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We study how the Eurosystem Collateral Framework for corporate bonds helps the European Central Bank (ECB) fulfill its policy mandate. Using the ECBs eligibility list, we identify the first inclusion date of both bonds and issuers. We find that due to the increased supply and demand for pledgeable collateral following eligibility, (i) securities lending market trading activity increases, (ii) eligible bonds have lower yields, and (iii) the liquidity of newly-issued bonds declines, whereas the liquidity of older bonds is una↵ected/improves. Corporate bond lending relaxes the constraint of limited collateral supply, thereby making the market more cohesive and complete. Following eligibility, bond-issuing firms reduce bank debt and expand corporate bond issuance, thus increasing overall debt size and extending maturity.
We study how the Eurosystem Collateral Framework for corporate bonds helps the European Central Bank (ECB) fulfill its policy mandate. Using the ECBs eligibility list, we identify the first inclusion date of both bonds and issuers. We find that due to the increased supply and demand for pledgeable collateral following eligibility, (i) securities lending market trading activity increases, (ii) eligible bonds have lower yields, and (iii) the liquidity of newly-issued bonds declines, whereas the liquidity of older bonds is unaffected/improves. Corporate bond lending relaxes the constraint of limited collateral supply, thereby making the market more cohesive and complete. Following eligibility, bond-issuing firms reduce bank debt and expand corporate bond issuance, thus increasing overall debt size and extending maturity.
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 study the many implications of the Eurosystem collateral framework for corporate bonds. Using data on the evolving collateral eligibility list, we identify the first inclusion dates of bonds and issuers and use these events to find that the increased supply and demand for pledgeable collateral following eligibility (a) increases activity in the corporate securities lending market, (b) lowers eligible bond yields, and (c) affects bond liquidity. Thus, corporate bond lending relaxes the constraint of limited collateral supply and thereby improves market functioning.