Abstract
Stock price prediction is the methodology of predicting the future value of a company stock. It is strenuous task to buy stock or invest in set of goods/assets; the financial market’s fast transformation makes it very difficult for prediction of future value of assets with high accuracy. Machine learning is the phenomenon of teaching computers to perform tasks that would normally need human intelligence, and is a major topic in scientific research at present. This article tries to develop a model for predicting future stock market rates by using the “Long Short Term Memory” model also called LSTM algorithm along with deep learning algorithms which include DENSE, DROP OUT, and SEQUENTIAL. In this work, we have considered mainly five factors that are open, close, low, high, and volume.
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References
Moritz B, Zimmermann T (2016) Tree-based conditional portfolio sorts: The relation between past and future stock returns. Available at SSRN 2740751
Batres-Estrada B (2015) Deep learning for multivariate financial time series
Takeuchi L, Lee YYA (2013) In Technical report. Stanford University
Wang S, Luo Y (2012) Signal processing: the rise of the machines. Deutsche Bank Quantitative Strategy
Paiva FD, Cardoso RTN, Hanaoka GP, Duarte WM (2018) Decision-making for financial trading: a fusion approach of machine learning and portfolio selection. Expert Syst Appl
Emerson S, Kennedy R, O’Shea L, O’Brien J (2019) Trends and applications of machine learning in quantitative finance. In: 8th international conference on economics and finance research (ICEFR 2019)
Siami-Namini S, Namin AS (2018) Forecasting economics and financial time series: Arima vs. LSTM. arXiv preprint arXiv:1803.06386
Patterson J (2017) Deep learning: a practitioner’s approach. O’Reilly Media
Heaton JB, Polson NG, Witte JH (2017) Deep learning for finance: deep portfolios. Appl Stoch Model Bus Ind 33(1):3–12
Olah C (2015) Understanding LSTM network—Colah’s blog. https://colah.github.io/
Khan MA, Kadry S, Parwekar P et al (2021) Human gait analysis for osteoarthritis prediction: a framework of deep learning and kernel extreme learning machine. Complex Intell Syst. https://doi.org/10.1007/s40747-020-00244-2
Mittal M, Satapathy SC, Pal V, Agarwal B, Goyal LM, Parwekar P (2021) Prediction of coefficient of consolidation in soil using machine learning techniques. Microprocess Microsyst 82:103830
Sadia H, Sharma A, Paul A, Padhi S, Sanyal S (2019) Stock market prediction using machine learning algorithms. IJEAT
Deepak RS, Uday SI, Malathi D (2017) Machine learning approach in stock market prediction. IJPAM
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Piyush, Amarjeet, Sharma, A., Kumar, S., Ansari, N.N. (2023). Stock Price Prediction Using Machine Learning. In: Mahapatra, R.P., Peddoju, S.K., Roy, S., Parwekar, P. (eds) Proceedings of International Conference on Recent Trends in Computing. Lecture Notes in Networks and Systems, vol 600. Springer, Singapore. https://doi.org/10.1007/978-981-19-8825-7_8
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DOI: https://doi.org/10.1007/978-981-19-8825-7_8
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