In a game of poker, it is usually said that when you do not know who the patsy is, you’re the patsy. The world of finance is not different. It is good to know who your counterparties are and which investors/traders drive the return of anomalies you focus on. We discussed that a few months ago in a short blog article called “Which Investors Drive Factor Returns?“. Different sets of investors and their approaches drive different anomalies, and we have one more paper that helps uncover the motivation of investors and traders for trading and their impact on anomaly returns.

We continue our short series of articles dedicated to the exploration of trading strategies that derive their functionality from the deep understanding of how Exchange Trading Funds (ETFs) work. In our first post, we discussed how we could use the ETF flows to predict subsequent daily ETF performance. In today’s article, we will analyze how we can use the information about the sensitivity of individual stocks to the ETF arbitrage activity to build a profitable equity factor trading strategy.

Exchange-traded funds (ETFs) are incredibly versatile investment vehicles. They have become more popular in recent years as investors have grown more comfortable with passive investing strategies. But ETFs can be very useful also in active trading strategies, as they can be used to gain exposure to specific markets, sectors, or themes. But when you invest in ETFs or trade them regularly; it’s really good to look under the hood and learn some tricks where to obtain a new source of alpha. And one such possible source or information advantage may be the possibility of analyzing the ETF flows data …

In recent years, financial markets have experienced a boom in passive and index-based strategies, which could have caused a change in the trading volume, volatility, beta or correlations. The reason is straightforward: the index investing causes a lot of stocks to move in the same direction. A novel research Shen and Shi (2020), using high-frequency data, suggests that over the last two decades, the patterns mentioned above have changed and the index investing is the cause. Both the trading volume and stock correlations are increased at the end of trading sessions. Betas are firstly dispersed, but in general, converge to one during the rest of the day. Trading volume has high dispersion at the market open, but low dispersion at the market close. Overall, the paper has many important implications for portfolio managers, risk managers and traders as well since it is closely related to the transaction costs, intraday price fluctuations, correlations or liquidity. Moreover, it is full of exciting charts that are worth seeing.

The pandemic of COVID-19 brought many changes for the whole humanity. The financial markets where no exception, but the trading has continued. Nowadays, the order can be placed from anywhere around the world and almost all stock exchanges are electronic and algorithmic. However, there is still one exchange where the floor trading exists – NYSE. During these tough times, NYSE was also purely electronic, the floor trading was closed, and human interaction was not possible. A novel study by Brogaard, Ringgenberg and Roesch examines the role of floor traders in the recent era driven by computers. The conclusion is clear: in the current digital age, floor traders still matter.

Authors: Jonathan Brogaard, Matthew C. Ringgenberg, Dominik Roesch

Title: Does Floor Trading Matter?