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.

The main theme of these days in the crypto world is unmistakenly clear, it’s the mayhem connected with the collapse of the FTX empire, insolvencies of various lenders, and questions about underlying holdings in GBTC OTC ETF and reserves of exchanges and Tether (or other stablecoins as well). With new information, nothing does paint a bright picture of this industry in the financial world now and in the near future. Calls for finally working regulations are getting stronger and stronger, while politicians (and central bankers) are still active on Central Bank Digital Currencies (CBDCs) proposals. While Bitcoin survived several crypto winters, long-term investors are continuing their DCA-ing and “stashing Satoshis” Are they safe? Do they pay attention to the surrounding news? In our blog entry, we will focus on the question of how news impact Bitcoin returns, being both the most famous cryptocurrency and also the one with the highest market capitalization.

The following article is a short distillation of the research paper Leveraging the Technical Competence of a Stock for the Purpose of Trading written by Rishabh Gupta. The author spent a summer internship at Quantpedia, investigating the Patent-to-Market (PTM) ratio developed by Jiaping Qiu, Kevin Tseng, and Chao Zhang. The PTM ratio uses public information about the number and dates of patents assigned to publicly listed companies, calculates an expected market value of patents, and tries to predict future stock performance.

Post–earnings-announcement drift (abbr. PEAD) is a well-researched phenomenon that describes the tendency for a stock’s cumulative abnormal returns to drift in the direction of an earnings surprise for some time (several weeks or even several months) following an earnings announcement. There have been many explanations for the existence of this phenomenon. One of the most widely accepted explanations for the effect is that investors under-react to the earnings announcements. Although we already addressed such an effect in some of our previous articles and strategies, we now present a handy method of improving the PEAD by using linguistic analysis of earnings call transcripts.

This blog post is the continuation (and finale) of series about Quantconnect’s Alpha market strategies. This part is related to the multi-factor strategies notoriously known from the majority of asset classes. We continue in the examination of factor strategies built on top of social trading strategies, but the investment universe is reduced based on the insights of the previous part. So, without further ado, we continue where we have left last time.