Hello all,

What have we accomplished in the last month?

– A new Automatic Trading Edge Analysis report
– 9 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 9 new backtests written in QuantConnect code
– And finally, 2+4 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

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

Have you ever wondered if your trading asset trends or mean-reverts? Everyone involved in trading or investments daily solves the task of – What trading strategy should I apply to my assets to generate profits? As always, we at Quantpedia will try to help you a bit with this never-ending task with our new tool/report, which will be unveiled next week for all Quantpedia Pro subscribers. The following article serves as an introduction to the methodology we will use to find new trading edges for you automatically.

Passive index funds are believed to offer low fees, nearly limitless liquidity, very low trading costs and (most of the time) they beat most active managers. Although not all of the above are accurate, there are still many arguments in favour of passive indexing. However, what is often left forgotten are avoidable travails linked to index funds. In general, after an index rebalances, traditional cap-weighted index funds buy high and sell low. Their tendency to add recent highfliers and drop unloved value stocks is what causes investors to lose. Arnott et al. (2022) target the stock selection problem around index rebalancing and propose several ideas on how to adjust index strategies in order to earn above-market returns. They present simple ways to construct an index, thanks to which it is possible to reduce both negative effects of buy-high/sell-low dynamic and the turnover costs of cap-weighted indices.

At the moment, there is a lot of attention surrounding overnight anomalies in various types of financial markets. While such effects have been well documented in research, especially in US equities and derivatives, there are other asset classes that are not as well addressed. A recent (2022) paper from Jiang, Luo, and Ye contributed appealing evidence in favor of validating these phenomena in the Chinese market. We highlight the finding that the market MKT factor beta premium is earned exclusively overnight and tend to reverse intraday (and in smaller potency also value HML and profitability RMW), which is the same finding as for the US equities. In contrast, the size SMB factor exhibit significantly opposite pattern: positive intraday premium and negative overnight premium (and the same for investment CMA factor).