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.
Lie groups applied to tech progress in economic growth.
Dematerialization is the reduction in the quantity of materials needed to produce something useful over time. Dematerialization fundamentally derives from ongoing increases in technical performance but it can be counteracted by demand rebound - increases in usage because of increased value (or decreased cost) that also…
Deep learning's success requires vast computing power, making future progress unsustainable.
NEAT algorithm optimizes stock trading with reduced risk.
Study improves cryptocurrency price prediction using neural networks and technical indicators.
RLVR training dynamics reveal an implicit curriculum that shapes learning progression.
Tokenized RWAs face liquidity issues despite promising markets.
Machine learning enhances cosmology through new tools and data analysis.
Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms. In a given paper, researchers might aspire to any subset of the following goals, among others: to theoretically characterize what is learnable, to obtain understanding through empiric…
Semantic code search is the task of retrieving relevant code given a natural language query. While related to other information retrieval tasks, it requires bridging the gap between the language used in code (often abbreviated and highly technical) and natural language more suitable to describe vague concepts and ideas…
Machine learning practitioners are often ambivalent about the ethical aspects of their products. We believe anything that gets us from that current state to one in which our systems are achieving some degree of fairness is an improvement that should be welcomed. This is true even when that progress does not get us 100%…
RLVR learning dynamics naturally create an implicit curriculum for transformers.
New simulation method tackles sign problem in quantum fields.
The subject of this paper is an optimal consumption/optimal portfolio problem with transaction costs and with multiple risky assets. In our model the transaction costs take a special form in that transaction costs on purchases of one of the risky assets (the endowed asset) are infinite, and transaction costs involving …
Our work improves Langevin dynamics convergence on manifolds.
Survey of reinforcement learning guarantees with data constraints.
The paper limits the profitability of technical trading rules and finds they are not better than random trading.
Fair ML systems can be safe ML systems by considering uncertainty.
We prove a global local rigidity result for character varieties of 3-manifolds into . Given a 3-manifold with toric boundary satisfying some technical hypotheses, we prove that all but a finite number of its Dehn fillings are globally locally rigid in the following sense: every irreducible repr…
In this article, we investigate when the set of primitive geodesic lengths on a Riemannian manifold have arbitrarily long arithmetic progressions. We prove that in the space of negatively curved metrics, a metric having such arithmetic progressions is quite rare. We introduce almost arithmetic progressions, a coarsific…
The paper explores the Thomas-Yau conjecture using holomorphic curves and Floer theory.
Survey on statistical learning theory for control, focusing on linear systems.
Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks. Many efforts have been devoted to enhancing the robustness of individual networks and then constructing a straightforward ensemble…
Deep learning (DL), a new-generation of artificial neural network research, has transformed industries, daily lives and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data…
Collaborative filtering is an important technique for recommendation. Whereas it has been repeatedly shown to be effective in previous work, its performance remains unsatisfactory in many real-world applications, especially those where the items or users are highly diverse. In this paper, we explore an ensemble-based f…
Abstract: A new approach to technical indicators without lag.
This paper uses decolonial theory to improve AI's ethical development.
In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…
Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix factorization via nonconvex optimization. While conventional wisdom often takes a dim view of nonconvex optimization algorithms due to their susceptibility to spurious local minima, simple iterative …
Technical trading rules have a long history of being used by practitioners in financial markets. Their profitable ability and efficiency of technical trading rules are yet controversial. In this paper, we test the performance of more than seven thousands traditional technical trading rules on the Shanghai Securities Co…
Revisits life insurance surplus models with new technical bases.
Dynamic memory prevents forgetting in continuous learning of medical images.
We investigate the performance of dynamic portfolios constructed using more than 21,000 technical trading rules on 12 categorical and country-specific markets over the 2004-2015 study period, on rolling forward structures of different lengths. We also introduce a discrete false discovery rate (DFRD+/-) method for contr…
Weak form of the Efficiency Market Hypothesis (EMH) excludes predictions of future market movements from historical data and makes the technical analysis (TA) out of law. However the technical analysis is widely used by traders and speculators who steadely refuse to consider the market as a "fair game" and survive with…
Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on…
Paper introduces a new curriculum generation method for reinforcement learning.
In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Mult…
In this dissertation, the main goal is visualisation of financial time series. We expect that visualisation of financial time series will be a useful auxiliary for technical analysis. Firstly, we review the technical analysis methods and test our trading rules, which are built by the essential concepts of technical ana…
This study improves stock price prediction for Apple Inc. using feature selection and regression models with technical indicators.
Study develops smart contract framework for procurement under demand variability.
ShotgunCSP predicts crystal structures using machine learning, achieving high accuracy with minimal computation.
Study finds traditional technical indicators underperform in high-frequency trading, suggesting risk management over prediction.
Bayesian model identifies health disparities in disease progression.
This paper tackles hidden technical debts in fair ML systems for Fintech.
New algorithm outperforms existing ones by focusing on mastering rate.
Foreign exchange markets show that currency units (= accounting or nominal price units) are variables. Technical and economic progress evidence that the consumer baskets (= purchasing power units or real price units) are also variables. In contrast, all physical measurement units are constants and either defined in the…
We present a solution to an optimal stopping problem for a process with a wide-class of novel dynamics. The dynamics model the support/resistance line concept from financial technical analysis.
TradingAgents uses LLM-powered multi-agent framework for financial trading.