Suchergebnisse

This doctoral dissertation consists of three chapters on the pricing of sovereign debt and inflation-linked products. The first chapter examines the relative pricing of nominal and inflation-linked debt of the three largest Eurozone sovereign issuers. Its main contribution is to present evidence of a selective default premium in real bond yields. The second chapter shifts its focus to the US inflation-linked product markets and quantifies liquidity premium in TIPS and inflation swap rates. The size of this compensation for exposure to asset level and liquidity risk helps to explain a large part of the TIPS-Treasury puzzle. The third chapter studies whether nominal bond markets are segmented across different maturities and contributes to the policy discussion on long term discount rates of the Solvency II Directive of the European Union.

We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump's tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump's decision to tweet about the economy.

We propose the "President reacts to news" channel of stock returns by studying the financial market impact of the Twitter account of the 45th president of the United States, Donald Trump. We use machine learning algorithms to classify topic and textual sentiment of 1,400 economy-related tweets to investigate whether they contain relevant information for financial markets. Analyzing high-frequency data, we find that after controlling for past market movements, most tweets are reactive and predictable, rather than novel and informative. The exceptions are tweet topics where the president has direct policy authority and his negative sentiment could adversely a↵ect economic outcomes. ; September 27, 2021