Predictive Analysis of Indonesian Stock Market Prices Using Deep Learning: An Application of Diffusion Variational Autoencoders

Abstract

This study introduces the application of the Diffusion Variational Autoencoder (D-VAE), a deep learning technique, for predicting stock prices in the Indonesian stock market. With the challenges presented by market volatility and complex data distributions, D-VAE is explored for its capability to encapsulate uncertainty and model complex distributions. This study is significant as it explores the potential of D-VAE in the context of the Indonesian stock market, which has not been widely studied before. Historical stock data from Yahoo Finance was collected over one year and preprocessed for training and validation of the model. The model is trained with an architecture designed to allow tuning of the latent space, utilizing ReLU and linear activation functions for the encoder and decoder. The model's performance is evaluated using the Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared metrics, yielding results that highlight the model's capability to enhance the accuracy of stock price predictions. By leveraging machine learning techniques in stock price prediction models, this study underscores the significant contribution such approaches can make to informed and successful investment decisions underpinned by robust data.