Probabilistic Prediction for Start-Up Success through Deep Learning based Stacked DAE Model

Researchers and financial entities have long been fascinated by the ability to accurately predict the success of start-ups. Modern start-ups, which are creative, tech-focused, and quickly growing companies, face this challenge more than most. This study introduces a deep learning Stacked Denoising Autoencoder (SDAE) model that uses important technology domains to forecast the likelihood of a startup's success. The three primary steps of the proposed approach are as follows: preprocessing, feature extraction, and training the model. To ensure statistical independence of signal components in multivariate datasets, preprocessing steps include min-max normalisation for data standardisation and Independent Component Analysis (ICA) for feature extraction. The predictive power of the SDAE model is then enhanced by training. The proposed model outperforms traditional methods like Decision Trees (DT) and Denoising Autoencoders (DAE) with an average accuracy of 94.28%, according to the experimental data. This makes it a strong contender for predicting startup success in changing technical contexts.