# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 6 Discussion  
This study's results highlight the effectiveness of our proposed framework, which combines ontology-based stock selection with state prediction utilizing LSTM calibrated by NMC and sentiment analysis through BERT. These findings support the research conducted \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stock** & **Accuracy (\%)** & **BnH (\%)** & **SP (\%)** & **ROC (AUC)** \\ \hline Stock \#1 & 64.49 & 46.42 & 55.03 & 0.79, 0.71, 0.82 \\ Stock \#2 & 59.81 & \(-\)9.19 & \(-\)4.67 & 0.90, 0.71, 0.89 \\ Stock \#3 & 77.57 & 7.55 & 10.93 & 0.89, 0.80, 0.86 \\ Stock \#4 & 83.18 & 9.87 & 9.81 & 0.80, 0.82, 0.86 \\ Stock \#5 & 73.83 & \(-\)11.53 & \(-\)2.74 & 0.89, 0.83, 0.84 \\ Total & - & 8.62 & 13.67 & - \\ S\&P500 & 97.20 & 1.08 & 6.36 & 1.00, 0.99, 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Buy and Hold (BnH) and State Predicted (SP) strategies with dynamic thresholds. by [PERSON] (2022) [4], [PERSON] (2023) [3], and [PERSON] (2023) [8]. Their work emphasizes the importance of integrating multiple aspects of market analysis--such as sentiment, liquidity, and technical indicators--to enhance prediction accuracy. Compared to [PERSON] and [PERSON] (2023) [3], who introduced a robust ontology-based stock classification methodology, our framework advances their work by integrating predictive models for market state transitions. This dynamic incorporation of real-time sentiment, as extracted by BERT, and exogenous variables, such as illiquidity and volatility indices, enables the framework to adapt to diverse market scenarios. In contrast, [PERSON] and [PERSON]'s methodology lacks a predictive component, focusing solely on static classification. The _State Predicted_ strategy consistently outperformed traditional approaches, such as the _Buy and Hold_ approach, achieving significant cumulative returns across the selected stocks and the S&P500 index. The integration of BERT for sentiment analysis offers a distinct advantage over methods like those in [PERSON] (2023) [8], where sentiment indices are constructed but not leveraged for direct state prediction. Furthermore, while [PERSON]'s work aggregates sentiment scores retrospectively, this study's forward-looking sentiment analysis (\(t+1\)) provides actionable insights, aligning with the recent advancements highlighted by [PERSON] (2024) [37]. From a technical perspective, this study also surpasses models relying solely on LSTM or GARCH, as seen in works by [PERSON] and [PERSON] (2018) [1] and [PERSON] and [PERSON] (2018) [2]. By combining LSTM predictions with NMC-calibrated state probabilities and integrating these with sentiment-driven insights, the proposed framework achieves higher accuracy and cumulative returns. This multimodal integration bridges the gap between fundamental, technical, and sentiment-based analyses, as suggested by [PERSON] (2022) [4]. Empirical results validate the robustness of this approach. While [PERSON] (2024) [37] reported predictive accuracies around 70% for BERT-enhanced models, our framework achieves superior performance, particularly when predicting future market states (\(t+1\)). This improvement highlights the advantage of fine-tuning BERT for the financial domain and integrating it with ontological and exogenous components. From a scalability standpoint, the framework was crafted to effectively adjust to market shifts, even when faced with rigorous computational limitations. [PERSON] (2023) [48] and [PERSON] (2024) [49] emphasize that these issues are particularly vital in high-frequency trading environments, where cutting-edge technology is crucial for enhancing performance and minimizing latency. To meet these requirements in real-time trading situations, the following strategies were employed:  
* Incremental Updates: Instead of reprocessing the entire pipeline, only critical variables and market states are updated, significantly reducing latency and computational costs.