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Journal ArticleDOI

Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest

TL;DR: 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.
Citations
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Book
01 Jan 1981

704 citations

Journal ArticleDOI
TL;DR: This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index, finding that average performance of ANN model was found significantly better than that of SVM model.
Abstract: Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).

701 citations


Cites background or methods or result from "Forecasting Stock Index Movement: A..."

  • ...It ensures that the larger value input attributes do not overwhelm smaller value inputs, and helps to reduce prediction errors (Kim, 2003; Manish & Thenmozhi, 2005)....

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  • ...We selected ten technical indicators as feature subsets by the review of domain experts and prior researches (Armano et al., 2005; Diler, 2003; Huang & Tsai, 2009; Kim, 2003; Kim & Han, 2000; Manish & Thenmozhi, 2005; Yao, Chew, & Poh, 1999)....

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  • ...ANN often exhibit inconsistent and unpredictable performance on noisy data (Kim, 2003; Kim & Han, 2000; Manish & Thenmozhi, 2005)....

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  • ...Thus, investors can hedge against potential market risks and speculators and arbitrageurs have opportunities to make profit by trading in stock index (Manish & Thenmozhi, 2005)....

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  • ...Manish and Thenmozhi (2005) used SVM and random forest to predict the daily movement of direction of S&P CNX NIFTY Market Index of the National Stock Exchange and compared the results with those of the traditional discriminant and logit models and ANN....

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Journal ArticleDOI
TL;DR: The results indicate that Random Forest is the top algorithm followed by Support Vector Machines, Kernel Factory, AdaBoost, Neural Networks, K-Nearest Neighbors and Logistic Regression in the domain of stock price direction prediction.
Abstract: We predict long term stock price direction.We benchmark three ensemble methods against four single classifiers.We use five times twofold cross-validation and AUC as a performance measure.Random Forest is the top algorithm.This study is the first to make such an extensive benchmark in this domain. Stock price direction prediction is an important issue in the financial world. Even small improvements in predictive performance can be very profitable. The purpose of this paper is to benchmark ensemble methods (Random Forest, AdaBoost and Kernel Factory) against single classifier models (Neural Networks, Logistic Regression, Support Vector Machines and K-Nearest Neighbor). We gathered data from 5767 publicly listed European companies and used the area under the receiver operating characteristic curve (AUC) as a performance measure. Our predictions are one year ahead. The results indicate that Random Forest is the top algorithm followed by Support Vector Machines, Kernel Factory, AdaBoost, Neural Networks, K-Nearest Neighbors and Logistic Regression. This study contributes to literature in that it is, to the best of our knowledge, the first to make such an extensive benchmark. The results clearly suggest that novel studies in the domain of stock price direction prediction should include ensembles in their sets of algorithms. Our extensive literature review evidently indicates that this is currently not the case.

368 citations

Journal ArticleDOI
TL;DR: Artificial Neural Network and Random Forest techniques have been utilized for predicting the next day closing price for five companies belonging to different sectors of operation and show that the models are efficient in predicting stock closing price.

218 citations

Journal ArticleDOI
TL;DR: Support vector machine and artificial neural network were found to be the most used machine learning algorithms for stock market prediction.
Abstract: The stock market is a key pivot in every growing and thriving economy, and every investment in the market is aimed at maximising profit and minimising associated risk. As a result, numerous studies have been conducted on the stock-market prediction using technical or fundamental analysis through various soft-computing techniques and algorithms. This study attempted to undertake a systematic and critical review of about one hundred and twenty-two (122) pertinent research works reported in academic journals over 11 years (2007–2018) in the area of stock market prediction using machine learning. The various techniques identified from these reports were clustered into three categories, namely technical, fundamental, and combined analyses. The grouping was done based on the following criteria: the nature of a dataset and the number of data sources used, the data timeframe, the machine learning algorithms used, machine learning task, used accuracy and error metrics and software packages used for modelling. The results revealed that 66% of documents reviewed were based on technical analysis; whiles 23% and 11% were based on fundamental analysis and combined analyses, respectively. Concerning the number of data source, 89.34% of documents reviewed, used single sources; whiles 8.2% and 2.46% used two and three sources respectively. Support vector machine and artificial neural network were found to be the most used machine learning algorithms for stock market prediction.

