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Synchronous social media and the stock market
The impact of margin requirements on voluntary clearing decisions
The volatility of stock investor returns
Understanding the impacts of dark pools on price discovery
Arbitrage opportunities and efficiency tests in crypto derivatives
We test the joint efficiency of the bitcoin and ether options and perpetual futures markets and identify the determinants of arbitrage opportunities. Our novel fiat-currency-free put–call parity relationship motivates new arbitrage tests for options-only and option–perpetual cross-markets. Bitcoin and ether derivatives markets are becoming more efficient, especially for options of maturity ≥ 15 days. Bitcoin derivative markets are generally more efficient than ether derivative markets, but arbitrage strategies can still be highly profitable even under conservative transaction cost scenarios, which include slippage for large orders, especially during periods of high trading volumes or when the blockchain traffic becomes more congested.
Extreme illiquidity and cross-sectional corporate bond returns
Robinhood, Reddit, and the news: The impact of traditional and social media on retail investor trading
We study the impact of social media posts and news articles on retail trading on Robinhood by analyzing the net buying activity before and after a (social) media publication. Our findings indicate that both publication types lead to an increase of holders. However, both effects are short-lived, and the effect of social media posts on retail trading is significantly higher. Furthermore, investors buy stocks that are recommended on Reddit regardless of text sentiment. However, they act as contrarians by selling stocks with positive news coverage and vice versa. We thus find evidence for both attention- and information-driven trading behavior.
Financial congestion
Algorithmic trading and market efficiency around the introduction of the NYSE Hybrid Market
I study the effect of algorithmic trading on market efficiency, taking into account past market and limit order flows alike. I find that an exogenous increase in algorithmic trading around the introduction of the NYSE Hybrid Market leads to a significant decrease in the predictive power of surprises in market order imbalance and limit order book imbalances, especially at the outer levels of the limit order book. However, the predictive power of past returns remains largely unchanged. This suggests that algorithmic trading improves market efficiency by facilitating the incorporation of information embedded in both market and limit order flows.