Research Article
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Year 2023, Volume: 12 Issue: 2, 91 - 101, 30.07.2023
https://doi.org/10.17261/Pressacademia.2023.1741

Abstract

References

  • Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction. Journal of Applied Mathematics, 2014, 1–7.
  • Chen, S. J., Billings, S. A., & Grant, P. J. (1990). Non-linear system identification using neural networks. International Journal of Control, 51(6), 1191–1214.
  • Cornell, B., & Damodaran, A. (2020). Valuing ESG: doing good or sounding good? Social Science Research Network. https://doi.org/10.2139/ssrn.3557432
  • Damodaran, A. (2011). The Little Book of Valuation: How to Value a Company, Pick a Stock and Profit. John Wiley & Sons.
  • Damodaran, A. (2011a). Applied corporate finance. In Wiley eBooks.https://pages.stern.nyu.edu/~adamodar/pdfiles/execs/cf1day2007notes.pdf
  • Fernandez, P. (2002). Valuation methods and shareholder value creation. In Elsevier eBooks. https://doi.org/10.1016/b978-0-12-253841-4.x5000-8
  • Komo, D., Chang, C., & Ko, H. (2002). Neural network technology for stock market index prediction. https://doi.org/10.1109/sipnn.1994.344854
  • Husain, T., Sarwani, Sunardi, N., & Lisdawati. (2020). Firm’s value prediction based on profitability ratios and dividend policy. Finance & Economics Review, 2(2), 13–26.
  • Menezes, J., & Barreto, G. A. (2008). Long-term time series prediction with the NARX network: An empirical evaluation. Neurocomputing, 71(16–18), 3335–3343.
  • Lin, T., Horne, B. G., & Giles, C. L. (1998). How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies. Neural Networks, 11(5), 861–868.
  • Narendra, K. S., & Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1), 4–27.
  • Oztemel, E. (2020). Yapay Sinir Agları. Papatya Publishing.
  • Ozturk, H. (2009). Sirket Degerlemenin Esasları. Turkmen Publishing.
  • Patel, M. B., & Yalamalle, S. R. (2014). Stock Price Prediction Using Artificial Neural Network. International Journal of Innovative Research in Science, Engineering and Technology, 3(16). http://ijirset.com/upload/2014/june/76_Stock.pdf
  • Di Persio, L., & Honchar, O. (2016). Artificial Neural Networks architectures for stock price prediction: comparisons and applications. International Journal of Circuits, Systems and Signal Processing, 10, 403–413. https://iris.univr.it/handle/11562/955101
  • Rather, A. M. (2011). A prediction-based approach for stock returns using autoregressive neural networks. https://doi.org/10.1109/wict.2011.6141431
  • Jordan, B., Westerfield, R., & Ross, S. (2012). Fundamentals of Corporate Finance Standard Edition. McGraw-Hill Education.
  • Samarasinghe, S. (2006). Neural networks for applied sciences and engineering: From Fundamentals to Complex Pattern Recognition. Auerbach Publications.
  • Wilimowska, Z., & Krzysztoszek, T. (2013). The use of artificial neural networks in company valuation process. In Studies in computational intelligence (pp. 279–288). https://doi.org/10.1007/978-3-642-34300-1_27
  • Wunsch, A., Liesch, T., & Broda, S. (2018). Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX). Journal of Hydrology, 567, 743–758.
  • Zhang, G. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/s0925-2312(01)00702-0

AN ARTIFICIAL NEURAL NETWORK BASED METHOD FOR COMPANY VALUATION

Year 2023, Volume: 12 Issue: 2, 91 - 101, 30.07.2023
https://doi.org/10.17261/Pressacademia.2023.1741

Abstract

Purpose - Company value is a crucial issue for everyone operating in financial markets. Both firm managers and investors should calculate the value of a firm, and there are many methods available to do so. Calculating valuation methods is challenging due to the multiple parameters and stages that are involved. Due to these reasons, it's possible for managers, owners, investors, and other stakeholders to estimate a company's value inaccurately or with difficulty. The main objective of this essay is to demonstrate how to evaluate a company's value using the NARX model as an alternative to other models.
Methodology – It is estimated that the firm value using an artificial neural network nonlinear external input autoregressive network model for 50 companies operating in the consumer products and industrial products and services sectors in the Euro Stoxx 50 index. The dataset covers the period from 2000 to 2021, and 20 financial ratios were included as input to the model, with FCFF as the output.
Findings- The NARX model with a 20-6-6-1 or 20-10-10-1 network structure provided the best value for both R and MSE at two-time delays. However, the 20-12-12-1 network structure of the NARX model with a time delay of three has a lower error rate after training and the best R value. The model's prediction success rate is 90.82% using the 20-12-12-1 network structure with a time delay of three.
Conclusion- As a result, this model can be used by investors and business managers to value a company. By using this method, businesses may gain access to more precise and unbiased appraisals that can guide resource allocation and strategic decision-making. By including macroeconomic factors that have an impact on the sector and employing a longer time frame, the study could be improved.

