AI Trading: Evaluating Large Language Models for Technical Market Analysis

Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents

Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making. Several studies have shown that Large Language Models (LLMs) perform well in many financial tasks, such as stock price movements and financial analytics. However, a critical task remains unexplored: the ability of LLMs to identify errors in financial documents. In this paper, we introduce \textbf{FinED-Bench}, the first publicly \textbf{Bench}mark for \textbf{Fin}ancial \textbf{E}rror \textbf{D}etection across three levels of cognitive complexity. FinED-Bench covers nine real-world financial scenarios, and includes over 900 documents reported in 2025 that are unseen by existing language models. We detail the benchmark construction process and evaluate several advanced LLMs (e.g., GPT-4o, Qwen3-14B) on this tasks, which requires both financial domain knowledge and reasoning capabilities. Experimental results show that current LLMs still struggle with this task, especially in high-complexity cases. Besides, supervised fine-tuning can significantly improve the performance of weaker LLMs on this task. Our data and code are available at https://github.com/hedyHe/FinED-Bench.

Why Large Language Models Fail at Tabular Prediction

The results show that the LLM's capability dissolves with dimension in a way no noise-corrupted classical learner mimics - which explains why LLMs, so capable elsewhere, keep losing to fifty-year-old baselines on tables, while leaving the mechanism of the prediction as an open question.

RAG-Powered Financial Trend Analysis System Using Borsa Istanbul Indices

A comparative review of modern large language model paradigms: GPT-4, BERT, Gemini, and DeepSeek

Comparison of GPT-4, BERT (bidirectional encoder representations from transformers), Gemini, and DeepSeek large language models (LLM), focusing on architectures, training methodologies, and real-world applications reveals GPT-4 excels in natural language generation and complex reasoning, supporting up to 128K tokens with moderate latency and higher costs making it effective for conversational artificial intelligence (AI).

Measuring the quality and efficiency of AI-generated codes for financial markets prediction with LSTM

Generative AI coding assistants are increasingly used to write machine-learning code, yet their ability to produce reliable LSTM implementations for financial prediction remains underexplored. This study evaluates the LSTM code generated by seven assistants ChatGPT 4.5, GitHub Copilot, Deepseek 3, Perplexity, Gemini 2.0 Pro, Claude 3.7 Sonnet, and Meta’s Llama from a single standardized prompt, on three indices (Nikkei 225, S&P 500, STOXX Europe 600). Each assistant’s generated script was re-executed over independent runs; accuracy (MAE, MSE, RMSE, R2, execution time) is reported as mean ± standard deviation on the original price scale, complemented by a static code-quality analysis (Pylint, Radon, SonarQube, Pytest, Bandit). The assistants converge on nearly identical LSTM architectures, so performance differences arise mainly from data-handling and code-correctness defects: Meta’s Llama near-zero errors are an artifact of normalized-scale metrics combined with a shuffled train/test split (data leakage), and once corrected its accuracy is among the weakest; Gemini 2.0 Pro, once its predictions are evaluated consistently on the price scale, is among the most accurate assistants. Differences are validated with Diebold–Mariano and Wilcoxon tests. AI-generated forecasting code can be accurate but is not uniformly trustworthy: its generated preprocessing and evaluation code must be audited before use.

Generative AI and Large Language Models for Financial Markets: From Behavioral Prediction to Autonomous Trading

Financial markets are one of the few machine-learning settings where feedback is fast, mistakes are expensive, and deployment is constrained by execution and regulation. Yet most existing tutorials still treat finance either as an NLP benchmark or as a classical forecasting problem. This tutorial takes a different view. We show how recent progress in generative AI becomes genuinely useful in markets when it helps model participant behavior, ground reasoning in live documents and order-flow data, and support research and execution workflows that can survive contact with production. The material therefore moves in three steps: behavioral and market foundations; grounded LLM systems, including RAG, critique models, and agentic workflows; and responsible deployment, including corrected backtesting, execution, risk controls, and governance. Two live demonstrations and three optional Colab notebooks connect the lecture material to implementation.