Environmental, social, and governance (ESG) investing is rapidly growing in popularity. As more investors grow interested in the ESG investing, the funds theoretically have more reason to highlight their engagement with the ESG-related activities. In the research paper by Andrikogiannopoulou et al. (2022), authors first use textual analysis to assess how and how much the funds talk about ESG-related topics in their prospectuses, and then they compare this measure with the funds’ actual ESG engagement. The discrepancy between the words in their prospectus (high rate of mentioning ESG investing-related topics) and the fund’s acts (not being as green as illustrated in the prospectus) allows the authors to identify the greenwashing funds and take a closer look at their performance.

Dear readers & clients,

The beginning of the new year is usually a traditional time for recapitulation. The year 2022 was a really challenging one on the financial markets, filled with unexpected events (Russia’s invasion of Ukraine or surge in inflation etc.). But I am again really proud of my whole team for their work as we continue fulfilling our primary mission to process academic research related to quant&algo trading to a more user-friendly form.

Last year was again really productive…

Four new strategies have been added. Seven new related research papers have been included into existing strategy reviews and four short free blog posts has been published during last few weeks. Plus, five trading strategies have been backtested in QuantConnect in the previous two weeks.

We have already published a few articles about how the different market cycles affect the performance of your portfolio and performance of market factors. So far, these states of the market were identified in-sample, with the benefit of hindsight. The full methodology of how we defined bull/ bear market, low/ high inflation, and rising/ falling interest rates is described in this article.

Today, we are going to define the same market states out-of-sample. We will describe our methodology and the thinking behind it all in this article. Both in sample and out of sample market cycle analysis may be useful for making investment decisions. It’s crucial to understand the differences and how to use this kind of analysis to your benefit.

As usual, at this time of the year, let us do a short recapitulation of posts on our blog in the previous 12 months. We have published a record 80 short analyses of academic papers and our own research articles on this blog in 2022. We want to use this opportunity to summarize 10 of them, which were the most popular (based on the Google Analytics tool). Maybe you will be able to find something you have not read yet …

Today, we analyze how all the factors we use in our Multi-Factor Regression Model performed during various Market Cycles (in sample), including the Bull/ Bear market, the High/ Low inflation, and the Rising/ Falling interest rates. Further, we also examine the performance of a Balanced Portfolio ETF – AOR, over past 100 years. This is done by creating the Factor AOR, which we constructed using our Multi-Factor Regression Model from AOR ETF. In addition to a chart comparison of equity curves, we also compare the performance of factor AOR to that of all the factors by means of risk/return tables, i.e. quantitatively. All the tables are sorted based on the Sharpe ratio from the best (at the top) to the worst (at the bottom).