ANN for FOREX Forecasting and Trading

Czekalski, Piotr; Niezabitowski, Michał; Styblinski, Rafal

doi:10.1109/cscs.2015.51

Cited by 15 publications

(6 citation statements)

References 13 publications

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“…In a paper made by piotr (Czekalski et al, 2015) using ANN in making forex predictions. ANN was then developed into RNN and developed again into LSTM.…”

Section: Methodsmentioning

confidence: 99%

“…speed in using the method. Other paper have discussed about forecasting FOREX using deep learning (Czekalski et al, 2015;Korczak & Hemes, 2017;Nagpure, 2019;Sezer et al, 2020). In this paper a comparison of forecasting performance between the two methods will be made where the method uses time series analysis specifically ARIMA and deep learning (LSTM) methods by observing the comparison of the Root Mean Squared Error (RMSE) values of the two methods and the speed of forecasting.…”

Section: A R T I C L E I N F O Abstractmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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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“…In a paper made by piotr (Czekalski et al, 2015) using ANN in making forex predictions. ANN was then developed into RNN and developed again into LSTM.…”

Section: Methodsmentioning

confidence: 99%

Section: A R T I C L E I N F O Abstractmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Putri

Halim

2020

Int. J. Ind. Optim.

View full text Add to dashboard Cite

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“…The market is also the most active financial market, because the number of transactions is very large with continuous operations and trading starts from 22:00 to 17:00 GMT Sunday to 22:00 GMT Friday (New York) [2]. The size of a currency market is up to thirty times the turnover of the stock market owned by the United States where there is approximately 3 trillion dollars in flow of funds every day [3]. Activities in buying and selling currencies are often also called forex trading.…”

Section: Introductionmentioning

confidence: 99%

Foreign exchange prediction based on indices and commodities price using convolutional neural network

Rassetiadi

Suharjito

2020

IJEECS

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The level of accuracy in predicting is the key in conducting forex trading activities in gaining profits. Some predictions are made only by using historical currency data to be predicted, this makes predictions less accurate because they do not consider external influences. This study examines external factors that can influence the results of predictions, by looking for the relationship between the value of indices such as NTFSE and S & P 500 and the value of commodities such as gold and silver to the prediction process of EUR / USD. Prediction carried out using a deep learning algorithm with the Convolutional Neural Network method uses 2 1-dimensional convolution layers with ReL activation. The data used is the value of Open, High, Low and Close prices on forex, indices and commodities which are combined into one with the close forex value target for the next 5 days. Testing of EUR / USD test data only gets MSE results of 0.00081894. While the results of testing of the combined test data between EUR / USD, indices and commodities producing MSE vary between 0.00068717 to 0.0109606 where the best combination is a combination of FTSE 100 and Natural Gas values. So it can be concluded that other factors included in predicting have an influence on the results obtained.

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“…Los estudios empíricos basados en arquitecturas de redes neuronales artificiales (ANN) y aprendizaje profundo (DL) han demostrado que son difíciles de configurar y muy sensibles a los valores de los hiperparámetros [522,530,531,519]. Los autores en [519] mediante la optimización de parámetros investigaron la mejor configuración de las redes neuronales de retropropagacion ANN para predecir los índices del mercado de valores de Lituania.…”

Section: Definición De Hiperparámetrosunclassified

Modelo de análisis multivariante aplicado a la predicción de la tendencia del tipo de cambio euro-dólar

Argotty Erazo

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Predecir los precios y las tendencias de los instrumentos financieros para mejorar la efectividad de las decisiones de inversión es un reto clave para la industria financiera y los agentes implicados. Aunque se han utilizado muchas técnicas eficaces de inteligencia artificial en el análisis de las series de tiempo, el problema de predecir la dirección del movimiento de los tipos de cambio en el mercado Forex aún requiere soluciones parsimoniosas, descifrables y precisas. Desde la perspectiva del análisis técnico, esta investigación presenta una metodología novedosa para clasificar la dirección de la tendencia de los tipos de cambio. La metodología utiliza puntos de inflexión y datos de mercado que miden la acción de los precios, junto con las diferencias multidimensionales entre tendencias, para construir una función lineal discriminante (LDA).La metodología propuesta consta de cinco fases: preparación de datos, selección de características, detección de estructuras subyacentes, formulación de una función discriminante lineal y evaluación del desempeño del modelo con datos dentro y fuera de la muestra. Los experimentos se realizaron con datos de mercado del tipo de cambio euro-dólar en marcos de tiempo de 15 minutos y 1 semana, y una colección de puntos de inflexión del mercado (ET) definidos por un modelo de negociación algorítmico. El período de muestra va desde enero de 1999 hasta abril de 2023.En contraste con algunos trabajos notables publicados en la literatura científica como la Memoria a Corto Plazo (LSTM), el Aprendizaje Profundo por Refuerzo (DRL), el Análisis Wavelet (WA), el Análisis de Sentimiento de Contenido Textual, las Máquinas de Vectores de Soporte (SVM) y los Algoritmos Genéticos (GA), la metodología propuesta logró una precisión de clasificación del 98.77% con datos fuera de muestra. Estos resultados respaldan la construcción de métodos de clasificación interpretables, generalizables, precisos y parsimoniosos, lo que sugiere mejoras significativas en el rendimiento financiero y la reducción del riesgo en las estrategias de negociación. Además, esta metodología es aplicable en la selección de variables y se adapta fácilmente a otros activos financieros.

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ANN for FOREX Forecasting and Trading

Cited by 15 publications

References 13 publications

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

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

Foreign exchange prediction based on indices and commodities price using convolutional neural network

Modelo de análisis multivariante aplicado a la predicción de la tendencia del tipo de cambio euro-dólar

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