Investment Analysis

Can ChatGPT Overcome Behavioral Biases in the Financial Sector? Classify-and-Rethink: Multi-Step Zero-Shot Reasoning in the Gold Investment ArXiv ID: 2411.13599 “View on arXiv” Authors: Unknown Abstract Large Language Models (LLMs) have achieved remarkable success recently, displaying exceptional capabilities in creating understandable and organized text. These LLMs have been utilized in diverse fields, such as clinical research, where domain-specific models like Med-Palm have achieved human-level performance. Recently, researchers have employed advanced prompt engineering to enhance the general reasoning ability of LLMs. Despite the remarkable success of zero-shot Chain-of-Thoughts (CoT) in solving general reasoning tasks, the potential of these methods still remains paid limited attention in the financial reasoning task.To address this issue, we explore multiple prompt strategies and incorporated semantic news information to improve LLMs’ performance on financial reasoning tasks.To the best of our knowledge, we are the first to explore this important issue by applying ChatGPT to the gold investment.In this work, our aim is to investigate the financial reasoning capabilities of LLMs and their capacity to generate logical and persuasive investment opinions. We will use ChatGPT, one of the most powerful LLMs recently, and prompt engineering to achieve this goal. Our research will focus on understanding the ability of LLMs in sophisticated analysis and reasoning within the context of investment decision-making. Our study finds that ChatGPT with CoT prompt can provide more explainable predictions and overcome behavioral biases, which is crucial in finance-related tasks and can achieve higher investment returns. ...

AI in Investment Analysis: LLMs for Equity Stock Ratings ArXiv ID: 2411.00856 “View on arXiv” Authors: Unknown Abstract Investment Analysis is a cornerstone of the Financial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), offers opportunities to enhance the equity rating process. This paper explores the application of LLMs to generate multi-horizon 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, specially when incorporating financial fundamentals. While integrating news data improves short-term performance, substituting detailed news summaries with 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 and efficient framework for generating accurate stock ratings, serving as a cost-effective alternative to traditional methods. Future work will extend to longer timeframes, incorporate diverse data, and utilize newer models for enhanced insights. ...

Into the Abyss: What If Nothing is Risk Free? ArXiv ID: ssrn-1648164 “View on arXiv” Authors: Unknown Abstract In corporate finance and investment analysis, we assume that there is an investment with a guaranteed return that offers both firms and investors a “risk free” Keywords: corporate finance, risk-free rate, investment analysis, cost of capital, capital budgeting, Corporate Equity Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper focuses on conceptual discussions and theoretical implications of the risk-free rate, with moderate mathematical notation but no complex derivations or empirical data; it lacks backtesting or implementation details. flowchart TD Q["Research Question: Is a truly Risk-Free Rate Possible?"] --> M["Methodology: Review & Analysis"] M --> D["Data: Historical Defaults & Macro Shocks"] D --> C["Computation: Modeling & Scenario Analysis"] C --> F["Key Findings: No True Risk-Free Asset Exists"] F --> O["Outcome: Adjusted Cost of Capital Models"]