Standard

Analyzing and forecasting P/E ratios using investor sentiment in panel data regression and LSTM models. / Dolaeva, A; Beliaeva, U; Grigoriev, D; Semenov, A; Rysz, M.

In: International Review of Economics and Finance, Vol. 98, 2025.

Research output: Contribution to journal › Article › peer-review

Harvard

Dolaeva, A, Beliaeva, U, Grigoriev, D, Semenov, A & Rysz, M 2025, 'Analyzing and forecasting P/E ratios using investor sentiment in panel data regression and LSTM models', International Review of Economics and Finance, vol. 98. https://doi.org/10.1016/j.iref.2025.103840

APA

Dolaeva, A., Beliaeva, U., Grigoriev, D., Semenov, A., & Rysz, M. (2025). Analyzing and forecasting P/E ratios using investor sentiment in panel data regression and LSTM models. International Review of Economics and Finance, 98. https://doi.org/10.1016/j.iref.2025.103840

Vancouver

Dolaeva A, Beliaeva U, Grigoriev D, Semenov A, Rysz M. Analyzing and forecasting P/E ratios using investor sentiment in panel data regression and LSTM models. International Review of Economics and Finance. 2025;98. https://doi.org/10.1016/j.iref.2025.103840

Author

Dolaeva, A ; Beliaeva, U ; Grigoriev, D ; Semenov, A ; Rysz, M. / Analyzing and forecasting P/E ratios using investor sentiment in panel data regression and LSTM models. In: International Review of Economics and Finance. 2025 ; Vol. 98.

BibTeX

@article{ae8438a1ed984368a2a5bfd10aa85d8e,
title = "Analyzing and forecasting P/E ratios using investor sentiment in panel data regression and LSTM models",
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",
author = "A Dolaeva and U Beliaeva and D Grigoriev and A Semenov and M Rysz",
note = "Times Cited in Web of Science Core Collection: 6 Total Times Cited: 7 Cited Reference Count: 48",
year = "2025",
doi = "10.1016/j.iref.2025.103840",
language = "Английский",
volume = "98",
journal = "International Review of Economics and Finance",
issn = "1059-0560",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Analyzing and forecasting P/E ratios using investor sentiment in panel data regression and LSTM models

AU - Dolaeva, A

AU - Beliaeva, U

AU - Grigoriev, D

AU - Semenov, A

AU - Rysz, M

N1 - Times Cited in Web of Science Core Collection: 6 Total Times Cited: 7 Cited Reference Count: 48

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - Sentiment analysis

KW - Deep learning

KW - FinBERT

KW - P/E ratio

KW - Panel data models

U2 - 10.1016/j.iref.2025.103840

DO - 10.1016/j.iref.2025.103840

M3 - статья

VL - 98

JO - International Review of Economics and Finance

JF - International Review of Economics and Finance

SN - 1059-0560

ER -

ID: 147931620