Llm for stock prediction. Ieee Access 7 (2019), 28299–28308.

Llm for stock prediction. • Tasks: o Provide buy/sell/hold recommendations.

Llm for stock prediction We utilized recently released language models for our LLM-based classifier, including GPT- 3 and 4, and LLaMA- 2 and 3 models. Oct 17, 2023 · Recent studies have demonstrated the ability of LLMs like ChatGPT to predict stock market returns by analyzing sentiment in news headlines, with findings indicating a significantly positive The goal of the Predict module is to fine-tune a LLM to generate good stock predictions and explanations for the unseen test period. (2020), it demonstrated impressive capabilities in financial sentiment analysis and stock prediction tasks. , formulating the numerical features (e. What you say and how you say it matters: Predicting stock volatility using verbal and vocal cues. Each stock has an associated list of news. (6 ph) o Simulate scenarios and validate predictions. creasing attention in stock prediction for their abil-ity to model inter-stock relations (Sawhney et al. As discussed by Yang et al. 1 MOTIVATION CHOICE OF PARAMETERS 5 days ago · Guided by background knowledge and identified factors, we leverage historical stock prices in textual format to predict stock movement. In quantitative investing, return forecasting is fundamental for subsequent tasks like stock picking, portfolio optimization, etc. An extensive evaluation of the LLMFactor framework across four benchmark datasets from both the U. The router dynamically selects the most suitable expert model for stock movement prediction based on the given context. (Lee et al. Tensorflow is an open-source Python framework, famously known for its to predict stock market returns using news headlines. It involves using statistical models and machine learning algorithms to analyze financial data and make predictions about the future performance of a stock. It has developed a user-friendly app for Android and iOS, with subscribers receiving 3 stock recommendations each week. Oct 29, 2024 · Financial trading has been a challenging task, as it requires the integration of vast amounts of data from various modalities. Furthermore, it extracts correlations between stocks, statistical trends, and timestamps from stock price data, transforming them into textual About. 5 % 85 0 obj /Filter /FlateDecode /Length 5608 >> stream xÚ­[K“ÛÈ‘¾Ï¯ÐÍè TáéÛHÖÈöj4 ©g'6l Ð šD 8xXnÿúýò ` å>ì…¨Ê* ^Y™_>øæþ‡ÿú) ^å~ž˜äÕýã«8z•šÌ lúê~ÿêo^uªÚqx}·3±ñ¾Uò,ê“ ÆŽž¡wî»»0öþYïç ò軇; xÓ0Jý±/NÕ ‰½owaàuýWyý±ë¥ ÛþI?E3 cÝ îþqÿ×WÁ«] øyœËœŠv ·³¹õÊît. This paper explores fine-tuning LLMs for predicting stock returns with financial newsflow. Aug 25, 2024 · Unlike recent FinLLMs, StockTime is specifically designed for stock price time series data. Jul 25, 2024 · The conventional way of applying financial news data to stock picking involves a multi-step extraction-and-validation process as illustrated in Fig. Jan 29, 2025 · Khaidem et al. Aug 25, 2024 · In this paper, we proposed StockTime, an efficient LLM-based architecture for stock price prediction. This repository is designed to provide financial insights using state-of-the-art natural language processing (NLP) and machine learning techniques. stock prediction [10, 76] are few, and use limited techniques such as pre-trained LLMs or instruction tuning. Yang (2019). Figure: Prediction by LLMs Dec 10, 2024 · Stock price/movement prediction is an extremely difficult task. May 13, 2024 · To tackle these issues, we propose our Summarize-Explain-Predict (SEP) framework, which utilizes a verbal self-reflective agent and Proximal Policy Optimization (PPO) that allow a LLM teach itself how to generate explainable stock predictions, in a fully autonomous manner. Python-based stock market analysis and forecasting tool using LLM and technical indicators for major tech stocks - RezaBaza/stock-forecast-llm. (Xu and Cohen,2018) uses tweets and historical prices to make temporally dependent predictions from stock data. abstain from quantifying a security’s inherent value; instead, they rely on stock charts to pinpoint configurations and trends that indicate the potential future behavior of a stock. Traders can effectively leverage these LLMs to analyze financial news and predict stock returns accurately. We We assess the predictive accuracy of LLM for stock returns at different time horizons, as well as the impact of transaction costs. , GPT-3. Jun 26, 2024 · Saffarian S Haratizadeh S (2024) LLM-Driven Feature Extraction for Stock Market Prediction: A Case Study of Tehran Stock Exchange 2024 15th International Conference on Information and Knowledge Technology (IKT) 10. A perfect mix of Fundamental and technical analysis could serve as a benefactor in the stock market trend prediction. ; Shen et al. 5 Stock movement