Investor finds a fair outcome in complex financial markets.
problem Finding a fair outcome in complex financial markets.
method Recalled and proved the existence of personal equilibrium in a multistep, generically incomplete financial market model.
result Personal equilibrium exists in a multistep, generically incomplete financial market model under appropriate assumptions.
Summarizes financial news for better investment decisions.
problem Information overload from financial news hinders timely investment decisions.
method Personalized Chain-of-Thought summarization framework integrating user-specified keywords.
result Personalized summaries highlight relevant market signals, improving investment narratives.
Unified survey of treatment effect heterogeneity and uplift modeling methods.
problem Estimating heterogeneous treatment effects and uplift modeling.
method Unified survey of treatment effect heterogeneity and uplift modeling approaches.
result Unified notations for comparing methods and applications in personalized marketing, medicine, and social studies.
Robo-advisor improves investment advice through client interaction.
problem Offering personalized financial advice to clients.
method Adaptive mean-variance portfolio optimization with client interaction.
result Optimal investment strategy includes both myopic and intertemporal hedging terms.
Statistical test evaluates if personalizing interventions is cost-effective.
problem Balancing the benefits of personalizing interventions with their potential costs.
method Developed a statistical hypothesis test to assess the performance of personalized interventions.
result The test shows that personalized interventions can outperform standard approaches under certain conditions.
Blockchain-based exchanges adopt based on token pair volatility and personal use.
problem Token value loss and arbitrage issues in decentralized exchanges.
method Investigation of Automated Market Makers (AMMs) using transaction-level data.
result AMMs are adopted for high personal use or highly correlated token price movements.
BanditLP optimizes personalized recommendations for large-scale systems.
problem Optimizing personalized recommendations for large-scale systems with constraints.
method Unified neural Thompson Sampling for learning and large-scale linear programming for action selection.
result Consistent gains over strong baselines in experiments and business win in LinkedIn's email marketing system.
Social interactions and personal tastes shape our consumption behavior of cultural products. In this study, we present a computational model of a cultural market and we aim to analyze the behavior of the consumer population as an emergent phenomena. Our results suggest that the final market shares of cultural products …
LLMs can simulate human investment attitudes based on personality traits.
problem Investigating how LLMs mimic human investment behaviors.
method Simulated investment task using LLM personas with specific Big Five personality profiles.
result LLMs can produce meaningful behavioural differences in investment tasks that align with human traits.
Reinforcement Learning improves personalized dialogue systems.
problem Personalized dialogue systems that generalize across users.
method Two RL-based approaches: single learner with user context features, and learner per context segmentation.
result RL methods outperform handcrafted systems in financial product recommendation.
Platform learns user preferences to avoid abandonment due to marketing fatigue.
problem Maximizing platform's cumulative payoff while avoiding user abandonment.
method Dynamic sequential choice model to balance exploration and exploitation.
result Proposed algorithm achieves optimal regret bound for online learning.
Dynamic pricing model considers ambiguity in endowment growth rate.
problem Dynamic asset pricing under ambiguous endowment growth rate.
method α-maxmin expected utility model for ambiguity, intra-personal equilibrium strategies, market equilibrium.
result Asset prices reflect ambiguity in endowment growth rate.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice.
problem Lack of consistent advisor expertise and difficulty in encoding it in LLM systems.
method Grounds financial advisor personas in fund disclosures, market context, and manager commentary through an agentic actor--scorer--patcher loop.
result Personas better recover portfolio decisions and manager interpretation than generic baselines.
This study examines how fashion consumption affects self-confidence and buying behavior in Iranian consumers.
problem Understanding the role of self-confidence in fashion buying behavior.
method A questionnaire was used to collect data from 400 consumers in Tehran's clothing market. Structural equations and factor analysis were employed to test the model.
result Interest in fashion, personal taste, utilitarianism, and new products positively impact self-confidence, and self-confidence positively impacts fashion buying behavior.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice and manager interpretation.
problem Lack of consistent and specific financial advisor expertise in personalized investment advice.
method Grounds financial advisor personas in fund disclosures, holdings transitions, market context, and manager commentary through an agentic actor--scorer--patcher loop.
result Personas better recover portfolio decisions and grounded manager interpretation than generic baselines.
