Using AI to Identify Relative Value Trading Opportunities in the US Credit Market
In financial markets, volatility inherently presents opportunities, as fluctuating prices can result in misaligned pricing . Relative value (RV) analysis involves comparing the valuation of similar securities to identify bonds which are over- or under-valued compared to their peers based upon recent trading history. Evaluating RV opportunities can help traders generate fresh trade ideas, maximize alpha generation, and more effectively manage risk.
The increase in electronic trading has led to narrower spreads, making alpha generation more challenging. The proliferation of algorithms, capable of rapidly evaluating thousands of CUSIPs, has further complicated the landscape by swiftly identifying and neutralizing many arbitrage opportunities. Consequently, the speed at which arbitrages collapse is significantly faster than just a few years ago.
Traditionally, relative value has been assessed manually, using spreadsheets incorporating statistical methods and financial metrics. However, in today’s increasingly sophisticated market, manual methods like human qualitative analysis and Excel modeling are often inadequate. While it’s important to consider qualitative factors like a company's management, clients, geographic exposure, competition, corporate governance, and industry trends, it’s improbable that an individual following a strictly qualitative approach can assess the entire universe of bonds with the speed and precision necessary to capitalize on trading opportunities.
With the increasing popularity of machine learning, more sophisticated anomaly detection techniques have become feasible, offering enhanced precision, speed and scalability. Many firms are developing advanced quantitative models to gain a competitive edge.