arXiv · arXiv · 2026
This paper develops an autonomous framework for systematic factor investing via agentic AI. Rather than relying on sequential manual prompts, our approach operationalizes the model as a self-directed engine that endogenously formulates interpretable trading signals. To mitigate data snooping biases, this closed-loop system imposes strict empirical discipline through out-of-sample validation and economic rationale req…
Allen Yikuan Huang, Zheqi Fan
arXiv · arXiv q-fin · 2023
Active investing aims to construct a portfolio of assets that are believed to be relatively profitable in the markets, with one popular method being to construct a portfolio via factor-based strategies. In recent years, there have been increasing efforts to apply deep learning to pursue "deep factors'' with more active returns or promising pipelines for asset trends prediction. However, the question of how to constru…
Zikai Wei, Bo Dai, Dahua Lin
arXiv · arXiv · 2025
Large Language Models (LLMs) have recently been leveraged for asset pricing tasks and stock trading applications, enabling AI agents to generate investment decisions from unstructured financial data. However, most evaluations of LLM timing-based investing strategies are conducted on narrow timeframes and limited stock universes, overstating effectiveness due to survivorship and data-snooping biases. We critically ass…
Weixian Waylon Li, Hyeonjun Kim, Mihai Cucuringu, Tiejun Ma
arXiv · arXiv q-fin · 2020
Cryptocurrencies (CCs) have risen rapidly in market capitalization over the last years. Despite striking price volatility, their high average returns have drawn attention to CCs as alternative investment assets for portfolio and risk management. We investigate the utility gains for different types of investors when they consider cryptocurrencies as an addition to their portfolio of traditional assets. We consider ris…
Alla Petukhina, Simon Trimborn, Wolfgang Karl Härdle, Hermann Elendner
arXiv · arXiv · 2026
In 1956 John Kelly wrote a paper at Bell Labs describing the relationship between gambling and Information Theory. What came to be known as the Kelly Criterion is both an objective and a closed-form solution to sizing wagers when odds and edge are known. Samuelson argued it was arbitrary and subjective, and successfully kept it out of mainstream economics. Luckily it lived on in computer science, mostly because of To…
Oscar Stiffelman
arXiv · arXiv · 2025
We present a multi-agent, AI-driven framework for fundamental investing that integrates macro indicators, industry-level and firm-specific information to construct optimized equity portfolios. The architecture comprises: (i) a Macro agent that dynamically screens and weights sectors based on evolving economic indicators and industry performance; (ii) four firm-level agents -- Fundamental, Technical, Report, and News …
Chujun He, Zhonghao Huang, Xiangguo Li, Ye Luo, Kewei Ma
arXiv · arXiv · 2023
In this article, we present a novel approach for the construction of an environment-friendly green portfolio using the ESG ratings, and application of the modern portfolio theory to present what we call as the ``green efficient frontier'' (wherein the environmental score is included as a third dimension to the traditional mean-variance framework). Based on the prevailing action levels and policies, as well as additio…
Shashwat Mishra, Rishabh Raj, Siddhartha P. Chakrabarty
arXiv · arXiv · 2017
This paper studies long term investing by an investor that maximizes either expected utility from terminal wealth or from consumption. We introduce the concepts of a generalized stochastic discount factor (SDF) and of the minimum price to attain target payouts. The paper finds that the dynamics of the SDF needs to be captured and not the entire market dynamics, which simplifies significantly practical implementations…
Dietmar Leisen, Eckhard Platen
arXiv · arXiv q-fin · 2023
In the field of quantitative finance, volatility models, such as ARCH, GARCH, FIGARCH, SV, EWMA, play the key role in risk and portfolio management. Meanwhile, factor investing is more and more famous since mid of 20 century. CAPM, Fama French three factor model, Fama French five-factor model, MSCI Barra factor model are mentioned and developed during this period. In this paper, we will show why we need adjust group …
Ke Zhang
arXiv · arXiv · 2026
Artificial intelligence (AI) now supports investment workflows from data and prediction through research, portfolios, execution, and tool use. Technical capability, however, is not evidence of investment profitability. This critical state-of-the-art review examines public research available through 31 August 2026 on listed equities, exchange-traded funds, centralized crypto spot, perpetual futures, and on-chain marke…
Linsen Zhu, Mengqing Cai
arXiv · arXiv q-fin · 2019
This paper acts as a collection of various trading strategies and useful pieces of market information that might help to implement such strategies. This list is meant to be comprehensive (though by no means exhaustive) and hence we only provide pointers and give further sources to explore each strategy further. To set the stage for this exploration, we consider the factors that determine good and bad trades, the noti…
Ravi Kashyap
OpenAlex · Quantitative Finance · 2014 · cites 91
In this book, Andrew Ang seeks to provide common rules and guidance for investment decisions to ‘nations, through sovereign wealth funds, collective owners like pension funds, endowments, and found...
Robert M. Anderson
arXiv · arXiv · 2026
This paper investigates whether large language models (LLMs) can generate reliable stock market predictions. We evaluate four state-of-the-art models - ChatGPT, Gemini, DeepSeek, and Perplexity - across three prompting strategies: a naive query, a structured approach, and chain-of-thought reasoning. Our results show that LLM-generated recommendations are hindered by recurring reasoning failures, including financial m…
Ricardo Crisostomo, Diana Mykhalyuk
arXiv · arXiv · 2026
We propose an explainable regime-aware portfolio construction framework based on a strictly causal Wasserstein Hidden Markov Model. The model combines rolling Gaussian HMM inference with predictive model-order selection and template-based identity tracking using the 2-Wasserstein distance between Gaussian components. This allows regime complexity to adapt dynamically while preserving stable economic interpretation. R…
Amine Boukardagha
arXiv · arXiv · 2025
In this review, we provide practical guidance on some of the main machine learning tools used in portfolio weight formation. This is not an exhaustive list, but a fraction of the ones used and have some statistical analysis behind it. All this research is essentially tied to precision matrix of excess asset returns. Our main point is that the techniques should be used in conjunction with outlined objective functions.…
Mehmet Caner Qingliang Fan
arXiv · arXiv · 2024
This project is a collaboration between industry and academia to delve into Finance Social Networks, specifically the Board of Directors of public companies. Knowing the connections between Directors and Executives in different companies can generate powerful stories and meaningful insights on investments. A proof of concept in the form of a Data Visualization tool reveals its strength in investigating corporate gove…
Alice Da Fonseca, Peter Lake, Ariana Barrenechea
arXiv · arXiv · 2022
This article develops a model that takes into account skewness risk in risk parity portfolios. In this framework, asset returns are viewed as stochastic processes with jumps or random variables generated by a Gaussian mixture distribution. This dual representation allows us to show that skewness and jump risks are equivalent. As the mixture representation is simple, we obtain analytical formulas for computing asset r…
Benjamin Bruder, Nazar Kostyuchyk, Thierry Roncalli
arXiv · arXiv · 2020
On a periodic basis, publicly traded companies report fundamentals, financial data including revenue, earnings, debt, among others. Quantitative finance research has identified several factors, functions of the reported data that historically correlate with stock market performance. In this paper, we first show through simulation that if we could select stocks via factors calculated on future fundamentals (via oracle…
Lakshay Chauhan, John Alberg, Zachary C. Lipton