prediction (SMP) The Stock Movement Prediction (SMP) task aims to predict the next day’s price movement (e. Recently, large language models (LLMs) have brought new ways to improve these predictions. Features StockLLM is a multimedia AI analysis tool that integrates various facets of stock market data, combining both structured and unstructured information to deliver comprehensive insights and Dec 17, 2024 · 文章浏览阅读2. (6 ph) o Tailor insights based on risk profiles. Abstract. Does it mean LLM is useless for stock forecasting? stock prediction [10, 76] are few, and use limited techniques such as pre-trained LLMs or instruction tuning. Then, the agent extract these factors from daily news and make predictions of stock price during trading. To use the AI-based stock Analysis, we simply need to provide it with a financial news article or another piece of text. To tackle the explainable stock prediction task using LLMs, we Jul 12, 2024 · The stock prediction model’s block diagram is presented in Fig. ,2023) and mining relational data from historical StonkBERT: Can Language Models Predict Medium-Run Stock Price Movements?1 Stefan Pasch2 Daniel Ehnes3 Abstract To answer this question, we fine-tune transformer-based language models, including BERT, on different sources of company-related text data for a classification task to predict the one-year stock price performance. and Chinese stock markets demonstrates its superiority over existing state-of-the-art methods and its Aug 25, 2024 · The stock price prediction task holds a significant role in the financial domain and has been studied for a long time. As this research topic has recently Apr 8, 2024 · Let’s predict the price for the next 4 days: import yfinance as yf import numpy as np from sklearn. Qin and Yang (2019) Qin, Y. In this example, the model will predict future stock prices based on historical data. Eg- sample input and output of the bot- 📈 AI Agents for Stock Market Prediction: A Multi-Agent Framework 🤖💹 The integration of LLM-based multi-agent systems into financial trading is an exciting development, and this framework Jul 25, 2024 · Illustration of the LLM-based return forecasting model for the stock-picking process. The goal of stock price prediction is to help investors make informed investment decisions by providing a Moreover, Zhang et al. (4 ph) • Role: Generates actionable strategies. For example, we could provide the stock Analysis with the following financial news Dec 28, 2023 · Introduction: The Indian stock market presents a unique challenge for retail investors, especially those without a finance background. Mar 30, 2024 · 4) Financial large language model FinMA-7B trained with StockNet training set yield inferior performance than Ploutos, indicating purely tuning LLM with stock related instruction data cannot get optimal performance for stock movement prediction task. InvestSmart. Dec 16, 2023 · Predicting stock prices is a challenging yet intriguing task in the field of machine learning. Diffusion variational autoencoder for tackling stochasticity in multi-step regression stock price prediction. 2018. Accurate stock market predictions following earnings reports are crucial for investors. This task is particularly challenging due to the need to integrate time-series problems with temporal dependencies extracted from text information. We pass the preprocessed historical data as input and specify the Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting, in arXiv 2023. In 2019 IEEE fifth international conference on big data computing service and applications (BigDataService), pp. Get real-time stock evaluations, market insights, and strategic investment opportunities tailored to help you make informed decisions and maximize your investment portfolio. Nov 14, 2024 · One way to achieve this is by providing direct instructions to the LLM. However, manual tuning of LSTM parameters significantly impacts model performance. preprocessing import MinMaxScaler # Fetch the latest 60 days of AAPL stock data data = yf. Traditional deep learning and reinforcement learning methods require large training data and often involve encoding various data types into numerical formats for model input, which limits the explainability of model behavior. Leverage Deep Learning Models to Forecast Stock Prices and Make Data Aug 25, 2024 · 4. To account for unobserved variations, these regressions include fixed effects for both firms and time, and we cluster standard pose the stock return prediction model into two sub-models: a Local model and a Global model. Once the model is fine-tuned, we can run it to generate predictions. Xie et al. 1970–1979. Drawing on multimodal technologies, [28] explored how audio features—such as tone, emo-tion, and speech rate—enhance stock movement predictions when combined with text analysis. We trained three models to predict stock returns for 5, 10, and 20 trading days ahead. 