Foreign currency exchange rate prediction using neuro-fuzzy systems

Yong, Yoke Leng; Lee, Yunli; Gu, Xiaowei; Angelov, Plamen; Ngo, David Chek Ling; Shafipour, Elnaz

doi:10.1016/j.procs.2018.10.523

Cited by 19 publications

(5 citation statements)

References 11 publications

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“…A variation of the model named subtractive clustering‐based adaptive neuro‐fuzzy is used for predicting apple stock data prices (Chandar, 2019). The NFS has also been used for predicting future foreign exchange rates (Yong et al, 2018). This study presents the application of ANFIS to predict UAE stock prices for several listed companies.…”

Section: Research Methodology and Analysismentioning

confidence: 99%

Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model

Mohamed

Ahmed

Mehdi

et al. 2021

Intell Sys Acc Fin Mgmt

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Predicting stock price remains one of the challenges for investors' investment strategies. This study helps with accurate prediction and the main factors affecting variations in stock prices. It applies an adaptive neuro-fuzzy model on 58 listed firms from both the Abu Dhabi Securities Exchange and the Dubai Financial Market for the period 2014-2018 to estimate the predictive power of corporate performance measures and their significance. After examining four performance predictors-return on asset (ROA), return on equity (ROE), earning per share (EPS), and profit margin (PM)-the study finds that ROE is the most significant predictor and ROA is the least. EPS is the most influential profitability measure and PM the least.

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Section: Research Methodology and Analysismentioning

confidence: 99%

Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model

Mohamed

Ahmed

Mehdi

et al. 2021

Intell Sys Acc Fin Mgmt

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“…The foreign exchange rates fluctuate complexly as the foreign exchange market would be affected by various external factors, making it not easy to predict the currency exchange rates. Yong et al proposed the Gaussian mixture model initialized neuro fuzzy (GMMINF) model to predict the exchange rates of the Australian dollar, the euro and the British pound against the US dollar, and obtain good prediction results at the closing price [11]. Henryíquez and Kristjanpoller suggested a hybrid model of an independent component analysis in combination with the neural network to predict the exchange rates of the euro, the British pound, the Japanese dollar, the Swiss franc and the Canadian dollar against the US dollar, where the results showed that the accuracy of the proposed hybrid model is higher than other econometric models [12].…”

Section: Stock Market and Foreign Exchange Marketmentioning

confidence: 99%

“…For the exchange rates of the foreign exchange market, this study uses the six foreign currencies are converted to the TWD in terms of the cash exchange rate and the spot exchange rate respectively, thereby a total of 12 variables. According to Yong et al [11] and Baffour et al [35], the exchange rates of the euro, the British pound and the US dollar were selected as the variables. The renminbi (CNY), the Japanese yuan (JPY) and the Hong Kong dollar (HKD) circulating in the regions near Taiwan are also included.…”

Section: Datasets Collectionmentioning

confidence: 99%

Stock and Futures Market Prediction Using Deep Learning Approach

Fan,

Huang,

Chen

2024

Business, Management and Economics

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In recent years, numerous studies have been devoted to predict the price fluctuations of financial markets. Taiwan 50 Exchange Traded Funds (ETF) is one of the important indicators to measure the volatility of the component stocks of the Taiwan 50 Index. With the development of the financial market, the trading volume of Taiwan Stock Index Futures is also increasing. The three markets play the important roles of economic development in the Taiwan. This study predicts the trend of Taiwan 50 ETF and Taiwan index futures applying machine learning and deep learning approaches which have excellent data exploration capabilities. This study applies the support vector regression (SVR), artificial neural networks (ANN), recurrent neural network (RNN), and long short-term memory network (LSTM) to predict the trend of the Taiwan stock market. This study uses various financial and technical factors as inputs, and extract variables from the factors affecting Taiwan’s economy to build models, and compares the benefits between models to explore future market.

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“…Forecasting on FOREX can be done by the method of Statistical Learning (time series analysis), Technical analysis (candle stick), and deep learning (Recurrent Neural Network, LSTM). There are some research about forecasting FOREX with any method such using deep learning (Czekalski et al, 2015;Korczak & Hemes, 2017;Nagpure, 2019;Sezer et al, 2020), ARIMA (Reddy SK, 2015), fuzzy neuron (Reddy SK, 2015) and neuro-fuzzy system (Yong et al, 2018). Forecasting provides factors to be able to predict further whether there will be a bullish or bearish.…”

Section: Introductionmentioning

confidence: 99%

Currency movement forecasting using time series analysis and long short-term memory

Putri

Halim

2020

Int. J. Ind. Optim.

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Foreign exchange is one type of investment, which its goal is to minimize losses that could occur. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using a time series analysis called Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day.

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Foreign currency exchange rate prediction using neuro-fuzzy systems

Cited by 19 publications

References 11 publications

Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model

Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model

Stock and Futures Market Prediction Using Deep Learning Approach

Currency movement forecasting using time series analysis and long short-term memory

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