Details

Autor(en) / Beteiligte

• Liapis, Charalampos M
• Karanikola, Aikaterini
• Kotsiantis, Sotiris

Titel

Investigating Deep Stock Market Forecasting with Sentiment Analysis

Ist Teil von

• Entropy (Basel, Switzerland), 2023-01, Vol.25 (2), p.219

Ort / Verlag

Switzerland: MDPI AG

Erscheinungsjahr

2023

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• When forecasting financial time series, incorporating relevant sentiment analysis data into the feature space is a common assumption to increase the capacities of the model. In addition, deep learning architectures and state-of-the-art schemes are increasingly used due to their efficiency. This work compares state-of-the-art methods in financial time series forecasting incorporating sentiment analysis. Through an extensive experimental process, 67 different feature setups consisting of stock closing prices and sentiment scores were tested on a variety of different datasets and metrics. In total, 30 state-of-the-art algorithmic schemes were used over two case studies: one comparing methods and one comparing input feature setups. The aggregated results indicate, on the one hand, the prevalence of a proposed method and, on the other, a conditional improvement in model efficiency after the incorporation of sentiment setups in certain forecast time frames.