2023. We cluster recent company-related news to assess its reach and influence, enriching prompts with more specific data and precise instructions. May 4, 2024 · template = """ Identify the sentiment towards the Apple(AAPL) stocks from the news article , where the sentiment score should be from -10 to +10 where -10 being the most negative and +10 being the most positve , and 0 being neutral Also give the proper explanation for your answers and how would it effect the prices of different stocks Article Nov 2, 2024 · Our dataset contains news articles collected from different sources, historic stock price, and financial report data for 20 companies with the highest trading volume across different industries in the stock market. Stock-market LLM: A Language Model for Financial Analysis and Prediction in Stock Markets. py # Entry point for running the Dec 27, 2024 · In the case of stock market prediction software, these include: Usability: The software should feature an intuitive user interface that can be easily navigated and understood by a diverse range of users, from novice traders to experienced market analysts. To achieve a better accuracy in sentiment classification, experiments are designed to compare six different models (GPT 4, Llama 3, Gemma 2, Mistral 7b, FinBERT, VADER) in financial news sentiment Jun 8, 2023 · We leverage the power of large language models to analyze historical stock data and generate predictions. TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series. To implement this we shall Tensorflow. Assume an investment universe of 3 stocks denoted by a,b,c. The steps for each stage is given below: Predicting whether the stock market would go up or down has always been a challenge for investors. Apr 20, 2023 · By integrating news reports about companies, the LLM can consider a more comprehensive set of factors when making stock market predictions. I'd integrate that with langchain so the LLM could query for stock predictions and make strategic decisions. A model involving information capacity constraints, limits to arbitrage, and LLMs rational-izes this predictability, which strengthens among smaller stocks and following negative news. 6 percent, with an average level of correctness of only 59. Two sets of researchers did so recently and found that large language models (LLMs) like ChatGPT and BERT can enhance the accuracy of predictions about the stock market and public opinion, at least as measured against historical data. The application of LLMs in stock prediction has been evolv-ing, with existing studies primarily focusing on methods such as pre-trained LLMs or instruction tuning, which require extensively annotated datasets [21, 28, 29]. Stock market trend prediction using high-order information of time series. Stock price forecasting involves three stages: (i) Calculation of feature vectors, ten historical technical indications (ii) Data preprocessing using min–max method and (iii) use of one-day-ahead stock price prediction using LSTM. ,2019). Second Nov 2, 2023 · For instance, if we have historical stock prices, we can convert them into sentences like “On January 1, 2020, the closing price of XYZ stock was $100. The combination of LLMs with traditional stock price prediction methods holds great Python-based stock market analysis and forecasting tool using LLM and technical indicators for major tech stocks - stock-forecast-llm/README. Traditional methods, particularly classical machine learning models, struggle with these predictions because they cannot effectively process and interpret extensive textual data contained in earnings reports and often overlook nuances that influence market movements. e. The Local model models stock-specific, intrinsic information (such as volume, price, and other technical features) to predict stock returns, corre-sponding to the αcomponent in asset pricing models. 1109/IKT65497. LLMs excel in analyzing sentiments about listed companies from financial news, providing critical insights for trading decisions. In this section, we discuss the overall fine-tuning process of the model and the subsequent inference procedure at test-time. In this blog post, we’ll explore how to use Long Short-Term Memory (LSTM), a type of recurrent Aug 8, 2023 · Once stock ticker is extracted correctly, in the later stages stock data, news, and financial statements are simply fetched by inputting the ticker symbol. In response, this project introduces an innovative approach leveraging knowledge graphs and Language Model (LLM) reasoning to enhance stock price prediction accuracy. txt # Project dependencies └── main. 其中，预测股价的未来变动是一项关键任务，它依赖于大型语言模型（LLM）对多种数据源的综合分析。 