Sovereign Wealth Funds (SWFs) have reached an enormous financial power and have tripled their assets under management to 7.4 trillion American dollars during the past ten years. So far, academia has focused on the most obvious characteristic of SWFs, the state ownership, and relating governance issues. This thesis answers the question of whether there are certain company characteristics, which are preferred by SWFs when investing, and which characteristics influence the ex-post probability of becoming a SWF target. For this purpose, the selected sample is compared to a set of Year, Industry, Size, and MTBV matched peers. First, systematic differences between targets and peers are identified using t-test and Wilcoxon rank sum test statistics. Second, the influence of variables on the ex-post probability of becoming a SWF target is analyzed using logistic panel regression models. The regression results are further discussed using odds ratios and marginal effects analysis. The main finding is that target companies are typically significantly larger than their matched peers, and the size of a company is identified to have the highest influence on the likelihood of becoming a target. This is accompanied by the finding that a higher proportion of held cash has a positive influence, and a higher book leverage ratio has a negative influence. Additionally, it is shown that there are no target characteristics, which would promote large or small-scale investments. As introduction into further research, it is analyzed how stock markets react on announced SWF investments. It is shown that excess returns of approximately one percent can be observed within a three-day event window.
1 Introduction
2 Introducing Sovereign Wealth Funds
2.1 The Rise of Sovereign Wealth Funds
2.2 Definition of “Sovereign Wealth Fund”
2.3 Funding Sources of SWFs
2.4 Scientific Discussion about SWFs
3 Data and Sample Overview
3.1 Data Sources and Sample Selection
3.1.1 Sovereign wealth fund transactions
3.1.2 Unique company identifiers
3.1.3 Financial information
3.2 Sample Description
3.2.1 Description of included observations
3.2.2 Description of included SWFs
4 Characteristics of SWF Targets
4.1 Identifying Matched Peers
4.2 Analysis of Target Characteristics
4.2.1 Comparison of targets with matched peers
4.2.2 The ex-post probability of getting targeted by SWFs
4.2.3 Target characteristics of large versus small-scale investments
5 Stock Market Reactions
6 Conclusion
Research Objectives and Core Themes
This thesis investigates the investment priorities of Sovereign Wealth Funds (SWFs) by examining whether specific company characteristics influence the ex-post probability of a firm becoming a target for SWF investment. The research aims to identify these determinants using a matched peer group approach and logistic panel regression models, while also providing initial insights into the stock market reaction to announced SWF investments.
- Analysis of systematic differences between SWF target companies and matched peers.
- Examination of firm-specific determinants (e.g., Size, Cash, Leverage) influencing target selection.
- Evaluation of whether large-scale and small-scale SWF investments exhibit distinct target characteristics.
- Investigation of short-term stock market reactions following the announcement of SWF investments.
Excerpt from the Book
4.2.1 Comparison of targets with matched peers
The first approach is to identify systematic differences between the targets and their matched peers. To achieve this, table 8 reports the difference of the mean values in column (4). It is negative, if the mean of the targets is smaller than the mean of the peer group. Further, t-statistics for the difference of the means, and the Wilcoxon signed rank statistics for the median difference is reported in column (5) to (9), together with the respective level of the statistical significance. The labeling of ***, ** and * indicate the significance at the 1%, 5% and 10% levels. The Wilcoxon statistic serves as a robustness check, since it is less influenced by extreme values and some variables display skewness or fat tails. Finally, the last five columns display the quintile distribution of targets within the matched sample.
Summary of Chapters
1 Introduction: Provides an overview of SWFs as globally significant financial players and outlines the thesis’s objective to identify target company characteristics.
2 Introducing Sovereign Wealth Funds: Discusses the history, growth, and definition of SWFs, while reviewing the existing scientific debate regarding their political influence and transparency.
3 Data and Sample Overview: Describes the construction of the transaction database, the selection criteria used, and the methodology for matching transactions with financial data.
4 Characteristics of SWF Targets: Analyzes the systematic differences between SWF targets and matched peers, and performs logistic regressions to determine the influence of company characteristics on targeting probability.
5 Stock Market Reactions: Presents an event study investigating the immediate impact of SWF investment announcements on stock prices in the US and UK markets.
6 Conclusion: Summarizes the key findings, including the high influence of firm size on target selection, and suggests avenues for future research.
Keywords
Sovereign Wealth Funds, SWF, Financial Markets, Investment Determinants, Logistic Panel Regression, Target Characteristics, Firm Size, Market Capitalization, Event Study, Abnormal Returns, Financial Transparency, Governance Issues, Equity Investments, Strategic Investments, Capital Markets
Frequently Asked Questions
What is the primary focus of this research?
The research focuses on identifying company characteristics that make a firm more likely to become a target for Sovereign Wealth Fund (SWF) investments.
What are the central themes discussed in the thesis?
Central themes include the role of SWFs in global capital markets, the drivers of SWF investment behavior, company-specific determinants of targeting, and the resulting stock market reactions to these investments.
What is the main research question of the work?
The work seeks to answer whether there are specific firm-level characteristics that SWFs prefer and that influence the ex-post probability of a company being targeted.
Which scientific methodology is employed?
The study utilizes a matched peer group approach for comparison, combined with logistic panel regression models and an event study methodology to analyze stock market reactions.
What subjects are covered in the main section?
The main section covers the identification of matched peer groups, analysis of target characteristics through statistical testing, logistic regression on targeting probability, and an evaluation of large-scale versus small-scale investments.
Which keywords best characterize this work?
Key terms include Sovereign Wealth Funds, investment determinants, firm size, matched peer analysis, and event studies regarding abnormal stock returns.
Why does the author use logistic panel regressions?
These models are used to identify the impact of independent variables (like size and cash) on the likelihood of a company becoming an SWF target, while allowing for adjustments through target country fixed effects.
What is the significance of the "Size" variable in this study?
Size is identified as the most significant determinant; larger companies are consistently more likely to be targeted by SWFs across all models tested.
Does the study find differences between small and large-scale SWF investments?
The analysis indicates that there are no observable systematic differences in the characteristics of companies targeted for large-scale versus small-scale investments within the examined sample.
What does the event study reveal about stock market reactions?
The event study reveals that SWF investment announcements are associated with positive abnormal returns of approximately one percent within a three-day event window.
- Quote paper
- Philipp Reinhold (Author), 2018, Characteristics of Sovereign Wealth Fund Targets, Munich, GRIN Verlag, https://www.grin.com/document/454690
