In this white paper, RavenPack Data Scientists sought to test the performance of insider transactions as a basis for trading European equities.
It follows on from a previous study in which the data yielded positive results when applied to a portfolio of global stocks.
Could the data work just as successfully on stocks in the European firmament, researchers wondered?
European Insider Transactions Data
The first step was to filter out - by size and importance - those insider transactions likely to have the greatest impact on prices.
These were then used as the basis of a trading strategy, which buys the stock when net transactions turn positive and sells when they turn negative.
A strategy with a one-day holding period produced excess annualized returns of 5.4% and 9.2%, and information ratios of 1.3 and 1.7 for large/mid cap stocks and small-cap stocks respectively.
Adding a sentiment overlay calculated from the company’s underlying news flow helped improve signal strength, especially during negative sentiment trends.
The results of combining the two can be seen in the chart below which clearly shows how a negative sentiment overlay, in particular, increases excess returns.
To isolate the portfolio specific returns from other factors and determine whether insider transactions were responsible for generating the strategy’s alpha, it was tested using the MSCI Barra GEM3 model.
“We find that the performance is very similar to that of excess returns across all aggregation periods and market capitalizations, demonstrating persistent alpha generation emanating from our corporate insider transaction data model within the European Universe,” says Peter Hafez, Chief Data Scientist at RavenPack.
Easily implement the results of this White Paper using the RavenPack Analytics Platform, which includes a comprehensive and high-quality database of global insider transactions. Request a trial here.Request White Paper