Integrating market sentiments for stock price prediction: A comparative study of Bi-LSTM and multilayer perceptions

Ahmet Akusta

Makale detayları

Integrating market sentiments for stock price prediction: A comparative study of Bi-LSTM and multilayer perceptions

Yazarlar:

Ahmet Akusta

Yayıncı:

Istanbul University

Ana Başlıklar:

Behavioural Finance

Dergi:

Istanbul Business Research Vol.54 Issue 1, pp.99-121

Belge Tipi:

Makale

Yayın Tarihi:

5/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350074

pISSN:

2630-5488

Anahtar kelimeler:

Özet:

 This study investigates the integration of sentiment analysis with machine learning models to forecast stock price movements using the Nvidia Corporation as a case study. Sentiment scores were derived from Nvidia-related financial news headlines using two sentiment analysis tools: FinBERT, a domain-specific tool, and TextBlob, a general-purpose tool. These scores were integrated into predictive frameworks based on bidirectional long short-term memory Bi-LSTM networks and multilayer perceptrons MLPs developed alongside historical stock price data. This study assesses the predictive performance over the entire observation period and across distinct market phases: bullish, stagnation, and strong bullish conditions. The findings indicate that sentiment features enhance predictive accuracy in specific contexts, particularly during stagnation phases, with TextBlob demonstrating superior performance to FinBERT in specific scenarios. In addition, Bi-LSTM models exhibit consistently superior performance in capturing temporal dependencies compared to MLPs. However, the impact of sentiment features diminished during strongly directional trends, such as those observed in strong bullish markets. The combination of FinBERT and TextBlob in the same dataset allows for a dual-perspective approach to sentiment analysis, thereby providing new insights into the dynamic relationship between market sentiment and stock price behavior. This research contributes to the existing literature on applying sentiment analysis to financial forecasting by advancing the integration of complementary sentiment tools and phase-specific evaluations.

 This study investigates the integration of sentiment analysis with machine learning models to forecast stock price movements using the Nvidia Corporation as a case study. Sentiment scores were derived from Nvidia-related financial news headlines using two sentiment analysis tools: FinBERT, a domain-specific tool, and TextBlob, a general-purpose tool. These scores were integrated into predictive frameworks based on bidirectional long short-term memory Bi-LSTM networks and multilayer perceptrons MLPs developed alongside historical stock price data. This study assesses the predictive performance over the entire observation period and across distinct market phases: bullish, stagnation, and strong bullish conditions. The findings indicate that sentiment features enhance predictive accuracy in specific contexts, particularly during stagnation phases, with TextBlob demonstrating superior performance to FinBERT in specific scenarios. In addition, Bi-LSTM models exhibit consistently superior performance in capturing temporal dependencies compared to MLPs. However, the impact of sentiment features diminished during strongly directional trends, such as those observed in strong bullish markets. The combination of FinBERT and TextBlob in the same dataset allows for a dual-perspective approach to sentiment analysis, thereby providing new insights into the dynamic relationship between market sentiment and stock price behavior. This research contributes to the existing literature on applying sentiment analysis to financial forecasting by advancing the integration of complementary sentiment tools and phase-specific evaluations.

 This study investigates the integration of sentiment analysis with machine learning models to forecast stock price movements using the Nvidia Corporation as a case study. Sentiment scores were derived from Nvidia-related financial news headlines using two sentiment analysis tools: FinBERT, a domain-specific tool, and TextBlob, a general-purpose tool. These scores were integrated into predictive frameworks based on bidirectional long short-term memory Bi-LSTM networks and multilayer perceptrons MLPs developed alongside historical stock price data. This study assesses the predictive performance over the entire observation period and across distinct market phases: bullish, stagnation, and strong bullish conditions. The findings indicate that sentiment features enhance predictive accuracy in specific contexts, particularly during stagnation phases, with TextBlob demonstrating superior performance to FinBERT in specific scenarios. In addition, Bi-LSTM models exhibit consistently superior performance in capturing temporal dependencies compared to MLPs. However, the impact of sentiment features diminished during strongly directional trends, such as those observed in strong bullish markets. The combination of FinBERT and TextBlob in the same dataset allows for a dual-perspective approach to sentiment analysis, thereby providing new insights into the dynamic relationship between market sentiment and stock price behavior. This research contributes to the existing literature on applying sentiment analysis to financial forecasting by advancing the integration of complementary sentiment tools and phase-specific evaluations.

 This study investigates the integration of sentiment analysis with machine learning models to forecast stock price movements using the Nvidia Corporation as a case study. Sentiment scores were derived from Nvidia-related financial news headlines using two sentiment analysis tools: FinBERT, a domain-specific tool, and TextBlob, a general-purpose tool. These scores were integrated into predictive frameworks based on bidirectional long short-term memory Bi-LSTM networks and multilayer perceptrons MLPs developed alongside historical stock price data. This study assesses the predictive performance over the entire observation period and across distinct market phases: bullish, stagnation, and strong bullish conditions. The findings indicate that sentiment features enhance predictive accuracy in specific contexts, particularly during stagnation phases, with TextBlob demonstrating superior performance to FinBERT in specific scenarios. In addition, Bi-LSTM models exhibit consistently superior performance in capturing temporal dependencies compared to MLPs. However, the impact of sentiment features diminished during strongly directional trends, such as those observed in strong bullish markets. The combination of FinBERT and TextBlob in the same dataset allows for a dual-perspective approach to sentiment analysis, thereby providing new insights into the dynamic relationship between market sentiment and stock price behavior. This research contributes to the existing literature on applying sentiment analysis to financial forecasting by advancing the integration of complementary sentiment tools and phase-specific evaluations.

Makale detayları

Integrating market sentiments for stock price prediction: A comparative study of Bi-LSTM and multilayer perceptions

Yazarlar:

Ahmet Akusta

Yayıncı:

Istanbul University

Ana Başlıklar:

Behavioural Finance

Dergi:

Istanbul Business Research Vol.54 Issue 1, pp.99-121

Belge Tipi:

Makale

Yayın Tarihi:

5/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350074

pISSN:

2630-5488

Bi-LSTM

Multilayer perceptrons

Sentiment analysis

Machine learning

Sayı

Makale detayları

Integrating market sentiments for stock price prediction: A comparative study of Bi-LSTM and multilayer perceptions

Yazarlar: Ahmet Akusta

Yayıncı: Istanbul University

Ana Başlıklar: Behavioural Finance

Dergi: Istanbul Business Research Vol.54 Issue 1, pp.99-121

Belge Tipi: Makale

Yayın Tarihi: 5/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001350074

pISSN: 2630-5488

Bi-LSTM

Multilayer perceptrons

Sentiment analysis

Machine learning

Sayı

Yazarlar:

Ahmet Akusta

Yayıncı:

Istanbul University

Ana Başlıklar:

Behavioural Finance

Dergi:

Istanbul Business Research Vol.54 Issue 1, pp.99-121

Belge Tipi:

Makale

Yayın Tarihi:

5/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001350074

pISSN:

2630-5488