• Title, Summary, Keyword: Moving Average

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Hybrid Model Approach to the Complexity of Stock Trading Decisions in Turkey

  • CALISKAN CAVDAR, Seyma;AYDIN, Alev Dilek
    • The Journal of Asian Finance, Economics and Business
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    • v.7 no.10
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    • pp.9-21
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    • 2020
  • The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.

A Genetic Algorithm for Optimal Period Forecasting Of Moving Average (유전자 알고리즘을 이용한 Moving Average의 최적 Period 예측 시스템 구현)

  • Kim, So-Young;Han, Chi-Geun
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.2447-2450
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    • 2002
  • 주가지수선물시장은 주식투자에 따르는 위험을 효과적으로 관리할 수 있는 제도적 장치로서 오늘날 불안한 주식시장 현황에 있어서 더욱더 중요한 위치를 갖고 있다. 현재 이러한 주가지수선물거래에 있어서 Moving Average 를 예측하고자 하는 여러 트레이딩 시스템을 선보이고 있다. 이 논문에서는 과거의 데이터를 토대로 한 Moving Average Line 분석에 있어서 일반적으로 기존방법보다 효과적이라고 알려진 유전자 알고리즘을 이용하여 Moving Average 의 최적 Period 예측 시스템을 구현한다.

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A Laplacian Autoregressive Moving-Average Time Series Model

  • Son, Young-Sook
    • Journal of the Korean Statistical Society
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    • v.22 no.2
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    • pp.259-269
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    • 1993
  • A moving average model, LMA(q) and an autoregressive-moving average model, NLARMA(p, q), with Laplacian marginal distribution are constructed and their properties are discussed; Their autocorrelation structures are completely analogus to those of Gaussian process and they are partially time reversible in the third order moments. Finally, we study the mixing property of NLARMA process.

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Decentralized Moving Average Filtering with Uncertainties

  • Song, Il Young
    • Journal of Sensor Science and Technology
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    • v.25 no.6
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    • pp.418-422
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    • 2016
  • A filtering algorithm based on the decentralized moving average Kalman filter with uncertainties is proposed in this paper. The proposed filtering algorithm presented combines the Kalman filter with the moving average strategy. A decentralized fusion algorithm with the weighted sum structure is applied to the local moving average Kalman filters (LMAKFs) of different window lengths. The proposed algorithm has a parallel structure and allows parallel processing of observations. Hence, it is more reliable than the centralized algorithm when some sensors become faulty. Moreover, the choice of the moving average strategy makes the proposed algorithm robust against linear discrete-time dynamic model uncertainties. The derivation of the error cross-covariances between the LMAKFs is the key idea of studied. The application of the proposed decentralized fusion filter to dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.

PRECISE ASYMPTOTICS OF MOVING AVERAGE PROCESS UNDER ?-MIXING ASSUMPTION

  • Li, Jie
    • Journal of the Korean Mathematical Society
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    • v.49 no.2
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    • pp.235-249
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    • 2012
  • In the paper by Liu and Lin (Statist. Probab. Lett. 76 (2006), no. 16, 1787-1799), a new kind of precise asymptotics in the law of large numbers for the sequence of i.i.d. random variables, which includes complete convergence as a special case, was studied. This paper is devoted to the study of this new kind of precise asymptotics in the law of large numbers for moving average process under $\phi$-mixing assumption and some results of Liu and Lin [6] are extended to such moving average process.

Distributed Fusion Moving Average Prediction for Linear Stochastic Systems

  • Song, Il Young;Song, Jin Mo;Jeong, Woong Ji;Gong, Myoung Sool
    • Journal of Sensor Science and Technology
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    • v.28 no.2
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    • pp.88-93
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    • 2019
  • This paper is concerned with distributed fusion moving average prediction for continuous-time linear stochastic systems with multiple sensors. A distributed fusion with the weighted sum structure is applied to the optimal local moving average predictors. The distributed fusion prediction algorithm represents the optimal linear fusion by weighting matrices under the minimum mean square criterion. The derivation of equations for error cross-covariances between the local predictors is the key of this paper. Example demonstrates effectiveness of the distributed fusion moving average predictor.

An Analysis on Combination Effect of Value Investment Strategy and Moving Average Method (가치투자전략과 이동평균법의 결합효과)

  • Chang, Kyung-Chun;Kim, Yeon-Gueon;Kim, Hyun-Seok
    • Management & Information Systems Review
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    • v.27
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    • pp.53-69
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    • 2008
  • In this paper we analyse performance of value strategy and moving average method among the non-financial listed companies whose fiscal year ends at December in the Korean Stock Exchange between 1996 and 2005. And we analyse combination investment performance of value investment and moving average method. After the analysis objective enterprises divide with the value stock and the growth stock, in accordance with moving average method we divide ascending stock and descending stock. And we compose 6 portfolios with combination of value stock, growth stock, ascending stock and descending stock. Using the difference of investment performance of these portfolios, when fundamental analysis and technical analysis method all considering we measure investment performance. The major findings of this research are as follows: First, the value strategy of buying value stocks and selling growth stocks were effective in the long-term investment. Second, using the moving average method, technical analysis were effective in the case of the short-term investment. Third, the portfolios combined fundamental analysis and technical analysis were more effective than investment performance of technical analysis.

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A Smoothing Method for Stock Price Prediction with Hidden Markov Models

  • Lee, Soon-Ho;Oh, Chang-Hyuck
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.4
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    • pp.945-953
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    • 2007
  • In this paper, we propose a smoothing and thus noise-reducing method of data sequences for stock price prediction with hidden Markov models, HMMs. The suggested method just uses simple moving average. A proper average size is obtained from forecasting experiments with stock prices of bank sector of Korean Exchange. Forecasting method with HMM and moving average smoothing is compared with a conventional method.

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The ARL of a Selectively Moving Average Control Chart (선택적 이동평균(S-MA) 관리도의 ARL)

  • Lim, Tae-Jin
    • Journal of the Korean Society for Quality Management
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    • v.35 no.1
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    • pp.24-34
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    • 2007
  • This paper investigates the average run length (ARL) of a selectively moving average (S-MA) control chart. The S-U chart is designed to detect shifts in the process mean. The basic idea of the S-MA chart is to accumulate previous samples selectively in order to increase the sensitivity. The ARL of the S-MA chart was shown to be monotone decreasing with respect to the decision length in a previous research [3]. This paper derives the steady-state ARL in a closed-form and shows that the monotone property is resulted from head-start assumption. The steady-state ARL is shown to be a sum of head-start ARL and an additional term. The statistical design procedure for the S-MA chart is revised according to this result. Sensitivity study shorts that the steady-state ARL performance is still better than the CUSUM chart or the Exponentially Weighted Moving Average (EWMA) chart.