Optimizing Investment: Combining Deep Learning for Price Prediction and Moving Average for Return-Risk Analysis

Widi Hastomo(1), Adhitio Satyo Bayangkari Karno(2), Masriyanda Masriyanda(3), Ellya Sestri(4), Aqwam Rosadi Kardian(5), Nur Azis(6), I. Joko Dewanto(7), Ahmad Rasyidin(8), Aries Sundoro(9), Nada Kamilia(10),

DOI: https://doi.org/10.15294/jte.v14i2.45002


(1) Ahmad Dahlan Institute of Technology and Business
(2) Gunadarma University
(3) Ahmad Dahlan Institute of Technology and Business
(4) Ahmad Dahlan Institute of Technology and Business
(5) STMIK Jakarta STI&K
(6) Tangerang Raya University
(7) Tangerang Raya University
(8) Tangerang Raya University
(9) Tangerang Raya University
(10) STMIK Jakarta STI&K

Abstract

The ability to analyze predictions marks something going up or down, as well as the level of possible risk taken into account by much-needed stock investors. In a study, this analysis of risk and correlation between shares was calculated using the method of moving averages (MA). Besides that, a dataset of 4 stocks (Apple, Google, Microsoft, and Amazon) also performed prediction mark stock in period time next (future) with the use of the neural network method (deep learning) Long Short-Term Memory (LSTM) model. The result of programming in the Python language is several visualizations for easy graph-reading information. This article presents new research that aims to fill the gap in understanding investment analysis for beginners by visualizing risk and return analysis on shares. The results reveal that changes in stock sales volume did not occur significantly, although the short- and long-term MA charts for the four stocks tended to fluctuate, offering new insights into investment analysis and providing a basis for future development. Thus, this research adds a new dimension to the literature and paves the way for further investigations in risk and return analysis and stock prediction using deep learning.

Keywords

Correlation; LSTM; Return; Risk; Prediction

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