Understanding seasonality in financial markets requires recognizing how predictable return patterns can be influenced by investor behavior. One underexplored aspect of this is the impact of front-running—where traders anticipate seasonal trends and act early, shifting returns forward in time. We have already explored seasonality front-running in commodities, stock sectors, and crisis hedge portfolios. Our new research examines whether this phenomenon extends to country ETFs, an asset class where seasonality has been less studied. By applying a front-running strategy to a dataset of country ETFs, we identify opportunities to capitalize on seasonal effects before they fully materialize. Our findings indicate that pre-seasonality drift is strongest in commodities but remains present in country ETFs, offering a potential edge in portfolio construction. Ultimately, our study highlights how front-running seasonality can enhance ETF investing, providing an additional layer of market timing beyond traditional trend-following approaches.

Can we truly rely on the opening price in OHLC data for backtesting? While the overnight drift effect is well-documented in equities, we investigated its presence in gold using the GLD ETF and then extended our analysis to the GDX – Gold Miners ETF, where we observed an unusually strong overnight return exceeding 30% annualized. However, when we tested execution at 9:31 AM using 1-minute data, the anomaly diminished significantly, suggesting that the extreme return was partially a data artifact. This finding highlights the risks of blindly trusting OHLC open prices and underscores the need for higher-frequency data to validate execution assumptions.

Seasonality plays a significant role in financial markets and has become an essential concept for both practitioners and researchers. This phenomenon is particularly prominent in commodities, where natural cycles like weather or harvest periods directly affect supply and demand, leading to predictable price movements. However, seasonality also plays a role in equity markets, influencing stock prices based on recurring calendar patterns, such as month-end effects or holiday periods. Recognizing these patterns can provide investors with an edge by identifying windows of opportunity or risk in their investment strategies.

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.

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.