Articles for Stock Price Sep 24, 2023 · Abstract: LLM-based Stock Market Trend Prediction Investor sentiment, which is driven by 'intriguing factors' such as news articles and options volume, has been historically resistant to effective use in quantitative methods for predictive market analysis. IEEE. This paper introduces an advanced LLM refused to give exact prediction, instead it told us ten well-known Chinese stocks, and suggested us to conduct a research before investing in stocks. Jan 11, 2025 · 4. Notably, we show that time series analysis (e. fit %PDF-1. This paper LLM based Finance Agent is a powerful tool that leverages large language models (LLMs) to automatically fetch news and predict historical stock prices to forecast future prices. , sentiments, topics, popularity, etc. Ieee Access 7 (2019), 28299–28308. **Stock Price Prediction** is the task of forecasting future stock prices based on historical data and various market indicators. Feb 19, 2024 · This article investigates the prediction of stock prices using state-of-the-art artificial intelligence techniques, namely Language Models (LMs) and Long Short-Term Memory (LSTM) networks. This paper introduces an advanced In the realm of stock market prediction, relying solely on historical data to predict stock market directions has proven to be inadequate. Jan 14, 2025 · Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. I'd also want to run simulations on past data to see how it would have performed at various times throughout stock market history. Section 5: Generating Future Stock Price Predictions To generate future stock price predictions, we use the OpenAI API and a suitable language model (e. Today, Large Language Models (LLMs) present a solution to this problem, given their known capabilities to generate human-readable explanations for their decision-making process. Feb 19, 2024 · Forecast Output: Leveraging its understanding of the textualized data and contextual prompts, the LLM produces forecasts for future time points, offering valuable insights into potential outcomes. For instance, given the sentence, “Due to the pandemic declaration, the S&P 500,” an LLM might predict "declined" as the next word based on the previous words. The goal is to train the LLM-based model f(·) to predict the future stock price pˆfor a forecast period of x days based on a lookback period of d days. ai is worth considering if you’re looking to receive AI stock predictions on your phone. o Predict stock performance and generate signals. Stock Market price analysis is a Timeseries approach and can be performed using a Recurrent Neural Network. (4 ph) LLM 5. In this study, we propose a novel hybrid deep learning framework that integrates a large language model (LLM), a Linear Transformer (LT), and a Convolutional Neural Network (CNN) to enhance stock price prediction using solely historical market data. Sep 16, 2024 · In this article, we shall build a Stock Price Prediction project using TensorFlow. This approach may help the model capture market sentiment, industry trends, and external events that could influence stock prices. Traditional technical indicators such as moving averages and exponential moving averages (EMAs) are frequently insufficient for accurate forecasting, especially when the market is influenced by significant Apr 1, 2024 · This section assesses the ability of various LLMs to predict stock returns for the next day using regression models. 16 with some variation, but for a more confident prediction of Tesla’s price next month, human experts and consideration of current events are crucial. Oct 29, 2024 · In finance, for example, LLMs can analyse news, reports, or social media to provide insights for market predictions, risk management, and strategy development. The system features Bull and Bear researchers evaluating market conditions, a risk management Oct 7, 2024 · Stock_Analysis_Prediction_Model/ │ ├── data/ # Raw and processed stock data ├── src/ # Source code for data fetching and model training ├── models/ # Saved trained models ├── tests/ # Unit tests for various components ├── images/ # Model performance visualization ├── requirements. (2023) Qianqian Xie, Weiguang Han, Yanzhao Lai, Min Peng, and Jimin Huang. 4. Our regression with Eq. - Mousami7/LLM-Performance-Evaluation-for-Price-Prediction-in-Crypto-Stocks Jul 26, 2024 · Illustration of the LLM-based return forecasting model for the stock-picking process. 