A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Examines how extending home loan durations affects French households financially.
New mortgage contracts reduce underwater default by adjusting loan balances, but must balance prepayment incentives.
Optimal buying and selling times for homes in fluctuating interest rates.
Strong regulations in the financial industry mean that any decisions based on machine learning need to be explained. This precludes the use of powerful supervised techniques such as neural networks. In this study we propose a new unsupervised and semi-supervised technique known as the topological hierarchical decomposi…
Improved credit scoring model with explainability.
Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoT-based applications is smart homes for healthcare, especially for elders. IoT-based smart homes enable elders' health to be properly monitored and taken care of. However, elder…
The paper introduces mortgage-rate-adjusted home prices to help buyers and adjust housing indices.
XGBoost predicts bank loan defaults with improved accuracy.
In this paper we first introduce two new financial products: stock loan and capped stock loan. Then we develop a pure variational inequality method to establish explicitly the values of these stock loans. Finally, we work out ranges of fair values of parameters associated with the loans.
In a voice-controlled smart-home, a controller must respond not only to user's requests but also according to the interaction context. This paper describes Arcades, a system which uses deep reinforcement learning to extract context from a graphical representation of home automation system and to update continuously its…
New method learns time-varying home field advantage in football.
This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that uses consumer behavior data only and uses machine learning to suggest actions for inhabitants to reduce the energy consumption of their ho…
Two models predict net loan losses using Bayesian and frequentist regression.
Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target ho…
Current auto loans converge to super-prime credit despite remaining underwater.
Study uses SVM to predict weather-induced home insurance claims and losses.
An integrated and extendable approach for stress-testing loan portfolios
A stock loan is a loan, secured by a stock, which gives the borrower the right to redeem the stock at any time before or on the loan maturity. The way of dividends distribution has a significant effect on the pricing of the stock loan and the optimal redeeming strategy adopted by the borrower. We present the pricing mo…
Extends ASRF model for green and brown loans, accounting for systematic and idiosyncratic risks.
Paper solves stock loan pricing with finite maturity using integral equations.
This paper discusses how usage patterns and preferences of inhabitants can be learned efficiently to allow smart homes to autonomously achieve energy savings. We propose a frequent sequential pattern mining algorithm suitable for real-life smart home event data. The performance of the proposed algorithm is compared to …
Logistic Regression and Support Vector Machine algorithms, together with Linear and Non-Linear Deep Neural Networks, are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans. A two phase model is proposed; the first phase predicts loan rejection,…
Derivatives impact U.S. banking sector's systemic risk, but loan and leverage ratios are more significant.
We derive a "semi-analytic" solution for a stock loan in which the lender forces liquidation when the loan-to-collateral ratio drops beneath a certain threshold. We use this to study the sensitivity of the contract to model parameters.
Optimal student loan repayment strategies vary based on loan size.
Analyzes national real estate investment risks and returns.
This paper works out fair values of stock loan model with automatic termination clause, cap and margin. This stock loan is treated as a generalized perpetual American option with possibly negative interest rate and some constraints. Since it helps a bank to control the risk, the banks charge less service fees compared …
Paper calculates loan loss after default using Bayesian model.
A stock loan is a contract whereby a stockholder uses shares as collateral to borrow money from a bank or financial institution. In Xia and Zhou (2007), this contract is modeled as a perpetual American option with a time varying strike and analyzed in detail within a risk--neutral framework. In this paper, we extend th…
Kiva is an online non-profit crowdsouring microfinance platform that raises funds for the poor in the third world. The borrowers on Kiva are small business owners and individuals in urgent need of money. To raise funds as fast as possible, they have the option to form groups and post loan requests in the name of their …
In 1979 following a decade of hyperinflation, Iceland introduced Verðtryggð lán, negatively amortised, index-linked loans whose outstanding principal is increased by the rate of the consumer price inflation index(CPI). The loans were part of a general government policy which used indexation to the CPI to address the ec…
Flashot visualizes Flash Loan attacks in DeFi systems.
Paper uses BERT to assess P2P borrowers' credit risk from loan descriptions.
Study integrates ESG factors into home price predictions for U.S. cities.
The study examines how limited liability and haircut affect a bank's loan portfolio's liquidity risk.
Zero-Liquidation loans protect ETH borrowers from liquidation risks.
Retail investors set interest rates for P2P loans based on borrower characteristics.
The authors examine the concept of probability of default for asset-backed loans. In contrast to unsecured loans it is shown that probability of default can be defined as either a measure of the likelihood of the borrower failing to make required payments, or as the likelihood of an insufficiency of collateral value on…
Online Peer to Peer Lending (P2PL) systems connect lenders and borrowers directly, thereby making it convenient to borrow and lend money without intermediaries such as banks. Many recommendation systems have been developed for lenders to achieve higher interest rates and avoid defaulting loans. However, there has not b…
Quantum mechanics applied to credit loans for better repayment schedules.
Credit Scores are ubiquitous and instrumental for loan providers and regulators. In this paper we showcase how micro-loan credit system can be developed in real setting. We show what challenges arise and discuss solutions. Particularly, we are concerned about model interpretability and data quality. In the final sectio…
Optimizes loan recovery timing by forecasting cash flows.
5D AI model detects bad loans without biased features, improving consumer protection.
Model assesses loan profitability under changing credit conditions.
High-value transactions between Australian banks are settled in the Reserve Bank Information and Transfer System (RITS) administered by the Reserve Bank of Australia. RITS operates on a real-time gross settlement (RTGS) basis and settles payments sourced from the SWIFT, the Austraclear, and the interbank transactions e…
Deep neural networks reduce loan portfolio risk.
This paper studies the payoff amounts in simple interest loans without arbitrage.
This paper optimizes DC pension plan investments using O-U process and loan.