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Ensemble Stock Market Prediction using SVM,LSTM, and Linear Regression
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  • Amila Indika ,
  • Nethmal Warusamana ,
  • Erantha Welikala ,
  • Sampath Deegalla
Amila Indika
Department of Computer Engineering

Corresponding Author:[email protected]

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Nethmal Warusamana
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Erantha Welikala
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Sampath Deegalla
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Abstract: Stock forecasting is challenging because of stock volatility and dependability on external factors, such as economic, social, and political factors. This motivates investors to seek tools to identify stock trends to reap profits.
In this research, we compared several heterogeneous ensembles for financial forecasting, including averaging, weighted, stacking, and blending ensembles. In addition, we used a random forest regressor as the baseline.
Regression was used to predict the next day’s closing stock price. We used classification to label closing stock value as HIGH or LOW by comparing with the opening stock value of a particular company. We used Long Short Term Memory (LSTM) models, Linear Regression, and Support Vector Machines (SVM) as individual models. Further, we analyzed 10 years of historical data of the most active 20 companies of the NASDAQ stock exchange for implementing ensemble models.
In conclusion, experimental results depict blending ensembles perform the best out of compared ensembles in financial forecasting. Further, they reveal SVM is under-performing, LSTM outputs are satisfactory, while linear regression produced promising results.
Data: Data for this research was gathered from online available sources from the NASDAQ American stock exchange.
We gathered data for most active 20 companies and 10 years of historical data from 21st September 2019 backwards. We used 40044 data points in total.