_value:.2f}")
```

Chapter 2: Time Value of Money

* Summary:
* Concept of future and present value.
* Annuities and perpetuities.
* Applications in investment and financial planning.
* Example: Calculating the present value of an annuity using Python:

```python
import math

annual_payment = 1000
interest_rate = 0.05
years = 20

present_value = annual_payment * ((1 - (1 + interest_rate)(-years)) / interest_rate)
print(f"Present value of {years} annual payments of {annual_payment}: {present_value:.2f}")
```

Chapter 3: Risk and Return

* Summary:
* Measuring risk and return using standard deviation and Sharpe ratio.
* Diversification and portfolio optimization.
* Efficient frontier concept.
* Example: Calculating the Sharpe ratio of a portfolio using Python:

```python
import numpy as np
import pandas as pd

returns = np.array([0.10, 0.05, -0.02, 0.15, 0.08])
risk_free_rate = 0.02

sharpe_ratio = (np.mean(returns) - risk_free_rate) / np.std(returns)
print(f"Sharpe ratio of the portfolio: {sharpe_ratio:.2f}")
```

Chapter 4: Capital Budgeting

* Summary:
* Net present value (NPV) and internal rate of return (IRR) methods.
* Payback period and profitability index.
* Applications in investment decisions.
* Example: Calculating the NPV of a project using Python:

```python
import numpy as np

initial_investment = -10000
cash_flows = np.array([2000, 3000, 4000, 5000])
discount_rate = 0.10

npv = np.npv(discount_rate, cash_flows, initial_investment)
print(f"Net present value of the project: {npv:.2f}")
```

Chapter 5: Corporate Finance

* Summary:
* Capital structure and its impact on firm value.
* Dividend policy and stock valuation.
* Mergers and acquisitions.
* Example: Calculating the weighted average cost of capital (WACC) using Python:

```python
import numpy as np

debt_ratio = 0.75
equity_ratio = 0.25
cost_of_debt = 0.05
cost_of_equity = 0.08

wacc = (debt_ratio * cost_of_debt) + (equity_ratio * cost_of_equity)
print(f"Weighted average cost of capital: {wacc:.2f}")
```