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.
Improving software quality through effective organizational learning.
A new indicator measures project risk from activity durations.
Simplifying machine learning (ML) application development, including distributed computation, programming interface, resource management, model selection, etc, has attracted intensive interests recently. These research efforts have significantly improved the efficiency and the degree of automation of developing ML mode…
Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed machine learning (ML) at the top of research, economic and political agendas. Such unprecedented interest is fuelled by a vision of ML applica…
The process of exploring and exploiting Oil and Gas (O&G) generates a lot of data that can bring more efficiency to the industry. The opportunities for using data mining techniques in the "digital oil-field" remain largely unexplored or uncharted. With the high rate of data expansion, companies are scrambling to develo…
Deployment of machine learning (ML) algorithms in production for extended periods of time has uncovered new challenges such as monitoring and management of real-time prediction quality of a model in the absence of labels. However, such tracking is imperative to prevent catastrophic business outcomes resulting from inco…
The results of data mining endeavors are majorly driven by data quality. Throughout these deployments, serious show-stopper problems are still unresolved, such as: data collection ambiguities, data imbalance, hidden biases in data, the lack of domain information, and data incompleteness. This paper is based on the prem…
We present the "Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles" (ABOUT ML) project as an initiative to operationalize ML transparency and work towards a standard ML documentation practice. We make the case for the project's relevance and effectiveness in consolidating dispa…
A framework combining HSMM and survival analysis for lifecycle-oriented mobility analysis.
The paper optimizes DIA purchase policies using lifecycle models and asset allocation.
A dynamic model of the product lifecycle of (nearly) homogeneous durables in polypoly markets is established. It describes the concurrent evolution of the unit sales and price of durable goods. The theory is based on the idea that the sales dynamics is determined by a meeting process of demanded with supplied product u…
Homeownership boosts wealth and welfare compared to renting, according to new research.
Focuses on monitoring and explaining models in real-world applications.
RED-2400 is a public benchmark of trading events from a Solana exchange, labeled by algorithmic rejection.
A new microeconomic model is presented that aims at a description of the long-term unit sales and price evolution of homogeneous non-durable goods in polypoly markets. It merges the product lifecycle approach with the price dispersion dynamics of homogeneous goods. The model predicts a minimum critical lifetime of non-…
The paper proposes a method to identify fair features in ML data integration.
Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering chal…
We extend the lifecycle model (LCM) of consumption over a random horizon (a.k.a. the Yaari model) to a world in which (i.) the force of mortality obeys a diffusion process as opposed to being deterministic, and (ii.) a consumer can adapt their consumption strategy to new information about their mortality rate (a.k.a. h…
The Australian Government uses the means-test as a way of managing the pension budget. Changes in Age Pension policy impose difficulties in retirement modelling due to policy risk, but any major changes tend to be `grandfathered' meaning that current retirees are exempt from the new changes. In 2015, two important chan…
The paper introduces deep learning for ALM, enhancing asset and liability management.
The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine lear…
Quantum computers can optimize foreign exchange reserves management.
GenAI offers financial benefits but requires risk management.
This review classifies electricity price models for risk management.
Quantum computing offers financial industry new optimization and risk management tools.
Portfolio management is the art and science in fiance that concerns continuous reallocation of funds and assets across financial instruments to meet the desired returns to risk profile. Deep reinforcement learning (RL) has gained increasing interest in portfolio management, where RL agents are trained base on financial…
AI enhances bank credit risk management through deep learning and data analysis.
Deep learning enhances financial asset management through new models and data sources.
The paper audits trading filters, finding a high save-to-miss ratio.
Machine learning improves wildfire science and management, but requires expert knowledge.
Model cash management under ambiguity using maxmin preferences and diffusion.
The paper uses clustering and integer programming to optimize stock selection for investment funds.
Financial institutions face new model risks with AI, requiring enhanced model risk management.
Traditional centralized energy systems have the disadvantages of difficult management and insufficient incentives. Blockchain is an emerging technology, which can be utilized in energy systems to enhance their management and control. Integrating token economy and blockchain technology, token economic systems in energy …
In this chapter the complex systems are discussed in the context of economic and business policy and decision making. It will be showed and motivated that social systems are typically chaotic, non-linear and/or non-equilibrium and therefore complex systems. It is discussed that the rapid change in global consumer behav…
New characterization of second-order stochastic dominance with applications in risk management.
Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems. We leverage the insights from the interviews and we enumerate the gaps in perspective in securing machine l…
Deep learning enhances water resources management through data analysis.
The 20/60/20 rule improves risk management and portfolio optimization in finance.
Three methods detect informed trading on prediction markets, each focusing on different aspects.
Paper presents a risk management framework for blockchain protocols.
Deep quantum neural networks applied to finance for efficient risk management.
The paper tackles revenue management with time-varying demand using posterior sampling.
Study of portfolio management under relative performance concerns using mean field games.
We construct new multivariate copulas on the basis of a generalized infinite partition-of-unity approach. This approach allows - in contrast to finite partition-of-unity copulas - for tail-dependence as well as for asymmetry. A possibility of fitting such copulas to real data from quantitative risk management is also p…
Combines VaR and ES forecasts for cryptocurrency market risk management.
Neural networks predict ETF performance using financial data.
This study examines non-retail trading on Polymarket, revealing unique behavior patterns and structural limitations.