Mean-Reverting Portfolios: Tradeoffs Between Sparsity and Volatility

TL;DR

This paper explores the tradeoffs between sparsity and volatility in constructing mean-reverting portfolios, proposing algorithms to optimize these properties for more practical trading strategies.

Contribution

It introduces algorithmic methods to balance mean reversion, sparsity, and volatility in portfolio construction, addressing limitations of traditional cointegration-based approaches.

Findings

Algorithms can effectively trade off sparsity and volatility.

Sparse portfolios still exhibit strong mean reversion.

Enhanced portfolios improve practical trading profitability.

Abstract

Mean-reverting assets are one of the holy grails of financial markets: if such assets existed, they would provide trivially profitable investment strategies for any investor able to trade them, thanks to the knowledge that such assets oscillate predictably around their long term mean. The modus operandi of cointegration-based trading strategies [Tsay, 2005, {\S}8] is to create first a portfolio of assets whose aggregate value mean-reverts, to exploit that knowledge by selling short or buying that portfolio when its value deviates from its long-term mean. Such portfolios are typically selected using tools from cointegration theory [Engle and Granger, 1987, Johansen, 1991], whose aim is to detect combinations of assets that are stationary, and therefore mean-reverting. We argue in this work that focusing on stationarity only may not suffice to ensure profitability of cointegration-based strategies. While it might be possible to create syn- thetically, using a large array of financial assets, a portfolio whose aggre- gate value is stationary and therefore mean-reverting, trading such a large portfolio incurs in practice important trade or borrow costs. Looking for stationary portfolios formed by many assets may also result in portfolios that have a very small volatility and which require significant leverage to be profitable. We study in this work algorithmic approaches that can take mitigate these effects by searching for maximally mean-reverting portfo- lios which are sufficiently sparse and/or volatile.