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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Developing new Merger Arbitrage Strategy using Google Trends Dataset |
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| Language |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 40 |
| Date | 2020 |
| Abstract Text | In this thesis the forecasting ability of a merger event is measured by using search frequency from the Google Trends database as a proxy for investor attention. Former papers have already proved that this publicly available dataset provides the opportunity to detect economic and social trends by analyzing the searching patterns of internet users on the Google platform. Finance related research in this field has expanded as well since the data became accessible in 2004, but the investigation of a merger event in combination with Google Trends remains almost unexplored. Thus, I strived to take the analysis of this dataset to the next level by following a novel anticipation methodology. The sample of the study covers mergers of US targets from 2010 to 2019 with a transaction value of at least 10 billion USD of a total of 103 target firms and 82 acquirer firms. From each individual merger a 60-day data period prior to the announcement is assessed and combined with the other mergers to mean values. It will be analyzed if target names (T) and acquirer names (A) had been searched on Google on a higher frequency than usual. Furthermore, the same process is done for the combination of target and acquirer names in a single inquiry (TA) and a combination of the target or acquirer name with the keyword "Merger" (TM/AM). The results of the daily internet inquiry have shown a continuous abnormal rise in the searching requests for the names of target (T) and acquirer (A) and the combination (TA) starting one to five days prior to the merger announcement. In addition, higher volatility of the searching intensity is detected particularly for the searching term (TA). Furthermore, the correlation between the search intensity of target and acquirer firm names increase significantly eight days prior to the announcement. In the same manner, the comparison of the search volume of the companies with its traded stock volume on the same day illustrates a higher level of correlation. After sorting out specific data points for the robustness tests, the outputs did not change enormously, which supports the previous findings. Moreover, I have analyzed the search volume data with a comparative setting of 115 benchmarking mergers in the same period. It generates big differences to the original sample in terms of abnormal search volume and correlation coefficient movements. Consequently, when the searching intensity differs extensively between real merger companies and other non-involved companies of the same industry sector, it gives evidence of an unconvential searching behavior for the real merger-involved companies prior to this event. As the final step, I examined a merger arbitrage strategy approach that includes the trading instrument Bollinger Bands. By diverting it from its intended use, the Bollinger Bands are applied as a trigger signal for the correlation coefficient between searching volumes and trading volumes of target and acquirer firms. With this simplified method, it is possible to anticipate a merger seven days prior to the announcement and profit from abnormal return. The findings are very promising, but they have to be examined critically hereafter. Small searching volumes for specific individual target and acquirer firms are a problematic issue which may distort the mean values of the evaluation. But overall, the continuous upward movements of diverse calculation methods prior to the announcement shed light on a potential of this public database to benefit from a merger arbitrage strategy. It is particularly a novelty that acquirer data (A) and the combination of target and acquirer names (TA) are involved in the analysis. In earlier studies only target data (T) with the given dataset was assessed prior to a merger. In conclusion, the study represents the basis for further research in this field of digital merger traces through Google Trends. |
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