Liquidity effect

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. 

The paper “Through Stormy Seas: How Fragile is Liquidity Across Asset Classes?” is a very interesting examination of how liquidity properties have evolved over the past decade. Although the average bid–ask spread has declined, the kurtosis and skewness of the spread distribution have increased. What does this imply? On average, markets appear more liquid; however, liquidity evaporates more rapidly during stress events, amplifying tail risk and increasing execution slippage.

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.

Today, we will evaluate some specifics that are akin to the now second-largest market in the world – China. The abundance of “shell companies” creates a problem when researchers try to uncover sources of alpha in the Chinese market. We present recent research by Zhiyong Li and Xiao Rao (2022) that proposes a new alternative filter, which excludes the stocks with a high estimated shell probability when constructing equity factor models.

The growth of passive investing and ETFs is indisputable. Consequently, this boom also affects financial markets (e.g., market elasticity or by creating predictable buys and sells) and assets that ETFs track. Even though all passive ETFs aim to replicate some benchmark index, there are two distinct approaches to doing so. The first approach is directly replicating the benchmark (by buying underlying assets) either by full direct replication or sampling. The second approach consists of synthetic replication using derivatives – most commonly by total return swaps (or futures). How do replication methods influence tracking error?

As we have mentioned several times, the best course of action for a quant analyst who wants to develop a new trading strategy is to understand a well-known investment anomaly/factor fundamentally and then improve it. Quantpedia is a big fan of transferring ideas derived from academic research from one asset class to another. But that’s not the only possibility of improvement – we can try to embrace Roger Ibbotson’s theory of popularity, which states that popular assets/securities are usually overpriced compared to less-known (exotic) assets/securities. Additionally, more professional investors usually follow popular assets, and this market segment is probably significantly more efficient.

So, we went in this direction. We took a well-known commodity momentum factor strategy and investigated its performance among commodity futures that were part of the S&P GSCI respectively BCOM commodity indexes and then compared the strategy’s performance with a variant that traded only non-indexed commodity futures. As we had expected, the trading strategy using exotic assets performed significantly better.