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With this paper, I propose a simple asset pricing model that accounts for the influence from social interaction. Investors are assumed to make up their mind about an asset’s price based on a forecasting strategy and its past profitability as well as on the contemporaneous expectations of other market participants. Empirically analysing stocks of the DAX30 index, I provide evidence that social interaction rather destabilises financial markets. Based on my results, I state that at least, it does not have a stabilising effect.
An analyst who works in Germany is more likely to publish a high (low) price target regarding a DAX30 stock if other Germany based analysts are also optimistic (pessimistic) about the same stock. This finding is not biased by the fact that DAX30 companies are headquartered in Germany. In times of bull markets, price targets of analysts who regularly exchange their opinion are higher correlated compared to other analysts. This effect vanishes in a bearish market environment. This suggests that communication among analysts indeed plays an important role. However, analysts’ incentives induce them not to deviate too much from the overall average during an economic downturn.
In this paper, I analyse the reciprocal social influence on investment decisions within an international group of roughly 2,000 mutual fund managers who invested in companies in the DAX30. Using a robust estimation procedure, I provide empirical evidence that the average fund manager puts 0.69% more portfolio weight on a particular stock, if his peers on average assign a weight to the corresponding position which is 1% higher compared to other stocks in the portfolio. The dynamics of this influence on the choice of portfolio weights suggest that fund managers adjust their behaviour according to the prevailing market situation and are more strongly influenced by others in times of an economic downturn. Analysing the working locations of the fund managers, I conclude that more than 90% of the magnitude of influence stems from the social learning. While this form of influence varies much over time, the magnitude of influence resulting from the exchange of opinion is more or less constant.
With this paper, I propose a simple asset pricing model that accounts for the influence from social interaction. Investors are assumed to make up their mind about an asset's price based on a forecasting strategy and its past profitability as well as on the contemporaneous expectations of other market participants. Empirically analysing stocks in the DAX30 index, I provide evidence that social interaction rather destabilises financial markets. At least, it does not have a stabilising effect.
An analyst who works in Germany is more likely to publish a high (low) price target regarding a DAX30 stock if other Germany based analysts are also optimistic (pessimistic) about the same stock. This finding is not biased by the fact that DAX30 companies are headquartered in Germany. In times of bull markets, price targets of analysts who regularly exchange their opinion are higher correlated compared to other analysts. This effect vanishes in a bearish market environment. This suggests that communication among analysts indeed plays an important role. However, analysts’ incentives induce them not to deviate too much from the overall average during an economic downturn.
In this paper, I analyse the reciprocal social influence on investment decisions within an international group of roughly 2000 mutual fund managers that invested in companies of the DAX30. Using a robust estimation procedure, I provide empirical evidence that in the average a fund manager puts 0.69% more portfolio weight on a particular stock, if other fund managers increase the corresponding position by 1%. The dynamics of this influence on portfolio weights suggest that fund managers adjust their behaviour according to the prevailing market situation and are more strongly influenced by others in times of an economic downturn. Analysing the working locations of the fund managers, I conclude that more than 90% of the magnitude of influence is due to pure observation. While this form of influence varies much in time, the magnitude of influence resulting from the exchange of opinion is more or less constant.
Ziel meiner Dissertation ist die empirische Analyse von Auswirkungen der sozialen Interaktion zwischen Akteuren auf Finanzmärkten. Die folgenden Aufsätze sind Bestandteil dieser kumulativen Dissertation:
1. Frederik König (2012): Does Social Interaction destabilise Financial Markets?
2. Frederik König (2012) : Analyst Behaviour: the Geography of Social Interaction
3. Frederik König (2012) : Fluctuations of Social Influence: Evidence from the Behaviour of Mutual Fund Managers during the Economic Crisis 2008/09
In meinem ersten Aufsatz stelle ich ein Marktpreismodell vor, welches dem Einfluss durch soziale Interaktion Rechnung trägt. Mit Hilfe dieses Modells gehe ich der Fragestellung nach, ob soziale Interaktion zwischen Marktteilnehmern eine stabilisierende oder eine destabilisierende Wirkung auf Finanzmärkte hat. Mit meinem zweiten Aufsatz untersuche ich das Verhalten von Aktienanalysten, die als wesentlicher Impulsgeber für Finanzmärkte gelten. Konkret stelle ich heraus, ob Analysten stärker von anderen Analysten beeinflusst werden, wenn diese im gleichen Land bzw. in der gleichen Stadt arbeiten oder wenn sogar ein regelmäßiger Meinungsaustausch erfolgt. Beides setzte ich ins Verhältnis zum vorherrschenden Marktumfeld. In meinem dritten Aufsatz beschäftige ich mich mit der sozialen Interaktion zwischen Fondsmanagern. Diese verwalten in etwa ein Drittel des frei handelbaren Aktienvermögens und haben folglich einen nennenswerten Einfluss auf Finanzmärkte. Mit Hilfe einer neuartigen Schätzmethode bestimme ich die Größe des sozialen Einflusses und untersuche auch hier temporale Variationen im Verhältnis zum zu Grunde liegenden Marktumfeld. Des Weiteren zerlege ich die Gesamtgröße des sozialen Einflusses in zwei Komponenten, die zum einen den Einfluss im Rahmen der reinen Beobachtung und zum anderen den Einfluss durch Kommunikation reflektieren.