171 citations

References
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01 Jan 1998
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Abstract: A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

26,531 citations

Journal ArticleDOI
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.

18,794 citations


"Forecasting Stock Index Movement: A..." refers background in this paper

  • ...Hornik et al. (1989) in their theoretical work found that single hidden layer is sufficient for the network to approximate any complex non-linear function with any desired accuracy....

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Journal ArticleDOI
TL;DR: For example, this paper found that expected returns on common stocks and long-term bonds contain a term or maturity premium that has a clear business-cycle pattern (low near peaks, high near troughs).

4,110 citations


"Forecasting Stock Index Movement: A..." refers methods in this paper

  • ...Using time-series analysis, Fama and Schwert (1977), Rozeff (1984), Keim and Stambaugh (1986), Campbell (1987), Fama and Bliss (1987), and Fama and French (1988, 1988, 1990) found out that macroeconomic variables such as short-term interest rates, expected inflation, dividend yields, yield spreads…...

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Journal ArticleDOI
TL;DR: In this article, the power of dividend yields to forecast stock returns, measured by regression R2, increases with the return horizon, and the authors offer a two-part explanation: high autocorrelation causes the variance of expected returns to grow faster than the return-horizon.

3,811 citations


"Forecasting Stock Index Movement: A..." refers background or methods or result in this paper

  • ...(1989), and Fama and French (1992) are good examples of this group of research. Further, studies based on European markets report similar findings. The results of Ferson and Harvey (1993) indicate that returns are, to a certain extent, predictable across a number of European markets (e....

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  • ...(1989), and Fama and French (1992) are good examples of this group of research. Further, studies based on European markets report similar findings. The results of Ferson and Harvey (1993) indicate that returns are, to a certain extent, predictable across a number of European markets (e.g., UK, France, Germany). In their study which is aimed at forecasting the UK stock prices, Jung and Boyd (1996) report “reasonably good” performance of their forecasts, suggesting that the predictive strength of their stock return models are not negligible. For the Japanese stock market, the empirical investigations by Jaffe and Westerfield (1985) and Kato et al. (1990) also find some evidence of predictability in the behavior of index returns....

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  • ...(1989), and Fama and French (1992) are good examples of this group of research. Further, studies based on European markets report similar findings. The results of Ferson and Harvey (1993) indicate that returns are, to a certain extent, predictable across a number of European markets (e.g., UK, France, Germany). In their study which is aimed at forecasting the UK stock prices, Jung and Boyd (1996) report “reasonably good” performance of their forecasts, suggesting that the predictive strength of their stock return models are not negligible....

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  • ...Using time-series analysis, Fama and Schwert (1977), Rozeff (1984), Keim and Stambaugh (1986), Campbell (1987), Fama and Bliss (1987), and Fama and French (1988, 1988, 1990) found out that macroeconomic variables such as short-term interest rates, expected inflation, dividend yields, yield spreads…...

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  • ...(1989), and Fama and French (1992) are good examples of this group of research....

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Journal ArticleDOI
TL;DR: This article found that a slowly mean-reverting component of stock prices tends to induce negative autocorrelation in returns, which is weak for the daily and weekly holding periods common in market efficiency tests but stronger for long-horizon returns.
Abstract: A slowly mean-reverting component of stock prices tends to induce negative autocorrelation in returns. The autocorrelation is weak for the daily and weekly holding periods common in market efficiency tests but stronger for long-horizon returns. In tests for the 1926-85 period, large negative autocorrelations for return horizons beyond a year suggest that predictable price variation due to mean reversion accounts for large fractions of 3-5-year return variances. Predictable variation is estimated to be about 40 percent of 3-5-year return variances for portfolios of small firms. The percentage falls to around 25 percent for portfolios of large firms.

3,015 citations


"Forecasting Stock Index Movement: A..." refers methods in this paper

  • ...Using time-series analysis, Fama and Schwert (1977), Rozeff (1984), Keim and Stambaugh (1986), Campbell (1987), Fama and Bliss (1987), and Fama and French (1988, 1988, 1990) found out that macroeconomic variables such as short-term interest rates, expected inflation, dividend yields, yield spreads…...

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