Hello all,

What have we accomplished in the last month?

– One new Quantpedia Pro report – the Rebalancing Analysis
– 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, 5+1 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

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…

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 …

Hello all,

– One new Quantpedia Pro report – the One-Day Shocks & Historical Event Analysis
– 10 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 10 new backtests written in QuantConnect code
– And finally, 4+1 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

Quantpedia’s mission is simple – we want to analyze and process academic research related to quant/algo trading and simplify it into a more user-friendly form to help everyone who looks for new trading strategy ideas. It also means that we are a highly focused quant-research company, not an asset manager, and we do not manage any clients’ funds or managed accounts. But sometimes, our readers contact us with a request to help them to translate strategy backtests performed in Quantconnect into paper trading or real-trading environment. The following article is a short case study that contains a few useful tips on how to do it.

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.