Journal ArticleDOI
Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest
Manish Kumar,M. Thenmozhi +1 more
TLDR
Empirical experimentation suggests that the SVM outperforms the other classification methods in terms of predicting the direction of the stock market movement and random forest method outperforms neural network, discriminant analysis and logit model used in this study.Abstract:
There exists vast research articles which predict the stock market as well pricing of stock index financial instruments but most of the proposed models focus on the accurate forecasting of the levels (i.e. value) of the underlying stock index. There is a lack of studies examining the predictability of the direction/sign of stock index movement. Given the notion that a prediction with little forecast error does not necessarily translate into capital gain, this study is an attempt to predict the direction of S&P CNX NIFTY Market Index of the National Stock Exchange, one of the fastest growing financial exchanges in developing Asian countries. Random forest and Support Vector Machines (SVM) are very specific type of machine learning method, and are promising tools for the prediction of financial time series. The tested classification models, which predict direction, include linear discriminant analysis, logit, artificial neural network, random forest and SVM. Empirical experimentation suggests that the SVM outperforms the other classification methods in terms of predicting the direction of the stock market movement and random forest method outperforms neural network, discriminant analysis and logit model used in this study.read more
Citations
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Journal ArticleDOI
Navigating Random Forests and related advances in algorithmic modeling
TL;DR: The primary methods by which researchers can visualize results, the relationships between covariates and responses, and the out-of-bag test set error are introduced.
Journal ArticleDOI
Technical analysis and sentiment embeddings for market trend prediction
TL;DR: This work aims to combine both technical and fundamental analysis through the application of data science and machine learning techniques and produces a robust predictive model able to forecast the trend of a portfolio composed by the twenty most capitalized companies listed in the NASDAQ100 index.
Journal ArticleDOI
Prediction of Stock Market Index Movement by Ten Data Mining Techniques
Phichhang Ou,Hengshan Wang +1 more
TL;DR: 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.
Journal ArticleDOI
A comprehensive evaluation of ensemble learning for stock-market prediction
TL;DR: An extensive comparative analysis of ensemble techniques such as boosting, bagging, blending and super learners (stacking) suggests that an innovative study in the domain of stock market direction prediction ought to include ensemble techniques in their sets of algorithms.
Journal ArticleDOI
Prediction of cryptocurrency returns using machine learning
TL;DR: Machine learning classification algorithms reach about 55–65% predictive accuracy on average at the daily or minute level frequencies, while the support vector machines demonstrate the best and consistent results in terms of predictive accuracy compared to the logistic regression, artificial neural networks and random forest classification algorithms.
References
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