Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

Shynkevich, Yauheniya; McGinnity, T.M.; Coleman, Sonya; Belatreche, Ammar

doi:10.1016/j.dss.2016.03.001

Cited by 71 publications

(33 citation statements)

References 27 publications

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“…Moreover, as already asserted, Twitter and other social platforms are prone to noise. On the other hand, web-based news data exhibit less vulnerability to noise and have recently been adopted in several prediction studies [46][47][48]. The strength of these news data is due to real-time online accessibility and rigorous professional editing.…”

Section: Related Workmentioning

confidence: 99%

Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study

Jang¹,

Kim²,

Kim³

2021

JMIR Med Inform

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Background Each year, influenza affects 3 to 5 million people and causes 290,000 to 650,000 fatalities worldwide. To reduce the fatalities caused by influenza, several countries have established influenza surveillance systems to collect early warning data. However, proper and timely warnings are hindered by a 1- to 2-week delay between the actual disease outbreaks and the publication of surveillance data. To address the issue, novel methods for influenza surveillance and prediction using real-time internet data (such as search queries, microblogging, and news) have been proposed. Some of the currently popular approaches extract online data and use machine learning to predict influenza occurrences in a classification mode. However, many of these methods extract training data subjectively, and it is difficult to capture the latent characteristics of the data correctly. There is a critical need to devise new approaches that focus on extracting training data by reflecting the latent characteristics of the data. Objective In this paper, we propose an effective method to extract training data in a manner that reflects the hidden features and improves the performance by filtering and selecting only the keywords related to influenza before the prediction. Methods Although word embedding provides a distributed representation of words by encoding the hidden relationships between various tokens, we enhanced the word embeddings by selecting keywords related to the influenza outbreak and sorting the extracted keywords using the Pearson correlation coefficient in order to solely keep the tokens with high correlation with the actual influenza outbreak. The keyword extraction process was followed by a predictive model based on long short-term memory that predicts the influenza outbreak. To assess the performance of the proposed predictive model, we used and compared a variety of word embedding techniques. Results Word embedding without our proposed sorting process showed 0.8705 prediction accuracy when 50.2 keywords were selected on average. Conversely, word embedding using our proposed sorting process showed 0.8868 prediction accuracy and an improvement in prediction accuracy of 12.6%, although smaller amounts of training data were selected, with only 20.6 keywords on average. Conclusions The sorting stage empowers the embedding process, which improves the feature extraction process because it acts as a knowledge base for the prediction component. The model outperformed other current approaches that use flat extraction before prediction.

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Section: Related Workmentioning

confidence: 99%

Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study

Jang¹,

Kim²,

Kim³

2021

JMIR Med Inform

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“…the financial news published on non-trading day to the latest trading day. We adopt the bag-of-words model [29] to quantize the text news. First, Jieba 5 is used to realize Chinese word segmentation, and 23818 words are obtained after filtering the stop words.…”

Section: B Contrast Data Sources For Forecasting Sz50 1) Sz50 Technimentioning

confidence: 99%

“…Such studies primarily focused on classifying price direction [27], [28]. With the development of the Internet, finance related websites and applications constantly provide a large amount of textual data that increasingly affect market participants future price expectations [29]. [30] selected semantically relevant features to predict stock price movement and thus reduced the overfitting problem.…”

Section: Introductionmentioning

confidence: 99%

Can the Exchange Rate Be Used to Predict the Shanghai Composite Index?

et al. 2020

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Stock index price forecasting is a consistent focus of business intelligence. Various factors influence stock index price forecasting, such as technical indicators, financial news, business status, and the macroeconomics situation. In addition, many studies have shown that the exchange rate is related to the stock index price; however, no study has examined whether the exchange rate can be used to forecast stock index prices. Therefore, this paper focuses on this topic and uses exchange rate to predict China stock index price for the first time. Firstly, we compare the association of China stock index price with different data sources to illustrate the feasibility of using the exchange rate to predict stock index prices. Then, we generate some additional technical features of the exchange rate and propose a strategy to predict the stock index price. Finally, we compare the forecast results of China's stock index price based on four data sources, i.e., technical indicators, exchange rate data, US market index data and finance news data from January 3, 2017 to March 20, 2019. Experimental results demonstrate that the performance of exchange rate data for stock index prediction is comparable to other popular data sources and that, in some prediction periods, the exchange rate outperforms such data sources. The results confirm that the exchange rate could be used for forecasting the Shanghai Composite Index prices. INDEX TERMS Stock index prediction, exchange rate, finance news, technical indicators, SZ50, rolling window.

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“…Other scholars compared artificial neural network (ANN), support vector machine (SVM) and set algorithm based on recurrent neural network and k-means clustering method to predict the price of daily Bitcoin exchange rate. In summary, it is found that the application of machine learning algorithms can significantly improve the accuracy of predictive models [19,21]. Therefore, this paper combines transaction data from multiple markets and social media data and applies a variety of machine learning methods to construct a price prediction model for cryptocurrencies.…”

Section: Introductionmentioning

confidence: 99%

Cryptocurrency Price Prediction based on Multiple Market Sentiment

Wang¹,

Chen²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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With the rapid development of the Internet, cryptocurrencies have been gaining increasing amounts of attention dramatically. As a digital currency, it is not only used worldwide for online payments, but also traded as an investment tool on the market. Therefore, the ability to predict the price volatility will facilitate future investment and payment decisions. However, there are many uncertainties in the price movement of cryptocurrencies, and the prediction is extremely difficult.To this end, based on the transaction data of three different markets and the number and content of user comments and responses from online forums, this paper constructs a price prediction model of cryptocurrencies using a variety of machine learning and deep learning algorithms. It turns out that the trading price premium rate in different markets will affect the price to be predicted, and adding social media comment features can significantly improve the accuracy of the forecast. This article is conducive to investors who encrypt currencies to make more scientific decisions.

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Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

Cited by 71 publications

References 27 publications

Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study

Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study

Can the Exchange Rate Be Used to Predict the Shanghai Composite Index?

Cryptocurrency Price Prediction based on Multiple Market Sentiment

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