Equity investments in illiquid markets: Managing the trade-off between returns and costs

Factor investing is an attractive investment strategy, but it is essential to remember that most academic research in this area has been conducted on large and mid-cap U.S. stocks. If we naively apply these strategies to less liquid markets, factor investing can appear even more attractive at first glance. Two prime examples are emerging markets and European small caps. Therefore, our research department strives to increase risk premiums after implementation costs, especially in these markets.

We start by comparing the performance of multi-factor portfolios against MSCI benchmark indices in these two markets and in large- and mid-cap U.S. stocks. The multi-factor-signal used to construct the factor portfolios is an equal-weighted mix of value, size, momentum, profitability, investment (asset growth) and low beta.

While a simple multifactor model for US equities has significantly outperformed the market over the past 22 years, this outperformance is even greater for emerging markets and European small caps. However, these markets are considerably less liquid than US large and mid caps. Trading factor signals in these markets without taking market frictions into account would therefore lead to high transaction costs. The following chart illustrates the average daily trading volume over the last 22 years:

Portfolio optimization requires a short-term liquidity forecast

To manage trading costs, we need to manage liquidity demand. If we know that low volume is traded in a particular stock, we can only buy small amounts at a time. But the problem is more complex. When we optimize a portfolio, the resulting orders are traded in the future. Given the limited liquidity in smaller markets, it can take several days for a trade to be fully executed without demanding too much liquidity. Therefore, we cannot only use long-term average liquidity in the portfolio optimization process but also need a short-term liquidity forecast. If short-term liquidity movements are ignored when making investment decisions, the probability of falling into a costly “liquidity trap” increases. A liquidity trap occurs when liquidity turns out to be much lower than expected during portfolio construction.

An advantage of a quantitative investment process is that the tradeoff between expected outperformance and expected transaction costs can be incorporated into investment decisions, resulting in higher returns net of costs. We can include not only expectations about returns and volatility in portfolio construction but also expectations of trading costs. Our research shows that ex-ante cost management in terms of reliable liquidity estimates can improve performance after transaction costs across market cycles and segments. Ex-ante cost management means finding the sweet spot between return and costs for the underlying investment strategy. Doing so, we estimate a transaction costs² reduction of 20% in our Emerging Markets strategy and of 25% in the European small caps strategy.

^{1) Daily traded volumes are a measure of historically executed stock orders, not order book data as limit order data etc. The level of transaction costs depends on the required liquidity of a trade, which is measured by comparing the size of a trade to the typical volume traded in that stock in a given period. If a large amount of average liquidity is required, the impact of the trade on the market will move the price against the investor. While these adverse effects are limited for a small retail investor, managing institutional funds requires the systematic incorporation of liquidity in the investment process.}

^{2) The transaction costs are comprised of fees, the half bid-ask spread, and adverse price movements induced by market impact.}