arXiv · arXiv q-fin · 2025
We investigate the application of quantum cognition machine learning (QCML), a novel paradigm for both supervised and unsupervised learning tasks rooted in the mathematical formalism of quantum theory, to distance metric learning in corporate bond markets. Compared to equities, corporate bonds are relatively illiquid and both trade and quote data in these securities are relatively sparse. Thus, a measure of distance/…
Joshua Rosaler, Luca Candelori, Vahagn Kirakosyan, Kharen Musaelian, Ryan Samson
arXiv · arXiv q-fin · 2011
Diffusion in a linear potential in the presence of position-dependent killing is used to mimic a default process. Different assumptions regarding transport coefficients, initial conditions, and elasticity of the killing measure lead to diverse models of bankruptcy. One "stylized fact" is fundamental for our consideration: empirically default is a rather rare event, especially in the investment grade categories of cre…
Yuri A. Katz
arXiv · arXiv q-fin · 2022
The primary aim of this research was to find a model that best predicts which fallen angel bonds would either potentially rise up back to investment grade bonds and which ones would fall into bankruptcy. To implement the solution, we thought that the ideal method would be to create an optimal machine learning model that could predict bankruptcies. Among the many machine learning models out there we decided to pick fo…
Harrison Mateika, Juannan Jia, Linda Lillard, Noah Cronbaugh, Will Shin
arXiv · arXiv q-fin · 2010
We derive simple return models for several classes of bond portfolios. With only one or two risk factors our models are able to explain most of the return variations in portfolios of fixed rate government bonds, inflation linked government bonds and investment grade corporate bonds. The underlying risk factors have natural interpretations which make the models well suited for risk management and portfolio design.
Matti Koivu, Teemu Pennanen
arXiv · arXiv q-fin · 2018
We present two methodologies on the estimation of rating transition probabilities within Markov and non-Markov frameworks. We first estimate a continuous-time Markov chain using discrete (missing) data and derive a simpler expression for the Fisher information matrix, reducing the computational time needed for the Wald confidence interval by a factor of a half. We provide an efficient procedure for transferring such …
Marius Pfeuffer, Goncalo dos Reis, Greig smith
arXiv · arXiv q-fin · 2012
Maximum likelihood estimation applied to high-frequency data allows us to quantify intermittency in the fluctu- ations of asset prices. From time records as short as one month these methods permit extraction of a meaningful intermittency parameter λ characterising the degree of volatility clustering of asset prices. We can therefore study the time evolution of volatility clustering and test the statistical significan…
Martin Rypdal, Espen Sirnes, Ola Løvsletten, Kristoffer Rypdal
OpenAlex · The Journal of Finance · 2007 · cites 1130
ABSTRACT We find that liquidity is priced in corporate yield spreads. Using a battery of liquidity measures covering over 4,000 corporate bonds and spanning both investment grade and speculative categories, we find that more illiquid bonds earn higher yield spreads, and an improvement in liquidity causes a significant reduction in yield spreads. These results hold after controlling for common bond‐specific, firm‐spec…
Long Chen, David A. Lesmond, Jason Zhanshun Wei
arXiv · arXiv q-fin · 2009
We use the theory of large deviations to study the pricing of investment-grade tranches of synthetic CDO's. In this paper, we consider a simplified model which will allow us to introduce some of the concepts and calculations.
Richard B. Sowers
arXiv · arXiv q-fin · 2009
We use the theory of large deviations to study the pricing of investment-grade tranches of synthetic CDO's. In this paper, we consider a heterogeneous pool of names. Our main tool is a large-deviations analysis which allows us to precisely study the behavior of a large amount of idiosyncratic randomness. Our calculations allow a fairly general treatment of correlation.
Richard B. Sowers
arXiv · arXiv q-fin · 2025
We create a time series model for annual returns of three asset classes: the USA Standard & Poor (S&P) stock index, the international stock index, and the USA Bank of America investment-grade corporate bond index. Using this, we made an online financial app simulating wealth process. This includes options for regular withdrawals and contributions. Four factors are: S&P volatility and earnings, corporate BAA rate, and…
Andrey Sarantsev, Angel Piotrowski, Ian Anderson
arXiv · arXiv q-fin · 2010
We consider the effect of recovery rates on a pool of credit assets. We allow the recovery rate to depend on the defaults in a general way. Using the theory of large deviations, we study the structure of losses in a pool consisting of a continuum of types. We derive the corresponding rate function and show that it has a natural interpretation as the favored way to rearrange recoveries and losses among the different t…
Konstantinos Spiliopoulos, Richard B. Sowers
Semantic Scholar · Financial Innovation · 2024 · cites 1
Financing sources for urban construction have garnered significant attention globally. Among various financing methods, the urban construction investment bond (UCIB) is unique to China. The UCIB credit spread, which represents the compensation for credit risk, has become a focal point for researchers. However, owing to shortcomings of previous approaches, few scholars have accurately assessed the impact of implicit g…
Rongda Chen, Han Li, Xuhui Tang, Chenglu Jin, Shuonan Zhang
arXiv · arXiv q-fin · 2026
Invoice or payment dilution is the gap between the approved invoice amount and the actual collection is a significant source of non credit risk and margin loss in supply chain finance. Traditionally, this risk is managed through the buyer's irrevocable payment undertaking (IPU), which commits to full payment without deductions. However, IPUs can hinder supply chain finance adoption, particularly among sub-invested gr…
Pavel Koptev, Vishnu Kumar, Konstantin Malkov, George Shapiro, Yury Vikhanov
arXiv · arXiv · 2026
This paper compares a series of contemporary portfolio construction approaches by employing ten U.S. stocks (TSLA, WMT, BAC, GS, LLY, MRK, GOOG, META, AAPL and XOM) in a time frame from September 2023 to December 2025. The paper explores both basic mean-variance optimization, constrained optimization, Fama French five factor regression modeling, Monte Carlo simulation, and the Black-Litterman model to determine how c…
Ajay Kumar Verma, Shravya Barkam
arXiv · arXiv · 2026
We provide a large-scale empirical audit of DEX routing using 2.98 million WETH-USDC swaps on Ethereum. Comparing realized routes with optimized benchmarks, we measure an average shortfall of 2.02 bps per trade or \$24 million. To attribute losses, we introduce three reproducible optimal benchmarks: a Support-Constrained Optimum (SCO) that evaluates split quality conditional on the pools actually used; a Full-Venue O…
Weiye Xi, Ciamac C. Moallemi
arXiv · arXiv · 2026
We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance Spot paper policies after assumed costs. Numerical results come from scripted fixed-seed model runs and deterministic simulators; human-supervised AI agents supported the July 20 evidence-integrity revision through literature retrieval, separately tasked critique, artifa…
Ayoub Jadouli