# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
2 FITec--Technological Innovations, Casi do Apolo, 222:12\" andar, Recife 50030-230, Pernambuco, Brazil; 2  
[PERSON]J.E.L.d.O.) 2 FITec--Technological Innovations, Casi do Apolo, 222:12\" andar, Recife 50030-230, Pernambuco, Brazil; 2  
[PERSON]J.E.L.d.O.) 2 FITec--Technological Innovations, Casi do Apolo, 222:12\" andar, Recife 50030-230, Pernambuco, Brazil; 2  
[PERSON]J.E.L.d.O.) 2 FITec--Technological Innovations, Casi do Apolo, 222:12\" andar, Recife 50030-230, Pernambuco, Brazil; 2  
[PERSON]J.E.L.d.O.) 2 FITec--Technological Innovations, Casi do Apolo, 222:12\" andar, Recife 50030-230, Pernambuco, Brazil; 2  
[PERSON]J.E.L.d.O.) 2 FITec--Technological Innovations, Casi do Apolo, 222:12\" andar, Recife 50030-230, Pernambuco, Brazil; 2  
[PERSON]C.M.d.O.R. ); [EMAIL_ADDRESS] (J.E.L.d.O.) 2 FITec--Technological Innovations, Casi do Apolo, 222:12\" andar, Recife 50030-230, Pernambuco, Brazil; 2  
[PERSON]J.E.L.d.O.) 2 FITec--Technological Innovations, Casi do Apolo, 222:12\" andar, Recife 50030-230, Pernambuco, Brazil; 2  
[PERSON]J.E.L.d.O.) 2 FITec--Technological Innovations, Casi do Apolo, 222:12\" andar, Recife 50030-230, Pernambuco, Brazil; 2  
[PERSON]C.M.d.O.R. ); [EMAIL_ADDRESS] (J.E.L. However, traditional methods often struggle to encompass the multidimensional nature of financial markets within a single framework--considering elements like investor sentiment, liquidity, and fundamental analysis. For instance, statistical models like GARCH and ARIMA are effective at identifying historical price patterns but fail to incorporate macroeconomic indicators or real-time shifts in sentiment; this issue is highlighted by [PERSON] and [PERSON] (2018) [2]. Similarly, although sentiment analysis models excel at processing textual data related to public mood or news sentiments about stocks or sectors, they frequently exclude company-specific financial metrics, which limits their broader applicability across different contexts. Market states, often categorized as bull (upward trend), bear (downward trend), or neutral (sideways movement), represent distinct phases of market behavior. These states are derived from both historical and real-time data, offering simplified yet insightful representations of market dynamics. Accurate forecasting of market states is essential for enabling investors to dynamically adjust their strategies. For example, recognizing an impending bull market can support risk-on strategies aimed at maximizing returns, whereas anticipating a bear market may prompt risk-off approaches to minimize losses. Identifying neutral phases allows for balanced portfolio strategies, preserving capital during periods of uncertainty. Integrating these market state predictions into financial models enhances their robustness by incorporating both quantitative and qualitative factors, bridging the gap between theoretical research and practical applications in the investment industry. These challenges have driven the development of hybrid models that integrate diverse data sources and predictive techniques. [PERSON] and [PERSON] (2023) [3], for example, introduced financial ontologies to classify companies based on key metrics such as liquidity, profitability, and growth, offering a structured approach to stock selection. However, their methodology lacks integration with advanced machine learning models capable of predicting dynamic market conditions. Similarly, [PERSON] et al. (2022) [4] proposed a multimodal framework combining sentiment analysis with machine learning but did not leverage ontological analysis for stock classification, leaving a critical gap between asset selection and market state prediction. This study addresses these gaps by proposing a unified framework that integrates ontology-based stock selection with advanced machine learning techniques to predict market states. The framework introduces several key innovations:  
1.