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.
The professional services sector is at a turning point, with some industries showing growth opportunities.
Study proposes new methods to calculate probabilistic benchmarks in noisy data.
Mean Field Games applied to finance and economics.
New method estimates corporate default probabilities using indirect data.
Deep learning enhances solving complex mean field games in finance.
Reviews six finance topics, including 'radical complexity'.
We study an agent-based model of evolution of wealth distribution in a macro-economic system. The evolution is driven by multiplicative stochastic fluctuations governed by the law of proportionate growth and interactions between agents. We are mainly interested in interactions increasing wealth inequality that is in a …
Labor productivity was studied at the microscopic level in terms of distributions based on individual firm financial data from Japan and the US. A power-law distribution in terms of firms and sector productivity was found in both countries' data. The labor productivities were not equal for nation and sectors, in contra…
I introduce Forecastable Component Analysis (ForeCA), a novel dimension reduction technique for temporally dependent signals. Based on a new forecastability measure, ForeCA finds an optimal transformation to separate a multivariate time series into a forecastable and an orthogonal white noise space. I present a converg…
We propose a vector auto-regressive (VAR) model with a low-rank constraint on the transition matrix. This new model is well suited to predict high-dimensional series that are highly correlated, or that are driven by a small number of hidden factors. We study estimation, prediction, and rank selection for this model in …
We show that a steady-state stock-flow consistent macro-economic model can be represented as a Constraint Satisfaction Problem (CSP).The set of solutions is a polytope, which volume depends on the constraintsapplied and reveals the potential fragility of the economic circuit,with no need to study the dynamics. Several …
In both finance and economics, quantitative models are usually studied as isolated mathematical objects --- most often defined by very strong simplifying assumptions concerning rationality, efficiency and the existence of disequilibrium adjustment mechanisms. This raises the important question of how sensitive such mod…
We show that an economic system populated by multiple agents generates an equilibrium distribution in the form of multiple scaling laws of conditional PDFs, which are sufficient for characterizing the probability distribution. The existence of the double scaling law is demonstrated empirically for the sales and the lab…
We introduce an innovative theoretical framework to model derivative transactions between defaultable entities based on the principle of arbitrage freedom. Our framework extends the traditional formulations based on Credit and Debit Valuation Adjustments (CVA and DVA). Depending on how the default contingency is accoun…
The study compares profitability of conventional and Islamic banks in Bangladesh.
This paper presents two cases of random banking data generators based on migration matrices and scoring rules. The banking data generator is a new hope in researches of finding the proving method of comparisons of various credit scoring techniques. There is analyzed the influence of one cyclic macro--economic variable …
The relationship between micro-structure and macro-structure of complex systems using information geometry has been dealt by several authors. From this perspective, we are going to apply it as a geometrical structure connecting both microeconomics and macroeconomics . The results lead us to introduce new modified quant…
In this article we will show that the Macro-Economy and its growth can be modelled and explained exactly in principle by commonly known Field Theory from theoretical physics. We will show the main concepts and calculations needed and show that calculation and prediction of economic growth then gets indeed possible in D…
I propose a frequency domain adaptation of the Expectation Maximization (EM) algorithm to group a family of time series in classes of similar dynamic structure. It does this by viewing the magnitude of the discrete Fourier transform (DFT) of each signal (or power spectrum) as a probability density/mass function (pdf/pm…
GPR ensemble method predicts stock returns efficiently.
A new method learns DAGs from Gaussian data without verifying acyclicity.
In this paper we provide a comprehensive analysis of a structural model for the dynamics of prices of assets traded in a market originally proposed in [1]. The model takes the form of an interacting generalization of the geometric Brownian motion model. It is formally equivalent to a model describing the stochastic dyn…
Unfulfilled expectations from macro-economic initiatives during the Great Recession and the massive shift into globalization echo today with political upheaval, anti-establishment propaganda, and looming trade/currency wars that threaten domestic and international value chains. Once stable entities like the EU now look…
The importance of adequately modeling credit risk has once again been highlighted in the recent financial crisis. Defaults tend to cluster around times of economic stress due to poor macro-economic conditions, {\em but also} by directly triggering each other through contagion. Although credit default swaps have radical…
Machine learning models predict housing prices using macroeconomic factors.
This paper compares traditional econometric and contemporary machine/deep learning techniques for forecasting foreign exchange rates.
Threadneedle is a multi-agent simulation framework, based on a full double entry book keeping implementation of the banking system's fundamental transactions. It is designed to serve as an experimental test bed for economic simulations that can explore the banking system's influence on the macro-economy under varying a…
Analysis of the 2007-8 credit crisis has concentrated on issues of relaxed lending standards, and the perception of irrational behaviour by speculative investors in real estate and other assets. Asset backed securities have been extensively criticised for creating a moral hazard in loan issuance and an associated incre…
Research develops a DSS for stock selection and asset allocation using fundamental data.
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…
The aim of this work is to explore the possible types of phenomena that simple macroeconomic Agent-Based models (ABM) can reproduce. We propose a methodology, inspired by statistical physics, that characterizes a model through its 'phase diagram' in the space of parameters. Our first motivation is to understand the lar…
Context-aware recommender systems (CARSs) apply sensing and analysis of user context in order to provide personalized services. Adding context to a recommendation model is challenging, since the addition of context may increases both the dimensionality and sparsity of the model. Recent research has shown that modeling …
Enhances activity recognition in wearable computing with context awareness and uncertainty quantification.
MLPs can approximate any function in context, challenging the importance of in-context universality.
Paper argues context equals environment, improving AI generalization.
Scales attention for long contexts in LLMs.
Enhances neural processes to learn from multiple related datasets.
Transformers can scale both context and task, but MLPs can only scale task.
We introduce a stochastic contextual bandit model where at each time step the environment chooses a distribution over a context set and samples the context from this distribution. The learner observes only the context distribution while the exact context realization remains hidden. This allows for a broad range of appl…
Transformers can be hijacked by context, but deeper models are more robust.
This study examines how sequential correlations affect in-context learning in sequence models.
New method for contextual bandits with corrupted context.
Paper proposes linear transformers for efficient in-context learning without context length limitations.
Mobile context determination is an important step for many context aware services such as location-based services, enterprise policy enforcement, building or room occupancy detection for power or HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the…
The paper tackles long-context linear system identification with improved sample complexity bounds.
NOTMAD estimates context-specific Bayesian networks without breaking datasets.
MCPCA analyzes shared factors across multiple data contexts.
Thompson Sampling tackles noisy context in stochastic bandits.