Forecasting stock market trends using support vector regression and perceptually important points

2020 ◽  
Author(s):  
Mojtaba Azimifar ◽  
Babak N. Araabi ◽  
Hadi Moradi
Keyword(s):  
Stock Market ◽  
Support Vector Regression ◽  
Support Vector ◽  
Market Trends

Support vector regression with chaos-based firefly algorithm for stock market price forecasting

2013 ◽  
Vol 13 (2) ◽  
pp. 947-958 ◽  
Author(s):  
Ahmad Kazem ◽  
Ebrahim Sharifi ◽  
Farookh Khadeer Hussain ◽  
Morteza Saberi ◽  
Omar Khadeer Hussain
Keyword(s):  
Stock Market ◽  
Support Vector Regression ◽  
Firefly Algorithm ◽  
Market Price ◽  
Support Vector ◽  
Price Forecasting

scholarly journals Stock Market Prediction and Risk Analysis using NLP and Machine Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 813-815
Keyword(s):  
Machine Learning ◽  
Risk Analysis ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Support Vector Regression ◽  
Processing Speed ◽  
Storage Systems ◽  
Support Vector ◽  
Expected Return ◽  
Retail Investors

The stock market has been an instrument of investment for more than 200 years now. The price movements in the stock market have been an enigma for many financial analysts and they have tackled this problem with very little success. The phenomenal advancement in technology led to increased storage systems, higher processing speed and better algorithms. Thus, it is more possible now to develop a system for predicting stock markets. People have this taboo that only big investors can profit from the stock market and it is a trap for retail investors or small players. The solution we propose here is easy to understand and implement. We first do the sentiment analysis of the selected stock and then suggest whether to buy, sell or hold. Secondly, we calculate the maximum risk involved in the investment using a threefold approach; market risk, sector risk and stock risk. Finally, using Support vector Regression (SVR) with three different approaches, we calculate expected return and compare them with actual returns.

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