Lolita Nazarov

A research paper examining how investor composition affects future returns, trading volume, and price volatility in music NFT collections. It finds that a higher prevalence of small investors predicts higher short-term returns and trading volume, with effects reversing over longer horizons. Broader ownership is associated with lower, while greater clientele concentration is associated with higher, future trading volume. The paper also discusses how an artist's presence on music streaming platforms mitigates price overreactions, while social media engagement amplifies them.

A research paper proposing an early diagnosis prediction model using LSTM (Long Short-Term Memory) neural networks. It addresses missing data values in medical data through various imputation techniques and introduces Multi-Label Early Detection (MED) for predicting patient diagnoses early in their hospital stay. The model's performance is compared to state-of-the-art baselines using the MIMIC-III database.