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Machine Learning In Fixed Income Markets

Machine Learning In Fixed Income Markets

Can Machine Learning manage Fixed Income? 

There are two aspects to be considered. 

The first one is based on an ongoing UCLA Applied Finance Project about the use of Machine Learning for accelerating risk management in Fixed Income; The second one is based on inspiration from “Future Possibilities in Finance Theory and Finance Practice” (Merton, 2002).  

As derivatives are commonly used as a risk management tool for fixed income investors, it is vitally important to develop models that could process large sizes of information efficiently and  provide accurate predictions, especially in this information-exploded era. 

In terms of valuing derivatives and calculating Greeks, the traditional ways such as Monte Carlo simulation and  Binomial Trees are computationally intensive and suffer from the high dimensionality when the input space is large. Especially when constructing a hedging portfolio, each market environment change is followed by a re-evaluation of the contract. 

Therefore, the purpose of the project we are working on is to accelerate the computational process by training the Neural Networks with the data generated by Monte Carlo simulation. A well-trained Neural Network could accelerate the process of option valuation and calculations of Greeks and restore the clean prediction when the data is noisy. Though the project is still at the very beginning stage, the exploration so far and the goal of this project has stated the potential of Machine Learning in risk management in Fixed Income. 

Machine Learning as a tool could be adopted to implement risk management strategies faster than traditional methods. Especially when one considers that United’s stock fell 76% within minutes of a Google News post about a 6-year-old Chicago Tribune story about United’s 2002 bankruptcy. 

Applying Machine Learning could help achieve better management in fixed income seems to be more convincing as the market could react so fast to a piece of information. 

And the application could far more beyond only adjusting hedging portfolios, but also in processing non-reliable information, predicting possible influence on portfolios, etc. Nevertheless, applying Machine Learning has some potential risks such as overfitting when the model is trained to fit given data so well and may not be able to predict robustly when future environments change. 

Despite being nearly 20 years old, the introduction of “Future  Possibilities in Finance Theory and Finance Practice” still provides some insights into how the future fixed income markets could be affected by the application of Machine Learning.  

In the paper, the author Robert C. Merton mentioned that there are four markets including households, endowments, non-financial firms, and governments for financial innovation, while among all the four sectors, one can see how Machine Learning could be a game changer in terms of financial services for households. 

The current dilemma created by technological innovation and widespread deregulation is that households must make a wide range of important and risky financial decisions which they do not have to and are trained to in the past. 

Therefore, to adapt to the future trend of customer-centric marketing, the application of Machine Learning  and Artificial Intelligence could be the solution. 

Instead of general advice from the Internet, households could seek more customized fixed income investing plans provided by a well-trained model that reacts fast and predicts accurately by aggregating historical data, regulations, ongoing news, and more importantly, the different situations households are facing.  

Overall, Machine Learning has the potential to lead an evolution, meanwhile, the potential risks should also be fully considered. 

ReferenceMerton, R. C. (2002). Future possibilities in finance theory and finance practice. In Mathematical Finance—Bachelier Congress 2000 (pp. 47-73). Springer, Berlin, Heidelberg.

Machine Learning In Fixed Income Markets

Written by Xinyan X Zhang

Edited by Jimei Shen & Calvin Ma

Leading Artificial Intelligence and Financial Advisor

Rebellion Research