A Cluster based Non-Linear Regression Framework for Periodic Multi-Stock Trend Prediction on Real Time Stock Market Data

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.

ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System

“…For implementing aspect-based sentiment analysis, studies have been carried out using rule-based approaches and machine learning techniques [25], [26]. As inspired with the existing studies and approaches in sentiment analysis, in this paper a finegrained procedure for ABSA is proposed.…”

Modelo de análisis multivariante aplicado a la predicción de la tendencia del tipo de cambio euro-dólar

“…El uso combinado de distintos tipos de indicadores como variables de entrada en la formulación de modelos también ha aumentado considerablemente en los últimos años. Los casos más usuales incluyen datos de mercado e indicadores técnicos [389]. Estudios con un enfoque más amplio integran datos de mercado, indicadores técnicos y datos fundamentales [390].…”

“…En particular, cuando se desconocen los principios y méritos de los modelos utilizados en la selección y clasificación. Especialmente cuando se desea mejorar la precisión de las predicciones [389].…”