Studies using machine learning techniques for return forecasting have shown considerable promise. However, as in empirical asset pricing, researchers face numerous decisions around sampling methods and model estimation. This raises an important question: how do these methodological choices impact the performance of ML-driven trading strategies? Recent research by Vaibhav, Vedprakash, and Varun demonstrates that even small decisions can significantly affect overall performance. It appears that in machine learning, the old adage also holds true: the devil is in the details.

Short sellers often have a bad reputation, seen as market disruptors who profit from declining prices. Yet, they play a crucial role in making markets more efficient by identifying overvalued assets and correcting mispricings. A recent study uncovers another surprising aspect of their behavior: rather than just demanding liquidity, the most informed short sellers actually provide it. Using transaction-level data, the research shows that these traders supply liquidity, especially on news days and when trading on known anomalies, challenging the conventional view of short sellers as merely aggressive market participants.

Our research will explore the intriguing phenomenon of the Pre-Holiday effect in commodities, particularly crude oil and gasoline. Historical data reveals a short-term price drift prior to major U.S. holidays, suggesting a trend in these markets. We hypothesize that this anomaly may be driven by increased demand for oil and its derivatives, such as gasoline, as people prepare for travel, often by car, during the holiday season. This seasonal behavior offers unique opportunities for market participants.

Today, we will venture a little into the fundamental analysis corner, and we will give you a glimpse of an intriguing paper (Hillenbrand and McCarthy, 2024) that discusses the advantages of using ‘Street’ earnings over traditional GAAP earnings. The paper suggests that ‘Street’ earnings provide better valuation estimates and improved financial analysis. Is this a way how to improve the performance of the struggling equity value factor?

Uninformed long-term investors provide an easy target for short-term traders, and they often unscrupulously take advantage of them. But ETF investors with long investment time horizons can mitigate some of the front-running costs if they take transactional costs into account to calculate whether it is economically optimal to participate in these “market games” (exchange and broker fees + classical opportunity costs of actively participating in strategy execution). Today, we will turn our attention to the paper “ETF Rebalancing, Hedge Fund Trades, and Capital Market” from Wang, Yao, and Yelekenova to better understand complex relationship between ETFs (their investors) and hedge funds.

Cryptocurrencies are no longer just a whim of computer nerds, they are part of the mainstream finance and often accepted part of fixed allocation for an overall diversified portfolio. We will not try to predict, whether they are here to stay in the future or will be subject to failure. This is a topic that has been touched on infinitely. Our interest caught up a purely practical paper by Austin Adams, Markus Ibert, and Gordon Liao, in which the authors apply classic macro-finance principles to identify the impact of monetary policy and risk sentiment in conventional markets on crypto asset prices. So let’s explore their results …