IKNet: Interpretable Stock Price Prediction via Keyword-Guided Integration of News and Technical Indicators

• URL: http://arxiv.org/abs/2510.07661v2
• Date: Mon, 27 Oct 2025 08:47:49 GMT
• Title: IKNet: Interpretable Stock Price Prediction via Keyword-Guided Integration of News and Technical Indicators
• Authors: Jinwoong Kim, Sangjin Park,
• Abstract summary: We propose an interpretable keyword-guided network (IKNet) to model the semantic association between individual news keywords and stock price movements.<n>IKNet identifies salient keywords via FinBERTbased contextual analysis, processes each embedding through a separate nonlinear projection layer, and integrates their representations with the time-series data of technical indicators to forecast next-day closing prices.<n> Empirical evaluations of S&P 500 data from 2015 to 2024 demonstrate that IKNet outperforms baselines, including recurrent neural networks and transformer models, reducing RMSE by up to 32.9% and improving cumulative returns by 18.5%.
• Score: 3.5795275871379704
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing influence of unstructured external information, such as news articles, on stock prices has attracted growing attention in financial markets. Despite recent advances, most existing newsbased forecasting models represent all articles using sentiment scores or average embeddings that capture the general tone but fail to provide quantitative, context-aware explanations of the impacts of public sentiment on predictions. To address this limitation, we propose an interpretable keyword-guided network (IKNet), which is an explainable forecasting framework that models the semantic association between individual news keywords and stock price movements. The IKNet identifies salient keywords via FinBERTbased contextual analysis, processes each embedding through a separate nonlinear projection layer, and integrates their representations with the time-series data of technical indicators to forecast next-day closing prices. By applying Shapley Additive Explanations the model generates quantifiable and interpretable attributions for the contribution of each keyword to predictions. Empirical evaluations of S&P 500 data from 2015 to 2024 demonstrate that IKNet outperforms baselines, including recurrent neural networks and transformer models, reducing RMSE by up to 32.9% and improving cumulative returns by 18.5%. Moreover, IKNet enhances transparency by offering contextualized explanations of volatility events driven by public sentiment.

Related papers

• PriceSeer: Evaluating Large Language Models in Real-Time Stock Prediction [47.70107097572211]
  We introduce PriceSeer, a benchmark specifically designed for large language models performing stock prediction tasks.<n>PriceSeer includes 110 U.S. stocks from 11 industrial sectors, with each containing 249 historical data points.<n>We evaluate six cutting-edge LLMs under different prediction horizons, demonstrating their potential in generating investment strategies.
  arXiv  Detail & Related papers  (2025-12-31T08:35:46Z)
• GroupSHAP-Guided Integration of Financial News Keywords and Technical Indicators for Stock Price Prediction [5.287763385823119]
  GroupSHAP quantifies contributions of semantically related keyword groups rather than individual tokens.<n>We employed FinBERT to embed news articles from 2015 to 2024, clustered them into coherent semantic groups, and applied GroupSHAP to measure each group's contribution to stock price movements.<n> Empirical results from one-day-ahead forecasting of the S&P 500 index throughout 2024 demonstrate that our approach achieves a 32.2% reduction in MAE and a 40.5% reduction in RMSE.
  arXiv  Detail & Related papers  (2025-10-27T08:33:18Z)
• Multimodal Stock Price Prediction [0.0]
  It has become increasingly critical to carefully integrate diverse data sources with machine learning for accurate stock price prediction.<n>This paper explores a multimodal machine learning approach for stock price prediction by combining data from diverse sources, including traditional financial metrics, tweets, and news articles.
  arXiv  Detail & Related papers  (2025-01-23T16:38:46Z)
• FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs [8.303827622646086]
  We propose a data-driven approach that enhances sentiment-based stock movement predictions by incorporating news dissemination breadth, contextual data, and explicit instructions.<n>Our approach improves prediction accuracy by 8% compared to existing methods.
  arXiv  Detail & Related papers  (2024-12-14T13:04:42Z)
• MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction [3.456620177234167]
  We introduce the Market Attention-weighted News Aggregation Network (MANA-Net) MANA-Net learns the relevance of news sentiments to price changes and assigns varying weights to individual news items. We evaluate MANA-Net using the S&P 500 and NASDAQ 100 indices, along with financial news spanning from 2003 to 2018.
  arXiv  Detail & Related papers  (2024-09-09T15:12:24Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• Earnings Prediction Using Recurrent Neural Networks [0.0]
  This study develops a neural network to forecast future firm earnings, using four decades of financial data. It addresses analysts' coverage gaps and potentially revealing hidden insights. It is able to produce both fiscal-year-end and quarterly earnings predictions.
  arXiv  Detail & Related papers  (2023-11-10T13:04:34Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information [73.40830291141035]
  Several methods were recently proposed to mine the shared information through stock concepts extracted from the Web to improve the forecasting results. Previous work assumes the connections between stocks and concepts are stationary, and neglects the dynamic relevance between stocks and concepts. We propose a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts.
  arXiv  Detail & Related papers  (2021-10-26T14:04:04Z)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.