Abstract
Analyzing the past records and precisely predicting the future trends is an important facet in a financially volatile market like the stock market! This paper employs Auto-ARIMA and Holt-Winters models for predicting the future value of stocks and Linear programming for portfolio optimization. Continuously training the model using the latest market data, helps the model get trained on the latest market behavior, so that it can be deployed further for more no. of portfolios in future; The trend obtained can be analyzed for the same to predict better investment plans in the stock market. Thus, helping people optimally invest in the stocks, depending upon the present market analysis, would help them fetch maximum return and suffer comparatively lesser loss.
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Roy, S. (2020). Stock Market Prediction and Portfolio Optimization Using Data Analytics. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_32
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DOI: https://doi.org/10.1007/978-981-13-8676-3_32
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