A new framework for discovering macro-level causes and effects from micro-level data.
problem Understanding macro-level causal relations from poorly understood micro-level data.
method Generalizes Chalupka et al. (2015) to discover macro-variable causes and effects from micro-level measurements.
result Identifies multiple levels of causal structure under specific conditions.
Paper analyzes churn behavior in mobile games at micro and macro levels.
problem Understanding churn behavior in mobile games, especially at micro and macro levels.
method Developed a semi-supervised and inductive embedding model for micro-level churn prediction and constructed a relationship graph for macro-level churn ranking.
result Accurate micro-level churn prediction and macro-level churn ranking were achieved using novel techniques.
In the present work we propose an original analytical model of coopetitive game. We try to apply this analytical model of coopetition - based on game theory and conceived at a macro level - to the Greek crisis, suggesting feasible solutions in a cooperative perspective for the divergent interests which drive the econom…
Unified model predicts stock and systemic risks from diverse financial data.
problem Isolating financial tasks leads to missed cross-scale dependencies.
method Shared Transformer backbone with modular task heads for cross-modal attention and multi-task optimization.
result Uni-FinLLM significantly outperforms baselines in stock forecasting, credit-risk assessment, and systemic-risk detection.
The paper tackles performative policy learning with strategic agents, improving scalability and generalizability.
problem Strategic agents adjust their features in response to a released policy, causing endogenous distribution shifts.
method Relaxing parametric assumptions, the paper uncovers a low-dimensional structure in distribution shifts and proposes a gradient-based policy optimization algorithm.
result The proposed algorithm achieves high sample efficiency and provides theoretical guarantees for convergence.
Proposes a method to explain black-box models using causal learning.
problem Existing explainability methods focus on micro-level inputs, not interpretable features.
method Learns causal graphical representations to differentiate between causal and confounding influences.
result Graphs can differentiate between interpretable and confounding features.
The paper tackles multi-level fairness in algorithmic systems, addressing bias at both individual and structural levels.
problem Algorithmic systems can unfairly impact marginalized groups, especially when considering only individual-level bias.
method Formalizes multi-level fairness using causal inference tools, addressing effects of sensitive attributes at multiple levels.
result Illustrates the importance of accounting for macro-level sensitive attributes in fairness assessments.
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…
CRF model improves protein secondary structure prediction.
problem Improving secondary structure prediction of proteins.
method Applied Conditional Random Fields (CRF) to protein classification.
result CRF model leads to extremely accurate protein secondary structure predictions.
The study explains how market-makers' hedging affects stock volatility during gamma-squeeze events.
problem Endogenous volatility amplification in option markets during gamma-squeeze events.
method Developed a theoretical framework linking hedging behavior and market turbulence, incorporating beta-normalized volatility.
result Low-beta stocks amplify volatility more during gamma-squeeze events.
Stylized facts can be regarded as constraints for any modeling attempt of price dynamics on a financial market, in that an empirically reasonable model has to reproduce these stylized facts at least qualitatively. The dynamics of market prices is modeled on a macro-level as the result of the dynamic coupling of two dyn…
Consistent SEMs predict the same effects of interventions.
problem Consistency of causal models in SEMs.
method Formalised consistency of SEMs through transformations, emphasizing well-specified interventions.
result Causal models at different levels of detail are consistent.
Neuromorphic column performs online unsupervised clustering.
problem Real-time clustering of streaming data.
method Localized, spike timing-dependent plasticity (STDP) neural column.
result Prototype column performs similarly to k-means clustering.
A new framework for performative prediction robust to distributional misspecification.
problem Performative prediction models can be influenced by their own predictions, leading to suboptimal outcomes.
method Introduces distributionally robust performative prediction (DRPO) to approximate the true performative optimum (PO) robustly.
result DRPO provides provable guarantees as a robust approximation to the true PO when the nominal distribution map is misspecified.
Business cycles affect startup valuations, both directly and indirectly.
problem How do business cycles impact startup valuations?
method Structural Equation Model approach using a dataset of 1,089 venture capital investments.
result Business cycles impact startup valuations both directly and indirectly.
The well-known Ising model used in statistical physics was adapted to a social dynamics context to simulate the adoption of a technological innovation. The model explicitly combines (a) an individual's perception of the advantages of an innovation and (b) social influence from members of the decision-maker's social net…
This paper evaluates random forest models for predicting stock price trends.
problem Predicting stock price trends to assist investors in making informed decisions.
method Random forest models combined with artificial intelligence, using optimal parameters.
result Random forest models show better predictive performance and time efficiency.
