Ever thought about investing in cryptocurrencies? Before diving in, it’s worth understanding the shadowy practice of wash trading, where fake trade volumes distort the market. Our latest blog explores how this manipulation impacts major assets like Bitcoin and Ethereum, the methods used to detect it, and why regulatory clarity is key to a more trustworthy crypto ecosystem.

For those who have not yet had the chance to read it, we recommend the latest empirical study by Minghui Chen, Matthias X. Hanauer, and Tobias Kalsbach, which shows that design choices in machine learning models, such as feature selection and hyperparameter tuning, are crucial to improving portfolio performance. Non-standard errors in machine learning predictions can lead to substantial portfolio return variations, and authors are highlighting the importance of robust model evaluation techniques.

Seasonality is a well-known phenomenon in the commodity markets, with certain sectors exhibiting predictable patterns of performance during specific times of the year. These patterns often attract investors who aim to capitalize on anticipated price movements, creating a self-reinforcing cycle. But what if you could stay one step ahead of the crowd? By front-running these seasonal trends—buying sectors with expected positive performance (or shorting those with negative seasonality) before their favorable months begin—you can potentially gain a significant edge over traditional seasonality-based strategies. In this blog post, we explore how to construct and backtest a systematic strategy using commodity sector ETFs to exploit this seasonal front-running effect.

The rise of passive investing has been one of the most profound trends in the asset management industry in the past two decades. However, how does the popularity of passive funds impact market risk? We can rely on the data, and a recent research paper shows that the impact is significant, mainly through a substantial increase in stock correlations. As more investors flock to passive funds, which track indices, the prices of stocks within those indices tend to move more in tandem, increasing market-wide risk.

Do the quants possess a crystal ball? The recent research hints, that if we try to process the Twiter images, then we may get a small glimpse into the future. The Federal Open Market Committee (FOMC) meetings significantly influence financial markets, drawing global attention from traders and investors, especially regarding equity risk premia. Recent research indicates that combining sentiment analysis of Twitter images with text analysis can more accurately predict stock performance on FOMC days than text alone.

As interest in cryptocurrencies continues to surge, driven by each new price rally, crypto assets have solidified their position as one of the main asset classes in global markets. Unlike traditional assets, which primarily trade during standard working hours, cryptocurrencies trade 24/7, presenting a unique landscape of liquidity and volatility. This continuous trading environment has prompted us to investigate how Bitcoin, the flagship cryptocurrency, behaves across intraday and overnight periods. With Bitcoin’s growing availability to both retail and institutional investors through ETFs and other investment vehicles, we hypothesized that trading activity in these distinct timeframes could reveal patterns similar to those seen in traditional markets, where returns are often impacted by liquidity shifts during off-peak hours.