Hello all,

What have we accomplished in the last month?

– Three new Quantpedia Pro reports to accelerate the building of multi-strategy overlays and/or new ETF trading strategies
– 9 new Quantpedia Premium strategies have been added to our database
– 13 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, 1+5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

Exchange-traded funds (ETFs) have been recently booming in popularity and enjoy great praise for their flexibility and accessibility in terms of liquidity. They allow investors convenient exposure to less liquid assets such as corporate bonds. But liquid ETF instrument based on illiquid assets is a recipe for a lot of hidden problems (and sometimes disasters), especially in such a turbulent period on fixed income markets as it’s now. There are various certain specifics which come with creation of new ETFs and problems for buying of underling prospects to match the fund’s NAV. Chris Reilly’s paper (2022) revolves around the point that ETF managers encourage Authorized Participants (APs) to more aggressively arbitrage tracking errors to the benefit of ETF investors while simultaneously allowing APs to interact strategically with ETF portfolios at the expense of ETF investors. Underlying asset liquidity is a first-order determinant of optimal security design for ETFs. While these ETFs do underperform their benchmark by greater than their stated net expense ratios (as much as claimed 48 bps p.a.), they still offer a liquid alternative for investors that do not have the resources to manage their own fixed income portfolio. This summary could be taken as a good reminder that investors’ expenses to obtain liquidity in the fixed income space are often quite substantial.

Overnight and seasonality effects or analysis of sentiment are favorite themes in quantitative academic research. Novel and very recent research from Baoqing Gan, Vitali Alexeev, and Danny Yeung (August 2022) presents us with an opportunity to discover new findings related to both these phenomena. The main takeaway is that the accumulated sentiment from the overnight non-trading period can predict the next period’s intraday stock return.

Trading on non-public information has been very profitable in the past (and probably still is). Prominent insiders use their knowledge and share it with influential, wealthy institutional investors who earn money in an illegal way. And especially, options provide attractive leverage and relatively viable ways to “hide” sources of this illegal advantage. But after several big scandals, the resurgence of some forms of insider trading was stopped in 2009 after a trial with hedge fund superstar Raj Rajaratnam. The question is: What is the situation now?