> ## Content Index
> Fetch the complete content index at: https://fsc.stevens.edu/llms.txt
> Use this file to discover other available public pages before exploring further.

# Corporate Bond Yield Predictability: A Statistical and Machine Learning Approach
- URL: https://fsc.stevens.edu/corporate-bond-yield-predictability-a-statistical-and-machine-learning-approach/
- Published: 2023-01-25T17:29:41.000Z
- Updated: 2026-05-16T22:33:05.000Z
- Description: Investing in bonds is becoming more popular among investors due to the increasing uncertainty of the financial world. This research investigates the out-of-sample predictability of corporate bond yield using BVAL curve values for the period April 2011 to June 2020 for three
- Author: hfsladmin
- Tags: 2020, #Import 2024-05-30 10:26, #Import 2025-11-08 22:14

**Author:** Agathe Sadeghi  
**Degree:** M.S. in Financial Engineering  
**Year:** 2020  
**Advisory Committee:** Dr. Dragos Bozdog, Dr. Ionut Florescu

**Abstract:** Investing in bonds is becoming more popular among investors due to the increasing uncertainty of the financial world. This research investigates the out-of-sample predictability of corporate bond yield using BVAL curve values for the period April 2011 to June 2020 for three tickers of JPM, GS and IBM. Multiple models using statistical and machine learning approaches are developed and the best models are selected based on the performance measures. The random forest model has a better performance when taking forward rates as the explanatory variable. In contrast, the autoregressive integrated moving average models demonstrate a higher performance measure values when the independent variable is considered as forward-spot spreads.