# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 2 Related Work
### Ontology-Based Financial Analysis  
The use of financial ontologies has emerged as an innovative approach for stock selection. Financial ontologies are structured frameworks that organize and represent financial concepts--such as liquidity, profitability, debt, and growth--into hierarchical categories. By providing a systematic classification of financial data, these ontologies enable consistent evaluation and facilitate the identification of high-performing stocks based on predefined financial criteria. [PERSON] et al. (2015) [PERSON] et al. (2015) highlighted how ontologies can represent financial concepts systematically, allowing for more structured and coherent data analysis.  
[PERSON] and [PERSON] (2023) [PERSON] and [PERSON] (2023) introduced a methodology that maps financial indicators into an ontological structure to classify stocks. Their approach demonstrated significant potential in categorizing companies based on fundamental metrics. However, despite its strengths, their work is limited to static classification and lacks integration with dynamic market state prediction models. Furthermore, it does not incorporate real-time sentiment analysis, which is crucial for adapting to rapidly evolving market environments.  
The proposed framework builds upon and extends this ontology-based classification by addressing these limitations. Unlike prior approaches, it incorporates a dynamic component that continuously updates financial indicators within the ontological structure, reflecting changes in real-time market conditions. This dynamic integration creates a feedback loop, allowing the ontology to adapt as new data become available. As highlighted by [PERSON] et al. (2023) [PERSON] et al. (2023), such integration of ontologies with dynamic updates and machine learning enhances decision-making by aligning classifications with evolving market dynamics.  
Moreover, the framework leverages the ability of financial ontologies to integrate diverse data sources, including fundamental financial metrics and sentiment analysis. The work by [PERSON] et al. (2023) [PERSON] et al. (2023) further supports the utility of ontologies in combining structured data with real-time updates for enhanced stock selection processes. This structured classification is seamlessly integrated into the broader framework, ensuring that the identified stocks are potential candidates for further analysis by advanced predictive techniques.  
The novelty of this framework lies in its ability to dynamically organize fundamental financial data and facilitate a continuous pipeline for asset selection. After identifying high-potential stocks, the framework transitions to a predictive stage where Non-Stationary Markov Chains (NMCs), Bidirectional Encoder Representations from Transformers (BERT) for sentiment analysis, and Long Short-Term Memory (LSTM) are employed to forecast market states for the selected assets. These predictions guide decision-making in the subsequent stages of the pipeline, enabling the execution of optimized buy-and-sell strategies.  
By leveraging these advanced techniques, the framework aims to maximize returns and consistently outperform traditional strategies, such as Buy and Hold.  
To the best of our knowledge, no prior work has integrated financial ontologies with dynamic market state prediction and real-time sentiment analysis. This novel integration provides a comprehensive solution that bridges the gap between structured asset classification and dynamic predictive modeling, offering a robust tool for quantitative trading and investment strategies.