Convolutional neural networks (CNNs), inspired by the human brain’s ability to recognize visual patterns, excel in tasks like object detection, facial recognition, and image classification, making them powerful tools for extracting insights from visual data. However, we are traders, so a natural question arises: Can we use that in trading? A recent paper shows that we can actually do it. Utilizing CNNs, Niklas Paluszkiewicz introduces a novel approach to pairs trading by visually analyzing historical price movements while converting traditional time series data into image representations. 

This is the moment we all have been waiting for, and today, we would like to acknowledge the accomplishments of the researchers behind innovative studies in quantitative trading. So, what do the top five look like, and what will the authors of the papers receive?

Let’s find out …

Quantpedia Days 2024

– Celebrate with us a crossing of the threshold of 1000 strategies
– You can now subscribe to any of our services, be it 3-, 12- or 36-months Quantpedia Prime, Premium, or Pro subscription, and get the same 2nd subscription for your co-worker or fellow researcher for free – an offer valid between 24th May and 31st May 2024
– The winner of the first season of our Quantpedia Awards 2024 competition will be announced on Monday, 27th May 2024
– What’s your favorite paper from the presented top 10?

The topic of today’s short blog post concerns a subject that’s connected to everybody participating in financial markets worldwide: different subjective return expectations. It is reasonable to have some expected returns you can count on if you are putting your money at risk. But how do they differ between different market professionals? And are return expectations influenced by recessions? We will look closely at financial analysts and their views on risk premia. The main point from the authors of the analyzed paper stresses the idea that analysts are counter-cyclical.

Have we mentioned, that we love history? Probably more than just once. What we like on the academic studies which use longterm data is that they offer a bird-like view on the financial markets. The daily noise and ebbs and flows retreat into the background and macroeconomic and geopolitical trends emerge. This top-down analysis helps to design the asset allocation or shape the overall structure of the portfolio of systematic trading strategies that may then trade on the higher frequency. Bryan Taylor’s paper offers a treasure of tables and charts depicting over 800 years of history of returns of global stocks, bonds and bills.

The replication crisis in social sciences (and, of course, finance) is an often covered topic (see also our articles How do Investment Strategies Perform After Publication and In-Sample vs. Out-of-Sample Analysis of Trading Strategies). In vs. out-of-sample tests are usually performed on equity factors as data are available. However, the Copenhagen Business Schools, in close cooperation with AQR Capital Management, went in a different direction and built a database of realistic corporate bond data and took a closer look at the precision of corporate bonds forecasting methodologies. We applaud them for that, as working with the corporate bond data is challenging, and their work sheds a little light on this important part of the financial markets.