Comparison of Stock Price Prediction Models Using News Articles, Currency Exchange Rates and Global Indicator Performance

2020 ◽  
Vol 12 (SP7) ◽  
pp. 1668-1676
Author(s):  
Rakesh Kumar Sharma
Author(s):  
Vijay Kumar Dwivedi ◽  
Manoj Madhava Gore

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.


2014 ◽  
Vol 50 (2) ◽  
pp. 426-441 ◽  
Author(s):  
Enric Junqué de Fortuny ◽  
Tom De Smedt ◽  
David Martens ◽  
Walter Daelemans

2020 ◽  
Vol 10 (5) ◽  
pp. 1597 ◽  
Author(s):  
Yoojeong Song ◽  
Jongwoo Lee

In Korea, because of the high interest in stock investment, many researchers have attempted to predict stock prices using deep learning. Studies to predict stock prices have been continuously conducted. However, the type of stock data that is suitable for deep learning has not been established, and it has not been confirmed that the developed stock prediction model can actually result in a profit. To date, designing a good deep learning model depends on how well the user can extract the features that represent all the characteristics of the training data. Among the various available features for training and test data, we determined that the use of event binary features can make stock price prediction models perform better. An event binary feature refers to a 0 or 1 value describing whether an indicator is satisfied (1) or not (0) for any given day and stock. We proposed and compared a stock price prediction model with three different feature combinations to verify the importance of binary features. As a result, we derived a prediction model that defeated the market (KOSPI and KODAQ (KOSPI (Korea Composite Stock Price Index) and KOSDAQ (Korean Securities Dealers Automated Quotations) is Korean stock indices)). The results suggest that deep learning is suitable for stock price prediction.


2004 ◽  
Vol 2 (2) ◽  
pp. 207
Author(s):  
Vinicius Ratton Brandi ◽  
Beatriz Vaz de Melo Mendes

The investigation of the stochastic behavior of financial series has become widespread over the literature. There is empirical and theoretical evidence that the total stock price change over a long period is usually concentrated in the a few hectic runs of trading days. The drawdown is a random variable which provides information on alternative characteristics of market behavior during these periods. In this work, we use distributions from extreme value theory to model the severity of drawdowns and drawups. We illustrate using nine currency exchange rates and gold.


Author(s):  
Marwa Sharaf ◽  
Ezz El-Din Hemdan ◽  
Ayman El-Sayed ◽  
Nirmeen A. El-Bahnasawy

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