The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the "Natural Language Toolkit" that uses machine...

We identify a component of monetary policy news that is extracted from high-frequency changes in risky asset prices. These surprises, which we call "risk shifts", are uncorrelated, and therefore complementary, to risk-free rate surprises. We show that (i) risk shifts capture the lion's share of...

This paper investigates the effect of the conventional and unconventional (e.g. Quantitative Easing - QE) monetary policy intervention on the insurance industry. We first analyze the impact on the stock performances of 166 (re)insurers from the last QE programme launched by the European Central...

Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the implemented techniques and the type of source on which...

We define a sentiment indicator based on option prices, valuation ratios and interest rates. The indicator can be interpreted as a lower bound on the expected growth in fundamentals that a rational investor would have to perceive in order to be happy to hold the market. The lower bound was...

How people form beliefs is crucial for understanding decision-making under uncertainty. This is particularly true in a situation such as a pandemic, where beliefs will affect behaviors that impact public health as well as the aggregate economy. We conduct two survey experiments to shed light on...

We study how stock price informativeness changes with the presence of highfrequency trading (HFT). Our estimate is based on the staggered start of HFT participation in a panel of international exchanges. With HFT presence market prices are a less reliable predictor of future cash ows and...

We show that High Frequency Traders (HFTs) are not beneficial to the stock market during flash crashes. They actually consume liquidity when it is most needed, even when they are rewarded by the exchange to provide immediacy. The behavior of HFTs exacerbate the transient price impact, unrelated...

We study the introduction of single-market liquidity provider incentives in fragmented securities markets. Specifically, we investigate whether fee rebates for liquidity providers enhance liquidity on the introducing market and thereby increase its competitiveness and market share. Further, we...

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...