Are stock returns predictable?

Table of Contents

A Introduction

B Theoretical Foundations

I. Capital Market Efficiency

II. Portfolio Management

III. Financial Analysts

C Equity Valuation

I. Technical Analysis

1. Dow Theory

2. Elliott Wave Theory

3. Chart Patterns

4. Indicators

II. Fundamental Analysis

1. Global Analysis

2. Industry Analysis

3. Company Analysis

D Empirical Study

I. Own Empirical Study

1. Introduction and Literature Review

2. Data

3. Test for Stationarity

4. Statistical Properties of the Data

II. Methodological Foundations of Time Series Analysis

1. ARMA Model

2. Estimation of ARMA Models

III. Methodological Foundations of Multivariate Regression Analysis

1. Model Formulation

2. Estimation and Testing of the Regression Function

3. Testing of the Regression Coefficients

IV. Empirical Results

E. Summary and Conclusion

Research Objectives and Core Topics

This thesis investigates the predictability of stock returns on the German stock market, aiming to determine whether empirical models can outperform historical averages or simple random walk expectations. The research addresses the tension between the efficient-market hypothesis and various analytical approaches used by market participants to forecast future developments.

• Theoretical foundations of capital market efficiency and portfolio management.
• Comprehensive analysis of technical and fundamental equity valuation methods.
• Methodological development of time series (ARMA) and multivariate regression models.
• Empirical evaluation of forecasting quality using in-sample and out-of-sample data for DAX constituents.

Excerpt from the Book

Summary of Chapters

A Introduction: Outlines the core challenge of predicting stock returns and introduces the fundamental theories and hypotheses governing capital market research.

B Theoretical Foundations: Explores the concepts of capital market efficiency, the structure of portfolio management, and the role of financial analysts.

C Equity Valuation: Details the methodologies behind technical and fundamental analysis used to evaluate stocks and derive potential price forecasts.

D Empirical Study: Presents the author's own research, covering the literature review, data processing, model methodologies, and the resulting performance analysis of the forecasting models.

E. Summary and Conclusion: Summarizes the thesis findings, stating that despite the extensive analytical effort, no model consistently outperformed simple benchmarks in predicting monthly returns.

Keywords

Stock returns, predictability, German stock market, DAX, capital market efficiency, random walk hypothesis, technical analysis, fundamental analysis, ARMA models, multivariate regression, forecasting quality, in-sample, out-of-sample, finance.

Frequently Asked Questions

What is the core focus of this Master's thesis?

The thesis investigates whether stock returns on the German stock market are predictable using historical economic data and various quantitative modeling techniques.

What are the primary analytical fields covered?

The study covers capital market theory, portfolio management, financial analysis, technical analysis (charts, indicators), and fundamental analysis (global, industry, and company analysis).

What is the central research goal?

The goal is to develop and test forecasting models, specifically ARMA models and multivariate regressions, to see if they can effectively predict monthly returns of DAX stocks.

Which methodologies are employed for the study?

The author uses time series analysis (ARMA models) to account for dependencies in returns and multivariate regression analysis to assess the influence of micro- and macroeconomic variables.

What does the main part of the thesis discuss?

The main part explains the theoretical frameworks, the setup of the empirical study including data transformation and stationarity testing, and the performance testing of different forecasting models.

Which keywords best describe this research?

The research is best characterized by terms such as stock return predictability, capital market efficiency, DAX constituents, time series analysis, and quantitative forecasting models.

Why did the author specifically choose BASF for the detailed analysis?

The author selected BASF because complete data sets were available, and the stock is considered representative of the broader market performance across DAX constituents.

What was the general conclusion regarding the predictability of returns?

The author concludes that none of the models achieved adequate forecasting quality, suggesting that short-term stock returns are largely influenced by incalculable factors, making consistent outperformance difficult.