can chatgpt predict the stock market?

can chatgpt predict the stock market?

Quick answer: can chatgpt predict the stock market? Short version: LLMs like ChatGPT can extract useful textual signals and help generate trade ideas, but they are not a plug‑and‑play oracle for reliably predicting returns. This guide explains why, summarizes empirical evidence, and offers practical, risk‑aware steps for practitioners and Bitget users.

Background and context

Since late 2022, large language models (LLMs) such as ChatGPT have seen rapid adoption across industries — including finance. As of Jan 2023, public reports indicated ChatGPT reached about 100 million monthly active users within months of launch, demonstrating fast consumer uptake and broad interest in using LLMs for business tasks (public press coverage, Jan 2023). As of 2026-01-17, according to public research summaries and media coverage, financial teams increasingly deploy LLMs for text understanding, sentiment extraction, and idea generation while the research community explores their limits in forecasting asset returns.

When asking "can chatgpt predict the stock market?" it helps to distinguish two broad approaches:

• Using LLMs to process unstructured text (news, filings, transcripts, social media) and produce features that feed into quantitative models; and
• Training generative numeric models directly on price and volume time series (models sometimes called StockGPT or price‑token models), an approach distinct from text‑focused ChatGPT.

The rest of this article explains how each approach is used, what the evidence shows, and what practitioners should watch for.

How ChatGPT and LLMs are applied to market prediction

Textual signal extraction

One common workflow asks whether can chatgpt predict the stock market indirectly by converting raw text into structured signals. Use cases include:

• Summarizing earnings calls and flagging management tone changes (e.g., cautious vs. optimistic language).
• Producing event tags and severity scores from news (mergers, regulatory actions, supply shocks).
• Scoring social media posts or forum threads for sentiment and relevance to specific tickers.

In these setups, ChatGPT is not asked to output a price forecast directly; instead it produces sentiment scores, topic importance weights, or human‑readable summaries that become inputs to trading rules or machine‑learning models. For many firms, the value is in scaling text processing across thousands of documents per day and surfacing unusual events that traditional numeric factors miss.

Prompt engineering and human‑in‑the‑loop workflows

A pragmatic approach combines analysts with LLMs. Analysts design prompts to extract consistent labels (e.g., 1–5 downside risk, or categories like "product, guidance, litigation"), then review outputs and correct errors. Typical pipeline:

1. Ingest news, filings, and social posts.
2. Use scripted prompts to get category, sentiment, and confidence scores from the LLM.
3. Analysts validate a sample and adjust prompts or re‑label problem cases.
4. Aggregate LLM outputs into daily signals that feed quant models.

These human‑in‑the‑loop workflows help control for hallucinations, tailor outputs to the investment process, and keep governance and audit trails for model decisions.

Fine‑tuning, annotation and hybrid models

Firms also fine‑tune LLMs on domain data or use LLMs to annotate large datasets that train downstream models. Typical techniques:

• Use ChatGPT to label historical Reddit or Twitter posts for a supervised sentiment model; then train a dedicated classifier (e.g., RoBERTa) on those labels for lower latency at scale.
• Fine‑tune LLMs on a corpus of earnings‑call transcripts with expert labels so the model learns finance‑specific phrasing.
• Combine textual LLM features with numeric models (TabNet, gradient boosted trees) that incorporate fundamentals and price history.

These hybrid approaches are attractive because they separate (1) the effort of creating labeled datasets from (2) the need to deploy robust, efficient inference models in production.

Generative numeric models (StockGPT style)

Separately, researchers have trained generative models directly on price and volume time series. These models treat returns or return discretizations as tokens and learn temporal patterns without relying on natural language. While conceptually similar to language generation, these models are optimized for numeric sequence modeling and differ from ChatGPT’s text training. Some experimental results show that purpose‑built generative numeric models can capture short‑term autocorrelations and regime patterns that are useful for backtested strategies, though they bring distinct risks around overfitting and nonstationarity.

Empirical evidence from research

Academic and preprint literature has begun to evaluate whether can chatgpt predict the stock market and related outcomes. Findings are mixed and highly dependent on design choices. Selected studies (short summaries):

• LoGrasso, M., “Could ChatGPT have earned abnormal returns? A retrospective test from the U.S. stock market” (2025).

  • Retrospective tests reported that GPT‑4–based signals produced positive alphas over long holding periods in specific experimental setups after controlling for common risk factors. Results were sensitive to transaction cost assumptions and out‑of‑sample design.

• Chen, S., Green, T.C., Gulen, H., Zhou, D., “What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts” (SSRN, 2024–2025).

  • Finds LLMs display extrapolative bias (overweighting recent performance) and calibration issues in point forecasts; interval estimates were sometimes better than human survey baselines but still miscalibrated in stressed regimes.

