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Figure 8 b presents the resulting structure. A hyperparameter is a parameter that has a significant impact on the learning process. Maximizing model performance by finding optimal hyperparameter values to minimize a loss function is called hyperparameter optimization.
This method is widely used in machine learning and deep learning. In this study, the well-known grid search method was adopted. A grid search finds the best parameters among a parameter set defined by a user and applies several parameter candidates to the model sequentially to identify the cases with the best performance.
If there are few parameter candidates, optimal values can be obtained rapidly. However, if there are many candidates, optimization requires exponentially more time. In this study, we adopted the grid search algorithm because it is the simplest and most widely used algorithm for obtaining optimal hyperparameters Schilling et al. Although a random search can perform much better than a grid search on high-dimensional problems according to Hutter et al. These are the main reasons why we adopted the grid search algorithm Sun et al.
The Python technological stack was used for our experiments. We implemented the machine learning algorithms and grid search using the Scikit Learn, Keras, and TensorFlow packages. We used a grid search to identify and apply optimal parameters for each section of our model. The optimized parameters are the batch size, activation function, and optimizer function.
Two or three candidate groups were defined for each parameter. More parameters and candidate groups could be defined, but it would increase training time significantly. We divided the data into three intervals and attempted to compare two models, thereby limiting the candidate groups to make the most of our limited resources. Next, we optimized three parameters for stochastic gradient descent. The candidate batch sizes were 50 and , the activation functions were linear and ReLU, and the optimization functions were Adam, rmsprop, and nadam.
The learning rates were default values built into each activation function sprosprop: 0. Finally, the autoencoder and autoencoder-LSTM models were unified into four layers: two encoding layers and two decoding layers. Based on the small amount of testing data, this small depth was determined to be sufficient. We used the aforementioned grid search to find optimal parameter combinations. Among a total of 12 parameter combinations, the best parameters were identified and six optimizations were performed for the two models LSTM and autoencoder-LSTM and three periods in the same manner.
The results obtained via hyperparameter optimization are listed in Table 3. We compare the forecasting performances of our models in terms of distributions and outliers. To this end, the forecasting results are split by period and separated by index.
Additionally, distributions were defined by variances and standard deviations. To identify extreme outliers, multiplication by 1. Our main findings can be summarized as follows. First, the opportunities to learn volatility and forecast accuracy have a proportional relationship. In other words, there are many sections that rise and fall in the training data and learning these trends can improve prediction accuracy.
As shown in Figure 9 , the distribution is broad and there are many outliers in the order of a outliers in Period 1, c outliers in Period 3, and b outliers in Period 2. In situations where variance and outliers exist in moderation, the LSTM model using an autoencoder, which can derive the features of inputs accurately, performs better than the model without an autoencoder.
Third, among the deep learning methods, the autoencoder-LSTM exhibits the best prediction performance. Visual graphs of this trend are presented in Figures 10 — The goal of this study was to develop a hybrid model based on deep learning models for forecasting FX volatility. Therefore, this study is meaningful because the FXVIX, which is related to the US and the global economy, sensitively reflects international economic trends.
Data-driven methods are more powerful than model-driven methods for forecasting asset price time-series data see Kim et al. In this study, we investigated how event-driven data, which focus on events such as outliers in data-driven analysis, contribute to model performance. According to Shahid et al. Because there is only one type of outlier in the data considered in this study, comparing differences in model performance accordingly is meaningful.
Our empirical results provide several interesting conclusions with useful practical implications. First, the spread of data and presence of outliers increase the accuracy of forecasting performance of the proposed model. Based on the empirical findings in Section 4 , some implications can be observed. First, because the neural network model is a model created by mimicking the human brain, the data to be learned are important.
As shown in this study, the forecasting accuracy of the hybrid model is affected by the number of cases for which variability and outliers can be learned. Next, the use of an autoencoder, which can transform important properties of input data, similar to principal component analysis, is meaningful.
Autoencoders are used for denoising images, watermark removal, dimensionality reduction, and feature variation among other tasks. In this study, we conceived the concept of feature variation. Additionally, several studies using autoencoders to predict time series have been recently published Gensler et al. Our study contributes to the literature by introducing a new approach called the autoencoder-LSTM for forecasting time series.
