Corpus ID: 236469182

Combining Machine Learning Classifiers for Stock Trading with Effective Feature Extraction

@article{Ullah2021CombiningML,
  title={Combining Machine Learning Classifiers for Stock Trading with Effective Feature Extraction},
  author={Amin Ullah and F. Imtiaz and Miftah Uddin Md Ihsan and Md. Golam Rabiul Alam and Mahbub Alam Majumdar},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.13148}
}
The unpredictability and volatility of the stock market render it challenging to make a substantial profit using any generalized scheme. This paper intends to discuss our machine learning model, which can make a significant amount of profit in the US stock market by performing live trading in the Quantopian platform while using resources free of cost. Our top approach was to use ensemble learning with four classifiers: Gaussian Naive Bayes, Decision Tree, Logistic Regression with L1… Expand

References

SHOWING 1-10 OF 37 REFERENCES
Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques
TLDR
Experimental results show that the performance of all the prediction models improve when these technical parameters are represented as trend deterministic data, and random forest outperforms other three prediction models on overall performance. Expand
Predicting stock returns by classifier ensembles
TLDR
This study aims at investigating the prediction performance that utilizes the classifier ensembles method to analyze stock returns and indicates that multiple classifiers outperform single classifiers in terms of prediction accuracy and returns on investment. Expand
Automated Stock Trading Using Machine Learning
Predicting stock market movements is a well-known problem of interest. Now-a- days social media is perfectly representing the public sentiment and opinion about current events. Especially, TwitterExpand
Prediction of Stock Market Index Movement by Ten Data Mining Techniques
TLDR
Ten different techniques of data mining are discussed and applied to predict price movement of Hang Seng index of Hong Kong stock market and experimental results show that the SVM and LS-SVM generate superior predictive performances among the other models. Expand
Predicting Stock Price Direction using Support Vector Machines
Support Vector Machine is a machine learning technique used in recent studies to forecast stock prices. This study uses daily closing prices for 34 technology stocks to calculate price volatility andExpand
Using AI to Make Predictions on Stock Market
In the world of finance, stock trading is one of the most important activities. Professional traders have developed a variety of analysis methods such as fundamental analysis, technical analysis,Expand
Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index
TLDR
Empirical results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study. Expand
A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression
TLDR
The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model. Expand
Application of Deep Learning to Algorithmic Trading
Deep Learning has been proven to be a powerful machine learning tool in recent years, and it has a wide variety of applications. However, applications of deep learning in the field of computationalExpand
Fluctuation prediction of stock market index by Legendre neural network with random time strength function
  • F. Liu, Jun Wang
  • Mathematics, Computer Science
  • Neurocomputing
  • 2012
TLDR
This paper investigates and forecast the price fluctuation by an improved Legendre neural network and tests the predictive effect of SAI, SBI, DJI and IXIC in the established model, and the corresponding statistical comparisons of the above market indexes are exhibited. Expand
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