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.
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…
Model predicts EU carbon prices using market and political factors.
LLM forecasting benchmarks suffer from information leakage, which confounds model performance.
The study examines the generalization of Macro-AUC in multi-label learning, identifying label imbalance as a critical factor.
Enhances time-series regression trees with latent factors for robust financial analysis.
A new risk measure (FRM) for EM FI returns helps investors protect against volatility and policy instability.
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…
Crypto simulations show HODL strategy loads risk onto most investors, with macro-sentiment affecting returns.
Business cycles affect startup valuations, both directly and indirectly.
Solves the equity premium puzzle without calibrated values.
A new statistical concept, lepto-variance, is defined for stock returns using Regression Trees.
User behavior data in recommender systems are driven by the complex interactions of many latent factors behind the users' decision making processes. The factors are highly entangled, and may range from high-level ones that govern user intentions, to low-level ones that characterize a user's preference when executing an…
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 …
A new model explains asset returns with a single factor, improving cross-sectional performance.
LLM generates coherent macroeconomic stress scenarios for portfolio risk assessment.
New approach improves black-box planning efficiency by discovering focused macros.
The paper tackles multi-level fairness in algorithmic systems, addressing bias at both individual and structural levels.
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…
Hierarchical AI multi-agent framework optimizes equity portfolios in China's A-share market.
Aggregated variables can mask causal effects, turning unconfounded into confounded relations.
Study analyzes crypto asset risk exposures using a divide-and-conquer approach.
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. (…
The study compares profitability of conventional and Islamic banks in Bangladesh.
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…
Cryptocurrency forecasting model considers macro, sentiment, and technical indicators.
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 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 …
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 ubiquity of sound synthesizers has reshaped music production and even entirely defined new music genres. However, the increasing complexity and number of parameters in modern synthesizers make them harder to master. Hence, the development of methods allowing to easily create and explore with synthesizers is a cruci…
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…
A new method learns DAGs from Gaussian data without verifying acyclicity.
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…
I study the behavior and the performance of the long-term forecasts issued by financial analysts with respect to the Extrapolation Hypothesis. That hypothesis states that investors, extrapolating from the firms' recent performances, are too optimistic about growth and large firms and too pessimistic about value and sma…
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…
This paper proposes a continuous timing strategy for growth vs. defensive style allocation.
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…
New methods for equity fund selection and portfolio construction using mutual fund top holdings.
Proposes a method to explain black-box models using causal learning.
MarketSenseAI system outperforms passive benchmarks by 25.2% on S&P 500, adding value over random selection.
Improved forecasting of investment dynamics across heterogeneous panels using a two-stage model.
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…