Machine Learning: Origins, Developments and Implications
First published:
ISBN: 978-1-78272-445-2
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Predicting corporate bond returns
Preface
Introduction: human-machine entanglement
Machine learning: origins
Useful tools
Decision trees
Introduction to neural networks
Back-propagation
Regularisation
Optimisation
Building neural networks
Early applications of machine learning
Interpreting neural network decisions
Predicting corporate bond returns
Deep learning networks
Applications of deep learning networks
Machine intelligence
Consciousness
The future and its challenges
Artificial intelligence and the military
Final thoughts
Appendix
Epilogue
Acknowledgements
This chapter presents models designed to predict directional moves in corporate bond prices. Again, I necessarily present work of mine,11 This work was done with Glen McDermott, Alexi Kroujiline and Jure Skarabot. given the lack of access to the work of others. I have been fortunate for the opportunity to bring several models to production, and to share my experiences with the reader. To understand the models and their rationale, some background knowledge on financial domains is useful. Accordingly, this chapter begins with a brief tutorial on financial markets.
12.1 THE CORPORATE BOND MARKET
Credit is the extension of access to liquid assets today (typically cash) in return for a promise to pay in the future. When we think of credit instruments (ie, loans and bonds), we think of the debt that one party owes to another. In a debt transaction, there is usually a lender and a borrower. Common debt instruments (in particular, those with maturities longer than one year) are coupon-bearing instruments called bonds and loans. One type of debt without coupons is a zero-coupon bond or discount bond. Figure 12.1 shows the cashflows for a single US dollar invested at time t = 0 to be repaid at
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