4 percent across the four This repository provides tools and workflows for stock analysis using large language models (LLMs). grated with the stock price data in the latent space at time t. Feed it various stock data and have it make predictions. Running the Model for Predictions. Assume an investment universe of 3 stocks denoted by a, b, c. However, the task Jun 25, 2023 · The rapid advancement of Large Language Models (LLMs) has spurred discussions about their potential to enhance quantitative trading strategies. The four LLMs that were tested have generated predictions that were correct in only 51. Conclusion The researchers have developed a novel framework that combines a large language model with Quantized Low-Rank Adaptation to harness the power of corporate earnings reports for stock price Oct 9, 2023 · The remarkable achievements and rapid advancements of Large Language Models (LLMs) such as ChatGPT and GPT-4 have showcased their immense potential in quantitative investment. In today’s fast-paced world, making informed investment decisions in the stock market is crucial. Recently, large language models (LLMs) have brought new ways to Apr 21, 2024 · The concept is to Tokenize the time series data and then use a foundational LLM that predicts the probabilistic forecast values for every upcoming data point for n times and aggregating the Greetings to r/BudgetGamingLaptop, the ultimate destination for gaming laptop enthusiasts! Here, you'll find a vibrant hub buzzing with discussions, news, reviews, and expert advice to help you discover the perfect gaming laptop. Nov 5, 2023 · Photo by Markus Spiske on Unsplash. We will use the llama-2 model without fine-tuning. This step culminates in the output of forecasts, completing the LLM-Mixer framework processing pipeline. The wall street neophyte: A zero-shot analysis of chatgpt over multimodal stock movement prediction challenges. Discover AI-powered stock analysis and engage with large language models (LLM) including OpenAI's creations. Jul 25, 2024 · This paper explores fine-tuning LLMs for stock return forecasting with financial newsflow. Apr 18, 2023 · If you want a picture of the future, imagine asking a large language model for a prediction. Leveraging state-of-the-art NLP techniques to analyze market sentiment, predict trends, and provide insights for informed decision-making. (), which were pretrained on 13 datasets that do not include single stock data or stock indices, using the datasets of the residual returns of American single stocks published by Guijarro-Ordonnez et al. ai: Receive 3 Weekly AI Stock Predictions via an Android or iOS App Candlestick. We formulate the model to include text representation and forecasting modules. To simplify this process, we’ve developed an AI-powered Dec 14, 2024 · In this paper, we propose a data-driven approach that enhances LLM-powered sentiment-based stock movement predictions by incorporating news dissemination breadth, contextual data, and explicit instructions. arXiv preprint arXiv:2304. On the other hand, the Global model captures the Users can easily forecast stock price trends by simply providing an API key, making sophisticated financial analysis accessible and user-friendly. It leverages the natural ability of LLMs to predict the next token by treating stock prices as consecutive tokens, extracting textual information such as stock correlations, statistical trends and timestamps directly from these stock prices. Figure 3 presents these three models’ annual returns, Sharpe ratios, and turnover rates. 2. The ease with which users can interpret and act on the stock predictions is paramount. • Tasks: o Provide buy/sell/hold recommendations. Whether you’re a seasoned investor or a newcomer, having We introduce TradingAgents, a novel stock trading framework inspired by trading firms, utilizing multiple LLM-powered agents with specialized roles such as fundamental, sentiment, and technical analysts, as well as traders with diverse risk profiles. Oct 12, 2023 · FinGPT-RAG: We present a retrieval-augmented large language model framework specifically designed for financial sentiment analysis, optimizing information depth and context through external knowledge retrieval, thereby ensuring nuanced predictions. 5 Turbo). , forward return, volatility, etc. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Nov 10, 2024 · 大语言模型（LLM：Large language model，下文称“LLM”）是基于深度学习技术来理解、处理并生成人类自然语言的的人工智能系统。是当今人工智能领域的一大重大突破性技术，基于大量的密集的文本数据的训练，通过自我监督和半监督学习的方式，从文本文档中训练学习相关的统计关系以达到对人类 May 13, 2024 · To tackle these issues, we propose our Summarize-Explain-Predict (SEP) framework, which utilizes a verbal self-reflective agent and Proximal Policy Optimization (PPO) that allow a LLM teach itself how to generate explainable stock predictions, in a fully autonomous manner. This focus aligns Jun 29, 2023 · They found that ChatGPT — as compared to models such as BERT, GPT-1, and GPT-2 — performed the best and only more advanced models like ChatGPT can analyze large amounts of data to successfully predict the stock market. 