Active Share is a popular metric used to gauge how actively managed a portfolio is compared to its benchmark, but its predictive power for fund performance is questionable. Our research suggests that high Active Share often reflects exposure to systematic equity factors rather than genuine stock-picking skill. Additionally, inaccuracies in benchmark selection can distort the metric’s insights, making it unreliable as a standalone measure. A more effective approach is to conduct a factor analysis of alpha to better understand a manager’s performance and true sources of over/underperformance.

Hello all,

What have we accomplished in the last month?

– An extension of the Trading Edge Analysis report
– 9 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
– 4 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

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.