In practice, our findings can be helpful to researchers in economic research laboratories or policy managers who determine national economic policies because FXVIXs reveal important trends for FX that impact the global economy and volatility, meaning they can reveal market participant psychology. For example, Menkhoff et al. Guo et al. Additionally, we expect that we can improve prediction accuracy by learning and incorporating data that can affect each index based on the results of this study, where we only considered FXVIXs.
Additionally, hyperparameter optimization was performed using only a grid search, which is a commonly used machine learning algorithm, but we could increase the reliability of prediction by considering additional optimization algorithms. The data used to support the findings of this study are available from the corresponding author upon request.
The work of S. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors.
Read the winning articles. Journal overview. Special Issues. Academic Editor: Benjamin Miranda Tabak. Received 28 Dec Revised 30 Jan Accepted 20 Mar Published 31 Mar Abstract Since the breakdown of the Bretton Woods system in the early s, the foreign exchange FX market has become an important focus of both academic and practical research.
Introduction Among various financial asset markets, the foreign exchange FX market has become increasingly volatile and fluid over the past decade. Literature Review There is a vast body of literature on forecasting financial time series. Data Description and Methodologies 3. Figure 1. Table 1.
Figure 2. Figure 3. Table 2. Descriptions of the training and testing datasets for each period. Figure 4. Figure 5. Figure 6. Figure 7. Figure 8. Table 3. Figure 9. Table 4. Table 5. Table 6. Figure References W. Huang, K. Lai, Y. Nakamori, and S. Vasilellis and N. Knopf, J. Nam, and J. Brownlees and G. Gallo and E. Bollerslev, B. Hood, J. Huss, and L. Vee, P. Gonpot, and S.
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Liu, H. Yeung, J. Yin, Y. Chung, and X. Sun, W. Rong, J. Zhang, Q. Your Money. Personal Finance. Your Practice. Popular Courses. What Is Forex Forecasting Software? Key Takeaways Forex forecasting software refers to computer-based technical analysis software geared to currency markets.
The goal is to automate identification of technical indicators or chart patterns across a range of currency pairs in order to identify trade entry and exit points. In addition to technical analysis tools, macroeconomic data may be incorporated, combining both bottom-up and top-down indicators. Several platforms exist, many offering free demos to potential users to try them before you buy them. Compare Accounts. The offers that appear in this table are from partnerships from which Investopedia receives compensation.
This compensation may impact how and where listings appear. Investopedia does not include all offers available in the marketplace. A forex chart graphically depicts the historical behavior, across varying time frames, of the relative price movement between two currency pairs. Forex Charting Software Forex charting software helps traders analyze foreign currency pairs price trends, enabling them to make informed trading decisions.
Currency Trading Platform A currency or forex trading platform is a type of trading platform used to help currency traders with forex trading analysis and trade execution. Technical Analysis of Stocks and Trends Technical analysis of stocks and trends is the study of historical market data, including price and volume, to predict future market behavior. Trading Software Definition Trading software facilitates the trading and analysis of financial products, such as stocks or currencies.
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Forex Strategies Resources is a site continuously updated forex resources are many.! Note:This is not a newspaper site, informative, or a site of comments on financial news, but just a place where are shared ideas for trading and mathematical algorithms. Video Youtube. Projections Future.
Installation Renko Chart on MT4. MT4 Indicators. MT5 indicators. Expert Advisors. Blue-Red Forex Strategy. Traders Dynamic Index How to use. Money Manager EA. MM Masaniello. Radar signal update. Elliott Wave indicators update. Median Renko Scalper. When an individual trader uses them together, it can provide them with useful and indispensable information about the movement of currency trends. Learning how to make Forex predictions is hard and takes time, but having that extra knowledge will prove to be invaluable in your Forex career.
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It is quite a challenging task to generate a forecast of good quality, but we will describe four methods of doing so based on a level of high proficiency. This method is perhaps the most popular one due to its inclusion in economic textbooks. The PPP forecasting technique is rooted in the theoretical 'Law of One Price', which in fact states that identical goods in various countries should have identical prices.
