An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

Yasir, Muhammad; Durrani, Mehr Yahya; Afzal, Sitara; Maqsood, Muazzam; Aadil, Farhan; Mehmood, Irfan; Rho, Seungmin

doi:10.3390/app9152980

Cited by 35 publications

(15 citation statements)

References 54 publications

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“…All the experiments were carried out with Python on AMD processor A8-7410 APU with AMD Radeon R5 Graphics with 8GB RAM. We have used accuracy as an evaluation metric in this research work [25,[28][29][30][31]. The proposed methods are evaluated over 2 datasets under different cooperative and strict covariate conditions.…”

Section: B Results and Discussionmentioning

confidence: 99%

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

et al. 2021

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Section: B Results and Discussionmentioning

confidence: 99%

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

et al. 2021

Self Cite

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“…Other studies were aimed at predicting stock prices given textual information from the financial news [44][45][46]. For instance, Akita et al [44] converted newspaper articles into distributed representations via paragraph vectors and modeled the temporal effects of past events with an LSTM on predicting opening prices of stocks on the Tokyo Stock Exchange.…”

Section: Feature Selection For Stock Price Predictionmentioning

confidence: 99%

A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning

Nguyen

Yoon

2019

Applied Sciences

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Stock price prediction has always been an important application in time series predictions. Recently, deep neural networks have been employed extensively for financial time series tasks. The network typically requires a large amount of training samples to achieve high accuracy. However, in the stock market, the number of data points collected on a daily basis is limited in one year, which leads to insufficient training samples and accordingly results in an overfitting problem. Moreover, predicting stock price movement is affected by various factors in the stock market. Therefore, choosing appropriate input features for prediction models should be taken into account. To address these problems, this paper proposes a novel framework, named deep transfer with related stock information (DTRSI), which takes advantage of a deep neural network and transfer learning. First, a base model using long short-term memory (LSTM) cells is pre-trained based on a large amount of data, which are obtained from a number of different stocks, to optimize initial training parameters. Second, the base model is fine-tuned by using a small amount data from a target stock and different types of input features (constructed based on the relationship between stocks) in order to enhance performance. Experiments are conducted with data from top-five companies in the Korean market and the United States (US) market from 2012 to 2018 in terms of the highest market capitalization. Experimental results demonstrate the effectiveness of transfer learning and using stock relationship information in helping to improve model performance, and the proposed approach shows remarkable performance (compared to other baselines) in terms of prediction accuracy.

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“…The study concluded that Twitter data could be used to predict the direction of exchange rate change. Yasir et al [7] conducted a sentiment analysis using a Twitter dataset and investigated the impact of Twitter on the exchange rate. In this study, both exchange rates and oil and gold prices were examined as numerical data.…”

Section: Literature Reviewmentioning

confidence: 99%

US Dollar/Turkish Lira Exchange Rate Forecasting Model Based on Deep Learning Methodologies and Time Series Analysis

Yasar¹,

Kilimci

2020

Symmetry

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Exchange rate forecasting has been an important topic for investors, researchers, and analysts. In this study, financial sentiment analysis (FSA) and time series analysis (TSA) are proposed to form a predicting model for US Dollar/Turkish Lira exchange rate. For this purpose, the proposed hybrid model is constructed in three stages: obtaining and modeling text data for FSA, obtaining and modeling numerical data for TSA, and blending two models like a symmetry. To our knowledge, this is the first study in the literature that uses social media platforms as a source for FSA and blends them with TSA methods. To perform FSA, word embedding methods Word2vec, GloVe, fastText, and deep learning models such as CNN, RNN, LSTM are used. To the best of our knowledge, this study is the first attempt in terms of performing the FSA by using the combinations of deep learning models with word embedding methods for both Turkish and English texts. For TSA, simple exponential smoothing, Holt–Winters, Holt’s linear, and ARIMA models are employed. Finally, with the usage of the proposed model, any user who wants to make a US Dollar/Turkish Lira exchange rate forecast will be able to make a more consistent and strong exchange rate forecast.

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An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

Cited by 35 publications

References 54 publications

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning

US Dollar/Turkish Lira Exchange Rate Forecasting Model Based on Deep Learning Methodologies and Time Series Analysis

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