This blog post will give you a short recapitulation of the five quick market/portfolio insights built from Quantpedia Pro reporting.

– Gold displays a strong seasonal tendency in returns in days around US public holidays.

– The performance of Bitcoin is usually the worst during the same time as stock market experiences the bear market.

– Cryptocurrency market correlation slowly increases, and we can’t rule out the financialization of the crypto market (the same process that happened in commodities approximately ten years ago).

– Skewness-based trading strategies could serve as a practical hedge/diversification during stock market drawdowns.

– We show the main attribute of most of the risk parity portfolios – lower total returns but significantly lower risk measures.

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.

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.

As we have promised, we present a short article as an introduction into the methodology of the Quantpedia Pro CPPI reports. Quantpedia Pro clients can use the model portfolio built in the Portfolio Manager as a risky asset to test various variants – Basic CPPI, Drawdown Based CPPI and Dynamic Multiplier CPPI.

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

Quantitative based community is represented by the Quantconnect – Algorithmic Trading Platform, where quants can research, backtest and trade their systematic strategies. Additionally, similar to Seeking Alpha, there is a possibility to follow other quants/analysts through the open free market – Alpha Market.
To our best knowledge, the literature on community/social media alpha is scarce, and this paper aims to fill this gap. In the first part, we evaluate the benchmark strategy that consists of all strategies in the alpha market that are equally weighted. Moreover, through multidimensional scaling and clustering analysis, we examine how well can significantly lower amount of strategies track the aforementioned benchmark. This could solve the problem of costly and inconvenient following of every strategy in the market. Overall, this approach can lead to a strategy that follows the benchmark with drastically reduced costs, and these strategies can be even more profitable and less volatile.

Stay tuned for the 2nd, 3rd and 4th part of this series, where we will step on the gas and explore factor meta-strategies built on top of the QuantConnect’s Alpha Market.