• Chen, J., Tang, G., Zhou, G., Zhu, W., “ChatGPT and DeepSeek: Can They Predict the Stock Market and Macroeconomy?” (arXiv, 2025).

  • Reports that LLMs can extract predictive information from newswires for aggregate market returns and that certain prompt/model variants outperformed simpler baselines on macro return forecasts in controlled tests.

• Anic, N., Barbon, A., Seiz, R., Zarattini, C., “ChatGPT in Systematic Investing — Enhancing Risk‑Adjusted Returns with LLMs” (arXiv, 2025).

  • Demonstrates momentum strategies improved by model‑produced news evaluations, showing higher Sharpe ratios versus standard momentum benchmarks in their backtests.

• Kmak, M. et al., “Predicting stock prices with ChatGPT‑annotated Reddit sentiment” (arXiv, 2025).

  • Explores ChatGPT annotation of social media; finds limited or weak correlations for many tickers, though retail‑driven names occasionally show stronger effects. Simple volume or engagement metrics sometimes outperformed LLM annotations.

• Mai, D., “StockGPT: A GenAI Model for Stock Prediction and Trading” (arXiv, 2024).

  • Presents a generative numeric model trained directly on long histories of returns that yielded measurable factor‑adjusted alphas in tests, illustrating potential for purpose‑built numeric generative models.

• Stanco, J., Chung, K.H., “ChatGPT and the Stock Market” (SSRN, 2025).

  • Documents market microstructure effects after ChatGPT’s launch and examines impacts on volatility, liquidity and analyst forecast behavior.

These papers highlight heterogeneity: performance depends on dataset choice, forecast horizon, prompt design, model version, and evaluation method. In short, the literature shows that LLMs can produce useful signals in some contexts, but results are fragile and sensitive to methodology. This mixed evidence is central when answering whether can chatgpt predict the stock market.

Strengths and potential advantages

When evaluating whether can chatgpt predict the stock market, consider these strengths:

• Scale: LLMs can process vast volumes of unstructured text (news, filings, social feeds) in near real time, creating features that manual teams cannot scale to.
• Nuance: They capture contextual cues (tone, hedging language, named entities) that simple keyword counts miss.
• Rapid idea generation: LLMs can propose hypotheses, event taxonomies, and checklists that accelerate research.
• Readability: Outputs are human‑readable summaries and rationales, easing integration into analyst workflows and documentation.

These advantages make LLMs valuable as signal generators and augmentation tools, even if they do not produce standalone price or return oracles.

Limitations and failure modes

While useful, LLMs have important limits when answering whether can chatgpt predict the stock market.

Behavioral and statistical biases

LLMs inherit biases from training data and human annotators. Common issues:

• Extrapolation bias: overweighting recent trends in forecast generation.
• Optimism/negativity bias: reflecting sentiment skew in sources.
• Calibration problems: overconfident point estimates and misestimated probabilities.

These biases can create systematic forecasting errors if not corrected.

Data leakage, backtest contamination and overfitting

A frequent pitfall is evaluating LLM outputs without a strict out‑of‑sample split. LLMs trained or evaluated on data that overlap with test periods can exploit future information (data leakage). Researchers must use strict temporal holdouts, rolling‑window evaluation, and conservative transaction‑cost assumptions to avoid inflated results.

Market microstructure, costs and scalability

Even if an LLM produces a predictive signal, implementing it costs money:

• Transaction costs and slippage can erase small predictive edges.
• Market impact and limited capacity mean some signals are not scalable to large AUM.
• Latency and compute costs affect the practicality of high‑frequency implementations.

Backtests must reflect realistic execution assumptions; otherwise reported gains may not be achievable in live trading.

Nonstationarity and model drift

Financial markets change. Models trained on one regime may lose performance as market dynamics shift. LLMs that rely on historical language patterns can degrade as communication norms or news flows evolve. Continuous monitoring, periodic retraining, and robust validation are essential.

Interpretability and reproducibility

LLMs are sensitive to prompt wording, model version, and sampling settings. This sensitivity makes replication and governance difficult. Reproducible pipelines, version control for prompts, and human audits help but do not eliminate opacity.

Applications to cryptocurrencies (brief)

Asking whether can chatgpt predict the stock market often includes crypto analogues. Parallels and differences:

• Text and on‑chain signals both matter in crypto. Social media, developer activity, and on‑chain metrics (transaction counts, active addresses, staking flows) are informative.
• Crypto markets tend to be more retail‑driven and sometimes more sentiment‑sensitive, which can increase the short‑term efficacy of social‑signal‑based models.
• However, crypto also exhibits higher volatility, frequent forks, and coordinated behavioral events, increasing nonstationarity and model risk.

For Web3 wallet and execution needs, Bitget Wallet is recommended for a secure, integrated user experience when interacting with on‑chain data or decentralized applications. For trading or derivatives exposure, practitioners should consider Bitget execution services while accounting for liquidity and cost constraints.

