On-device research index

arXiv research

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.

168,742 papers · 148 categories

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5 results for “profit-driven”

Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspectiveecon.EM

This paper compares forecasting techniques for sales data, focusing on profit-driven models.

problem Choosing the best forecasting technique for sales data is challenging.

method Compares various forecasting methods including ML, statistics, and econometrics.

result Simple seasonal models consistently outperform other methodologies.

sales forecasting profit-driven tactical forecasting time series analysis machine learning

2020-02-03abs ↗pdf ↗

Profit Driven Decision Trees for Churn Predictionstat.ML

Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of predicting customer churn can be presented as a binary classification problem. Using data on historic behavior, classification algorithms are bu…

2017-12-21abs ↗pdf ↗

Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learningq-fin.TR

This paper combines RL with CPPI and TIPP for better trading strategies.

problem Challenges in quantitative trading due to swift dynamics and uncertainties.

method Fusion of CPPI and TIPP with MADDPG framework for multi-agent reinforcement learning.

result CPPI-MADDPG and TIPP-MADDPG outperform traditional strategies in real-market shares.

reinforcement learning quantitative markets multi-agent portfolio insurance portfolio protection

2023-03-15abs ↗pdf ↗

Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-makingstat.ML

DFL framework improves action and outcome fairness in policy learning.

problem Fairness in policy learning, especially action and outcome fairness.

method Integrates action and outcome fairness into a multi-objective optimization problem using a lexicographic weighted Tchebyshev method.

result DFL framework improves both action and outcome fairness with minimal value reduction.

fairness policy learning action fairness outcome fairness multi-objective optimization

2026-01-27abs ↗pdf ↗

Profit over Proxies: A Scalable Bayesian Decision Framework for Optimizing Multi-Variant Online Experimentsstat.AP

This paper introduces a Bayesian framework for optimizing online experiments to maximize profit.

problem Statistical flaws and reliance on proxy metrics in A/B tests compromise their effectiveness.

method Hierarchical Bayesian model for estimating conversion probability and monetary value, decision-theoretic stopping rule.

result The framework ensures experiments conclude when no variant offers a significant profit improvement, conserving resources.

Bayesian decision framework A/B tests profit optimization online experiments statistical integrity

2025-09-16abs ↗pdf ↗