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Improving Returns on Strategy Decisions through Integration of Neural Networks for the Valuation of Asset Pricing: The Case of Taiwanese Stock

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  • Yi-Chang Chen

    (School of Finance and Accounting, Fuzhou University of International Studies and Trade, Fuzhou 350202, China)

  • Shih-Ming Kuo

    (Department of Nursing, Fooyin University, Kaohsiung City 83102, Taiwan)

  • Yonglin Liu

    (School of Electrical and Computer Engineering, Nanfang College Guangzhou, Guangzhou 510970, China)

  • Zeqiong Wu

    (School of Electrical and Computer Engineering, Nanfang College Guangzhou, Guangzhou 510970, China)

  • Fang Zhang

    (School of Public Finance and Taxation, Guangdong University of Finance & Economics, Guangzhou 510320, China)

Abstract

Most of the growth forecasts in analysts’ evaluation reports rely on human judgment, which leads to the occurrence of bias. A back-propagation neural network (BPNN) is a financial technique that learns a multi-layer feedforward network. This study aims to integrate BPNN and asset pricing models to avoid artificial forecasting errors. In terms of evaluation, financial statements and investor attention were used in this case study, demonstrating that modern analysts should incorporate the evaluation advantages of big data to provide more reasonable and rational investment reports. We found that assessments of revenue, index returns, and investor attention suggest that stock prices are prone to undervaluation The levels of risk-taking behaviors were used in the classification of robustness analysis. This study showed that when betas range from 1% to 5%, both risk-taking levels of investors can hold buying strategies for the long term. However, for lower risk-taking preferences, only when the change exceeds 10 percent, the stock price is prone to overvaluation, indicating that investors can sell or adopt a more cautious investment strategy.

Suggested Citation

  • Yi-Chang Chen & Shih-Ming Kuo & Yonglin Liu & Zeqiong Wu & Fang Zhang, 2022. "Improving Returns on Strategy Decisions through Integration of Neural Networks for the Valuation of Asset Pricing: The Case of Taiwanese Stock," IJFS, MDPI, vol. 10(4), pages 1-15, October.
    • Handle: RePEc:gam:jijfss:v:10:y:2022:i:4:p:99-:d:954724
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    References listed on IDEAS

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    1. Acharya, Viral V. & Pedersen, Lasse Heje, 2005. "Asset pricing with liquidity risk," Journal of Financial Economics, Elsevier, vol. 77(2), pages 375-410, August.
    2. Zheng,Yongnian & Huang,Yanjie, 2018. "Market in State," Cambridge Books, Cambridge University Press, number 9781108461573, November.
    3. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    4. John Y. Campbell & Tuomo Vuolteenaho, 2004. "Bad Beta, Good Beta," American Economic Review, American Economic Association, vol. 94(5), pages 1249-1275, December.
    5. Zheng,Yongnian & Huang,Yanjie, 2018. "Market in State," Cambridge Books, Cambridge University Press, number 9781108473446, November.
    6. Ou, Jane A. & Penman, Stephen H., 1989. "Financial statement analysis and the prediction of stock returns," Journal of Accounting and Economics, Elsevier, vol. 11(4), pages 295-329, November.
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    1. Fatih Konak & Mehmet Akif Bülbül & Diler Türkoǧlu, 2024. "Feature Selection and Hyperparameters Optimization Employing a Hybrid Model Based on Genetic Algorithm and Artificial Neural Network: Forecasting Dividend Payout Ratio," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1673-1693, April.

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