Practical considerations for practitioners

Best practices

If you explore whether can chatgpt predict the stock market in your workflow, follow these best practices:

• Strict time‑based out‑of‑sample tests and rolling validation windows.
• Control benchmarks: compare LLM‑augmented strategies against simple baselines (momentum, value, market) and include factor regressions.
• Realistic trading cost assumptions, slippage, and capacity analysis.
• Ensemble approaches: combine LLM textual features with numeric factors and diversify model families to reduce single‑model risk.
• Human‑in‑the‑loop validation to catch hallucinations and rare event misclassifications.
• Logging, prompt version control, and audit trails for governance.

Deployment and operational issues

Operational factors that affect whether LLM signals are useful in production:

• Latency: real‑time ingestion of news and social streams may require low latency paths and streaming prompts.
• API costs and compute: frequent LLM queries can be expensive; consider batching or using lower‑cost fine‑tuned models for inference.
• Model versioning: track the exact model family and weights used for scoring — performance can change when providers update models.
• Compliance and recordkeeping: maintain records of prompts, outputs, and reviewer decisions for audit and regulatory review.

For execution and custody, practitioners can use Bitget’s trading services and Bitget Wallet for on‑chain interactions while ensuring compliance and KYC requirements are met.

Regulatory, ethical and governance issues

Using LLMs in finance raises regulatory and ethical considerations relevant to whether can chatgpt predict the stock market:

• Market manipulation: automated systems that amplify false or misleading social content risk legal exposure.
• Use of non‑public information: training or prompting practices must avoid incorporating insider or non‑public data.
• Model governance: organizations should have policies for model validation, monitoring, incident response, and human oversight.
• Disclosure: regulators may expect disclosure of automated trading or advisory systems in certain contexts.

Compliant deployments require legal review, robust controls, and conservative operational guardrails.

Future directions and open research questions

Key areas where progress could change the answer to whether can chatgpt predict the stock market:

• Better calibration techniques for probability and interval forecasts from LLM outputs.
• End‑to‑end systems combining LLM textual signals with numeric time‑series architectures.
• Real‑time ingestion and evaluation of streaming news and social data with rigorous live out‑of‑sample experiments.
• Domain‑specific fine‑tuning and anonymized labeled datasets to improve finance‑specific responses.
• Crypto‑centric LLM applications that jointly model on‑chain metrics and off‑chain text.
• Community standards for reproducibility, including shared benchmarks, datasets, and rigorous cost‑adjusted backtests.

Summary and practical answer

So, can chatgpt predict the stock market? The practical answer is nuanced:

• ChatGPT and similar LLMs are powerful tools for extracting signals from unstructured text and can improve parts of the investment process, such as idea generation, event detection, and news scoring.
• Several studies report incremental or even economically meaningful improvements in controlled settings, while others document biases, miscalibration, and fragile gains.
• LLMs are not a guaranteed source of reliable, standalone return predictions: gains depend heavily on data, prompts, model versions, evaluation rigor, and execution realism.

In practice, treat LLMs as augmentative: use them to generate or enrich signals, but validate through strict out‑of‑sample testing, realistic execution modeling, and robust governance before allocating capital.

See also

• large language model
• sentiment analysis in finance
• momentum strategies
• algorithmic trading
• cryptocurrency sentiment
• model governance

References (selected)

• LoGrasso, M., “Could ChatGPT have earned abnormal returns? A retrospective test from the U.S. stock market” (2025).
• Chen, S., Green, T.C., Gulen, H., Zhou, D., “What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts” (SSRN, 2024–2025).
• Chen, J., Tang, G., Zhou, G., Zhu, W., “ChatGPT and DeepSeek: Can They Predict the Stock Market and Macroeconomy?” (arXiv, 2025).
• Anic, N., Barbon, A., Seiz, R., Zarattini, C., “ChatGPT in Systematic Investing — Enhancing Risk‑Adjusted Returns with LLMs” (arXiv, 2025).
• Mai, D., “StockGPT: A GenAI Model for Stock Prediction and Trading” (arXiv, 2024).
• Kmak, M. et al., “Predicting stock prices with ChatGPT‑annotated Reddit sentiment” (arXiv, 2025).
• Stanco, J., Chung, K.H., “ChatGPT and the Stock Market” (SSRN, 2025).

Notes on timeliness: As of 2026-01-17, according to public research summaries and media coverage, institutional and retail interest in LLMs for finance continues to grow. Practitioners should consult primary research and regulatory guidance when designing deployments.

For users interested in applying text‑driven signals to trading or exploring on‑chain data, consider Bitget’s trading services and Bitget Wallet for secure custody and integrated workflows. Explore Bitget features to learn how LLM‑augmented signals can fit into disciplined, governed investment processes.