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Enhancement of stock market forecasting using an improved fundamental analysis-based approach

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Abstract

Stock investment is regarded as a high-risk financial activity, in which life savings can be destroyed when investors fail to consider factors in stock price variation or do not master professional knowledge and experiences related to investment. To enhance decision-making quality and profitability for investors, numerous different methods for forecasting stock market prices have appeared. However, these forecasts usually do not appear as expected because of uncertainties in the stock market. Therefore, how to effectively use stock information to help investors make stock investment decisions has become a primary issue in stock investment. Based on the bottom-up approach that considers financial conditions of listed companies, industrial environment, macroeconomics, and financial news respectively, an improved fundamental analysis-based approach for stock market forecasting is developed for selecting optimal stocks from the stock market and predicting their future price trends to provide a reference for investor decisions. This study involves the following tasks: (1) design an improved fundamental analysis-based approach to stock market forecasting; (2) develop techniques related to fundamental analysis-based stock market forecasting, and (3) demonstrate and evaluate the proposed fundamental analysis-based approach to stock market forecasting. The improved fundamental analysis-based approach to stock market forecasting involves techniques such as calculating the weight of financial indicators, evaluating and selecting individual stocks, selecting financial news features, determining stock trading signals based on financial news, and forecasting stock price trend.

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References

  • Antweiler W, Frank MZ (2004) Is all that talk just noise? The information content of internet stock message boards. J Finance 59(3):1259–1294

    Article  Google Scholar 

  • Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8

    Article  Google Scholar 

  • Chang YW, Hsieh CJ, Chang KW, Ringgaard M, Lin CJ (2010) Training and testing low-degree polynomial data mappings via linear SVM. J Mach Learn Res 11:1471–1490

    MathSciNet  MATH  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444

    Article  MathSciNet  MATH  Google Scholar 

  • Chen B, Liu SH, Chu FH (2009) Training data selection for improving discriminative training of acoustic models. Pattern Recognit Lett 30(13):1228–1235

    Article  Google Scholar 

  • Chen J, Huang H, Tian S, Qu Y (2009) Feature selection for text classification with Naïve Bayes. Expert Syst Appl 36(3 (Part 1)):5432–5435

    Article  Google Scholar 

  • Cheng Z, Zhang Y (2010) Classification models of estrogen receptor-B ligands based on PSO-Adaboost-SVM. J Converg Inf Technol 5(2):67–83

    Article  MathSciNet  Google Scholar 

  • Gidófalvi G (2001) Using news articles to predict stock price movements. University of California, Department of Computer Science and Engineering

  • Gilbert E, Karahalios K (2010) Widespread Worry and the Stock Market. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media

  • Lam M (2004) Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decis Support Syst 37(4):567–581

    Article  Google Scholar 

  • Lazarevic-McManus N, Renno JR, Makris D, Jones GA (2008) An object-based comparative methodology for motion detection based on the F-Measure. Comput Vis Image Underst 111(1):74–85

    Article  Google Scholar 

  • Li X, Wang L, Sung E (2008) AdaBoost with SVM-based component classifiers. Eng Appl Artif Intell 21(5):785–795

    Article  Google Scholar 

  • Meyer C, Schramm H (2006) Boosting HMM acoustic models in large vocabulary speech recognition. Speech Commun 48(5):532–548

    Article  Google Scholar 

  • Mittermayer M (2004) Forecasting intraday stock price trends with text mining techniques. In: Proceedings of the 37th Hawaii International Conference on System Sciences, Kailua-Kona, HI

  • Niu B, Jin Y, Lu W, Li G (2009) Predicting toxic action mechanisms of phenols using AdaBoost Learner. Chemom Intell Lab Syst 96(1):43–48

  • Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Google Scholar 

  • Romero E, Màrquez L, Carreras X (2004) Margin maximization with feed-forward neural networks: a comparative study with SVM and AdaBoost. Neurocomputing 57:313–344

  • Schumaker RP, Chen H (2009) Textual analysis of stock market prediction using breaking financial news: the AZFin text system. ACM Trans Inf Syst 27(2) Article 12

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423

    Article  MathSciNet  MATH  Google Scholar 

  • Shiue YR, Guh RS, Lee KC (2012) Development of machine learning-based real time scheduling systems: using ensemble based on wrapper feature selection approach. Int J Prod Res 50(20):5887–5905

  • Stern H, Chassidim Y, Zofi M (2006) Multiagent visual area coverage using a new genetic algorithm selection scheme. Eur J Oper Res 175(3):1890–1907

    Article  MATH  Google Scholar 

  • Tetlock PC (2007) Giving content to investor sentiment: the role of media in the stock market. J Finance 62(3):1139–1168

    Article  Google Scholar 

  • Thawornwong S, Enke D (2004) The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing 56:205–232

    Article  Google Scholar 

  • Tsai CF, Hsiao YC (2010) Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches. Decis Support Syst 50(1):258–269

    Article  Google Scholar 

  • Tsai CF, Lin YC, Yen DC, Chen YM (2011) Predicting stock returns by classifier ensembles. Appl Soft Comput 11(2):2452–2459

    Article  Google Scholar 

  • Tsang E, Yung P, Li J (2004) EDDIE-Automation, a decision support tool for financial forecasting. Decis Support Syst 37(4):559–565

    Article  Google Scholar 

  • Vapnik CC (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  • Wang X, Wang F, Zhong S (2012) Electrical, information engineering and mechatronics 2011. In: Proceedings of the 2011 International Conference on Electrical, Information Engineering and Mechatronics (Eiem 2011). Springer

  • Wong TT (1998) Generalized dirichlet distribution in Bayesian analysis. Appl Math Comput 97(2–3):165–181

    MathSciNet  MATH  Google Scholar 

  • Wuthrich B, Cho V, Leung S, Permunetilleke D, Sankaran K, Zhang J (1988) Daily stock market forecast from textual web data. In: IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA

  • Zhou S, Wu L, Yuan X, Tan W (2007) Parameters selection of SVM for function approximation based on differential evolution. In: Proceedings of the 2007 International Conference Proceedings on Intelligent Systems and Knowledge Engineering

  • Zhou X, Jiang W, Tian Y, Shi Y (2010) Kernel subclass convex hull sample selection method for SVM on face recognition. Neurocomputing 73(10–12):2234–2246

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank the National Science Council of the Republic of China, Taiwan for financially supporting this research under Contract No. NSC100-2410-H-327-003-MY2. The authors thank the editor and the reviewers for their constructive comments and suggestions.

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Authors and Affiliations

  1. Department of Accounting and Information Systems, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan, ROC

    Yuh-Jen Chen

  2. Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan, ROC

    Yuh-Min Chen & Chang Lin Lu

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  1. Yuh-Jen Chen
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  2. Yuh-Min Chen
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  3. Chang Lin Lu
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Correspondence to Yuh-Jen Chen.

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The authors declare that there is no conflict of interest.

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Communicated by V. Loia.

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Chen, YJ., Chen, YM. & Lu, C.L. Enhancement of stock market forecasting using an improved fundamental analysis-based approach. Soft Comput 21, 3735–3757 (2017). https://doi.org/10.1007/s00500-016-2028-y

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  • DOI: https://doi.org/10.1007/s00500-016-2028-y

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