Settling the Size Matter

Equity factors are not as straightforward as they may seem to be. There is an ongoing debate about their usability or expected return since they have a cyclical nature. Moreover, the modern trend of smart beta only fuels this debate. Novel research by Blitz and Hanauer examines the size factor and sheds some light on this elusive anomaly. The size seems to be weak as a stand-alone factor, but it’s far from useless. The academic paper suggests that the size factor can be an important addition to the other equity factors as it helps to unlock the full potential of the quality, value or momentum factors.

Authors: Blitz, David and Hanauer, Matthias Xaver

Title: Settling the Size Matter

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ETF Liquidity

Exchange-traded funds (ETFs) have become popular and important investment vehicles in the financial markets. However, that is not a shock given the numerous benefits connected with ETFs. Naturally, they have caught the interest of academics, and there is plenty of literature about strategies on ETFs. While the profits and trading strategies are probably the most important research topics for practitioners, liquidity in the financial markets is almost equally important. Concerning liquidity in the ETFs, novel research by Pham et al. shows when exactly are ETFs the most liquid. Looking on the spreads, they are the lowest at market close. Such a finding can be an essential part of an optimal trading position making, where the aim is to minimize the trading costs.

Authors: Pham, Son Duy and Marshall, Ben R. and Nguyen, Nhut (Nick) Hoang and Visaltanachoti, Nuttawat 

Title: Predicting ETF Liquidity

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Quantpedia in August 2020

Once again, welcome to our summary of Quantpedia’s research. Ten new Quantpedia Premium strategies have been added into our database, and eleven new related research papers have been included in existing Premium strategies during last month.

Additionally, we have produced 15 new backtests written in QuantConnect code. Our database currently contains over 340 strategies with out-of-sample backtests/codes.

Also, four new blog posts, that you may find interesting, have been published on our Quantpedia blog:

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