Abstract
Trend prediction is and has been one of the very important tasks in the stock market since day one. For a sophisticated trend prediction using real time stock market data, stock sentiment news and technical analysis plays a vital role. While predicting the trend in the conventional way, technical indicators are delayed due to temporal data and less historic data. All the conventional stock trend predicting methods sustained without sentiment scores, technical scores and time periods for trend prediction. Considering the fact that all the previous conventional methods of stock trend predictions are bound to take single stock for trend prediction due to high computational memory and time, this prototype of highly functioning algorithms focus on trend prediction with multi stock data breaking all the conventional rules. This multi stock trend prediction model commissions and implements the effectively programmed algorithms on real time stock market data set. In this multi-stock trend prediction model, a new stock technical indicator and new stock sentiment score are proposed in order to improve the stock feature selection for trend prediction. In order to find the best real time feature selection model, a technical feature selection measure and stock news sentiment score are developed and incorporated. We used integrated stock market data to make a hybrid clustered model to find the relational multi stocks. Giving a final verdict, this is a cluster based nonlinear regression multi stock framework in order to predict the time-based trend prediction. The multi stock trend regression accuracy is bettered by 12% and recall by 11% while we cross check the experimental outcomes, henceforth making this model more accurate and precision furnished.
Highlights
Stock markets provide investors with the most profitable avenue to spend their money
Proposed model is compared to the traditional stock market classification models to verify the performance of the hybrid feature selection-based clustering and classification model to the traditional models
Most of the conventional single stock trend prediction models are depend on data size and limited feature space, it is difficult to find a novel feature selection measure on the stock technical data and stock news data
Summary
Stock markets provide investors with the most profitable avenue to spend their money. The trend of the price maybe hiked or inflated at times at any point These common outlines the flow of market trading. The stock price depreciates steadily over a period that may include some brief rises If he follows these signals the investor can be benefited. The risk of the stock increases, this is measured by standard deviation statistic This is the dispersion from what is required of the real. Looking back at our previous contributions, we have developed a single stock trend prediction using the technical and news data in an intraday process. We propose a single stock trend prediction model using the technical and news data in different periodic time intervals. A multi-stock clustering algorithm and classification models are developed in order to predict the periodic multi-stock trend
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