Automatic stock decision support system based on box theory and SVM algorithm

Wen, Qinghua; Yang, Zhongzhu; Song, Yong; Jia, Peifa

doi:10.1016/j.eswa.2009.05.093

Cited by 87 publications

(40 citation statements)

References 8 publications

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“…The important issue here is that both INDRA and ENAGAS have very accurate predictions in RSI14. The most feasible explanation is the chaotic data nature label given in the literature (e.g., Wen et al, 2010). However, the authors rely on iRSI development, and as future work suggest expanding the sample in order to investi gate this phenomenon.…”

Section: Samplementioning

confidence: 99%

“…Forecasting price movements in stock markets has been a major challenge for common investors, busi nesses, brokers and speculators (Majhi, Panda, & Sahoo, 2009). The stock market is considered a highly complex and dynamic sys tem with noisy, non stationary and chaotic data series (Wen, Yang, Song, & Jia, 2010), and hence, difficult to forecast (Oh & Kim, 2002;Wang, 2003). However, in spite of its volatibility, it is not entirely random (Chiu & Chen, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…According to Wen et al (2010), the study of the stock market is a hot topic, because if successful, the result will transfer to fruitful rewards. Thus, it is obvious that predicting the stock market's movement is the long cherished desire of investors, speculators, and industries (Kim, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Most of the work is the combination of soft computing technology and technical analysis in stock analysis (Chen, Mabu, Shimada, & Hirasawa, 2009;Wen et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Samplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Most of the work is the combination of soft computing technology and technical analysis in stock analysis (Chen, Mabu, Shimada, & Hirasawa, 2009;Wen et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

View full text Add to dashboard Cite

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A hybrid multicategory framework for generating automated trading systems

Kumar

Sahoo

Thakur

2023

Concurrency and Computation

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This work proposes a framework for producing automatic trading systems that suggest making trading decisions in financial markets. The trading systems thus generated utilize multicategory classifiers. A collection of important technical indicators are considered as input features for the underlying multicategory classifiers. The proposed trading systems also have an option to use random forest (RF) algorithms for feature selection. The trading range breakout strategy is used to train the proposed trading systems thus generating “BUY/SELL/WAIT” trading signals on daily open prices. The performances of the proposed trading systems are evaluated over five future indices. Empirical findings suggest that the day trading systems based on the proposed multicategory SVM classifiers along with the RF technique outperform the day trading systems built using the multicategory classifiers taken from the literature. The trading range breakout strategy‐based systems are found to be superior to the traditional BUY‐HOLD strategy and that of RF‐PSVM (BUY/SELL) based strategy.

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