Today there are dozens of papers existing which investigate the relationship between macroeconomic variables such as GDP growth, exchange rates, inflation, etc. and the 4 factors used in the Carhart 4-factor model. However, most of the papers select corresponding control variables a priori and might miss some macroeconomic variables which hold much information about one of the factors. Overcoming this problem constitutes the core of this paper. With a three tiered statistical procedure which comprises the use of clustering and LASSO regressions I am aiming at solving that challenge. I start with more than 300 macroeconomic control variables which proxy for all possible variables out there and select those with the highest explanatory power.
Table of Contents
1. Introduction
2. Literature Review
2.a. Potential pitfalls
2.b. Results and conclusions
3. Empirical analysis
3.a. Data
3.b. Variable grouping
3.c. Variable selection
3.d. Regression
4. Conclusion
Research Objectives and Themes
This thesis investigates the relationship between microeconomic factors of the Carhart 4-factor model (size, value, and momentum) and a comprehensive set of macroeconomic variables for the German equity market from 2006 to 2015, aiming to determine whether these factors act as proxies for macroeconomic risks.
- Application of prototype clustering to mitigate high intercorrelations among macroeconomic variables.
- Utilization of LASSO regression for effective variable selection in high-dimensional datasets.
- Execution of OLS estimations to evaluate the predictive power of macroeconomic variables on factor loadings.
- Critical examination of the stability and explanatory power of microeconomic factor models in dynamic market environments.
Excerpt from the Book
3. Empirical analysis
Currently there exist various research institutions offering free of charge time series data of the size, value and momentum factor for the German capital market. The individual data sets differ especially in respect to the timespan covered and some of them do not include the momentum factor. The explanatory power, however, of the factor loadings when compared over time might be low as accounting standards varied over the last decades. In Germany, most of financial statements published before 2005 were based on German Law (HGB) whereas after 2005 they were grounded on international financial reporting standards (IFRS) (Brückner et al., 2015). As HGB aims at protecting investors and IFRS tries to provide the investor with a true and fair view of financial performance, the accounts of a company might look very unequal when regarded through the lens of diverse accounting systems. Differences in asset valuations might, for instance, occur in the fields of hedge accounting, pension commitments and software development, which affect the profit and loss statement as well as the balance sheet. To overcome the potential risk of using, ceteris paribus, varying asset prices due to the use of diverse accounting standards I decided to use time series data from 2006 onwards. As Hanauer et al. (2013) provide time series data, which cover the longest period after 2005 including the momentum factor, I chose this data set to represent microeconomic factor loadings in my empirical analysis (the data set reaches from 2006 to 2015).
Summary of Chapters
1. Introduction: Outlines the goal of the study, which is to examine if microeconomic factors act as proxies for macroeconomic risks using a novel statistical approach.
2. Literature Review: Provides a conspectus of potential statistical pitfalls in linkage studies and reviews prior empirical findings regarding the relationship between factor models and macroeconomic indicators.
3. Empirical analysis: Details the methodology for data preparation, the clustering of macroeconomic variables, the LASSO-based selection process, and the subsequent OLS regression analysis.
4. Conclusion: Summarizes the key finding that while the developed model outperforms prior research in terms of adjusted R-squared, the overall explanatory power remains limited, suggesting that current statistical approaches may be too static.
Keywords
Carhart 4-factor model, German equity market, macroeconomic variables, prototype clustering, LASSO, OLS regression, size effect, value effect, momentum effect, financial distress, variable selection, prediction accuracy, risk premia, factor loadings, time-varying dependencies.
Frequently Asked Questions
What is the fundamental purpose of this research?
The research aims to reinvestigate whether microeconomic factors in the Carhart 4-factor model serve as proxies for macroeconomic risks in the German equity market between 2006 and 2015.
Which factors are analyzed in this study?
The study focuses on the three prominent microeconomic anomalies: size, value, and the momentum factor.
What is the primary research goal?
The primary goal is to address the limitations of existing research by employing a comprehensive set of 383 macroeconomic variables to explain factor performance.
Which statistical methods are employed?
The author uses a three-tiered approach: prototype clustering for variable grouping, LASSO for variable selection, and OLS regression for final estimation.
What does the empirical analysis cover?
It covers the selection of time series data, the mathematical transformation of variables, the clustering algorithm to handle intercorrelations, and the regression model comparisons.
Which keywords characterize the work?
Key terms include Carhart 4-factor model, German equity market, LASSO, prototype clustering, and macroeconomic risk factors.
Why is the German market chosen for this specific period?
The choice is motivated by the availability of high-quality data following the transition to IFRS accounting standards after 2005, which ensures comparability.
How does the author handle the problem of highly intercorrelated macroeconomic variables?
The author utilizes prototype clustering with minimax linkage to group variables and select the most central representative of each cluster.
What conclusion does the author draw regarding the explanatory power of these factors?
The author concludes that microeconomic factors only partially proxy for macroeconomic risks and suggests that a more dynamic statistical modeling approach is necessary.
- Citar trabajo
- Marwin Zimmermann (Autor), 2018, The effect of macroeconomic variables on the size, value and momentum factor in Germany, Múnich, GRIN Verlag, https://www.grin.com/document/449782
