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.
Modeling investor behavior from financial advisor notes using NLP.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice and manager interpretation.
Investors usually resort to financial advisors to improve their investment process until the point of complete delegation on investment decisions. Surely, financial advice is potentially a correcting factor in investment decisions but, in the past, the media and regulators blamed biased advisors for manipulating the ex…
Financial advisors use KYC info but not client behaviours to guide investments.
Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. The viability of robo-advisors crucially depends on their ability to offer personalized financial advice. We introduce a novel framework, in which a robo-advisor interacts with a client to solve an adaptiv…
Study shows LLM-advisors match human performance in eliciting preferences but struggle with conflicting needs and trust.
In the last few years, the financial advisory industry has been impacted by the emergence of digitalization and robo-advisors. This phenomenon affects major financial services, including wealth management, employee savings plans, asset managers, etc. Since the robo-advisory model is in its early stages, we estimate tha…
Study shows human advisors use context to improve student outcomes in algorithm-assisted advising.
Robo-advisors use MPC to create dynamic investment strategies.
In mutual fund, an investment adviser gives advice to clients about investing in securities such as stocks, bonds, mutual funds, or exchange traded funds. Some investment advisers manage portfolios of securities. In this paper, we analyze advisor portfolio for each advisor so as to recognize the pattern in each adviser…
Weibo experts predict stock market better than non-experts.
FinGPT democratizes financial data for LLMs, enabling innovation.
Shifu2 discovers advisor-advisee relationships in collaboration networks.
Summarizes financial news for better investment decisions.
Enhances robo-advisors with client investment preference inference.
We consider tackling a single-agent RL problem by distributing it to learners. These learners, called advisors, endeavour to solve the problem from a different focus. Their advice, taking the form of action values, is then communicated to an aggregator, which is in control of the system. We show that the local plan…
Study shows fiduciary duty reduces municipal bond yields by 9% after SEC rule.
Risk Advisor predicts and mitigates ML deployment failures.
Stock trend prediction is a challenging task due to the market's noise, and machine learning techniques have recently been successful in coping with this challenge. In this research, we create a novel framework for stock prediction, Dynamic Advisor-Based Ensemble (dynABE). dynABE explores domain-specific areas based on…
Reinforcement Learning improves insulin bolus decisions for type-I diabetes patients.
This paper considers method of creation of an advisor and indicator based on the spectral stochastic analysis model, both with linear and non-linear approximation. The problem of entrance to one or another trade position is solved on the basis of combined analysis of dynamics of quotations of all currency pairs, what a…
We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor's risk preference, but learns it over time by observing her portfolio choices in different market environments. We develop an exploration-exploitation algorithm which trades off costly solicitations of …
ADVISOR dynamically balances imitation and reinforcement learning to overcome the imitation gap.
This paper examines the risk-adjusted performance and differential fund flows for socially responsible mutual funds (SRMF). The results show that SRMF rated high on ESG, perform better than lower rated ESG funds during the period of economic crisis. The findings also show that low ESG rated SRMF had higher differential…
Vanguard uses AI to create personalized financial plans.
We analyze and quantify, in a financial market with parameter uncertainty and for a Constant Relative Risk Aversion investor, the utility effects of two different boundedly rational (i.e., sub-optimal) investment strategies (namely, myopic and unconditional strategies) and compare them between each other and with the u…
Dynamic retirement glidepaths evolve over time based on some measure such as the retiree's funded status or current market valuations. Conversely, static glidepaths are fixed at a starting point and selected under the assumption that they will not change. In practice, new static glidepaths may be derived periodically m…
LLMs prefer Bitcoin under crisis frames, affecting financial decisions.
Robo-advisors estimate clients' risk aversion using interactive questionnaires.
This paper considers the problem of isolating a small number of exchange traded funds (ETFs) that suffice to capture the fundamental dimensions of variation in U.S. financial markets. First, the data is fit to a vector-valued Bayesian regression model, which is a matrix-variate generalization of the well known stochast…
Flexible algorithm of multicurrency trade on Forex market has been built on the grounds of non-linear stochastic wavelets (NSW) model. Probability of the loss-free trade has been evaluated. Results of the algorithm's real-time testing and issues of the algorithm's development are discussed.
Study uses FDA to analyze discount functions of different temperaments.
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…
We analyze four structured products that have caused severe losses to investors in recent years. These products are: return optimization securities, yield magnet notes, reverse exchangeable securities, and principal-protected notes. We describe the basic structure of these products, analyze them probabilistically using…
Robo-advisor uses ML to optimize investment performance.
DeePM is a deep-learning portfolio manager that outperforms classical strategies in diversified futures markets.
Smooth functions on manifolds with degenerate singular submanifolds
Paper uses inverse optimization to measure risk preference from investment portfolios.
Research explores how local communities and corporations interact in finance.
The performance of trend following strategies can be ascribed to the difference between long-term and short-term realized variance. We revisit this general result and show that it holds for various definitions of trend strategies. This explains the positive convexity of the aggregate performance of Commodity Trading Ad…
In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforcement learning with policy advice (RLPA) algorithm which leverages this input set and learns to use the best policy in the set for the reinfo…
Most known regret bounds for reinforcement learning are either episodic or assume an environment without traps. We derive a regret bound without making either assumption, by allowing the algorithm to occasionally delegate an action to an external advisor. We thus arrive at a setting of active one-shot model-based reinf…
This paper uses Bayesian models to analyze CTA returns across short and long-term trends.
Portfolio optimization emerged with the seminal paper of Markowitz (1952). The original mean-variance framework is appealing because it is very efficient from a computational point of view. However, it also has one well-established failing since it can lead to portfolios that are not optimal from a financial point of v…
Investigation shows CTAs and STRTs can benefit from coexistence.
The influence of Commodity Trading Advisors (CTA) on the price process is explored with the help of a simple model. CTA managers are taken to be Kelly optimisers, which invest a fixed proportion of their assets in the risky asset and the remainder in a riskless asset. This requires regular adjustment of the portfolio w…
Training deep reinforcement learning agents complex behaviors in 3D virtual environments requires significant computational resources. This is especially true in environments with high degrees of aliasing, where many states share nearly identical visual features. Minecraft is an exemplar of such an environment. We hypo…