Summary Besides arbitrage and implicit subsidies, relative value trading is a well-established investment strategy widely used in the portfolios of hedge funds, pension funds, and investment banks. This strategy achieves relatively stable returns by capturing price differences between related assets, rather than relying on overall market trends. Such strategies perform exceptionally well in volatile market environments, but the potential challenges must be carefully assessed. Originally published on November 18, 2025 Take a Step Forward: Relative Value Trading Let's start taking more risks. Institutions aren't always just arbitrageurs or "free riders"; they also try to profit from asset price fluctuations, but not in the way depicted in the movie "The Big Short"—making a fortune by betting huge sums on a particular direction or event—even in the "Wild West" era of decades ago, most portfolio managers did not like that. "Relative value trading" is what they prefer. The core of relative value trading lies in identifying and exploiting price inconsistencies among related assets. Investors typically adopt a long-short pairing approach: going long on undervalued assets while shorting overvalued ones, anticipating that the spread will gradually converge to historical averages or theoretical equilibria, thereby generating profits. This strategy applies to various asset classes, including bonds, stocks, commodities, and derivatives. For instance, in fixed income markets, it can be executed through yield curve comparisons; in equity markets, it often takes the form of pairs trading among companies in similar industries. Unlike risk-free pure arbitrage, the convergence of spreads in relative value trading depends on market dynamics, thus involving uncertainty. Its key feature is market neutrality: the strategy does not bet on macroeconomic trends or price movements. Instead, it focuses on relative pricing inefficiencies, making it particularly suitable for operations driven by quantitative models, where institutions can leverage big data and algorithms to efficiently identify opportunities. This method can consistently generate alpha (excess returns) in efficient markets, rather than mere beta (market returns). To facilitate understanding, the following table illustrates a simple pairs trading example: This table demonstrates the basic logic of the strategy: capturing deviations by comparing historical data. Institutional investors favour relative value trading primarily due to its advantages in risk diversification and return optimisation. According to relevant studies, over 70% of institutional portfolios incorporate such strategies to address market uncertainties. Firstly, its market-neutral characteristic ensures relatively stable performance across bull and bear cycles. Regardless of the overall market trend, as long as asset correlations persist, the strategy can operate independently, which is crucial for institutions pursuing absolute returns. Secondly, this strategy facilitates the application of leverage and the integration of resources. By minimising directional exposure, institutions can amplify potential returns while controlling overall volatility. In subfields such as volatility trading, institutions can also act as liquidity providers, earning additional premiums and reallocating risk. Furthermore, relative value trading integrates easily into diversified frameworks, reducing tail risk impacts and providing smoother return curves. Institutions, with their advanced data tools and execution systems, hold significant advantages in this strategy, explaining its prevalence in professional circles. One More Step, More Risk Although relative value trading is designed to minimise risk, it still faces multiple potential challenges that may amplify losses under extreme market conditions. Key risks include basis risk: spreads may fail to converge or even widen, often due to macroeconomic events or asset-specific factors disrupting correlations. Historical cases, such as the 1998 Long-Term Capital Management (LTCM) collapse, where the Russian debt crisis shattered correlations and leverage effects led to rapid fund liquidation, highlight the severity of this risk. Execution risk also warrants attention: the strategy requires precise timing control, where market fluctuations or delays may cause slippage losses. Liquidity risk is particularly prominent during stress periods, with difficulties in asset realisation potentially leading to forced liquidations, especially when leverage is involved. Model risk stems from reliance on historical data; if deviations or cognitive biases (such as anchoring effects) exist, valuation accuracy will be compromised. Systemic risks may affect the entire strategy category, such as global geopolitical events. To manage these risks effectively, institutions built robust risk control systems that include real-time monitoring of correlation indicators, asset diversification, dynamic stop-loss thresholds, and regular stress testing. Leverage levels are adjusted based on market conditions to ensure strategy sustainability. Studies show that strict risk controls can keep the annualized volatility of such strategies within reasonable ranges. Retail Investor Perspective: Learning and Application Although relative value trading is more common among institutions, retail investors can also draw valuable lessons from it, such as the market-neutral philosophy: avoiding over-reliance on single-asset directional predictions and instead focusing on relative asset relationships. This helps diversify risks in volatile markets and fosters data-driven investment habits. Moreover, retail investors can enhance overall decision-making quality and avoid emotional trading by understanding correlation analysis. Secondly, emphasising risk management: retail investors should start with small capital, set stop losses, and avoid high leverage to prevent low-probability events from amplifying losses. Overall, this strategy teaches retail investors to focus on pricing inefficiencies rather than short-term speculation, promoting long-term investment thinking. Of course, retail investors can try simplified relative-value strategies, such as pairs trading: Selecting assets with high historical correlations, like stocks from similar industries (e.g., Pepsi vs. Coca-Cola), and going long on the undervalued and shorting the overvalued when spreads abnormally widen, expecting convergence. Another feasible approach is to use ETFs for relative-value operations, such as comparing deviations across sector ETFs. Studies indicate that such strategies at the retail level can achieve annualised returns of 5-10%, but must be combined with fundamental analysis to reduce risks. However, with limited resources, retail investors are advised to practise on simulation accounts first to avoid excessive exposure to real capital. So, What About Crypto Traders? In the crypto market, relative value trading is increasingly popular, especially amid the rise in institutional inflows in 2025, which is driving market maturity. Institutions commonly use strategies, including relative valuation: using indicators such as market cap-to-transaction volume ratios to assess cryptocurrencies relative to similar assets and going long on undervalued coins and shorting overvalued ones accordingly. Retail investors can learn simplified versions of these strategies: executing mainstream crypto pairs trading in the spot market and using data to monitor spreads. In 2025, with the proliferation of ETFs and DeFi tools, retail investors can achieve low-threshold operations. The key lies in risk control: using small positions and combining multiple charts. Institutional experience shows that such strategies can generate stable alpha, but retail investors should avoid excessive leverage to avoid some extreme scenarios. To sum up, relative value trading, as a core component of the financial toolkit, can create value for both institutions and retail investors in complex environments. However, its success depends on a profound understanding of market structures and continuous optimisation. Investors should view it as an auxiliary tool rather than a sole reliance, enhancing execution efficiency through data-driven methods. Under current market dynamics, this strategy will continue to play a role, but balancing returns and risks is essential to achieve long-term sustainable returns. Stay tuned… Disclaimer: The information provided herein does not constitute investment advice, financial advice, trading advice, or any other sort of advice, and should not be treated as such. All content set out below is for informational purposes only. Original Post Editor's Note: The summary bullets for this article were chosen by Seeking Alpha editors.
