Shorting IG ETFs can hedge bond portfolios during market drawdowns effectively.
problem Managing downside risk in bond portfolios during market crises.
method Constructing three signals (Momentum, Liquidity, Credit) to dynamically hedge short IG positions.
result Dynamic hedge removes when predicted hedged return mean reverts, achieving higher returns and Sortino ratios.
QCML improves bond similarity learning in illiquid markets.
problem Improving similarity learning for illiquid corporate bonds.
method Quantum Cognition Machine Learning (QCML) for supervised distance metric learning.
result QCML outperforms classical tree-based models in high-yield markets.
Paper proves a spinorial version of Aubin's estimate for the Yamabe problem.
problem Solving the Yamabe problem for spin manifolds.
method Spinorial approach using the Dirac operator and conformal metrics.
result Shows a spinorial analogue to Aubin's estimate for the Yamabe problem.
RIG extends IG to Riemannian manifolds for explainable AI.
problem Lack of explainability in AI models.
method Extension of Integrated Gradients to Riemannian manifolds.
result RIG restricts to IG in Euclidean space.
Incremental gradient (IG) methods, such as stochastic gradient descent and its variants are commonly used for large scale optimization in machine learning. Despite the sustained effort to make IG methods more data-efficient, it remains an open question how to select a training data subset that can theoretically and pra…
Adapts IG for better feature attributions and robustness.
problem Reliability concerns in feature attributions for deep learning models.
method Adaptation of path-based feature attribution to Riemannian geometry of data manifolds.
result IG along geodesics generates more intuitive and robust explanations.
PS-IG improves feature attribution by reducing noise and variance.
problem Improving feature attribution in machine learning models.
method Path-sampled integrated gradients (PS-IG) computes expected value over sampled baselines.
result PS-IG reduces attribution variance by a factor of 1/3 under uniform sampling.
Codebook for Institutional Grammar 2.0 simplifies policy encoding.
problem Facilitating consistent policy encoding for diverse analytical needs.
method Revised Institutional Grammar with multiple levels of expressiveness.
result Enhanced flexibility and specificity in policy encoding.
In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing acti…
Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarit…
IG-RL learns adaptive traffic signals for any network, outperforming existing methods.
problem Adaptive traffic signal control for large networks with combinatorial state and action spaces.
method Graph-Convolutional Networks for decentralized, flexible control.
result IG-RL generalizes to new networks and traffic conditions without additional training.
New formulas for feature importance tests in regression models.
problem Identifying important features in regression models.
method Established formulas for AUC criteria and proposed alternative metrics.
result Integrated Gradients (IG) performs nearly as well as Kernel SHAP (KS) but is faster.
We introduce Generalized Integrated Gradients (GIG), a formal extension of the Integrated Gradients (IG) (Sundararajan et al., 2017) method for attributing credit to the input variables of a predictive model. GIG improves IG by explaining a broader variety of functions that arise from practical applications of ML in do…
An emerging problem in trustworthy machine learning is to train models that produce robust interpretations for their predictions. We take a step towards solving this problem through the lens of axiomatic attribution of neural networks. Our theory is grounded in the recent work, Integrated Gradients (IG), in axiomatical…
A three dimensional supergravity theory which generalizes the super IG theory of Witten and resembles the model discussed recently by Mann and Papadopoulos is displayed. The partition function is computed, and is shown to be a three-manifold invariant generalizing the Casson invariant.
We develop a theory of higher-order feature attribution for complex models.
problem Interpreting feature contributions in models with interactions is challenging.
method We extend Integrated Gradients (IG) to higher-order feature attributions.
result We establish natural connections to statistics and topological signal processing.
Enhanced visual feature attribution via adaptive baseline weighting.
problem IG's sensitivity to baseline images leads to noisy or unstable explanations.
method Weighted Integrated Gradients (WG) evaluates and weights baselines for improved reliability.
result WG improves over Expected Gradients (EG) by up to 36% across various models.
