Abstract

Stocks are one of the aspects that affect the economy in the world. In the stock market, the price of a stock changes every time. Investors who are able to predict the increase in stock prices will tend to get profit, but investors who are unable to predict the increase in stock prices will tend to experience losses. This study aims to find statistical models and use them to predict the probability of an increase in the price of a stock in the stock market. The method used in this study is a literature study regarding stochastic models and selected suitable stochastic models. This study finds stochastic models and uses them to predict the probability of an increase in stock prices. The novelty in this research lies in the stochastic model triangulation. The increase in stock prices is predicted using two stochastic models, namely: Bernoulli and autoregressive. The decision on stock prediction is determined by the results of the triangulation of the two methods. The proposed method in this study has the advantage that decision making is based on more than one stochastic model. The results of this study can be applied to the financial sector, especially in the stock market.

Highlights

  • Stocks are one of the things that affect the economy of people in the world [1]

  • This study aims to find stochastic models and use them to predict the probability of an increase in stock prices by means of triangulation methods

  • This study focuses on the Bernoulli distribution and autoregressive models

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Summary

Introduction

Shares can be traded on the stock market, for example the Indonesia Stock Exchange. Stock prices will increase when the number of investors who buy shares is larger than the number of investors who sell shares. The stock price will fall when the number of investors buying shares is smaller than the number of investors who sell shares. An autoregressive model is a time series model used to forecast economic output such as the gross national product of the United States [2] and the autoregressive model is applied for forecasting on several market indices and asset return [3]. Researchers use one method without considering other methods in decision making. If only one method is used, the results of the research are usually less accurate

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