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.
Survey explores geometric aspects of policy optimization in control systems.
Study of participating policies with guaranteed minimum interest rate and surrender option.
Study examines how economic policy uncertainty impacts stock markets.
Algorithmic stablecoins optimize monetary policy to balance price stability.
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…
We present an elementary analysis of the dynamical aspects of the GDP / government surplus multiplier with relevance to the assessment of a country's debt repayment policy. We show the (at first) counter intuitive result that in order to reduce the Debt/GDP ratio, countries with high Debt to GDP should go into further …
This study analyzes EU ETS literature trends using bibliometric methods.
This paper examines market misconduct in DeFi and proposes regulatory solutions.
Policy shifts between Trump and Biden impact ESG investments, creating volatility.
Unified minimax value interval for off-policy evaluation and optimization.
This work characterizes reward function partial identifiability and its impact on policy optimization.
We explain a persistent cost-of-carry spread in EUA market and suggest ECB policy change.
FPGs use structure to improve policy learning in complex tasks.
Paper combines RL with policy regularization for inventory policies.
Log-ergodic model improves velocity of money prediction.
We investigate statistical uncertainty quantification for reinforcement learning (RL) and its implications in exploration policy. Despite ever-growing literature on RL applications, fundamental questions about inference and error quantification, such as large-sample behaviors, appear to remain quite open. In this paper…
The paper investigates the effects of invalid action masking in policy gradient algorithms.
This study shows how trade policy uncertainty affects stock-T bill correlations.
Proposes a framework to reconcile policy learning and profit maximization in CATE estimation.
Machine Learning community is recently exploring the implications of bias and fairness with respect to the AI applications. The definition of fairness for such applications varies based on their domain of application. The policies governing the use of such machine learning system in a given context are defined by the c…
Germany's tax admin costs likely exceed 20% of total revenue, requiring system improvement.
Study assesses risks of European Safe Bonds using credit risk models.
This paper outlines a critical gap in the assessment methodology used to estimate the macroeconomic costs and benefits of climate policy. It shows that the vast majority of models used for assessing climate policy use assumptions about the financial system that sit at odds with the observed reality. In particular, the …
Study off-policy evaluation and learning in dynamic pricing with context.
This study analyzes public debts and deficits between European countries. The statistical evidence here seems in general to reveal that sovereign debts and government deficits of countries within European Monetary Unification-in average- are getting worse than countries outside European Monetary Unification, in particu…
PPO's gradients are heavy-tailed, affecting learning; a robust estimator improves performance.
New algorithm learns policies without uniform overlap assumption.
Venice used 'helicopter money' to subsidize during famine and plague, but it caused instability.
We are witnessing an increasing use of data-driven predictive models to inform decisions. As decisions have implications for individuals and society, there is increasing pressure on decision makers to be transparent about their decision policies. At the same time, individuals may use knowledge, gained by transparency, …
The paper uses machine learning to optimize rework policies in semiconductor manufacturing.
Study shows climate change can cause a 'run on fossil fuels' affecting prices and production.
Learning from demonstration has been widely studied in machine learning but becomes challenging when the demonstrated trajectories are unstructured and follow different objectives. This short-paper proposes PODNet, Plannable Option Discovery Network, addressing how to segment an unstructured set of demonstrated traject…
Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. While it is often difficult to directly define the behavior of the agents, simple communicatio…
When recruiting job candidates, employers rarely observe their underlying skill level directly. Instead, they must administer a series of interviews and/or collate other noisy signals in order to estimate the worker's skill. Traditional economics papers address screening models where employers access worker skill via a…
We consider the relationship between economic activity and intervention, including monetary and fiscal policy, using a universal dynamic framework. Central bank policies are designed for growth without excess inflation. However, unemployment, investment, consumption, and inflation are interlinked. Understanding dynamic…
New approach to off-policy evaluation connects causal graph to policy effects.
Evaluating AI investment strategies
There is an emerging trend in the reinforcement learning for healthcare literature. In order to prepare longitudinal, irregularly sampled, clinical datasets for reinforcement learning algorithms, many researchers will resample the time series data to short, regular intervals and use last-observation-carried-forward (LO…
Study examines UK firms' financial performance linked to corporate governance.
As reinforcement learning agents are tasked with solving more challenging and diverse tasks, the ability to incorporate prior knowledge into the learning system and to exploit reusable structure in solution space is likely to become increasingly important. The KL-regularized expected reward objective constitutes one po…
The study categorizes reward errors in reinforcement learning, finding some can be beneficial.
The paper studies reward concentration in MDPs, covering asymptotic and non-asymptotic settings.
Analyzing available FAO data from 176 countries over 21 years, we observe an increase of complexity in the international trade of maize, rice, soy, and wheat. A larger number of countries play a role as producers or intermediaries, either for trade or food processing. In consequence, we find that the trade networks bec…
Information systems experience an ever-growing volume of unstructured data, particularly in the form of textual materials. This represents a rich source of information from which one can create value for people, organizations and businesses. For instance, recommender systems can benefit from automatically understanding…
The aim of this paper is to introduce a method for computing the allocated Solvency II Capital Requirement (SCR) of each Risk which the company is exposed to, taking in account for the diversification effect among different risks. The method suggested is based on the Euler principle. We show that it has very suitable p…
An agent learning through interactions should balance its action selection process between probing the environment to discover new rewards and using the information acquired in the past to adopt useful behaviour. This trade-off is usually obtained by perturbing either the agent's actions (e.g., e-greedy or Gibbs sampli…
Paper investigates differentiable fuzzy implications and their suitability for learning.
New framework for robust reinforcement learning policies in uncertain environments.