---
title: IPO Pricing Prediction with Lasso-Neural Networks
url: https://www.ml-quant.com/papers/ssrn/4990940/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4990940
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4990940
featured: 2024-10-23
citations: unknown
topic: ML & AI Methods
---


# IPO Pricing Prediction with Lasso-Neural Networks

A new model using Lasso neural networks has been developed to predict IPO pricing for Chinese companies, with retained earnings per share being a key factor.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4990940
- Identifier: SSRN 4990940
- Released: 2024-10-17
- First featured: Quant Letter No. 71 (2024-10-23): https://www.ml-quant.com/issues/2024-10-23/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Generalized Factor Neural Network Model for High-dimensional Regression](https://www.ml-quant.com/papers/arxiv/2502.11310/): A novel method combining non-parametric regression, factor models, and neural networks has been introduced, showing effectiveness in predicting equity ETF indices prices and macroeconomic data.
- [Neural Network Logarithm Entropy Estimator](https://www.ml-quant.com/papers/ssrn/5274641/): A new LogDet estimator has been proposed to address the challenges of handling high-dimensional samples in machine learning using entropy estimators.
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://www.ml-quant.com/papers/arxiv/2312.00752/): Sequence Modeling: Mamba, a neural network architecture that doesn't use attention or MLP blocks, provides faster inference and better performance in language, audio, and genomics than Transformers.
- [Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models](https://www.ml-quant.com/papers/arxiv/2501.01423/): The paper proposes a new model, VA-VAE, that aligns the latent space with pre-trained vision foundation models, enabling faster convergence of Diffusion Transformers in high-dimensional latent spaces and achieving top performance on ImageNet 256x256 generation.
- [Graph Mamba: Towards Learning on Graphs with State Space Models](https://www.ml-quant.com/papers/arxiv/2402.08678/): Graph Mamba Networks, a new type of Graph Neural Networks, have been introduced, which achieve excellent performance in various benchmark datasets despite lower computational cost.
- [Edge Directionality Improves Learning on Heterophilic Graphs](https://www.ml-quant.com/papers/arxiv/2305.10498/): The study presents Directed Graph Neural Network (Dir-GNN), a new deep learning framework for directed graphs that surpasses traditional models in heterophilic benchmarks.