Publication Library
Instruct FinGPT Financial Sentiment Analysis by Instruction Tuning of General Purpose Large Language Models
Description: Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements. Despite the impressive capabilities of large language models (LLMs) in financial natural language processing (NLP), they still struggle with accurately interpreting numerical values and grasping financial context, limiting their effectiveness in predicting financial sentiment. In this paper, we introduce a simple yet effective instruction tuning approach to address these issues. By transforming a small portion of supervised financial sentiment analysis data into instruction data and finetuning a general-purpose LLM with this method, we achieve remarkable advancements in financial sentiment analysis. In the experiment, our approach outperforms state-of-the-art supervised sentiment analysis models, as well as widely used LLMs like ChatGPT and LLaMAs, particularly in scenarios where numerical understanding and contextual comprehension are vital.
Created At: 14 December 2024
Updated At: 14 December 2024
AI in Investment Analysis LLMs for Equity Stock Ratings
Description: Investment Analysis is acornerstoneoftheFinancial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), offers opportunities to enhance the equity stock rating process. This paper explores the application of LLMs to predict stock performance and generate stock ratings by ingesting diverse datasets. Traditional stock rating methods rely heavily on the expertise of financial analysts, and face several challenges such as data overload, inconsistencies in filings, and delayed reactions to market events. Our study addresses these issues by leveraging LLMs to improve the accuracy and consistency of stock ratings. Additionally, we assess the efficacy of using different data modalities with LLMs for the financial domain. We utilize varied datasets comprising fundamental financial, market, and news data from January 2022 to June 2024, along with GPT-4-32k (v0613) (with a training cutoff in Sep. 2021 to prevent information leakage). Our results show that our benchmark method outperforms traditional stock rating methods when assessed by forward returns. Specifically, incorporating financial fundamentals enhances ratings accuracy. While integrating news data improves short-termperformance,substitutingdetailednewssummarieswith sentiment scores reduces token use without loss of performance. In many cases, omitting news data entirely enhances performance by reducing bias. Our research shows that LLMs can be leveraged to effectively utilize large amounts of multimodal financial data, as showcased by their effectiveness at the stock rating prediction task. Our work provides a reproducible framework for generating consistent and accurate stock ratings, offering a cost-effective and efficient alternative to traditional methods. Future work will extend the analysis to longer time horizons, incorporating more diverse data, and utilizing newer models to enhance detailed investment analysis and reports.
Created At: 14 December 2024
Updated At: 14 December 2024
Time-Causal VAE Robust Financial Time Series Generator
Description: We build a time-causal variational autoencoder (TC-VAE) for robust generation of financial time series data. Our approach imposes a causality constraint on the encoder and decoder networks, ensuring a causal transport from the real market time series to the fake generated time series. Specifically, we prove that the TC-VAE loss provides an upper bound on the causal Wasserstein distance between market distributions and generated distributions. Consequently, the TC-VAE loss controls the discrepancy between optimal values of various dynamic stochastic optimization problems under real and generated distributions. To further enhance the model’s ability to approximate the latent representation of the real market distribution, we integrate a RealNVP prior into the TC-VAE framework. Finally, extensive numerical experiments show that TC-VAE achieves promising results on both synthetic and real market data. This is done by comparing real and generated distributions according to various statistical distances, demonstrating the effectiveness of the generated data for downstream financial optimization tasks, as well as showcasing that the generated data reproduces stylized facts of real financial market data.
Created At: 14 December 2024
Updated At: 14 December 2024
GPT-Guided Monte Carlo Tree Search for Symbolic Regression in Financial Fraud Detection
Description: With the increasing number of financial services available online, the rate of financial fraud has also been increasing. The traffic and transaction rates on the internet have increased considerably, leading to a need for fast decision-making. Financial institutions also have stringent regulations that often require transparency and explainability of the decision-making process. However, most stateof-the-art algorithms currently used in the industry are highly parameterized black-box models that rely on complex computations to generate a score. These algorithms are inherently slow and lack the explainability and speedoftraditional rule-based learners. This work introduces SR-MCTS (Symbolic Regression MCTS), which utilizes a foundational GPT modelto guidetheMCTS,significantly enhancing its convergence speed and the quality of the generated expressions which are further extracted to rules. Our experiments show that SR-MCTS can detect fraud more efficiently than widely used methods in the industry while providing substantial insights into the decision-making process.
Created At: 14 December 2024
Updated At: 14 December 2024
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
Description: Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies for multiple time series, however, is a challenging subject, owing to the intricate interdependencies among the constituent series. We hypothesize that anomalies occur in low density regions of a distribution and explore the use of normalizing flows for unsupervised anomaly detection, because of their superior quality in density estimation. Moreover, we propose a novel flow model by imposing a Bayesian network among constituent series. A Bayesian network is a directed acyclic graph (DAG) that models causal relationships; it factorizes the joint probability of the series into the product of easy-to-evaluate conditional probabilities. We call such a graph-augmented normalizing flow approach GANF and propose joint estimation of the DAG with f low parameters. We conduct extensive experiments on real-world datasets and demonstrate the effectiveness of GANF for density estimation, anomaly detection, and identification of time series distribution drift.
Created At: 14 December 2024
Updated At: 14 December 2024