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Über dieses Buch

This advanced undergraduate/graduate textbook teaches students in finance and economics how to use R to analyse financial data and implement financial models. It demonstrates how to take publically available data and manipulate, implement models and generate outputs typical for particular analyses. A wide spectrum of timely and practical issues in financial modelling are covered including return and risk measurement, portfolio management, option pricing and fixed income analysis. This new edition updates and expands upon the existing material providing updated examples and new chapters on equities, simulation and trading strategies, including machine learnings techniques. Select data sets are available online.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Prices

Abstract
The price of an asset is the amount the buyer pays and seller receives in a transaction. In this chapter, we introduce the student to Yahoo Finance data and show how to perform basic data manipulation techniques. We then show how to compare the capital gains between securities and several common analyses using price data, such as moving average, volume-weighted average prices, as well as how to create candlestick charts and 2-axis charts.
Clifford S. Ang

Chapter 2. Individual Security Returns

Abstract
This chapter demonstrates how to calculate individual security returns. We show how to calculate arithmetic return, logarithmic returns, total returns, daily returns, weekly returns, and monthly returns. We then discuss how to compare the performance of multiple securities.
Clifford S. Ang

Chapter 3. Portfolio Returns

Abstract
This chapter demonstrates how to calculate portfolio returns. We also show how to construct equal-weighted and value-weighted portfolios. We end the chapter by showing how to implement time-weighted rate of returns and money-weighted rate of returns.
Clifford S. Ang

Chapter 4. Risk

Abstract
This chapter discusses investment risks. We first demonstrate how to analyze individual security risk, and then we analyze portfolio risk. We discuss Value-at-Risk, expected shortfall, and other alternative risk measures.
Clifford S. Ang

Chapter 5. Factor Models

Abstract
This chapter discusses factor models, which are models that explain the variation in expected stock returns using various proxies. We begin by discussing the most commonly used factor models, capital asset pricing model (CAPM) and Fama–French three factor model. We show how to analyze for consistency of results to various assumptions. We also discuss a popular application of factor models, the event study, which allows us to determine the effect of the disclosure of new information on the firm’s stock price. We end the chapter by showing a methodology that allows you to select the best set of factors.
Clifford S. Ang

Chapter 6. Risk-Adjusted Portfolio Performance Measures

Abstract
To achieve higher returns, we have to take on more risk. In this chapter, we demonstrate how to calculate various commonly used risk-adjusted portfolio performance measures, which allows us to rank different investments by their risk-return profile. These include the Sharpe ratio, Roy’s safety first ratio, Treynor ratio, among others.
Clifford S. Ang

Chapter 7. Markowitz Mean–Variance Optimization

Abstract
This chapter discusses mean–variance optimization based on the work of Harry Markowitz. We demonstrate the intuition of identifying mean–variance efficient portfolios and construction of the mean–variance efficient frontier through a simple two-asset example. We then show how to use quadratic programming to extend the two-asset portfolio to a multi-asset portfolio.
Clifford S. Ang

Chapter 8. Equities

Abstract
This chapter discusses analyses and models related to equities. We show how to analyze projections. Then, we look at two components of the cost of equity: equity risk premium and betas. We then show how to perform relative valuation using regression analysis. We end the chapter by showing techniques that can help identify shifts in stocks returns.
Clifford S. Ang

Chapter 9. Fixed Income

Abstract
This chapter covers fixed income securities. We first show how to analyze economic and fixed income market data. Then, we demonstrate how to implement basic fixed income valuation models as well as the calculation of duration and convexity. We end the chapter with a discussion of the Vasicek and Cox, Ingersoll, and Ross interest rate models.
Clifford S. Ang

Chapter 10. Options

Abstract
This chapter demonstrates how to analyze options data. We show how to analyze data from the Chicago Board of Options Exchange (CBOE). Next, we show how to implement the Black–Scholes–Merton and Binomial Options Pricing Models by Cox, Ross, and Rubinstein. We then discuss the calculation of implied volatilities. We end the chapter by showing ways to estimate the value of American options, using the Cox, Ross, and Rubinstein model and the Bjerksund–Stensland approximation.
Clifford S. Ang

Chapter 11. Simulation

Abstract
Many asset pricing problems cannot be solved using closed-form formulas like the Black–Scholes–Merton model. This chapter discusses the application of simulation techniques in finance. We show different ways to model stock prices using a Geometric Brownian Motion, including simulating two correlated assets. We also show how simulations can be used in the Value-at-Risk calculation. We then show how simulations can be used in options pricing, including how to price several exotic options.
Clifford S. Ang

Chapter 12. Trading Strategies

Abstract
This chapter discusses basics of developing trading strategies. We start with a discussion of the efficient markets hypothesis, which grounds us in the theory that it is difficult to identify consistently profitable trading strategies. We then show several applications of technical analysis and how we can apply these technical trading rules to develop a simple trading strategy. We then end this chapter with a discussion of how machine learning can be applied to trading. In particular, we discuss how to use k-nearest neighbor algorithms, regression with k-fold cross validation, and artificial neural networks.
Clifford S. Ang

Backmatter

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