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.

Hello all,

– A Quantpedia Awards 2024 competition update – Grading Phase
– A new Risk Monitor report
– 11 new Quantpedia Premium strategies have been added to our database
– 11 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 8 new backtests written in QuantConnect code
– 5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

Choosing the right investment strategy plays a crucial in portfolio allocation decisions, particularly when considering both private and public asset classes. While the reported performance of public assets typically matches their real-world performance, the same cannot be said for private assets due to the complexities of fund selection, commitment pacing, and return on uncalled and uncommitted capital. Fortunately, there are ways to incorporate public and private asset classes into one portfolio optimally. One example is the recent paper written by Xiang Xu, which introduces the Fair Comparison (FC) framework, which provides a methodology to measure the real-world performance of private investment strategies.

Over the last decade, investing responsibly and deploying capital for “ethically” correct and sustainable growth has been quite a theme. We dedicated a few blogs to this theme and have a separate ESG category for trading strategies in our database. It is often easy to commit financial resources to noble ideas during liquidity abundance. However, how do these methodologies fare during crisis times, such as when the GFC (Global Financial Crisis) or COVID-19 hit? That’s the question that a new paper by Henk Berkman and Mihir Tirodkar tries to answer.

In the fast-paced world of cryptocurrencies, understanding market sentiment can provide a crucial edge. As investors and traders seek to anticipate the volatile movements of Bitcoin, innovative approaches are continuously explored. One such method involves leveraging Google Trends data to gauge public interest and sentiment towards Bitcoin. This approach assumes that search volume on Google not only reflects current interest but can also serve as a predictive tool for future price movements. This blog post delves into the intricacies of using Google Trends as a sentiment predictor, exploring its potential to forecast Bitcoin prices and discussing the broader implications of sentiment analysis in the financial market.