A neural network with a case based dynamic window for stock trading prediction

Chang, Pei‐Chann; Liu, Chen-Hao; Lin, Jun-Lin; Fan, Chin-Yuan; Ng, Celeste See-Pui

doi:10.1016/j.eswa.2008.08.077

Cited by 136 publications

(56 citation statements)

References 25 publications

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“…Chang, Liu et al (Chang, Liu et al 2009) presented an integrated system, CBDWNN by combining dynamic time windows, case based reasoning (CBR), and neural network for stock trading prediction and it includes three different stages: (1) screening out potential stocks and the important influential factors; (2) using back propagation network (BPN) to predict the buy/sell points (wave peak and wave trough) of stock price and (3) adopting case based dynamic window (CBDW) to further improve the forecasting results from BPN. The system developed in this research is a first attempt in the literature to predict the sell/buy decision points instead of stock price itself.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model

Olatunji¹,

Al-Ahmadi²,

Elshafei³

et al. 2013

IJIIS

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Abstract:In this paper, we have proposed artificial neural network for the prediction of Saudi stock market. The proposed predictions model, with its high degree of accuracy, could be used as investment advisor for the investors and traders in the Saudi stock market. The proposed model is based mainly on Saudi Stock market historical data covering a large span of time. Achieving reasonable accuracy rate of predication models will surely facilitate an increased confidence in the investment in the Saudi stock market. We have only used the closing price of the stock as the stock variable considered for input to the system. The number of windows gap to determine the numbers of previous days to be used in predicting the next day closing price data has been choosing based on experimental simulation carried out to determine the best possible value. Our results indicated that the proposed ANN model predicts the next day closing price stock market value with a very low RMSE down to 1.8174, very low MAD down to 18.2835, very low MAPE of down to 1.6476 and very high correlation coefficient of up to 99.9% for the test set, which is an indication that the model adequately mimics the trend of the market in its prediction. This performance is really encouraging and thus the proposed system will impact positively on the analysis and prediction of Saudi stock market in general.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model

Olatunji¹,

Al-Ahmadi²,

Elshafei³

et al. 2013

IJIIS

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“…In recent years several techniques for regression and classification financial assets have been explored, from classical statistical methods to more complex algorithms for machine learning, such as Artificial Neural Networks [19], [20], Logistic Regression [17], [18], PLSR [21] and more recently Support Vector Machine [22], [23], [24]. Reference works in the area prove that these soft computing techniques are well accepted for the study and evaluation of financial series.…”

Section: Related Work In Assets' Predictionsmentioning

confidence: 99%

Prediction of assets behavior in financial series using machine learning algorithms

Silva¹,

Duarte²

2013

IJARAI

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Abstract-The prediction of financial assets using either classification or regression models, is a challenge that has been growing in the recent years, despite the large number of publications of forecasting models for this task. Basically, the non-linear tendency of the series and the unexpected behavior of assets (compared to forecasts generated in studies of fundamental analysis or technical analysis) make this problem very hard to solve. In this work, we present for this task some modeling techniques using Support Vector Machines (SVM) and a comparative performance analysis against other basic machine learning approaches, such as Logistic Regression and Naive Bayes. We use an evaluation set based on company stocks of the BVM&F, the official stock market in Brazil, the third largest in the world. We show good prediction results, and we conclude that it is not possible to find a single model that generates good results for every asset. We also present how to evaluate such parameters for each model. The generated model can also provide additional information to other approaches, such as regression models

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“…The RSI as a part of diverse calculations and formulas is commonly present in soft computing research (e.g., Chang & Liu, 2008;Chang et al, 2009;Chiam, Tan, & Al Mamun, 2009;Chiu & Chen, 2009;Kim, 2004;Lai, Fan, Huang, & Chang, 2009;Lu, Lee, & Chiu, 2009;Majhi et al, 2009;Tan, Quek, & Yow, 2008;Yao & Herbert, 2009). However, using soft computing methods in getting iRSI calculations is a research task with no pres ence in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, in the first decade of the 21st century, various stud ies using ANN have been developed in the fields of forecasting stock indexes (Chang, Liu, Lin, Fan, & Ng, 2009;Chavarnakul & Enke, 2008;Chen & Leung, 2004;Chen, Leung, & Daouk, 2003;Enke & Thawornwong, 2005;Lam, 2004;Lee & Chen, 2002;Lee & Chiu, 2002;Leigh, Hightower, & Modani, 2005;Thawornwong & Enke, 2004;Yao, Li, & Tan, 2000). The importance of further developments in soft computing led to several papers devoted to forecasting stock indexes using techniques such as support vector machines (e.g., Chiu & Chen, 2009;Huang, Nakamori, & Wang, 2005;Kim, 2003;Pai & Lin, 2005;Wen et al, 2010), fuzzy systems (e.g., Chang & Liu, 2008;Chang, Wang, & Liu, 2007;Huang & Yu, 2005;Wang, 2003), genetic algorithms (e.g., Chen et al, 2009;Oh, Kim, & Min, 2005;Oh, Kim, Min, & Lee, 2006;Potvin, Soriano, & Vallee, 2004) and mixed methods (e.g., Armano, Marchesi, & Murru, 2005;Armano, Murru, & Roli, 2002;Hassan, Nath, & Kirley, 2007;Kwon & Moon, 2007;Leigh, Purvis, & Ragusa, 2002).…”

Section: Introductionmentioning

confidence: 99%

CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

Rodríguez-González

García-Crespo

Colomo‐Palacios

et al. 2011

Expert Systems with Applications

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a b s t r a c tStock price predictions have been a field of study from several points of view including, among others, artificial intelligence and expert systems. For short term predictions, the technical indicator relative strength indicator (RSI) has been published in many papers and used worldwide.CAST is presented in this paper. CAST can be seen as a set of solutions for calculating the RSI using arti ficial intelligence techniques. The improvement is based on the use of feedforward neural networks to calculate the RSI in a more accurate way, which we call the iRSI. This new tool will be used in two sce narios. In the first, it will predict a market in our case, the Spanish IBEX 35 stock market. In the second, it will predict single company values pertaining to the IBEX 35. The results are very encouraging and reveal that the CAST can predict the given market as a whole along with individual stock pertaining to the IBEX 35 index.

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A neural network with a case based dynamic window for stock trading prediction

Cited by 136 publications

References 25 publications

Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model

Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model

Prediction of assets behavior in financial series using machine learning algorithms

CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

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