Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators

Yıldırım, Deniz Can; Toroslu, İsmail Hakkı; Fiore, Ugo

doi:10.1186/s40854-020-00220-2

Cited by 127 publications

(44 citation statements)

References 43 publications

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“…However, these machine learning methods were proven ineffective when handling large and noisy data (Munkhdalai et al, 2019). In contrast, LSTM-based models were highly effective due to their capacity to capture high-level temporal features from the foreign exchange time-series data (Ahmed et al, 2020;Rundo, 2019;Yıldırım et al, 2021). The main limitation of existing approaches is that only single LSTMs were used without considering both previous and future data patterns, something that is needed to effectively learn long-term dependencies in the data.…”

Section: Related Literaturementioning

confidence: 99%

Deep learning-based exchange rate prediction during the COVID-19 pandemic

et al. 2021

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This study proposes an ensemble deep learning approach that integrates Bagging Ridge (BR) regression with Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks used as base regressors to become a Bi-LSTM BR approach. Bi-LSTM BR was used to predict the exchange rates of 21 currencies against the USD during the pre-COVID-19 and COVID-19 periods. To demonstrate the effectiveness of our proposed model, we compared the prediction performance with several more traditional machine learning algorithms, such as the regression tree, support vector regression, and random forest regression, and deep learning-based algorithms such as LSTM and Bi-LSTM. Our proposed ensemble deep learning approach outperformed the compared models in forecasting exchange rates in terms of prediction error. However, the performance of the model significantly varied during non-COVID-19 and COVID-19 periods across currencies, indicating the essential role of prediction models in periods of highly volatile foreign currency markets. By providing an improved prediction performance and identifying the most seriously affected currencies, this study is beneficial for foreign exchange traders and other stakeholders in that it offers opportunities for potential trading profitability and for reducing the impact of increased currency risk during the pandemic.

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Section: Related Literaturementioning

confidence: 99%

Deep learning-based exchange rate prediction during the COVID-19 pandemic

et al. 2021

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“…AbuHamad et al [6] built a real-time integration of technical and fundamental analysis model in the Forex market. Yıldırım et al [10] proposed Forex directional forecasting using Long-short term memory (LSTM) with FD and TI. First, FD and TI were processed separately in two LSTMs.…”

Section: ) Fd and Ti Aggregation Based Prediction Methodsmentioning

confidence: 99%

“…All previous Forex forecasting models [5]- [8], [10]- [14] can be divided into 2 main categories as follows: This model chooses either FD or TI as input for Forex prediction [5], [7], [8], [11]- [14]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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BERTFOREX: Cascading Model for Forex Market Forecasting Using Fundamental and Technical Indicator Data Based on BERT

2022

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Foreign exchange (Forex) rate forecasting is presently pursued by many researchers as it plays an important role in financial technology and business. The challenge of Forex research lies in its characteristics, fluctuation, non-linearity, and random walk phenomena. Several related studies generate forecasting signals using fundamental data (FD) and technical indicator data (TI) to support Forex. FD is an indicator of country economic conditions, while TI shows the price pattern-based signal. However, these two indicators pose two major limitations on their deployment. First, modeling a sequential neural network causes gradient vanishing and information loss. Second, FD exerts a significant impact on currency price upon its quarterly update and release. The second limitation is known as FD releasing problem. Moreover, Forex forecasting using FD and TI is typically conducted in an equal aggregation manner, resulting in inaccurate prediction due to unequal data changing frequency. In this work, we propose BERTFOREX, a cascading model for Forex time-series forecasting. The proposed technique uses deep learning Bidirectional Encoder Representations from Transformer (BERT) based on FD and TI data characteristics. The technique first applies FD to extract the hidden patterns over the designated period. Then, these extracted hidden patterns of FD are aggregated as additional weights to TI since FD frequency changes slower than that of TI. This yields a combined aggregated pattern of FD and TI. BERT again applies the aggregated pattern to discover underlying patterns within TI and FD over other influencing days. We demonstrate the efficiency of BERTFOREX aggregated representation using a simple neural network in forecasting. The proposed method outperforms other methods in terms of percentage of correct signals, sensitivity, specificity, precision, and negative predictive value.

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“…Among various RNN models most of them were not able to store the past information from their inputs (Yıldırım et al , 2021). LSTM work well for time series forecasting as they have the feature to store the past information.…”

Section: Forecasting Momentum Using Customised Loss Functionmentioning

confidence: 99%

Forecasting the momentum using customised loss function for financial series

Prabakaran

Rajasekaran

Kannadasan

et al. 2021

IJICC

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PurposeWe propose a Machine Learning (ML) approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecasting of financial series. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms. The LSTM Classic will be used to forecast the momentum of the Financial Series Index and also applied to its commodities. The network will be trained and evaluated for accuracy with various sizes of data sets, i.e. weekly historical data of MCX, GOLD, COPPER and the results will be calculated.Design/methodology/approachDesirable LSTM model for script price forecasting from the perspective of minimizing MSE. The approach which we have followed is shown below. (1) Acquire the Dataset. (2) Define your training and testing columns in the dataset. (3) Transform the input value using scalar. (4) Define the custom loss function. (5) Build and Compile the model. (6) Visualise the improvements in results.FindingsFinancial series is one of the very aged techniques where a commerce person would commerce financial scripts, make business and earn some wealth from these companies that vend a part of their business on trading manifesto. Forecasting financial script prices is complex tasks that consider extensive human–computer interaction. Due to the correlated nature of financial series prices, conventional batch processing methods like an artificial neural network, convolutional neural network, cannot be utilised efficiently for financial market analysis. We propose an online learning algorithm that utilises an upgraded of recurrent neural networks called long short-term memory Classic (LSTM). The LSTM Classic is quite different from normal LSTM as it has customised loss function in it. This LSTM Classic avoids long-term dependence on its metrics issues because of its unique internal storage unit structure, and it helps forecast financial time series. Financial Series Index is the combination of various commodities (time series). This makes Financial Index more reliable than the financial time series as it does not show a drastic change in its value even some of its commodities are affected. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.Originality/valueWe had built the customised loss function model by using LSTM scheme and have experimented on MCX index and as well as on its commodities and improvements in results are calculated for every epoch that we run for the whole rows present in the dataset. For every epoch we can visualise the improvements in loss. One more improvement that can be done to our model that the relationship between price difference and directional loss is specific to other financial scripts. Deep evaluations can be done to identify the best combination of these for a particular stock to obtain better results.

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Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators

Cited by 127 publications

References 43 publications

Deep learning-based exchange rate prediction during the COVID-19 pandemic

Deep learning-based exchange rate prediction during the COVID-19 pandemic

BERTFOREX: Cascading Model for Forex Market Forecasting Using Fundamental and Technical Indicator Data Based on BERT

Forecasting the momentum using customised loss function for financial series

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