The aim of this work is to establish the personal income distribution from the elementary constituents of a free market; products of a representative good and agents forming the economic network. The economy is treated as a self-organized system. Based on the idea that the dynamics of an economy is governed by slow mod…
Fair market valuations ignore future worker profits in employee-owned firms.
problem Ignoring future worker profits in fair market valuations for employee-owned firms.
method Analyzing property rights and residual claimants in employee-owned firms.
result Fair market valuations are inappropriate for employee-owned firms.
Game theory models futures market dynamics with incomplete information.
problem Modeling competition and price dynamics in futures markets with incomplete information.
method Formalizes a multi-step, non-cooperative game for n players.
result Reduction in price gaps due to high competition improves market liquidity.
MobileNet-v2 improves re-ID on edge devices with mixed precision.
problem Real-time person re-identification in edge devices with limited resources.
method Mixed precision training (FP32 and FP16) for MobileNet-v2 on edge nodes.
result Mixed precision training improves inference throughput and reduces power consumption.
Efficiently models agent dependencies in large social networks.
problem Challenges in incorporating inter-agent dependencies in social reinforcement learning models.
method Clusters users based on payoff and contribution, combines cluster-level policies with personalized agent-level policies, and uses dynamic clustering.
result Our approach learns more accurate policy estimates and converges more quickly than existing methods.
AI enhances personalized drug development and decision-making in pharma.
problem Traditional drug development lacks personalized treatment plans.
method Application of AI in drug discovery, clinical trials, and post-marketing assessment.
result AI improves personalized medicine, optimizing health outcomes.
Isobenefit Lines can offer a certain range of applicability in Location Theory and Gravitational Models for Urban and Geography Economics, in positional decision processes made by citizens, and, last but not least, in land value and property market theories and analysis. The value of a land, or a property, in a generic…
Through a short sale, a person borrows a share of stock from a lender, sells the borrowed share to a third person at the current price, and purchases an identical share in the market at a future date and at a future price to replace the borrowed share of stock. This only makes sense if the short seller anticipates a do…
AI models assess psychological risks in currency trading.
problem Identifying psychological risks in currency traders.
method Developed a decision tree model to identify patterns in historical data.
result Enhanced decision-making through real-time alerts.
Following our previous investigation of the USA Standard and Poor index anti-bubble that started in August 2000, we analyze thirty eight world stock market indices and identify 21 anti-bubble. An ``anti-bubble'' is defined as a self-fulfilling decreasing price created by positive price-to-price feedbacks feeding overal…
Game-theoretic models predict asset prices in financial markets.
problem Understanding price formation in financial markets with limited liquidity.
method Developed game-theoretic models for many-person and mean-field games, derived analytical formulas, and numerically assessed results.
result The derived price converges to the mean-field counterpart under specific conditions.
The paper explores how machine learning personalization aligns with humanistic views of the person.
problem Aligning machine learning personalization with humanistic views of the person.
method Explicating the concept of personalization and contrasting it with humanistic views of the person.
result Proposes dimensions for evaluating the degree of personalization of ML personalized scores.
In financial markets, abnormal trading behaviors pose a serious challenge to market surveillance and risk management. What is worse, there is an increasing emergence of abnormal trading events that some experienced traders constitute a collusive clique and collaborate to manipulate some instruments, thus mislead other …
Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of the agent-based models from empirical data instead of setting them artificially was suggested. We first revi…
Two firms compete in a financial market, choosing dividend strategies to avoid default and maximize profits.
problem Strategic interaction between two financially constrained firms in a market with default risk.
method Construct Nash equilibria in feedback form for a class of two-person stochastic games of singular control.
result Explicit identification of optimal strategies and equilibrium payoffs for different initial conditions.
Federated learning framework extended for personalizing global models.
problem Evaluate personalization strategies for global models on-device.
method Extend federated learning framework, develop tools for analysis and evaluation.
result Personalization yields significant benefits for a large user population.
Adaptive personalized federated learning improves local model personalization.
problem Maximizing global model performance limits local model personalization.
method APFL algorithm trains local models while contributing to global model, with optimal mixing parameter and communication-efficient optimization.
result Demonstrates effectiveness of personalization schema and correctness of generalization theories.