1 Stock Movement Prediction using Textual Data With the advancement of natural language process-ing (NLP) techniques, many researchers leverage textual data to forecast stock market trends. , forecasting) can be cast as yet another "language task" that can be effectively tackled by an off-the-shelf LLM. 10892779 (59-65) Online publication date: 24-Dec-2024 Jan 10, 2024 · Through implementing the aforementioned methods, the principal contribution lies in a substantial enhancement of deep learning network accuracy specifically tailored for financial sentiment analysis. 1 (a), i. 1087–1096. 10892779 (59-65) Online publication date: 24-Dec-2024 Jan 1, 2025 · “FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs” 金融市场对新闻和社交媒体高度敏感，情绪分析在现代金融预测中至关重要。传统情绪分析通常将情绪分为正面、负面和中性，LLMs的出现提升 Stock price prediction using news sentiment analysis. S. Adjust the prompts accordingly. Then, given the return forecasts and ranks, stocks can be selected into long-only or long-short portfolios. Oct 24, 2023 · Imagine an LLM making a prediction based on a financial news article. LLM4TS: Two-Stage Fine-Tuning for Time-Series Forecasting with Pre-Trained LLMs. Recent research has enhanced these models by integrating stock news to improve forecasting ability. Explore AI image generation and stay updated on AI and stock market trends. Jan 24, 2025 · Deep learning was used with preprocessing methods by Bhatt et al. , 2024) assesses LLM performance (ranging from general purpose LLMs to fine-tuned) on QA and summarization for financial documents, text classification, generation, stock movement prediction and more, demonstrating many applications for LLMs in finance. Since stock data lacks inherent graph structures, various methods are em-ployed to construct graphs, including utilizing prior knowledge (Kim et al. The analysis reveals an average price of $217. Oct 9, 2024 · Each LLM was instructed to predict whether, 30 days later, the stock price would be higher or lower and to indicate its level of confidence in the prediction. 5 %âãÏÓ 736 0 obj > endobj 769 0 obj >/Filter/FlateDecode/ID[6CBBB1FFCF018A222068D1BDD5EF0526>69656EF1BB9E2245AE94E4294688D4B0>]/Index[736 150]/Info 735 0 Nov 12, 2024 · Abstract. and Y. applied Random Forest to predict the direction of stock market prices using historical stock data and technical indicators to forecast stock for major companies like Apple (AAPL) and General Electric (GE). reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) scaled_data = scaler. Nov 12, 2024 · The QLoRA-enhanced LLM approach offers a promising direction for improving stock price prediction and supporting informed investment decisions. Candlestick. Dec 3, 2024 · 5. Traditional models have leveraged either Convolutional Neural Networks (CNN) for spatial feature extraction or Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, with limited integration of external textual Nov 14, 2024 · Kelvin JL Koa, Yunshan Ma, Ritchie Ng, and Tat-Seng Chua. AI offers expert financial analysis powered by advanced AI and LLM Agents. ,2019;Zheng et al. g. , 2024a) first utilizes LLM’s reasoning capability to identify important factors by asking the LLM to analysis relationship between historical news and corresponding stock price movements. Visualization Agent Dec 3, 2024 · 5. The goal of stock price prediction is to help investors make informed investment decisions by providing a Figure 2: Illustration of the LLM-based return forecasting model for the stock-picking process. ; Braei and Wagner . download('AAPL', period='60d', interval='1d') # Select 'Close' price and scale it closing_prices = data['Close']. Oct 23, 2024 · 5) Forecast Generation: Finally, a trainable decoder, which is a simple linear transformation, is applied to the last hidden layer of the LLM to predict the next set of future time steps. Feb 18, 2024 · Recent advancements in large language models (LLMs) have opened new pathways for many domains. However, the full potential of LLMs in financial investments remains largely untapped. Once all the stock related information is available, it is then utilized by the LLM for the comprehensive stock analysis. May 23, 2024 · The stock market is influenced by many unpredictable factors, making precise predictions beyond historical trends difficult. 2024. (1) uses LLM-generated scores from news headlines as the main predictors. md at main · RezaBaza/stock-forecast-llm It is used in finance and business to predict the trend of indexes and stocks, in the field of intelligent manufacturing to predict anomaly detection and power load, and in the field of meteorology, agriculture, and navigation to predict temperature, humidity, and climate Singh and Malhotra ; Munir et al. There are two main challenges for typical deep learning-based methods for quantitative finance. Huizhe Wu, Wei Zhang, Weiwei Shen, and Jun Wang. ,2023) models the multi- Aug 13, 2024 · Accurate stock market predictions following earnings reports are crucial for investors. Feb 22, 2025 · Accurate trading volume