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Machine learning-aided modeling of fixed income instruments

The fixed income market is very important to the economy. Sovereign issues are influenced by central bank policy, and corporate issues are viewed relative to these sovereign issues. Compared to equities, bonds have lower liquidity and transparency; hence, there is less public data available. We demonstrate the applicability of machine learning models in forecasting interest rates for U.S. Treasuries of varying maturities, as well as clean prices of corporate issues. 1 The Fixed Income Market Fixed income markets have strategic importance to the global economy. The global bond market is valued over $100 trillion, as compared to S&P’s $64 trillion valuation of the global equity markets. [14] Although most bonds are available to individual investors, the majority are purchased by large institutional funds and insurance companies for the purpose of hedging risk. A bond is issued with a defined term (lifespan) and coupon (interest) rate. A credit rating agency rates bonds based on the issuer’s creditworthiness. As there is a greater variety of bonds than stocks, secondary trades of a bond do not take place in a market, but through direct trader-to-trader negotiations. This system reduces liquidity and transparency for trades, which increases risk for both the buyer and the seller. Having an improved ability to forecast prices would mitigate some pricing risk, and, given the size of institutional trades, avoid large losses. Sovereign Bonds. Sovereign bonds are fixed income debt issued by a sovereign government. These bonds are usually considered the lowest risk available in their respective countries, and thus offer the lowest rates in the country. The U.S. government currently holds a top AAA rating from major credit agencies. The interest rates are influenced by the monetary policies of the country’s central bank, which aims to avoid sudden changes. Corporate Bonds. Corporate bonds are issued by companies, and are usually considered higher risk than sovereign bonds. The perceived additional risk of investing in a particular company leads to higher interest rates for corporate bonds as compared to sovereign bonds. Prior to 2002, U.S. corporate bond trade prices were not publicly available except through compulsory reporting of large trades by insurance companies. [6] As buyers had limited information, this caused bond prices to be relatively volatile. To combat this information void, the Securities and Exchanges Commission (SEC), a government body issued new reporting requirements. To comply with these new requrirements, the Financial Industry Regulatory Authority (FINRA), a trade group, set up a public database (TRACE) where licensed security dealers would be required to report trade information. It officially launched in 2006. An expanded version of a paper presented at the NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, Montréal, Canada. Matrix Pricing. Many bonds trade infrequently. This complicates the task of determining a suitable purchase price. The most popular technique used in this scenario is called “matrix pricing” [45]. Matrix pricing involves adding an arbitrary spread to a roughly-comparable bond: a more-actively traded bond from the same company, a similar bond from a different company, or a U.S. Treasury bond [44]. Dealers will usually hold this quote barring large shifts in the price of the comparable [23]. The arbitrary nature of this technique can lead to discrepancies in valuation. Using machine learning on existing data, our goal is to forecast the prices of such bonds without finding comparables. Summary. The rest of our paper will be organized as follows. We will begin by examining prior forecasting work in Section 2. In Section 3 we introduce the machine learning models used in our experiments. We will continue with our bond experiments in Section 4. Finally, we discuss these results in Section 5, and delve into further avenues of exploration.