A prediction methodology for the change of the values of financial products

Kyoung Sook Moon, Heejean Kim, Hongjoong Kim

Research output: Contribution to journalArticle

Abstract

A systematic algorithm based on data smoothing and the Bayes' theorem is proposed to predict the increase or decrease of a financial time series, which can be used in trading financial products when decisions need to be made between long and short positions. The algorithm compares the observed product values with those in the history to find a similar pattern with the maximum likelihood, based on which future up-down movement of the value is predicted. Empirical studies with S&P 500 Index and stocks of several companies show that the proposed methodology improves the rate of the correct predictions by about 30% or more, relative to naive prior probability or moving average convergence divergence predictions.

Original languageEnglish
Pages (from-to)197-210
Number of pages14
JournalEconomic Computation and Economic Cybernetics Studies and Research
Volume51
Issue number3
Publication statusPublished - 2017 Jan 1

Fingerprint

Bayes' Formula
Prior Probability
Financial Time Series
Methodology
Prediction
Moving Average
Empirical Study
Maximum likelihood
Maximum Likelihood
Smoothing
Time series
Divergence
Predict
Decrease
Industry
Financial products
Movement
History
Moving average
Product value

Keywords

  • Bayes' theorem
  • Empirical study
  • Financial time series
  • Maximum likelihood estimation
  • Numerical prediction method
  • Smoothing

ASJC Scopus subject areas

  • Economics and Econometrics
  • Computer Science Applications
  • Applied Mathematics

Cite this

A prediction methodology for the change of the values of financial products. / Moon, Kyoung Sook; Kim, Heejean; Kim, Hongjoong.

In: Economic Computation and Economic Cybernetics Studies and Research, Vol. 51, No. 3, 01.01.2017, p. 197-210.

Research output: Contribution to journalArticle

@article{9fb3738cd43d406daf5bafdf82e396e7,
title = "A prediction methodology for the change of the values of financial products",
abstract = "A systematic algorithm based on data smoothing and the Bayes' theorem is proposed to predict the increase or decrease of a financial time series, which can be used in trading financial products when decisions need to be made between long and short positions. The algorithm compares the observed product values with those in the history to find a similar pattern with the maximum likelihood, based on which future up-down movement of the value is predicted. Empirical studies with S&P 500 Index and stocks of several companies show that the proposed methodology improves the rate of the correct predictions by about 30{\%} or more, relative to naive prior probability or moving average convergence divergence predictions.",
keywords = "Bayes' theorem, Empirical study, Financial time series, Maximum likelihood estimation, Numerical prediction method, Smoothing",
author = "Moon, {Kyoung Sook} and Heejean Kim and Hongjoong Kim",
year = "2017",
month = "1",
day = "1",
language = "English",
volume = "51",
pages = "197--210",
journal = "Economic Computation and Economic Cybernetics Studies and Research",
issn = "0585-7511",
publisher = "Editura Academia de studii economice",
number = "3",

}

TY - JOUR

T1 - A prediction methodology for the change of the values of financial products

AU - Moon, Kyoung Sook

AU - Kim, Heejean

AU - Kim, Hongjoong

PY - 2017/1/1

Y1 - 2017/1/1

N2 - A systematic algorithm based on data smoothing and the Bayes' theorem is proposed to predict the increase or decrease of a financial time series, which can be used in trading financial products when decisions need to be made between long and short positions. The algorithm compares the observed product values with those in the history to find a similar pattern with the maximum likelihood, based on which future up-down movement of the value is predicted. Empirical studies with S&P 500 Index and stocks of several companies show that the proposed methodology improves the rate of the correct predictions by about 30% or more, relative to naive prior probability or moving average convergence divergence predictions.

AB - A systematic algorithm based on data smoothing and the Bayes' theorem is proposed to predict the increase or decrease of a financial time series, which can be used in trading financial products when decisions need to be made between long and short positions. The algorithm compares the observed product values with those in the history to find a similar pattern with the maximum likelihood, based on which future up-down movement of the value is predicted. Empirical studies with S&P 500 Index and stocks of several companies show that the proposed methodology improves the rate of the correct predictions by about 30% or more, relative to naive prior probability or moving average convergence divergence predictions.

KW - Bayes' theorem

KW - Empirical study

KW - Financial time series

KW - Maximum likelihood estimation

KW - Numerical prediction method

KW - Smoothing

UR - http://www.scopus.com/inward/record.url?scp=85043487077&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85043487077&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85043487077

VL - 51

SP - 197

EP - 210

JO - Economic Computation and Economic Cybernetics Studies and Research

JF - Economic Computation and Economic Cybernetics Studies and Research

SN - 0585-7511

IS - 3

ER -