Open Access Open Access  Restricted Access Subscription or Fee Access

Stock Price Prediction Using DCF and Machine Learning

Anish Santosh, Dinesh Iyer, Mahalaxmi Aher, Dharti Mistry, Bhagyashree Dhakulkar


Understanding the long-term outlook on a particular stock is very important for investors to see the growth of their investment. This paper will give the idea about how to predict the fair price of the shares using DCF method and understanding short term outlook of that investment. The various technologies used in the paper are free cash flow, the terminal value, net present value, ARIMA, Linear Regression. Traditional techniques lack in covering long term stock price movements and so new approaches have been developed for analysis of stock price variations.


Stock Market, Prediction, Data Mining, Multiple Regression, Polynomial Regression, Linear Regression, ARIMA, Etc.

Full Text:



G. Gonzalez ‘Rivera and T. H. Lee, ‘‘Nonlinear time series in financial forecasting,’’ in Encyclopedia of Complexity and Systems Science. New York, NY, USA: Springer, 2009.

Attigeri, G.V., P.M.M. Manohara, R.M. Pai and A. Nayak, 2015. Stock market prediction: A big data approach. Proceedings of the IEEE Region 10th Conference on TENCON 2015, November 1-4, 2015, Macao, China, pp: 1-5.

M. Usmani, S. H. Adil, K. Raza and S. S. A. Ali, "Stock market prediction using machine learning techniques," 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), Kuala Lumpur, 2016, pp. 322-327.

K. Raza, "Prediction of Stock Market performance by using machine learning techniques," 2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT), Karachi, 2017, pp. 1-1.

K. Pahwa and N. Agarwal, "Stock Market Analysis using Supervised Machine Learning," 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, 2019, pp. 197-200, doi: 10.1109/COMITCon.2019.8862225

Tutorial on Discounted cash Flow

Discounted Cash Flow (DCF) Analysis

Tutorial on free cash flow

DCF concept

Tutorial on Net Present value

Tutorial on Basic Earning per share

Tutorial on Financial ratio analysis

Book Value

Tutorial on dividend

Tutorial on Operating Revenue

Raicharoen, T., Lursinsap, C., & Sanguanbhokai, P. (2003, May). Application of critical support vector machine to time series prediction. In Circuits and Systems, 2003. ISCAS'03. Proceedings of the 2003 International Symposium on (Vol. 5, pp. V-V). IEEE.

Zhang, G. P. (2007). A neural network ensemble method with jittered training data for time series forecasting. Information Sciences, 177(23), 5329-5346

Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.

M. S. Acharya, A. Armaan and A. S. Antony, "A Comparison of Regression Models for Prediction of Graduate Admissions," 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India, 2019, pp. 1-5, doi:10.1109/ICCIDS.2019.8862140.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.