Arbitrage

Alternative ETFs sit at an unusual intersection of public-market accessibility and hedge-fund-style investment techniques. They package managed futures, merger arbitrage, and option-based income strategies into exchange-traded products, yet they remain thinly traded and relatively niche compared to mainstream equity or bond ETFs. This combination makes them intriguing: they offer exposure to alternative risk premia, and their limited liquidity raises possibilities to build short-term reversal strategies. 

Prediction markets are financial platforms where participants trade contracts linked to future events, with prices reflecting collective probabilities. While these markets efficiently aggregate information, systematic inefficiencies create trading opportunities. Notable strategies include inter- and intra-market arbitrage, exploiting price differences across platforms or mispricing within a single market. Behavioral biases, such as the longshot bias, lead traders to overvalue underdogs and undervalue favorites, while bookmakers may manipulate odds to mislead naive participants. Experienced traders can exploit these patterns to secure profits. This article reviews common systematic edges in prediction markets, illustrates their practical application, and highlights the potential for profitable trading.

Cryptocurrencies have matured from experimental curiosities into a viable investable asset class whose return-generation and risk characteristics merit treatment within empirical asset pricing. A recent paper by Nicola Borri, Yukun Liu, Aleh Tsyvinski, Xi Wu summarizes ten facts from the literature that show cryptocurrencies share important similarities with traditional markets—comparable risk-adjusted performance and a small set of cross-sectional factors—while retaining distinctive features such as frequent large jumps and price signals embedded in blockchain data. Key themes include portfolio diversification, factor structure, market microstructure, and the evolving role of regulation and derivatives in shaping market discovery and stability.

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.

In a game of poker, it is usually said that when you do not know who the patsy is, you’re the patsy. The world of finance is not different. It is good to know who your counterparties are and which investors/traders drive the return of anomalies you focus on. We discussed that a few months ago in a short blog article called “Which Investors Drive Factor Returns?“. Different sets of investors and their approaches drive different anomalies, and we have one more paper that helps uncover the motivation of investors and traders for trading and their impact on anomaly returns.

We continue our short series of articles dedicated to the exploration of trading strategies that derive their functionality from the deep understanding of how Exchange Trading Funds (ETFs) work. In our first post, we discussed how we could use the ETF flows to predict subsequent daily ETF performance. In today’s article, we will analyze how we can use the information about the sensitivity of individual stocks to the ETF arbitrage activity to build a profitable equity factor trading strategy.