On Stock Volatility Forecasting Based on Text Mining and Deep Learning Under High Frequency Data

2021 ◽  
Author(s):  
Bolin Lei ◽  
Zhengdi Liu ◽  
Yuping Song
Keyword(s):  
Deep Learning ◽  
Text Mining ◽  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Volatility Forecasting ◽  
Stock Volatility

Fourier volatility forecasting with high-frequency data and microstructure noise

2012 ◽  
Vol 12 (2) ◽  
pp. 281-293 ◽  
Author(s):  
Emilio Barucci ◽  
Davide Magno ◽  
Maria Elvira Mancino
Keyword(s):  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Microstructure Noise ◽  
Volatility Forecasting

scholarly journals Gradient Boosting Machine and Deep Learning Approach in Big Data Analysis

2022 ◽  
Vol 15 (1) ◽  
pp. 1-20
Author(s):  
Ravinder Kumar ◽  
Lokesh Kumar Shrivastav
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Processing ◽  
High Frequency ◽  
Data Science ◽  
Processing System ◽  
High Frequency Data ◽  
Gradient Boosting ◽  
Frequency Data ◽  
Gradient Boosting Machine

Designing a system for analytics of high-frequency data (Big data) is a very challenging and crucial task in data science. Big data analytics involves the development of an efficient machine learning algorithm and big data processing techniques or frameworks. Today, the development of the data processing system is in high demand for processing high-frequency data in a very efficient manner. This paper proposes the processing and analytics of stochastic high-frequency stock market data using a modified version of suitable Gradient Boosting Machine (GBM). The experimental results obtained are compared with deep learning and Auto-Regressive Integrated Moving Average (ARIMA) methods. The results obtained using modified GBM achieves the highest accuracy (R2 = 0.98) and minimum error (RMSE = 0.85) as compared to the other two approaches.

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