The focus of this bachelor thesis is the equity market of the Netherlands. The Amsterdam Stock Exchange is one of the oldest or even the oldest stock exchange of the world. Several interesting companies like Adyen (fintech company) and ASML (semiconductor company) are listed at the Netherlands market. However, this thesis is not about predicting individual stock returns, but about predicting the Netherlands stock market in general, and therefore, a broad stock index (the Netherlands-Datastream Market) is investigated, that contains (nearly) every stock of the Netherlands.
Equity Market Prediction is an quite interesting topic for investment bankers and the academia. It plays an important role in topics like asset allocation, asset pricing, risk management and capital budgeting. Being able to predict the capital markets would result in a huge gain for investors. Even companies may benefit from equity market prediction, because they could time the market by deciding for example the optimal time of an initial public offering (IPO) or pricing this IPO correctly without leaving money on the table. Therefore, this bachelor thesis examines different predictor variables, that are grouped into market valuation, trend, sentiment, and macroeconomic (macro) variables.
Predictor variables are variables that are said to be able to predict the equity market. To test the predictability of these predictors this thesis runs several in-sample and out-of-sample prediction trials with a defined regression framework. In-sample, both univariate as well as multivariate regressions are carried out. Out-of-sample, the predictive power of each predictor is tested stand-alone and compared to a simple benchmark model. In the end a trading strategy resulting from these return predictions may be evaluated.
Table of Contents
1 Introduction
2 Literature review and variable motivation
2.1 Literature review
2.2 Variable motivation
3 Data and summary statistics
3.1 Data
3.2 Summary Statistics
4 Methodology
4.1 Predictive regression framework (In-sample)
4.2 Out-of-sample methodology
4.2.1 Econometric specification
4.2.2 Forecast evaluation
5 Empirical Analysis
5.1 In-sample return prediction
5.1.1 Univariate regression results
5.1.1.1 General analysis
5.1.1.2 Regression results with market valuation variables
5.1.1.3 Regression results with trend variables
5.1.1.4 Regression results with the sentiment variable
5.1.1.5 Regression results with macro variables
5.1.2 Multivariate regression results
5.1.2.1 Bivariate regression results with market valuation variables
5.1.2.2 Bivariate regression results with trend variables
5.1.2.3 Bivariate regression results with sentiment variables
5.1.2.4 Bivariate regression results with macro variables
5.2 Out-of-sample return prediction
6 Conclusion
Research Objectives and Themes
This thesis examines the predictability of the Netherlands equity market risk premium by utilizing a diverse set of predictor variables grouped into market valuation, trend, sentiment, and macroeconomic categories. The primary research goal is to determine which of these variables demonstrate significant forecasting power for Netherlands stock market returns in both in-sample and out-of-sample prediction trials.
- Investigation of various stock market return predictors outside the US context.
- Application of a rigorous regression framework for in-sample and out-of-sample forecasting.
- Analysis of the book-to-market ratio (BMR) as a primary valuation-based predictor.
- Evaluation of trend-following, sentiment (VIX), and macroeconomic variables on equity market performance.
Excerpt from the Thesis
5.1.1.2 Regression results with market valuation variables
In this section the regression results of the five market valuation variables are explained in more detail. First of all, one has to mention that the BMR predicts already 1% of total variance in the one-month horizon and total variance explained increases rapidly over the forecasting horizon. Finally in the four-year horizon the BMR even predicts 35% of total variation. These values are among the highest adj. R2 values seen in the overall academic literature that focuses on this predictive regression framework. Especially, the results for the four-year horizon are remarkable and also at the 12-month horizon the adj. R2 is already greater than 10%. Even the EICC variable, that performed best, with respect to adj. R2 values, in the examination of the US market by Li et al. (2013)102, doesn´t outperform the BMR in the case of the Netherlands. On the other hand, only the 48-month slope coefficient of the BMR is significant at the 5% significance level, in contrary to the EICC of Li et al (2013)103 that showed significant coefficients at multiple horizons. Moreover, in contrary to Li et al. (2013) the EICC of the Netherlands equity market performs quite worse in the examined sample, and the BMR as well as other predictors, like the DY, outperform the EICC.
Summary of Chapters
1 Introduction: Provides the motivation for equity market prediction and outlines the thesis structure regarding the Netherlands market.
2 Literature review and variable motivation: Summarizes existing research on stock return predictability and explains the theoretical basis for the selected predictor variables.
3 Data and summary statistics: Details the data aggregation process for the Netherlands-Datastream Market and presents descriptive statistics for the variables used.
4 Methodology: Outlines the in-sample predictive regression framework and the out-of-sample econometric specification used to evaluate forecast performance.
5 Empirical Analysis: Presents and discusses the regression results, evaluating both univariate and multivariate in-sample performance as well as out-of-sample predictive power.
6 Conclusion: Summarizes the key findings, assesses the predictive performance of the BMR, and offers suggestions for future research and trading strategies.
Keywords
Equity market prediction, Netherlands, stock returns, predictability, book-to-market ratio, BMR, dividend yield, regression analysis, out-of-sample, implied cost of capital, trend variables, momentum, sentiment, volatility index, macroeconomics.
Frequently Asked Questions
What is the core focus of this thesis?
The research investigates the predictability of the Netherlands equity market risk premium, contributing to the global debate by focusing on a non-US capital market.
Which predictor categories are examined?
The study evaluates four primary categories: market valuation (e.g., BMR, DY), trend variables (moving averages, momentum), sentiment (volatility index), and macroeconomic factors (inflation, interest rates).
What is the primary objective of this study?
The main goal is to test whether these predictors can successfully forecast future excess returns in the Netherlands stock market using both in-sample and out-of-sample regression methodologies.
Which scientific method is applied?
The study utilizes a standard predictive regression framework based on Li et al. (2013) and Fama and French (1988, 1989), employing both univariate and multivariate (bivariate) regressions alongside out-of-sample forecast evaluations.
What does the main part of the thesis cover?
The main part includes the detailed data construction and descriptive statistics in Chapter 3, the methodological setup in Chapter 4, and a thorough empirical analysis of the regression results in Chapter 5.
Which keywords best describe this work?
Key terms include Equity Market Prediction, Netherlands, Stock Returns, Book-to-market ratio (BMR), Predictive regression, and Out-of-sample analysis.
How does the book-to-market ratio (BMR) perform compared to other variables?
The BMR proved to be the most robust univariate predictor for the Netherlands market, displaying significant predictive power that increases over longer forecasting horizons, unlike many other variables which were less effective.
Why did the author use the volatility index (VIX)?
The AEX VIX was used as a proxy for market sentiment, specifically as a "fear gauge," to determine if volatility contains predictive information relevant to future excess returns in the Netherlands market.
Does the thesis support the Efficient Market Hypothesis?
The thesis provides evidence of predictability in the Netherlands market, suggesting that the equity market may exhibit inefficiencies that can be exploited by certain models, particularly when using the BMR.
- Citation du texte
- Nico Horstmann (Auteur), 2021, Equity Market Prediction. Evidence from Netherlands, Munich, GRIN Verlag, https://www.grin.com/document/1128787
