Generation and study of random sequence of elliptic curve points over finite rings for medical Image cryptography

Sowmya T.K

doi:10.1007/s11042-026-21790-w

Mood in the Market: Forecasting IPO Activity with Music Sentiment and LSTM

2026-02-02

0

OA

AI

D

Ding, Qinxu *

G

Guan, Chong *

Y

Yu, Yinghui

DOI：10.3390/fintech5010012

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We examine whether aggregate music mood derived from globally popular songs can help forecast primary equity issuance. We build a Friday-anchored weekly panel that merges SEC EDGAR counts of priced Initial Public Offerings (IPOs) with features from the Spotify Daily Top 200 (audio descriptors such as valence, energy, danceability, tempo, loudness, etc.) and Genius-scraped lyrics. We extract lyric sentiment by tokenizing Genius-scraped lyrics and aggregating lexicon-based affect scores (valence and arousal) into popularity-weighted weekly indices. To address sparsity and regime shifts in issuance, we train a leakage-safe Long Short-Term Memory (LSTM) network on a smoothed target-the forward 4-week sum of IPOs-and obtain next-week forecasts by dividing the predicted sum by 4. On a chronological holdout, a single LSTM with look-back K = 8 outperforms strong baselines-reducing MAE by 13.9%, RMSE by 15.9%, and mean Poisson deviance by 27.6% relative to the best baseline in each metric. Furthermore, we adopt SHapley Additive exPlanations (SHAP) to explain our LSTM model, showing that IPO persistence remains the dominant driver, but music and lyrics covariates contribute incremental and robust signal. These results suggest that aggregate music sentiment contains economically meaningful information about near-term IPO activity.

Keywords:

time series forecasting

neural networks

fintech

IPO activity

C45

G17

G24

G41

Mood in the Market: Forecasting IPO Activity with Music Sentiment and LSTM

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