@inproceedings{6dc635fe054142f6826342c396fe05ec,
title = "Prediction of Stock Prices Using Markov Chain Monte Carlo",
abstract = "Financial sector investment is an activity that attract a lot of public interest. One of them is investing funds in purchase company's shares. Stocks are proof of ownership of a company or business entity. Stocks are an attractive investment and quite challenging because they can provide large profits for investors if they predict correctly. Basically peoples buy stocks for long-Term investment to get profit from dividend, but there are also investors who want to get benefit from buying and selling stock prices in the short-Term periods. Predicting of stock prices become an attractive and challenges because it can be easy and hard based on fundamental and technical analysis. Apply Bayesian inference to create prediction model and Markov Chain Monte Carlo (MCMC) to generate predicted data. By mathematically predicting stock prices become more challenges, and spending much time to compute. But with parallel computing on Apache Spark requires less time.",
keywords = "Apache Spark, Bayesian Inference, Bayesian Statistics, MCMC, Stock",
author = "Mochammad Hariadi and Muhammad, {Alfin Alim} and Nugroho, {Supeno Mardi Susiki}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020 ; Conference date: 17-11-2020 Through 18-11-2020",
year = "2020",
month = nov,
day = "17",
doi = "10.1109/CENIM51130.2020.9297965",
language = "English",
series = "CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "385--390",
booktitle = "CENIM 2020 - Proceeding",
address = "United States",
}