EconPapers    
Economics at your fingertips  
 

Analyzing and forecasting P/E ratios using investor sentiment in panel data regression and LSTM models

Aishat Dolaeva, Uliana Beliaeva, Dmitry Grigoriev, Alexander Semenov and Maciej Rysz

International Review of Economics & Finance, 2025, vol. 98, issue C

Abstract: This study investigates several factors influencing the well-known price/earnings ratio (P/E), with particular emphasis on investor sentiment scores obtained from textual data using natural language processing models. Data consisting of various economic indicators and user-generated text messages from the social network Twitter were collected for several established firms that were categorized into two sectors. Sentiment scores from the textual data were obtained using the BERT and FinBERT language models and shown to exhibit a high level of accuracy. Fixed and random effect regression models considering panel data comprising the economics indicators and sentiment scores were constructed and revealed statistically significant influences of sentiment on the P/E ratio in one sector. A Long Short-Term Memory recurrent neural network model was then used to forecast the P/E ratio over a one year interval, which produced highly accurate results. Our analysis demonstrates the significance of investor sentiment as a factor in P/E ratio forecasting, emphasizing its contribution alongside other fundamental factors.

Keywords: Sentiment analysis; Deep learning; FinBERT; P/E ratio; Panel data models (search for similar items in EconPapers)
JEL-codes: C33 C53 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1059056025000036
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:98:y:2025:i:c:s1059056025000036

DOI: 10.1016/j.iref.2025.103840

Access Statistics for this article

International Review of Economics & Finance is currently edited by H. Beladi and C. Chen

More articles in International Review of Economics & Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-24
Handle: RePEc:eee:reveco:v:98:y:2025:i:c:s1059056025000036