The study analyzes co-movements in Indian stock market sectors using network techniques.
problem Understanding co-movements among Indian stock sectors.
method Mesoscopic network analysis, recurrence analysis, partial correlations, multidimensional scaling, minimum spanning tree.
result Minimum spanning tree effectively separates technologically related sectors.
ABM simulates OTC government bond market dynamics, enhancing liquidity and stability.
problem Understanding and ensuring market stability and liquidity in OTC government bond markets.
method Developed a bespoke ABM to simulate market-maker interactions and test hypotheses.
result Greater agent diversity enhances market liquidity and reducing market-making costs improves stability.
Study optimizes health incentives to balance efficiency and fairness.
problem Designing health incentives to balance efficiency and fairness.
method Inverse behavioral optimization framework integrating QALY-based incentives and adaptive learning.
result Modern health systems operate near an efficiency-saturated frontier, with small fairness adjustments yielding diminishing returns.
Study examines if LLMs' trading styles match real market behavior.
problem Lack of behavioral consistency in LLMs' trading strategies.
method Year-long simulations with LLMs, operationalizing behavioral finance drivers, and comparing with financial theory.
result LLMs' strategy switching is only partially consistent with behavioral finance theories.
Paper proposes ExsdHawkes to model LOBs, capturing volatility dynamics.
problem Modeling volatility signature plots in LOBs with high-frequency trading dynamics.
method Extended State-Dependent Hawkes Process (ExsdHawkes) with relaxed constraints.
result ExsdHawkes uniquely reproduces volatility signature plots, identifying MLOs as catalysts.
System predicts vehicle interactions and trajectories with uncertainty.
problem Predicting future vehicle trajectories with uncertainty.
method Hierarchical Bayesian Generative Modeling with categorized and real-valued coordination variables.
result Categorized coordination better captures multi-modality and generates more diverse samples.
Study forecasts vegetable prices in Nepal using a novel index and ensemble model.
problem High volatility and cultural influences on agricultural commodity prices.
method Developed KVPI, created features, evaluated multiple models, introduced Momentum-Corrected Online Stacking Ensemble.
result Achieved RMSE of 1.771, MAPE of 0.68%, and R-squared of 0.845 at 90-day horizon.
Paper develops fine-grain spatiotemporal risk scores using high-resolution mobility data.
problem Developing reliable spatiotemporal risk scores for safe economic reopening.
method Hawkes process-based technique leveraging high-resolution cell-phone location signals.
result Fine-grain spatiotemporal risk scores based on high-resolution mobility data provide useful insights for safe re-opening.
From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level. While graphlets have witnessed a tremendous success and impact in a variety of domains, there has yet to be a fast and …
Develops a new method to measure causal effects in continuous and discrete settings.
problem Measuring direct causal effects in complex scenarios with continuous and interventionally changing variables.
method Probabilistic Easy Variational Causal Effect (PEACE) method, developed for both continuous and discrete cases.
result PEACE can measure causal effects under various conditions and is stable under small changes.
Paper introduces attacks to infer GAN training dataset properties.
problem Security and privacy risks of generative models like GANs.
method Proposes a general attack pipeline for two attack scenarios.
result Demonstrates strong performance in inferring GAN training dataset properties.
We argue that the present crisis and stalling economy continuing since 2007 are rooted in the delusionary belief in policies based on a "perpetual money machine" type of thinking. We document strong evidence that, since the early 1980s, consumption has been increasingly funded by smaller savings, booming financial prof…
HC-GNN tackles long-range graph information and high-order neighbourhoods.
problem Costly encoding of long-range information and failure to encode high-order neighbourhoods.
method Hierarchical structure with multi-level super graphs and innovative intra- and inter-level propagation.
result HC-GNN efficiently captures long-range interactions and incorporates meso- and macro-level semantics.
DeepScalper uses RL to capture intraday trading opportunities, balancing risk and profit.
problem Capturing fleeting intraday trading opportunities in high-frequency markets.
method Dueling Q-network, reward function with hindsight bonus, encoder-decoder architecture, risk-aware auxiliary task.
result Significantly outperforms state-of-the-art baselines in financial criteria.
CRISP predicts individual-level COVID-19 risk based on contact data.
problem Estimating individual-level infection risk during the pandemic.
method Probabilistic graphical model using SEIR framework with contact data.
result Model accurately predicts infection spread and recovery times.