Do SPACs Generate Abnormal Returns?

Special Purpose Acquisition Companies (SPACs) raise capital through IPO under special conditions intending to acquire an existing company (private equity). On the one hand, it looks like an attractive opportunity for investors – SPACs bring a lot of excitement and prospects of large profits since the management can find a valuable opportunity. If no acquisition is made, then investors simply get their money back. For firms that are being acquired, it is a much easier and faster way how to get publicly traded – without investment banks and IPOs. On the other hand, SPACs are very speculative and even frequently overpriced, which attracts many critiques. While SPACs are nothing new, recently they have got quite popular, which raises several questions: are they worth attention or do they bring abnormal profits? A fascinating insight into SPACs provides a novel research paper of Chong et al. (2021). The study explains the fundamental principles of SPACs, but most importantly, it shows us the risks and returns of such investments. Despite the popularity and the seemingly attractive opportunity of SPACs, results show us that the invested capital could be instead used elsewhere. Although the success depends on the sector in which is the SPAC interested or whether the acquisition was successful, overall, it is hard to find abnormal returns in these investments.

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Man vs. Machine: Stock Analysis

Nowadays, we see an increasing number of machine learning based strategies and other related financial analyses. But can the machines replace us? Undoubtedly, AI algorithms have greater capacities to “digest” big data, but as always in the markets, everything is not rational. Cao et al. (2021) dives deeper into this topic and examines the stock analysts. Target prices and earnings forecasts are crucial parts of the investing practice and are frequently used by traders and investors (and even ML-based strategies). The novel research examines and compares the abilities of human analysts versus the AI algorithm in forecasting the target price. As a whole, AI-based analysts, on average, outperforms human analysts, but it is not that straightforward. While AI can learn from large datasets, humans do not seem to be replaced soon. There are certain fields where human uniqueness is valuable. For example, in illiquid and smaller firms or firms with asset-light business models. Moreover, it seems that rather than competing with each other, AI and human analysts are complementary. The novel technology can be used with great success to help us in areas where we lag, and the combined knowledge and forecasts of AI and humans outperform the AI analyst in each year.

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Quantpedia in June 2021

Hello all,

What have we accomplished in the last month? New Quantpedia Pro reports, new Position Sizing course and as usual – a lot of new Quantpedia Premium strategies, papers and backtests.

– 10 new Quantpedia Premium strategies have been added to our database
– 10 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 11 new backtests written in QuantConnect code
– We have prepared a series of three new CPPI reports (Constant Proportion Portfolio Insurance) for Quantpedia Pro
– In cooperation with QuantInsti, we have launched a new practically oriented course called Position Sizing in Trading, which aims to explain the money management techniques to intermediate level traders
– And finally, four new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

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