Ziele: Das Ziel dieser offiziellen Leitlinie, die von der Deutschen Gesellschaft für Gynäkologie und Geburtshilfe (DGGG) und der Deutschen Krebsgesellschaft (DKG) publiziert und koordiniert wurde, ist es, die Früherkennung, Diagnostik, Therapie und Nachsorge des Mammakarzinoms zu optimieren.
Methoden: Der Aktualisierungsprozess der S3-Leitlinie aus 2012 basierte zum einen auf der Adaptation identifizierter Quellleitlinien und zum anderen auf Evidenzübersichten, die nach Entwicklung von PICO-(Patients/Interventions/Control/Outcome-)Fragen, systematischer Recherche in Literaturdatenbanken sowie Selektion und Bewertung der gefundenen Literatur angefertigt wurden. In den interdisziplinären Arbeitsgruppen wurden auf dieser Grundlage Vorschläge für Empfehlungen und Statements erarbeitet, die im Rahmen von strukturierten Konsensusverfahren modifiziert und graduiert wurden.
Empfehlungen: Der Teil 1 dieser Kurzversion der Leitlinie zeigt Empfehlungen zur Früherkennung, Diagnostik und Nachsorge des Mammakarzinoms: Der Stellenwert des Mammografie-Screenings wird in der aktualisierten Leitlinienversion bestätigt und bildet damit die Grundlage der Früherkennung. Neben den konventionellen Methoden der Karzinomdiagnostik wird die Computertomografie (CT) zum Staging bei höherem Rückfallrisiko empfohlen. Die Nachsorgekonzepte beinhalten Untersuchungsintervalle für die körperliche Untersuchung, Ultraschall und Mammografie, während weiterführende Gerätediagnostik und Tumormarkerbestimmungen bei der metastasierten Erkrankung Anwendung finden.
Purpose: The aim of this official guideline coordinated and published by the German Society for Gynecology and Obstetrics (DGGG) and the German Cancer Society (DKG) was to optimize the screening, diagnosis, therapy and follow-up care of breast cancer.
Methods: The process of updating the S3 guideline dating from 2012 was based on the adaptation of identified source guidelines which were combined with reviews of evidence compiled using PICO (Patients/Interventions/Control/Outcome) questions and the results of a systematic search of literature databases and the selection and evaluation of the identified literature. The interdisciplinary working groups took the identified materials as their starting point to develop recommendations and statements which were modified and graded in a structured consensus procedure.
Recommendations: Part 1 of this short version of the guideline presents recommendations for the screening, diagnosis and follow-up care of breast cancer. The importance of mammography for screening is confirmed in this updated version of the guideline and forms the basis for all screening. In addition to the conventional methods used to diagnose breast cancer, computed tomography (CT) is recommended for staging in women with a higher risk of recurrence. The follow-up concept includes suggested intervals between physical, ultrasound and mammography examinations, additional high-tech diagnostic procedures, and the determination of tumor markers for the evaluation of metastatic disease.
Purpose: Molecular diagnostics including next generation gene sequencing are increasingly used to determine options for individualized therapies in brain tumor patients. We aimed to evaluate the decision-making process of molecular targeted therapies and analyze data on tolerability as well as signals for efficacy.
Methods: Via retrospective analysis, we identified primary brain tumor patients who were treated off-label with a targeted therapy at the University Hospital Frankfurt, Goethe University. We analyzed which types of molecular alterations were utilized to guide molecular off-label therapies and the diagnostic procedures for their assessment during the period from 2008 to 2021. Data on tolerability and outcomes were collected.
Results: 413 off-label therapies were identified with an increasing annual number for the interval after 2016. 37 interventions (9%) were targeted therapies based on molecular markers. Glioma and meningioma were the most frequent entities treated with molecular matched targeted therapies. Rare entities comprised e.g. medulloblastoma and papillary craniopharyngeoma. Molecular targeted approaches included checkpoint inhibitors, inhibitors of mTOR, FGFR, ALK, MET, ROS1, PIK3CA, CDK4/6, BRAF/MEK and PARP. Responses in the first follow-up MRI were partial response (13.5%), stable disease (29.7%) and progressive disease (46.0%). There were no new safety signals. Adverse events with fatal outcome (CTCAE grade 5) were not observed. Only, two patients discontinued treatment due to side effects. Median progression-free and overall survival were 9.1/18 months in patients with at least stable disease, and 1.8/3.6 months in those with progressive disease at the first follow-up MRI.
Conclusion: A broad range of actionable alterations was targeted with available molecular therapeutics.
However, efficacy was largely observed in entities with paradigmatic oncogenic drivers, in particular with BRAF mutations. Further research on biomarker-informed molecular matched therapies is urgently necessary.