That also implies that there should not be any arbitrage opportunities for someone to buy something cheap in one country, and then sell it in another in order to gain profit. Based on this principle, the PPP approach of forecasting Forex predicts that the exchange rate will change to counteract changes in prices, and this is due to inflation. In turn, this suggests that prices in the US are anticipated to rise faster in comparison to prices in Canada. This approach looks at the power of economic growth within various countries, in order to make a currency market forecast concerning the direction of exchange rates.
The logic behind this approach is that a powerful economic environment and high growth has a bigger likelihood of attracting foreign investors. Therefore, in order to purchase investments in the yearned country, an investor would have to purchase the country's currency.
This creates an increased demand that should eventually cause the currency to appreciate. The same will happen due to another factor that may draw the investors' attention - interest rates. High interest rates will undoubtedly attract investors looking for the highest yield on their investments, causing demand for the currency to increase.
On the other hand, low interest rates may result in investors avoiding investing in a country, or alternatively borrowing the currency of the country with low interest rates, to fund other investments. If we compare this approach to PPP, relative economic strength does not forecast the actual position of the exchange rate, but instead, provides a general sense of the currency's behaviour appreciate or depreciate , and the overall feel for the movement's strength.
The next method of currency market forecasts involves gathering factors that you anticipate to affect the movement of a particular currency, and then creating a model that relates those factors to the exchange rate. The factors applied in econometric models are usually based on economic theory, however, any variable can be added if it is thought to considerably influence the exchange rate.
The last method we will present to you is the time series model. This approach is entirely technical in nature, and is not formed on any economic theory. One of the time series sub-approaches is the autoregressive moving average process.
The reason for utilising this method is based on the idea of using past behaviour data and price patterns to predict future price behaviour. We have discussed Forex trading forecasting and the main techniques to used by professional traders. We have also exemplified the methods of forecasting the direction of exchange rates.
As you can see, the application of certain techniques requires complete understanding, and certain trading skills. Not every technique will be suitable for everyone - it is a subjective matter. For novices, forecasting can be a tedious task - especially in the early stages of their career - but it is worth doing, as the benefits have the potential to improve profitability.
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Start trading today! This material does not contain and should not be construed as containing investment advice, investment recommendations, an offer of or solicitation for any transactions in financial instruments. Please note that such trading analysis is not a reliable indicator for any current or future performance, as circumstances may change over time. Before making any investment decisions, you should seek advice from independent financial advisors to ensure you understand the risks.
Contact us. Start Trading. Personal Finance New Admirals Wallet. About Us. Rebranding Why Us? Login Register. Top search terms: Create an account, Mobile application, Invest account, Web trader platform. What is Forex Forecasting? Overview of the Main Methods There are a number of methods available to a trader when forecasting the Forex market. Methods of Forecasting Although these methods differ, each one can help Forex traders to understand how rates are affecting the trade of a certain currency.
Free Trading Webinars With Admiral Markets If you're just starting out with Forex trading, or if you're looking for new ideas, our FREE trading webinars are the best place to learn from professional trading experts. The Ways of Forecasting Currency Changes We would like to show you how you can forecast the Forex market by exemplifying Forex forecasting methods.
Relative Economic Strength This approach looks at the power of economic growth within various countries, in order to make a currency market forecast concerning the direction of exchange rates. Econometric Models The next method of currency market forecasts involves gathering factors that you anticipate to affect the movement of a particular currency, and then creating a model that relates those factors to the exchange rate.
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Later, the market may then start a new decline towards 1. Later, the market may correct to correct towards 1. Later, the market may correct to return to 1. Later, the market may grow to return to 1. If later the price breaks this range to the downside, the market may form a new descending structure with the target at 1. Later, the market may form a new descending structure to return to 1. Later, the market may start a new correction towards 1. After that, the instrument may start a new correction to return to 1.
After that, the instrument may start a new correction with the target at 1. Possibly, the pair may expand the range down to 1. After that, the instrument may correct to return to 1. After that, the instrument may correct to reach 1. Possibly, the pair may expand the range up to 1. After that, the instrument may resume trading downwards with the target at 1.
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