Vanguard uses AI to create personalized financial plans.
problem Challenges in choosing features for complex financial planning.
method Reinforcement learning for identifying optimal savings rates.
result Trains algorithms to model financial success trajectories.
A new personality-based recommender system tackles data sparsity without feedback.
problem Data sparsity without common feedback among users.
method Implicitly identifying users' personality type and incorporating it with personal interests and knowledge level.
result The model's effectiveness, especially in data sparsity situations, demonstrated on a real-world dataset.
Framework generates personalized insulin treatment strategies using deep models.
problem Developing optimal personalized treatment strategies for diabetes patients.
method Combines deep generative time series models with decision theory.
result Demonstrated improved personalized insulin treatment strategies for diabetes patients.
Paper uses language models to predict MBTI personality types with high accuracy.
problem Predicting Myers-Briggs personality types from text.
method Fine-tuned BERT model for predicting MBTI types and generating personality-specific language.
result BERT model achieves high accuracy in predicting MBTI types and personality-specific language generation.
Personalized explanations improve understanding of machine learning models.
problem Improving human understanding of machine learning models and decisions.
method Deriving a conceptualization of personalized explanation, categorizing explainee data, identifying key properties, and introducing new measures.
result Identification of three key properties amendable to personalization: complexity, decision information, and presentation.
How do macro-financial shocks affect investor behavior and market dynamics? Recent evidence on experience effects suggests a long-lasting influence of personally experienced outcomes on investor beliefs and investment, but also significant differences across older and younger generations. We formalize experience-based …
Framework generates precise synthetic populations for scalable modeling.
problem Generating accurate synthetic populations without personal data.
method Constraint-programming framework encoding aggregated statistics and structural relations.
result Exact control of demographic profiles without requiring microdata.
The DeepSurv model predicts purchase timing better than other survival models.
problem Predicting the exact purchase timing of consumers.
method Survival models (Kernel SVM, DeepSurv, Survival Random Forest, MTLR) were compared using various consumer attributes.
result DeepSurv model outperformed other models in predicting purchase completion.
FMP improves personalized ASR models on private devices.
problem Limited accuracy and privacy in federated fine-tuning of ASR models.
method FMP estimates global and personalized marginal distributions and adjusts NNLM probabilities.
result FMP achieves modest WER reductions on ASR rescoring tasks.
Background doesn't affect personality predictions in deep networks.
problem Understanding how background images influence personality attribution in deep learning models.
method Explicitly studied the effect of background images on personality prediction in deep residual networks, controlling for confounds.
result Adding background information to input decreases model performance for personality trait prediction.
Generative framework unifies and improves personalized learning and estimation methods.
problem Statistical heterogeneity in client data motivates personalized learning models.
method Generative framework unifies and suggests new personalized learning and estimation algorithms.
result AdaPeD algorithm numerically outperforms known algorithms.
A new federated multi-armed bandit framework with personalization balances generalization and personalization.
problem Balancing generalization and personalization in federated multi-armed bandits.
method Proposed a Personalized Federated Upper Confidence Bound (PF-UCB) algorithm to achieve a O(log(T)) regret. result PF-UCB achieves an O(log(T)) regret regardless of personalization degree and has similar instance dependency to lower bound. The paper explores methods to personalize deep learning models.
problem Optimizing deep learning models for individual performance.
method Curriculum learning and data augmentation techniques.
result Personalized methods improve performance on individual data but degrade on general datasets.
Unified proof for scalable personalized federated learning.
problem Personalized federated learning under asynchronous updates.
method Unified proof for asynchronous federated learning with bounded staleness applied to MAML and ME personalization frameworks.
result Unified proof for convergence to first-order stationary point for smooth and non-convex functions.
This paper analyzes the trade-off between accuracy and communication in personalized federated learning.
problem The accuracy-communication trade-off in personalized federated learning.
method The paper provides a quantitative characterization of the personalization degree on the trade-off, establishing minimax optimality.
result The paper offers theoretical insights for choosing the personalization degree and validates the results on synthetic and real-world datasets.
Personalized sleep staging achieved with single-night data using KL-divergence regularization.
problem Improving automatic sleep staging accuracy with limited single-night data.
method KL-divergence regularization for transfer learning from a pretrained model to a personalized model.
result Personalized sleep staging accuracy of 79.6% with KL-divergence regularization.