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Machine Learning for Better Models for Predicting Bond Prices

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  • Swetava Ganguli
  • Jared Dunnmon

Abstract

Bond prices are a reflection of extremely complex market interactions and policies, making prediction of future prices difficult. This task becomes even more challenging due to the dearth of relevant information, and accuracy is not the only consideration--in trading situations, time is of the essence. Thus, machine learning in the context of bond price predictions should be both fast and accurate. In this course project, we use a dataset describing the previous 10 trades of a large number of bonds among other relevant descriptive metrics to predict future bond prices. Each of 762,678 bonds in the dataset is described by a total of 61 attributes, including a ground truth trade price. We evaluate the performance of various supervised learning algorithms for regression followed by ensemble methods, with feature and model selection considerations being treated in detail. We further evaluate all methods on both accuracy and speed. Finally, we propose a novel hybrid time-series aided machine learning method that could be applied to such datasets in future work.

Suggested Citation

  • Swetava Ganguli & Jared Dunnmon, 2017. "Machine Learning for Better Models for Predicting Bond Prices," Papers 1705.01142, arXiv.org.
  • Handle: RePEc:arx:papers:1705.01142
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    Cited by:

    1. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
    2. Verner, Robert & Tkáč, Michal, 2023. "On the predictability of bonds," Finance Research Letters, Elsevier, vol. 57(C).
    3. Vitalija Serapinaitė & Audrius Kabašinskas, 2021. "Clustering of Latvian Pension Funds Using Convolutional Neural Network Extracted Features," Mathematics, MDPI, vol. 9(17), pages 1-45, August.
    4. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    5. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).

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