prediction is essential for portfolio optimization, market regulation, and financial risk control. LLM’s ability to process large-scale text data makes it a promising application in the financial field. We o Predict stock performance and generate signals. The stock price prediction task holds a significant role in the financial domain and has been studied for a long time. However, integrating LLMs into existing quantitative models presents two primary challenges: the insufficient The goal is to assess how well these models handle price prediction tasks in financial markets with different levels of volatility, such as cryptocurrencies and traditional stocks. The optimisation of stock functions was the primary emphasis of their model; nevertheless, it was unable to successfully assimilate and incorporate real-time market sentiment. While recent financial large language models (FinLLMs) have shown considerable progress in financial NLP tasks compared to smaller pre-trained language models (PLMs), challenges persist 5 days ago · Abstract Large language models (LLMs) and their fine-tuning techniques have demonstrated superior performance in various language understanding and generation tasks. 6 percent to 65. and Chinese stock markets demonstrates its superiority over existing state-of-the-art methods and its Jun 26, 2024 · Saffarian S Haratizadeh S (2024) LLM-Driven Feature Extraction for Stock Market Prediction: A Case Study of Tehran Stock Exchange 2024 15th International Conference on Information and Knowledge Technology (IKT) 10. ) [1, 36 Python-based stock market analysis and forecasting tool using LLM and technical indicators for major tech stocks - RezaBaza/stock-forecast-llm Jun 16, 2024 · In this paper, we propose a data-driven approach that enhances LLM-powered sentiment-based stock movement predictions by incorporating news dissemination breadth, contextual data, and explicit instructions. Dongjin Song. To achieve a better accuracy in sentiment classification, experiments are designed to compare six different models (GPT 4, Llama 3, Gemma 2, Mistral 7b, FinBERT, VADER) in financial news sentiment Jun 16, 2024 · Guided by background knowledge and identified factors, we leverage historical stock prices in textual format to predict stock movement. 205–208. Harness the power of AI for stock insights, interact with advanced LLMs, and create stunning visuals with leading AI tools. PSO-LSTM model leveraging PSO’s efficient swarm Dec 15, 2024 · It was found that recent large language models can outperform FinBERT and VADER, which are the most commonly used models in financial sentiment analysis, in stock price forecasting with the combination of latest transformer-based prediction models. Temporal Data Meets LLM - Explainable Financial Time Series Forecasting Conference acronym ’XX, June 03–05, 2018, Woodstock, NY forecasting weekly/monthly stock returns (defined as the percent-age change in stock price from the beginning to the end of the week/month) with accompanying explanations. (Citation 2023) to increase the accuracy of stock prediction. To tackle the explainable stock prediction task using LLMs, we Aug 18, 2024 · Accurate stock market predictions following earnings reports are crucial for investors. However, the performance of LLMs in this task varies substantially due to their inherent characteristics. 3k次，点赞19次，收藏18次。本项目演示了如何使用 Python 进行股票数据的获取、处理、预测与可视化。通过akshare获取数据，结合机器学习模型进行预测，再借助matplotlib绘制图表，最后生成简短的市场分析。 Jan 31, 2025 · LLMs, showing that incorporating LLM-based sentiment analysis into stock prediction models leads to significantly better performance, especially in uncertain market conditions. Researchers have tried several different methods in order to predict market movement, ranging from different statistical machine learning models to trends on social media, the goal being to find the optimal strategy to make the most amount of money. Sep 22, 2023 · Stock movement prediction from tweets and historical prices. Moreover, Zhang et al. With RLSP, the subsequent stock price movements serve as an evaluative metric, allowing the model to adjust its predictions in Jan 22, 2024 · Finance is a highly specialized and complex field that involves a great deal of data analysis, prediction, and decision making. An effective method for predicting trading volume involves building a graph to model relations between stock. This paper examines the effectiveness of recent large language model-based news sentiment estimation for stock price forecasting with the Figure 2: Illustration of the LLM-based return forecast-ing model for the stock-picking process. Time-LLM is a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact. - bauer-jan/stock-analysis-with-llm Sep 30, 2024 · The financial domain presents a complex environment for stock market