Takeover Factor Explains the Size Effect

18.July 2022

The size effect assumes a negative relationship between average stock returns and firm size. In other words, it states that low capitalization stocks outperform stocks with large capitalization. Although generally accepted, the size effect keeps being challenged. Researchers have been asking how important the firm size characteristic actually is, and whether it is possible to replace the traditional size factor of Fama and French asset pricing model (1993) with more accurate factor. Recently, one potential challenger has emerged – so-called takeover factor, employed by Easterwood et al. (2022). In their study, they work on the assumption that small firms are often targets of takeovers, which gives us a different perspective on merger and acquisition news in regards to size effect. Their results show that M&A component of average returns explains the size premium entirely.

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The Worst One-Day Shocks and The Biggest Geopolitical Events of the Past Century

11.July 2022

We dedicated several articles to how we created 100-year history for bonds, stocks, and commodities . Now we analyze the 50 worst one-day shocks and the following days in each of the abovementioned asset classes. In addition to that, we also look at how the multi-asset trend-following strategy performed during the same periods. Further, the second part of this article focuses on critical geopolitical events (the starts of major wars, international crises, and deterioration of US presidents’ health) and their effect on bonds, stocks, commodities, and the multi-asset trend-following strategy.

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Quantpedia in June 2022

6.July 2022

Hello all,

What have we accomplished in the last month?

– A new Screener for external strategy contributors, with 43 new factors and 2 new factor providers
– 8 new Quantpedia Premium strategies have been added to our database
– 20 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 9 new backtests written in QuantConnect code
– And finally, 3+2 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

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Quantpedia Introduces 3rd Party Factors

28.June 2022

Every year, Quantpedia’s team investigates thousands of academic research papers to bring you the most promising ideas from the academic world. We read papers, identify ideas and backtest them to build our unique database. As a result, we have already identified hundreds of factors and built tools to help you orient better in the broad universe of trading strategies and systematic investment factors.

And now, we are opening the possibility to all external researchers, quants, and portfolio managers to contribute to Quantpedia.

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Investor Sentiment and the Eurovision Song Contest

24.June 2022

The summer is slowly approaching; therefore, our new article will be on a little lighter tone. We will examine a research paper on a periodic event with sentiment implications. The authors (Abudy, Mugerman, Shust) focused on a specific song competition – the Eurovision Song Contest, an international song competition organized annually. They examined a positive swing in investor mood in the winning country the day after the Eurovision Song Contest and documented an average abnormal return of 0.381%. On the contrary, they did not find any negative sentiment in other participating countries.

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Skewness/Lottery Trading Strategy in Cryptocurrencies

21.June 2022

A recent spring 2022 crisis in the cryptocurrency market emphasized the importance of market-neutral crypto trading strategies. It’s not enough just to HODL crypto market and hope for the everlasting bull market. Therefore, we continue our series of research articles about the cryptocurrency market and offer an analysis of the skewness anomaly. So after our description of the skewness effect in commodities, an article about the multi-asset skewness strategy, and observation of the skewness/lottery effect in ETFs, we have one more asset class, where we can find lottery/skewness anomaly – in cryptocurrencies.

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