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.
Paper optimizes Bayesian optimization for complex functions with macro-actions.
We prove that each coarsely homogenous separable metric space is coarsely equivalent to one of the spaces: the sigleton, the Cantor macro-cube or the Baire macro-space. This classification is derived from coarse characterizations of the Cantor macro-cube and of the Baire macro-space given in this paper. Namely, we …
One problem in the application of reinforcement learning to real-world problems is the curse of dimensionality on the action space. Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis. However, previous studies relied on human…
New approach improves black-box planning efficiency by discovering focused macros.
Aggregated variables can mask causal effects, turning unconfounded into confounded relations.
New method uses label-weighted conformal prediction for macro-coverage guarantees in classification.
HANET combines LSTM and attention mechanisms for better financial forecasting.
We present a domain-general account of causation that applies to settings in which macro-level causal relations between two systems are of interest, but the relevant causal features are poorly understood and have to be aggregated from vast arrays of micro-measurements. Our approach generalizes that of Chalupka et al. (…
Background and objective: Stacking is an ensemble machine learning method that averages predictions from multiple other algorithms, such as generalized linear models and regression trees. An implementation of stacking, called super learning, has been developed as a general approach to supervised learning and has seen f…
Network embedding aims to embed nodes into a low-dimensional space, while capturing the network structures and properties. Although quite a few promising network embedding methods have been proposed, most of them focus on static networks. In fact, temporal networks, which usually evolve over time in terms of microscopi…
The study examines the generalization of Macro-AUC in multi-label learning, identifying label imbalance as a critical factor.
LLM forecasting benchmarks suffer from information leakage, which confounds model performance.
The 'macro F1' metric is frequently used to evaluate binary, multi-class and multi-label classification problems. Yet, we find that there exist two different formulas to calculate this quantity. In this note, we show that only under rare circumstances the two computations can be considered equivalent. More specifically…
Paper introduces hierarchical softmax for global hierarchical classification tasks.
We study conditional risk minimization (CRM), i.e. the problem of learning a hypothesis of minimal risk for prediction at the next step of sequentially arriving dependent data. Despite it being a fundamental problem, successful learning in the CRM sense has so far only been demonstrated using theoretical algorithms tha…
Model predicts EU carbon prices using market and political factors.
GTSNE improves data visualization for high-dimensional data.
In this paper, I discuss a method to tackle the issues arising from the small data-sets available to data-scientists when building price predictive algorithms that use monthly/quarterly macro-financial indicators. I approach this by training separate classifiers on the equivalent dataset from a range of countries. Usin…
We discuss a Pareto macro-economy (a) in a closed system with fixed total wealth and (b) in an open system with average mean wealth and compare our results to a similar analysis in a super-open system (c) with unbounded wealth. Wealth condensation takes place in the social phase for closed and open economies, while it …
This paper analyses the relationship between BitCoin price and supply-demand fundamentals of BitCoin, global macro-financial indicators and BitCoin attractiveness for investors. Using daily data for the period 2009-2014 and applying time-series analytical mechanisms, we find that BitCoin market fundamentals and BitCoin…
As mobile devices become more and more popular, mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. A critical challenge for these platforms and services is to understand the churn behavior in mobile games, w…
We study involuntary micro-movements of the eye for biometric identification. While prior studies extract lower-frequency macro-movements from the output of video-based eye-tracking systems and engineer explicit features of these macro-movements, we develop a deep convolutional architecture that processes the raw eye-t…
Crypto simulations show HODL strategy loads risk onto most investors, with macro-sentiment affecting returns.
Shaping in humans and animals has been shown to be a powerful tool for learning complex tasks as compared to learning in a randomized fashion. This makes the problem less complex and enables one to solve the easier sub task at hand first. Generating a curriculum for such guided learning involves subjecting the agent to…
Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism…
This paper presents a model of the dynamics of the wage income distribution.
Deep learning solves and estimates complex financial models.
DISPR uses diffusion models to predict 3D cell shapes from 2D images.
The paper tackles multi-level fairness in algorithmic systems, addressing bias at both individual and structural levels.
ART adapts class-wise resampling to improve imbalanced classification performance.
Unified model predicts stock and systemic risks from diverse financial data.
A new risk measure (FRM) for EM FI returns helps investors protect against volatility and policy instability.
LLM generates coherent macroeconomic stress scenarios for portfolio risk assessment.
Bayesian framework improves trading robustness against market shifts.
LLMs add value in commodity portfolio construction when information set and implementation rules are held fixed.
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…
Study uses ML to predict currency and bond returns from news sentiment.
Hierarchical AI multi-agent framework optimizes equity portfolios in China's A-share market.
Paper uses LLMs for sector allocation, showing better returns.
Trajectory owner prediction is the basis for many applications such as personalized recommendation, urban planning. Although much effort has been put on this topic, the results archived are still not good enough. Existing methods mainly employ RNNs to model trajectories semantically due to the inherent sequential attri…
Model shows how relaxed leverage can lead to asset price bubbles.
Entropy helps explain disorder in both macro and micro systems.
Enhances time-series regression trees with latent factors for robust financial analysis.
In this paper, making use of recent statistical physics techniques and models, we address the specific role of randomness in financial markets, both at the micro and the macro level. In particular, we review some recent results obtained about the effectiveness of random strategies of investment, compared with some of t…
Extracts coarse-grained PDEs from microscopic simulations.
The paper investigates non-linear and heavy-tailed predictability in transition-energy financial markets.
In arXiv:1207.0332 [cs.LO] was proposed a graphic lambda calculus formalism, which has sectors corresponding to untyped lambda calculus and emergent algebras. Here we explore the sector covering knot diagrams, which are constructed as macros over the graphic lambda calculus.
A new statistical concept, lepto-variance, is defined for stock returns using Regression Trees.