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.

169,051 papers · 148 categories

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11 results for “Robo-advisors”

Personalized Robo-Advising: Enhancing Investment through Client Interactionq-fin.PM

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.

Robo-advisor Personalized finance Portfolio optimization Client interaction Risk profile

2019-11-04abs ↗pdf ↗

Robust Asset Allocation for Robo-Advisorsq-fin.PM

Robo-advisors struggle with automated asset allocation.

problem Automated asset allocation in robo-advisors is challenging due to complex optimization.

method Use regularization and sparsity to optimize and rebalance portfolios without human intervention.

result Robo-advisors need robust asset allocation methods to handle optimization and rebalancing.

Robo-advisors Asset allocation Mean-variance optimization Regularization Sparsity

2019-02-20abs ↗pdf ↗

Robo-advising: Learning Investors' Risk Preferences via Portfolio Choicesq-fin.PM

Robo-advisor learns investor's risk preference through portfolio choices.

problem Learning investors' risk preferences without prior knowledge.

method Reinforcement learning framework with exploration-exploitation algorithm.

result Algorithm's value function converges to optimal over polynomial periods.

Robo-advising Risk preference Portfolio choices Reinforcement learning Exploration-exploitation

2019-11-05abs ↗pdf ↗

Inference of Utilities and Time Preference in Sequential Decision-Makingmath.OC

Enhances robo-advisors with client investment preference inference.

problem Accurately inferring clients' investment preferences from past activities.

method Stochastic control framework with continuous-time model and discounting scheme.

result Proves sufficient conditions for client investment preference identifiability.

investment preferences robo-advisors stochastic control discounting utility functions

2024-05-24abs ↗pdf ↗

Eliciting Risk Aversion with Inverse Reinforcement Learning via Interactive Questioningstat.ML

Robo-advisors estimate clients' risk aversion using interactive questionnaires.

problem Estimating risk aversion of non-expert clients using adaptive questionnaires.

method Model risk aversion with cost functions and spectral risk measures. Use inverse reinforcement learning to design questions maximizing distinguishing power.

result Designing questions by maximizing distinguishing power achieves satisfactory accuracy in learning risk aversion with fewer than 50 questions.

risk aversion reinforcement learning interactive questionnaires spectral risk measures distinguishing power

2023-08-16abs ↗pdf ↗

Robo-Advising in Motion: A Model Predictive Control Approachq-fin.PM

Robo-advisors use MPC to create dynamic investment strategies.

problem Static allocation methods limit robo-advisors' effectiveness.

method Combines MPC with Hidden Markov Model and Black-Litterman for dynamic asset allocation.

result MPC-based strategies outperform static approaches in dynamic and risk-budgeting criteria.

Robo-advising Model Predictive Control Dynamic Asset Allocation Hidden Markov Model Black-Litterman

2026-01-14abs ↗pdf ↗

FinGPT: Democratizing Internet-scale Data for Financial Large Language Modelscs.CL

FinGPT democratizes financial data for LLMs, enabling innovation.

problem Limited financial text datasets and disparities between general and financial text data.

method Automates collection and curation of real-time financial data from diverse Internet sources, fine-tuning with RLSP and LoRA.

result Democratizes access to financial data for LLMs, enabling innovation.

Financial LLMs Data democratization Reinforcement Learning Low-rank Adaptation Robo-advisor

2023-07-19abs ↗pdf ↗

Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learningq-fin.PM

Robo-advisor uses ML to optimize investment performance.

problem Maximizing investment performance with historical data.

method Inverse optimization and deep reinforcement learning.

result Robo-advisor consistently outperformed S&P 500.

Robo-advising Inverse Optimization Deep Reinforcement Learning Machine Learning Investment Management

2021-05-19abs ↗pdf ↗

Multivariate Density Modeling for Retirement Financeq-fin.GN

Prior to the financial crisis mortgage securitization models increased in sophistication as did products built to insure against losses. Layers of complexity formed upon a foundation that could not support it and as the foundation crumbled the housing market followed. That foundation was the Gaussian copula which faile…

2017-09-12abs ↗pdf ↗

Auditing Asset-Specific Preferences in Financial Large Language Models: Evidence from Bitcoin Representations and Portfolio Allocationq-fin.GN

LLMs prefer Bitcoin under crisis frames, affecting financial decisions.

problem Testing whether LLMs have built-in biases towards specific financial assets.

method Developed a three-level audit protocol to examine Bitcoin's representation and influence in LLMs.

result An identifiable internal feature in LLMs can be perturbed to move financial choices, but only within measurable limits.

Large Language Models Financial Preferences Bitcoin Behavioral Leverage Portfolio Allocation

2026-06-01abs ↗pdf ↗

Machine Learning Optimization Algorithms & Portfolio Allocationq-fin.PM

This paper explores how machine learning algorithms can improve portfolio optimization for large investment universes.

problem Traditional portfolio optimization models are inefficient for large investment universes due to computational intractability.

method The paper applies four large-scale optimization algorithms (coordinate descent, ADMM, proximal gradient, Dykstra's algorithm) to portfolio optimization problems.

result These algorithms can manage large-scale portfolio optimization problems, providing more efficient solutions than traditional methods.

portfolio optimization machine learning large-scale optimization investment universe coordinate descent

2019-09-23abs ↗pdf ↗