Irene Aldridge has co-authored research on interpreting artificial intelligence and building trading strategies from data. A draft of the working paper can be found on SSRN. Title: What Data Series Matter? Explaining key trends and factors generated by Artificial Intelligence Abstract: We show a simple way to let the data speak for themselves. Specifically, we show how a large mixed bag of data, potentially embedded with missing data points and collinearities, and therefore unsuitable for traditional econometric analysis, can be useful in building fast and meaningful big data and artificial intelligence analyses and predictions. What’s more, our technique helps the results of the analyses toRead More →

Irene Aldridge will speak on the panel titled “Fintech and the Future of Finance” on Thursday, March 4, 2021, along with Anand Autar of ING and Caroline Tarnok of Coinbase. The panel will be moderated by John Bowden of ING. Here is the link to the event: https://netforum.avectra.com/eweb/DynamicPage.aspx?Site=CMCAS&WebCode=210225FintechPanelRead More →

By Irene Aldridge Once you add Neural Networks to your existing analytical toolset, you can benefit from detecting patterns in data that cannot be captured otherwise. This article shows, with IBM as an example, how using Neural Networks unlocks statistical insight, you could not have achieved without it. Technical analysis is a discipline that has managed to survive it all over more than 100 years. Starting from the 1920s, and possibly even earlier, technical analysts persevered at distilling meaning from patterns in the data. Numerous generations of quants declared technical analysis to be dead, only to resurrect it in various Auto-Regressive Moving Average (ARMA) specifications. Read More →