References

  • Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction. Journal of Applied Mathematics, 2014, 1–7.
  • Chen, S. J., Billings, S. A., & Grant, P. J. (1990). Non-linear system identification using neural networks. International Journal of Control, 51(6), 1191–1214.
  • Cornell, B., & Damodaran, A. (2020). Valuing ESG: doing good or sounding good? Social Science Research Network. https://doi.org/10.2139/ssrn.3557432
  • Damodaran, A. (2011). The Little Book of Valuation: How to Value a Company, Pick a Stock and Profit. John Wiley & Sons.
  • Damodaran, A. (2011a). Applied corporate finance. In Wiley eBooks.https://pages.stern.nyu.edu/~adamodar/pdfiles/execs/cf1day2007notes.pdf
  • Fernandez, P. (2002). Valuation methods and shareholder value creation. In Elsevier eBooks. https://doi.org/10.1016/b978-0-12-253841-4.x5000-8
  • Komo, D., Chang, C., & Ko, H. (2002). Neural network technology for stock market index prediction. https://doi.org/10.1109/sipnn.1994.344854
  • Husain, T., Sarwani, Sunardi, N., & Lisdawati. (2020). Firm’s value prediction based on profitability ratios and dividend policy. Finance & Economics Review, 2(2), 13–26.
  • Menezes, J., & Barreto, G. A. (2008). Long-term time series prediction with the NARX network: An empirical evaluation. Neurocomputing, 71(16–18), 3335–3343.
  • Lin, T., Horne, B. G., & Giles, C. L. (1998). How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies. Neural Networks, 11(5), 861–868.
  • Narendra, K. S., & Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1), 4–27.
  • Oztemel, E. (2020). Yapay Sinir Agları. Papatya Publishing.
  • Ozturk, H. (2009). Sirket Degerlemenin Esasları. Turkmen Publishing.
  • Patel, M. B., & Yalamalle, S. R. (2014). Stock Price Prediction Using Artificial Neural Network. International Journal of Innovative Research in Science, Engineering and Technology, 3(16). http://ijirset.com/upload/2014/june/76_Stock.pdf
  • Di Persio, L., & Honchar, O. (2016). Artificial Neural Networks architectures for stock price prediction: comparisons and applications. International Journal of Circuits, Systems and Signal Processing, 10, 403–413. https://iris.univr.it/handle/11562/955101
  • Rather, A. M. (2011). A prediction-based approach for stock returns using autoregressive neural networks. https://doi.org/10.1109/wict.2011.6141431
  • Jordan, B., Westerfield, R., & Ross, S. (2012). Fundamentals of Corporate Finance Standard Edition. McGraw-Hill Education.
  • Samarasinghe, S. (2006). Neural networks for applied sciences and engineering: From Fundamentals to Complex Pattern Recognition. Auerbach Publications.
  • Wilimowska, Z., & Krzysztoszek, T. (2013). The use of artificial neural networks in company valuation process. In Studies in computational intelligence (pp. 279–288). https://doi.org/10.1007/978-3-642-34300-1_27
  • Wunsch, A., Liesch, T., & Broda, S. (2018). Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX). Journal of Hydrology, 567, 743–758.
  • Zhang, G. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/s0925-2312(01)00702-0
There are 21 citations in total.

Details

Primary Language English
Subjects Finance, Business Administration
Journal Section Articles
Authors

Pinar Unal Guner This is me 0000-0002-8408-4648

Seyma Nur Unal This is me 0000-0002-3475-7226

Publication Date July 30, 2023
Published in Issue Year 2023 Volume: 12 Issue: 2

Cite

APA Guner, P. U., & Unal, S. N. (2023). AN ARTIFICIAL NEURAL NETWORK BASED METHOD FOR COMPANY VALUATION. Journal of Business Economics and Finance, 12(2), 91-101. https://doi.org/10.17261/Pressacademia.2023.1741

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