Analyzing the Impact of Global Influencing Features with Weighted Attention Model for Stock Market Forecasting
Keywords:Stock Market Forecasting, Global Influencing Features, Weighted Attention Score, Regularization Factor, Forecasting Accuracy
The present volatility of the stock markets makes forecasting stock trends extremely challenging owing to several socio economic and political factors other than market trends. While machine learning models can be used to perform regression analysis based on historical data trends, it becomes extremely challenging to incorporate the variabilities which are non-numeric in nature. Some of the factors which govern the rise and fall of stock prices are socio economic conditions, trade wars, current pandemic situation and global market slowdown, reliability of a company among others. Hence, one of the most effective ways to incorporate these trends is analyzing public trends pertaining to the same. While public sentiments may not always be coherent with prevailing market trends, yet they often portray the existential trends in the market and opinions of the public regarding potential purchases of stocks of a particular company in a given time period. This paper presents an approach which is an amalgamation of deep nets with attention, and opinion mining for forecasting stock trends. The attention vector employed as an additional input computed on the moving average allows for current trend analysis along with opinion mining from public datasets to encompass both numeric data trends and non-numeric data parameters pertaining to global influencing features. The regression and forecasting accuracy have been computed on a diverse set of datasets to validate the performance of the proposed approach.
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