Novel NLP-based stock market price prediction and risk analysis framework
The prediction of stock market prices represents a significant challenge due to its volatile nature, influenced by unpredictable economic factors, company performance, and market sentiment. The assurance of these forecasts or the associated risk with these price estimations plays a pivotal role in t...
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| Formato: | Articulo |
| Lenguaje: | Inglés |
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2024
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/173717 |
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I19-R120-10915-173717 |
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dspace |
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Universidad Nacional de La Plata |
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I-19 |
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R-120 |
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SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Long Short-Term Memory (LSTM) network natural language processing deep learning stock price analysis Red de memoria larga a corto plazo (LSTM) procesamiento del lenguaje natural aprendizaje profundo análisis del precio de las acciones |
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Ciencias Informáticas Long Short-Term Memory (LSTM) network natural language processing deep learning stock price analysis Red de memoria larga a corto plazo (LSTM) procesamiento del lenguaje natural aprendizaje profundo análisis del precio de las acciones Zain-ul-Abideen Raja Hashim Ali Ali Zeeshan Ijaz Talha Ali Khan Novel NLP-based stock market price prediction and risk analysis framework |
| topic_facet |
Ciencias Informáticas Long Short-Term Memory (LSTM) network natural language processing deep learning stock price analysis Red de memoria larga a corto plazo (LSTM) procesamiento del lenguaje natural aprendizaje profundo análisis del precio de las acciones |
| description |
The prediction of stock market prices represents a significant challenge due to its volatile nature, influenced by unpredictable economic factors, company performance, and market sentiment. The assurance of these forecasts or the associated risk with these price estimations plays a pivotal role in the decision-making process. Existing models have either focused on stock price prediction or risk analysis but rarely integrate both, leaving a gap in providing a comprehensive tool for investors. In the current work, we present a novel framework for investment analysis designed to create ease for investors and provide a confidence measure along with the stock price to depict the risk involved in investing in stocks of a particular company. The model uses a stock price dataset depicting the original scores as numerals and textual data extracted from Reddit news articles as input. The stock price is predicted by LSTMs on individual stock prices, while the confidence is represented by a risk value calculated using XGBoost and LSTM output. We performed sentiment analysis and subjectivity analysis to extract features for further investigation in the study. The results show that an accuracy of 94% for stock trend prediction can be achieved using PCA as the feature extractor with tuned parameters for XGBoost and around 76% accuracy for stock price prediction with a tuned LSTM. Our study demonstrates the effective integration of risk analysis with stock price forecasting, illustrating that deep learning techniques are suitable for melding risk assessment with the prediction of stock prices. |
| format |
Articulo Articulo |
| author |
Zain-ul-Abideen Raja Hashim Ali Ali Zeeshan Ijaz Talha Ali Khan |
| author_facet |
Zain-ul-Abideen Raja Hashim Ali Ali Zeeshan Ijaz Talha Ali Khan |
| author_sort |
Zain-ul-Abideen |
| title |
Novel NLP-based stock market price prediction and risk analysis framework |
| title_short |
Novel NLP-based stock market price prediction and risk analysis framework |
| title_full |
Novel NLP-based stock market price prediction and risk analysis framework |
| title_fullStr |
Novel NLP-based stock market price prediction and risk analysis framework |
| title_full_unstemmed |
Novel NLP-based stock market price prediction and risk analysis framework |
| title_sort |
novel nlp-based stock market price prediction and risk analysis framework |
| publishDate |
2024 |
| url |
http://sedici.unlp.edu.ar/handle/10915/173717 |
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I19-R120-10915-1737172024-11-25T20:06:10Z http://sedici.unlp.edu.ar/handle/10915/173717 Novel NLP-based stock market price prediction and risk analysis framework Un novedoso framework basado en PLN para análisis de riesgos y predicción de precios bursátiles Zain-ul-Abideen Raja Hashim Ali Ali Zeeshan Ijaz Talha Ali Khan 2024-10 2024-11-25T18:03:08Z en Ciencias Informáticas Long Short-Term Memory (LSTM) network Reddit natural language processing deep learning stock price analysis Red de memoria larga a corto plazo (LSTM) procesamiento del lenguaje natural aprendizaje profundo análisis del precio de las acciones The prediction of stock market prices represents a significant challenge due to its volatile nature, influenced by unpredictable economic factors, company performance, and market sentiment. The assurance of these forecasts or the associated risk with these price estimations plays a pivotal role in the decision-making process. Existing models have either focused on stock price prediction or risk analysis but rarely integrate both, leaving a gap in providing a comprehensive tool for investors. In the current work, we present a novel framework for investment analysis designed to create ease for investors and provide a confidence measure along with the stock price to depict the risk involved in investing in stocks of a particular company. The model uses a stock price dataset depicting the original scores as numerals and textual data extracted from Reddit news articles as input. The stock price is predicted by LSTMs on individual stock prices, while the confidence is represented by a risk value calculated using XGBoost and LSTM output. We performed sentiment analysis and subjectivity analysis to extract features for further investigation in the study. The results show that an accuracy of 94% for stock trend prediction can be achieved using PCA as the feature extractor with tuned parameters for XGBoost and around 76% accuracy for stock price prediction with a tuned LSTM. Our study demonstrates the effective integration of risk analysis with stock price forecasting, illustrating that deep learning techniques are suitable for melding risk assessment with the prediction of stock prices. La predicción de los precios del mercado de valores representa un desafío importante debido a su naturaleza volátil, influenciada por factores económicos impredecibles, el desempeño de las empresas y el sentimiento del mercado. La seguridad de estas previsiones o el riesgo asociado a estas estimaciones de precios juega un papel fundamental en el proceso de toma de decisiones. Los modelos existentes se han centrado en la predicción del precio de las acciones o en el análisis de riesgos, pero rara vez integran ambos, lo que deja una brecha a la hora de proporcionar una herramienta integral para los inversores. En el trabajo actual, presentamos un marco novedoso para el análisis de inversiones diseñado para facilitar a los inversores y proporcionar una medida de confianza junto con el precio de las acciones para representar el riesgo que implica invertir en acciones de una empresa en particular. El modelo utiliza un conjunto de datos de precios de acciones que representa las puntuaciones originales como números y datos textuales extraídos de artículos de noticias de Reddit como entrada. Los LSTM predicen el precio de las acciones sobre los precios de las acciones individuales, mientras que la confianza está representada por un valor de riesgo calculado utilizando la salida de XGBoost y LSTM. Realizamos análisis de sentimiento y análisis de subjetividad para extraer características para una mayor investigación en el estudio. Los resultados muestran que se puede lograr una precisión del 94% para la predicción de la tendencia de las acciones utilizando PCA como extractor de características con parámetros ajustados para XGBoost y alrededor del 76% de precisión para la predicción del precio de las acciones con un LSTM ajustado. Nuestro estudio demuestra la integración efectiva del análisis de riesgos con la previsión de precios de las acciones, lo que ilustra que las técnicas de aprendizaje profundo son adecuadas para fusionar la evaluación de riesgos con la predicción de los precios de las acciones. Facultad de Informática Articulo Articulo http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 74-87 |