Abstract
Stock market trend analysis is very crucial for the understanding of the way stock market attributes can fluctuate with time. Also it helps investors to analyse when to buy and/or sell financial instruments. Even though the predictions can be made with a certain degree of accuracy, the ultimate aim is to minimise the risk associated with the predictions. In this work, Linear regression, Ridge regression, Bayesian Ridge regression, Lasso regression and FBProphet forecasting models are used and compared to predict stock market prices for a particular dataset with a benchmark accuracy. Also, on the basis of the used forecasting models, we have devised a new risk function for long-term stock market predictions. This risk function is derived from the risk functions proposed by NIST and MEHARI.
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Bose, R., Das, A., Poray, J., Bhattacharya, S. (2019). Risk Analysis for Long-Term Stock Market Trend Prediction. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-13-9939-8_34
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DOI: https://doi.org/10.1007/978-981-13-9939-8_34
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