Study of bonded knots and braids with new algebraic models.
problem Classifying and understanding physical or chemical bonds in knots and braids.
method Developed new algebraic models (bonded knots, braids, braidoids) and invariants.
result New algebraic structures and invariants for bonded knots and braids.
Developed algebraic theory of bonded braids, proving Markov theorem.
problem Studied bonded knots and their algebraic properties.
method Introduced bonded braid monoid, proved Alexander and Markov theorems.
result Every bonded knot is the closure of a bonded braid, and equivalent knots have related braid representatives.
Local explanation methods, also known as attribution methods, attribute a deep network's prediction to its input (cf. Baehrens et al. (2010)). We respond to the claim from Adebayo et al. (2018) that local explanation methods lack sensitivity, i.e., DNNs with randomly-initialized weights produce explanations that are bo…
Study finds it hard to establish common factor pricing in corporate bonds.
problem Difficulty in establishing common factor pricing in corporate bonds.
method Portfolio- and bond-level analyses using multifactor models.
result Common factor pricing in corporate bonds is not significantly explanatory.
In the present paper we show that the Binomial-tree approach for pricing, hedging, and risk assessment of Convertible bonds in the framework of the Tsiveriotis-Fernandes model has serious drawbacks. Key words: Convertible bonds, Binomial tree, Tsiveriotis-Fernandes model, Convertible bond pricing, Convertible bond Gree…
Model shows government incentives boost green bond investment.
problem Increasing green investments through government incentives.
method Optimal incentives indexed on bond prices and covariation, applied to a portfolio of bonds.
result Method outperforms current tax-incentives systems in green investments.
Model proteins with bonds using Kauffman bracket skein module.
problem Modeling proteins with bonds for structural analysis.
method Extend Kauffman bracket polynomial to bonded knots.
result Infinite generation and torsion-freeness of the bonded skein module.
This article presents valuation of Treasury Bonds (T-Bonds) on Macedonian Stock Exchange (MSE) and empirical test of duration, modified duration and convexity of the T-bonds at MSE in order to determine sensitivity of bonds prices on interest rate changes. The main goal of this study is to determine how standard valuat…
Paper uses PCA to analyze Chinese sovereign bonds and discusses bond immunization.
problem Analyzing factors affecting Chinese sovereign bond yield changes.
method Applied Principal Component Analysis (PCA) on bond yield data.
result Identified principal factors influencing Chinese sovereign bond yield changes.
This paper describes a new method of bond portfolio optimization based on stochastic string models of correlation structure in bond returns. The paper shows how to approximate correlation function of bond returns, compute the optimal portfolio allocation using Wiener-Hopf factorization, and check whether a collection o…
Study finds implicit government guarantee improves municipal investment bond ratings.
problem Questioning the objectivity of municipal investment bond ratings due to implicit government guarantee.
method Text mining of policy documents and PMC index model for implicit guarantee strength calculation.
result Implicit government guarantee boosts municipal investment bond ratings, especially in less developed regions.
Classifies uncolored bonded knots with up to 7 singularity points.
problem Classifying uncolored bonded knots with up to 7 singularity points.
method Generation of planar graphs, conversion into bonded knot diagrams, use of Yamada polynomial, and brute-force Reidemeister moves.
result Systematic classification of uncolored bonded knots with singularity number at most seven.
Paper proposes a framework for precise daily default risk prediction of Chinese credit bonds.
problem Inadequate and inaccurate bond information disclosure creates risk of default for investors.
method Framework includes summarizing factors impacting defaults, constructing a risk index system, and using ConvLSTM neural network for prediction.
result The model provides more responsive and accurate daily default risk predictions than authoritative ratings.
We show that the martingale component in the long-term factorization of the stochastic discount factor due to Alvarez and Jermann (2005) and Hansen and Scheinkman (2009) is highly volatile, produces a downward-sloping term structure of bond Sharpe ratios, and implies that the long bond is far from growth optimality. In…
Paper analyzes pricing model for bonds with early redemption.
problem Analyzing pricing of bonds with early redemption features.
method Structural approach for mathematical modeling of bond prices.
result Existence and uniqueness of default and early redemption boundaries proved.