prediction, characterized by volatile patterns and the influence of multifaceted data sources. Random Forest generates several decision trees, each trained on random subsets of features and data points. Visualization Agent Dec 12, 2024 · So we first conducted a zero-shot evaluation of the predictions from pretrained and fine-tuned supervised time series foundation LLMs Chronos by Ansari et al. 3 Ablation Study (RQ2） 消融研究 (RQ2) %PDF-1. Assume an investment universe of 3 stocks denoted by a;b;c . ChatGPT is an LLM based on generative pre-trained transformer architecture that was first introduced in November of 2022 by Sep 18, 2023 · Using the AI-Based Stock Analysis. The stock analyzer will then use the LLM to generate insights from the text and predict the stock price. , 2021b;Kim et al. Jan 31, 2025 · Accurately predicting stock prices remains a challenging task due to the volatile and complex nature of financial markets. First, they struggle to fuse textual and numerical information flexibly for stock movement prediction. It also gave us a warning about the volatility of the stock market. StockTime leverages the inherent token transitions of LLMs to extrapolate future stock prices. Each stock has an associated list of Aug 25, 2024 · StockTime is introduced, a novel LLM-based architecture designed specifically for stock price data that outperforms recent LLMs, as it gives more accurate predictions while reducing memory usage and runtime costs. ChatGPT scores significantly predict subsequent daily stock returns, outperforming traditional methods. zêÅÕñXIá\õøä methods to predict stock price volatility. Following by this, [40] further extends the idea of using multimodal data to improve risk prediction perfor- Jul 26, 2024 · LLMFactor(Wang et al. Personally, I don't think any of the stock prediction models out there shouldn't be taken for granted and blindly rely on them. up or down) based on historical prices and associated text data. Return prediction is fundamental for subsequent tasks like portfolio construction and optimization in quantitative investing. In the context of LLM-based agents, FinAgent proposed a multimodal LLM trading agent with market Feb 7, 2024 · Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights on important texts. ) with the expectation that these features have a predictive relationship with stock future performance (e. Let rs;t + ` 2 R be the `-step This is the official repository for "Empowering Time Series Analysis with Large Language Models: A Survey" (To appear in IJCAI-24 Survey Track) This repository is activately maintained by Yushan Jiang and Zijie Pan from UConn DSIS Group led by Dr. (Luo et al. Recently, LLM-based agents have Jun 28, 2024 · In this paper, we propose A gent-based S imulated F inancial M arket (ASFM), a stock market simulation framework based on language model agents. ” Training the LLM: Once the data is LLMoE processes historical stock prices and news headlines through an LLM-based router, which provides a comprehensive overview of the current instance. It combines financial data processing with advanced natural language understanding to deliver insights, trends, and predictions in the stock market. (). First, we constructed a simulated stock trading market, encompassing most industry sectors present in the real financial market and implementing an order-matching trading mechanism identical to that of real markets. [20] explored enhancing few-shot stock trend prediction using LLMs, showing that incorporating LLM-based sentiment analysis into stock prediction models leads to significantly better performance, especially in uncertain market conditions. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. Our work seeks to fill this gap by designing a reinforcement learning (RL) framework which can fine-tune a LLM to generate explanations for stock prediction. This paper examines the effectiveness of recent large language model-based news sentiment estimation for stock price forecasting with the combination of latest transformer-based prediction models. However, models might be able to predict stock price movement correctly most of the time, but not always. Developed an end-to-end stock price prediction model by integrating LLM-based sentiment analysis of financial news with time series forecasting, leveraging Python, TensorFlow, and Hugging Face Transformers; achieved enhanced prediction accuracy by incorporating sentiment data. (4 ph) o Align strategies with user goals. values. The proposed system employs automated bots equipped with news APIs and social media APIs to gather real-time textual data. This study relies exclusively on stock price data as input, which defines it as a univariate stock price prediction task. yfwk fiaiqqrc qzr faiwv xdknl dfrs onpv mctjc odjdubc krjbpv mjwra qvik brkj ilbyqjol frhexk