Abstract
In this age, there is ample investment opportunities present in the market. Choosing one out of it for putting in the resources so as to maximize the returns becomes a very tedious and volatile task as there are several factors affecting its performance. Here, there is need to deploy a software system which can overcome human biases and provide an insight into the various schemes and opportunities. The system will pick up data from various sources and merge together their interdependencies to provide a set of visualizations of its previous history and plot its expected future growths. It shall consider historical data and news factors. The classes of investment broadly considered for this project are Stocks, Gold and Real Estate. The data obtained is to be trained using methods such as support vector machine, deep neural networks like CNN and LSTM and compared for their performance and accuracy and error values. This aids the human in understanding the rate of investment as well as associated risks considering numerous variables present in the market which otherwise is ignored.
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Jain, S., Mandal, P., Singh, B., Kulkarni, P.V., Sayed, M. (2021). Prediction of Stock Indices, Gold Index, and Real Estate Index Using Deep Neural Networks. In: Gunjan, V.K., Suganthan, P.N., Haase, J., Kumar, A. (eds) Cybernetics, Cognition and Machine Learning Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6691-6_37
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DOI: https://doi.org/10.1007/978-981-33-6691-6_37
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