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Empirical Finance.epub
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Empirical Finance: Theory and Applicationoffers a modern, data-driven introduction to the field of finance, tailored for undergraduate students and practitioners seeking to bridge theory with real-world evidence. In an era defined by abundant data and computational power, this resource emphasizes hands-on learning by integrating financial theory, empirical analysis, and practical implementation using Python and R. Each chapter balances intuitive explanations with mathematical rigor, ensuring that readers not only understand key concepts but also learn how to test them with actual data.
Structured in two parts, the resource begins with a thorough review of essential quantitative tools—optimization, probability, and statistics—providing the foundation needed for empirical work. The second part applies these tools to core topics in finance, including asset pricing, portfolio choice, market efficiency, event studies, and volatility modeling. Real-world examples and case studies—such as testing the Efficient Markets Hypothesis, analyzing stock splits, and evaluating the equity premium—bring the material to life and illustrate how empirical methods can validate or challenge economic intuition.
A distinctive feature of this text is its emphasis on reproducibility and application. Code snippets, exercises, and datasets enable readers to replicate results and develop their own analyses. Topics like time-series properties of returns, portfolio management and behavioral finance are treated with both theoretical and empirical depth, preparing students for quantitative internships, graduate studies, or roles in the financial industry.
Ideal for courses in Empirical Finance, Financial Econometrics, or Quantitative Finance, this resource stands out for its clear exposition, relevance powers a new generation of finance students to think critically, work with data, and understand markets not as a set of abstract rules, but as a dynamic interplay of economics, data, and technology.
Key Features:
Seamlessly integrates hands-on coding in both Python and R with financial theory, enabling readers to replicate results and conduct their own empirical analysis.
Strikes a unique balance between financial intuition, mathematical clarity, and real-world application, avoiding the common extremes of abstract theory or mere data manipulation.
Structured in two distinct parts—first building essential quantitative tools (optimization, probability, statistics) before applying them to core finance topics—ensuring a solid foundation for empirical work.
Uses contemporary, relevant examples throughout, such as testing market anomalies, analyzing cryptocurrency returns, and conducting event studies on recent scandals.
Emphasizes a data-centric approach to validate or challenge economic reasoning, teaching students to treat finance as a dynamic, evidence-based discipline.
1Optimization
1.1Unconstrained Optimization
1.1.1Existence of a Minimum
1.1.2Necessary Conditions for a Minimum
1.1.3How Do We Find the Minimum of a General Function?
1.1.4Multivariate Optimization
1.2Constrained Optimization
1.3Key Points
1.4Exercises
2Probability
2.1Continuous Versus Discrete Random Variables
2.2Conditional Probability and Independence
2.3Bayes Rule
2.3.1Biased Coin
2.3.2Sequential Trading Model
2.4Games of Chance
2.5Horse Racing
2.5.1Implied Probability
2.6Key Points
2.7Exercises
3Statistics Review
3.1Hypothesis Testing and Confidence Intervals
3.2Correlation and Linear Regression
3.3Multiple Regression
3.4Survivorship Bias or Sample Selection
3.5Time Series
3.6Key Points
vi. IIApplications in Finance
4Economic Behavior Toward Time and Uncertainty
4.1The Time Value of Money
4.2Attitudes to Risk or Uncertainty
4.3The Birth of Utility Theory
4.4Key Points
4.5Exercises
5Financial Markets
5.1Fixed Income Markets
5.1.1Money Market
5.1.2Bond Market
5.2Yield Curve and Discount Function
5.2.1Time Series Variation in Yield Curve
5.3Stock Market
5.3.1Returns
5.3.2Compounding
5.3.3Converting Returns Over Different Horizons
5.3.4Statistical Properties of Daily Returns
5.4Other Asset Classes
5.4.1Commodities
5.4.2Foreign Exchange
5.4.3Cryptocurrencies
5.4.4UK House Prices
5.5Key Points
5.6Exercises
6Portfolio Choice
6.1Mean Variance Analysis
6.1.1One Risky and One Riskless Asset
6.1.2Portfolio of Two Risky Assets
6.1.3Portfolio of Three Risky Assets
6.1.4The General Case
6.1.5The General Case with Riskless Asset
6.1.6Practical Aspects
6.2Performance Measurement
6.3Key Points
6.4Exercises
7Efficient Markets Hypothesis
7.1Alternative Views of the Stock Market
7.1.1Efficient Markets Hypothesis
7.1.2The Random Walk Hypothesis
7.1.3Technical Analysts and Momentum Traders
7.1.4Fundamental Analysis
vii. 7.1.5Active Versus Passive Portfolio Management
7.2Key Points
7.3Exercises
8Testing of EMH Based on Autocorrelations
8.1Testing of EMH
8.2How to Compare Markets According to Their Deviations from This Theory?
8.3Simple Trading Strategy for SSEC
8.4Is Predictability Stable Over Time?
8.5Market Anomalies
8.5.1Econometrics of Calendar Effects
8.6Conclusion
8.7Key Points
8.8Exercises
9Event Studies
9.1Financial Applications
9.2Seven Steps in an Event Study
9.2.1Event Definition and Selection Criteria
9.2.2Null and Alternative Hypothesis
9.2.3Normal Return Definition
9.2.4Measuring Abnormal Return
9.2.5Volkswagen Emissions Scandal
9.2.6Aggregating Abnormal Returns for Statistical Power and Absence of Normality
9.3Stock Splits: Why Do Firms Split Their Stocks?
9.3.1Some Facts
9.3.2Literature
9.3.3Dow Splits
9.3.4Exxon Splits
9.4Extensions to the Methodology
9.4.1Matching Methodology
9.4.2Buy and Hold
9.4.3Market Wide Events
9.4.4Abnormal Trading Volume
9.4.5Exchange Rate Event Study
9.4.6Regression Approach
9.5Conclusion
9.6Key Points
10Equity Premium
10.1The Risk Free Rate
10.1.1Long Horizon Return on Fixed Income Investments
10.1.2Time Series Behavior of Daily Risk Free Rate
viii. 10.2Equity Premium
10.2.1Explanations for the Equity Premium Puzzle
10.2.2Statistical Illusion
10.2.3Does the US Risk Premium Vary Over Time?
10.2.4Conclusions
10.3Key Points
10.4Exercises
11Fundamentals Versus Bubbles
11.1Present Value Relations
11.1.1The Gordon Growth Model Special Case
11.1.2Comparative Statics of Gordon Growth Model
11.1.3Shiller Variance Bounds Tests
11.1.4Predictability of Long Horizon Returns
11.2Key Points
11.3Exercises
12Volatility and Risk
12.1What is Volatility?
12.2Why Care About Volatility?
12.3Rolling Window or Intra-period Volatility
12.4Volatility Measures Using Only Open, Close, High, and Low
12.5Implied Volatility from Option Prices
12.6Time Series GARCH Models
12.7Applications in Event Studies
12.8Risk Management
12.9Other Risk Measures
12.9.1Semivariance
12.10 Key Points
13Complements
13.1What is Research?
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