Forex Market Prediction Using NARX Neural Network with Bagging

Shahbazi, Nima; Memarzadeh, Masoud; Gryz, Jarek

doi:10.1051/matecconf/20166819001

Cited by 7 publications

(6 citation statements)

References 6 publications

(6 reference statements)

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“…Our partial search of the literature shows that several studies [5,9,10,12,22,28,30,31] have looked at the impact of essential macroeconomic variables such as current account balance, inflation, annual gross domestic product (GDP), growth rate, monetary policy-rate, quasi money supply per GDP, and the total external debt on the exchange rate of the local currency against foreign currencies. As seen in Table 1, a high percentage of these studies used conventional time series models.…”

Section: Related Workmentioning

confidence: 99%

“…Other studies [12,16,24,31,[34][35][36][37] applied soft computing paradigms to examine the predictability of ER as a means of overcoming the deficiencies in the conventional time series models. However, they examined ER predictability based on other macroeconomic variables, leaving enough information hidden in public views.…”

Section: Related Workmentioning

confidence: 99%

“…Subsequently, the adopted models used in [12,38] are prone to long-term dependency problems because they do not have a memory block and cannot store their previous values in a sequence dataset [16]. Therefore, they struggle to learn fluctuating datasets, such as the exchange rate.…”

Section: Related Workmentioning

confidence: 99%

“…(11) where x i = input independent variable, y i = correspondent dependent output, and d = dimension of the input space. The application of the SVR gives a linear regression function as defined in Equation (12).…”

Section: Support Vector Regressormentioning

confidence: 99%

See 3 more Smart Citations

Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi

Adekoya

Nti

Weyori

2021

FinTech

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An accurate prediction of the Exchange Rate (ER) serves as the basis for effective financial management, monetary policies, and long-term strategic decision making worldwide. A stable and competitive ER enables economic diversification. Economists, researchers, and investors have conducted several studies to predict trends and facts that influence the ER’s rise or fall. This paper used the Long Short-Term Memory Networks (LSTM) framework to predict the weekly exchange rate of one Ghanaian Cedis (GH₵) to three different currencies (United States Dollar, British Pound, and Euro), using Google Trends and historical macroeconomic data. We fused past exchange rates, fundamental macroeconomic variables, commodity prices (cocoa, gold, and crude oil) and public search queries (Google Trends) as input parameters. An empirical analysis using publicly available ER data from the Bank of Ghana (BoG) from January 2004 to October 2019 showed satisfactory results. We observed that the proposed LSTM model outperformed the Support Vector Regressor (SVR) and Back-propagation Neural Network (BPNN) models in accuracy and closeness metrics. That is, our LSTM model obtained (MAE = 0.033, MSE = 0.0035, RMSE = 0.0551, R2 = 0.9983, RMSLE = 0.0129 and MAPE = 0.0121) compared with SVR (MAE = 0.05, MAE = 0.005, RMSE = 0.0683, R2 = 0.9973, RMSLE = 0.0191 and MAPE = 0.0241) and BPNN (MAE = 0.04, MAE = 0.0056, RMSE = 0.0688, R2 = 0.9974, RMSLE = 0.0172 and MAPE = 0.0168). Moreover, we observed a strong positive correction (0.98–0.99) between Google Trends on the currency of focus and its exchange rate to the Ghanaian cedis. The study results show the importance of incorporating public search queries from search engines to predict the ER accurately.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Support Vector Regressormentioning

confidence: 99%

See 2 more Smart Citations

Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi

Adekoya

Nti

Weyori

2021

FinTech

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“…Many patents have been led for automatic Forex trading systems [17]- [21]. Also, the Arti cial Intelligence and Machine Learning has been used for price prediction of the Foreign Exchange market [3], [22]- [27].…”

Section: R W a Researchmentioning

confidence: 99%

Constraint-Based Inference of Heuristics for Foreign Exchange Trade Model Optimization

Ivanov,

Yan

2021

Preprint

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e Foreign Exchange (Forex) is a large decentralized market, on which trading analysis and algorithmic trading are popular. Research e orts have been focusing on proof of e ciency of certain technical indicators. We demonstrate, however, that the values of indicator functions are not reproducible and o en reduce the number of trade opportunities, compared to price-action trading.In this work, we develop two dataset-agnostic Forex trading heuristic templates with high rate of trading signals. In order to determine most optimal parameters for the given heuristic prototypes, we perform a machine learning simulation of 10 years of Forex price data over three low-margin instruments and 6 di erent OHLC granularities. As a result, we develop a speci c and reproducible list of most optimal trade parameters found for each instrument-granularity pair, with 118 pips of average daily pro t for the optimized con guration.

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Comparative analysis of naïve bayes and knn on prediction of forex price movements for gbp/usd currency at time frame daily

Pande

Divayana

Indrawan

2021

J. Phys.: Conf. Ser.

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This study aims to analyze the comparison of the Naïve Bayes and kNN on the Prediction of Forex Price Movements for GBP / USD on Time Frame Daily. The data used is taken from the metatrader-4 application which is often used by forex traders when making transactions. There are 2,145 data rows consisting of the date, hour, open price, high, low, close, and transaction volume columns. From this data, a column for the target class is created with the name ‘result’. The result column is filled with increasing or decreasing values. The value of increase or decrease is obtained from the comparison of the previous closing price with the closing price of the next day. This study analyzes the results of the comparison of the data mining classification of algorithm between the Naïve Bayes algorithm and kNN. The 2,145 data were divided into 2 parts, namely 80% for training data and 20% for testing data. The analysis is done by comparing the precision, recall, and accuracy test results for each algorithm. The conclusion of this study is that the kNN algorithm is better than the Naïve Bayes algorithm in case of predicting forex price movements for GBP/USD currency at time frame daily.

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Forex Market Prediction Using NARX Neural Network with Bagging

Cited by 7 publications

References 6 publications

Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi

Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi

Constraint-Based Inference of Heuristics for Foreign Exchange Trade Model Optimization

Comparative analysis of naïve bayes and knn on prediction of forex price movements for gbp/usd currency at time frame daily

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