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 nat…
A machine learning model improves relative valuation of municipal bonds.
problem Challenges in determining the value or relative value of municipal bonds.
method Proposes a supervised similarity framework using CatBoost algorithm to identify similar bonds based on risk profiles.
result The similarity-based method outperforms rule-based and heuristic-based methods in back-testing.
We propose an option approach for pricing bond illiquidity that is reminiscent of the celebrated work of Longstaff (1995) on the non-marketability of some non-dividend-paying shares in IPOs. This approach describes a quite common situation in the fixed income market: it is rather usual to find issuers that, besides liq…
Study proposes framework for cyber bonds to compensate cyber attack losses.
problem Cyber risk treatment in finance industry.
method Developed a framework, used publicly available data to determine loss distribution parameters, numerically simulated bond price and characteristics, considered two coupon calculation approaches.
result Numerical simulations of cyber bond price, yield, and characteristics.
Paper uses machine learning to uncover nonlinear dynamics in CAT bond pricing.
problem Traditional linear models miss nonlinear relationships in CAT bond pricing.
method Advanced machine learning techniques applied to CAT bond transaction records.
result Machine learning enhances CAT bond pricing accuracy and reveals complex risk interactions.
In this paper, we are concerned with the valuation of Catastrophic Mortality Bonds and, in particular, we examine the case of the Swiss Re Mortality Bond 2003 as a primary example of this class of assets. This bond was the first Catastrophic Mortality Bond to be launched in the market and encapsulates the behaviour of …
Deep learning speeds CAT bond valuation.
problem Valuation of Catastrophe bonds.
method Deep neural networks trained to price CAT bonds.
result Trained model provides fast and accurate pricing.
Paper proposes an analytical pricing model for puttable bonds with credit risk.
problem Analytical pricing of puttable bonds with credit risk.
method Developed a 2-factor structural PDE model and derived analytical pricing formula under specific conditions.
result Derived analytical pricing formula for puttable bonds with credit risk.
BondBERT improves sentiment analysis for bond markets.
problem Misleading sentiment models for bond markets due to domain-specific dynamics.
method Fine-tuned transformer model on bond-specific news.
result BondBERT produces positive correlations with bond returns and higher forecasting accuracy.
Study predicts bond yields using machine learning and ultimate forward rates.
problem Forecasting bond yields using ultimate forward rates.
method Applied de Kort-Vellekooptype methodology for UFR estimation, used linear and nonlinear machine learning techniques.
result Nonlinear machine learning models outperform linear models in bond yield forecasting.
The paper uses option theory to estimate corporate bond liquidity spreads.
problem Estimating liquidity spreads for corporate bonds.
method Option-theoretic approach considering risk-free rate volatility and credit risk.
result The model provides a robust tool for pricing illiquid bonds.
There is an observed basis between repo discounting, implied from market repo rates, and bond discounting, stripped from the market prices of the underlying bonds. Here, this basis is explained as a convexity effect arising from the decorrelation between the discount rates for derivatives and bonds. Using a Hull-White …
Investor optimizes consumption and investment in a bond market described by HJM model.
problem Maximizing consumption utility in a bond market with varying maturity bonds.
method Solves consumption-investment problem using HJM factor model and HJB equation.
result Proves the regularity of the solution and applicability of the verification theorem.
Study analyzes bond traders' views on equity market dynamics.
problem Understanding temporal shifts in equity market parameters.
method Utilizes Black-Derman-Toy model and zero-coupon bond pricing.
result Discovers correlations between risk-neutral probability and market variables.
An invariant for knots with colored bonds respects HOMFLYPT relation.
problem Modeling and distinguishing knots with colored bonds.
method Introducing a HOMFLYPT skein module for colored bonded knots.
result The non-rigid version of the module provides information about knottedness of bonds.
Deep learning models price convertible bonds with complex reset and call features.
problem Pricing convertible bonds with path-dependent reset and call provisions.
method Formulated as a PPDE, deep learning approximates conditional expectations.
result Deep learning produces stable and accurate prices across various model specifications.