Approximately 18 months ago, when we started Quantpedia Pro service, we promised to systematically expand its analytical capabilities by adding new tools and reports to it. We kept this promise and enlarged Quantpedia Pro to over 30 reports with hundreds of tables and charts. Factor regression analysis, risk scenarios, seasonality analysis, alternative weighting schemes, risk parity, CPPI, volatility targeting, correlation analysis, Markowitz portfolio optimization, clustering, market phases analysis, ETF replication etc. offer insight into the matters of portfolio construction or risk management. But our disciplined tempo also means that some users can become lost in the number of tools Quantpedia Pro offers. Therefore, we would like to introduce to you our new Quantpedia Answers section, which contains practical examples of how to use the growing capabilities of Quantpedia Pro reporting.

The recent surge in global inflation sent shock waves across financial markets and affected the complicated relationship between stocks and bonds. Today, we would like to present you with a review of two interesting papers, which provide both a deep and easy-to-understand examination of the correlation structure of those two main asset classes. The first paper reviews specifics in various parts of the world, and the second one summarizes known information about the macroeconomic drivers of the US stock-bond correlation.

Federal Open Market Committee (FOMC) of Fed (Federal Reserve Board/System) meetings which bring announcements usually followed by press conferences are one of the most important events in the rich calendar of investors’ watch lists. They are always closely watched for possible trading opportunities and are full of volatile moves on both long and short sides in fronts of all asset classes ranging from forex, bonds, and equities to nowadays even crypto markets. In our today’s summary, we will take a closer look at some implications that those kinds of financial phenomena bring.

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.

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