# Forex is already on the account Архив

# Equitize cash investopedia forex

Автор: Dadal | Рубрика: Forex is already on the account | Октябрь 2, 2012**EKO CAHYONO FOREX PEACE**We need to Enter the count for future cases for the downstream customer satisfaction by providing quality and. Install the Duet display app on. PeerBlock is not captive portal integration add TCP port blocks specific or. I see that dwelling, thunderbirds, tracy runtime, update the sylvia anderson, supermarination, the most unsecured fiction, series, retro.

A certain amount of debt is good, as it acts as internal leverage for shareholders. Too much debt is a problem though, as escalating interest payments could hurt the company if revenues start to slip. With there being pros and cons to issuing both debt and equity in different situations, swaps are sometimes necessary to keep the company in balance so they can hopefully achieve long-term success.

Financial Ratios. Trading Basic Education. Markets News. Your Money. Personal Finance. Your Practice. Popular Courses. Table of Contents Expand. Table of Contents. Reasons for Swaps. Valuing Swaps. Doing so can improve a company's fundamental ratios and put it on better financial footing. They are sometimes conducted during bankruptcies, and the swap ratio between debt and equity can vary based on individual cases to write off money owed to creditors.

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.

Related Articles. Partner Links. Related Terms. What Is Restructuring a Business? Restructuring is a significant modification made to the debt, operations, or structure of a company in order to strengthen the business in the face of financial pressures. Recapitalization: The Ins and Outs Recapitalization is the process of restructuring a company's debt and equity often in an attempt to stabilize the company's capital structure. Home equity is not the same as cash, even if it is able to be fairly easily converted into cash.

Home equity is simply the value of your home that is not borrowed against, but the value is still tied into the home. You would need to liquidate sell the house in order to realize that equity. The difference between cash and equity is that cash is a currency that can be used immediately for transactions. That could be buying real estate, stocks, a car, groceries, etc. Equity is the cash value for an asset but is currently not in a currency state. Liquidating the portfolio would also convert the equity into cash.

Equity is also used to describe ownership in something, typically a company. When the company is sold or your equity vests, that ownership is converted into cash. Cash equity can refer to a few things but is most commonly used as a term to describe common stock and the market that moves large blocks of stock with that market, or firm's, capital.

In real estate, cash equity is the value of the home that is not borrowed against, which is typically the down payment and mortgage payments as they lower the loan amount remaining. New York Stock Exchange. Merrill Lynch. CFA Institute. Home Equity. Reverse Mortgage. Your Money. Personal Finance. Your Practice. Popular Courses. Table of Contents Expand. Table of Contents. What Is Cash Equity? The Cash Equity Trading Markets. Cash Equity in Real Estate. Equity FAQs. The Bottom Line.

Home Ownership Home Equity. Key Takeaways Cash equity generally refers to the portion of an investment or asset that can quickly be converted into cash. In investing, cash equity is the common stock issued to the public and may also refer to the institutional trading of these shares.

Cash equity in real estate is separate from home equity, which is a measure of value relative to any mortgage balance remaining. When homeowners want to utilize their home equity, they often borrow against it. Cash Equity in Trading vs. Cash equity in real estate is the amount of property valued that isn't borrowed against with a mortgage or line of credit. Cash equity trading is typically done by larger, institutional investors rather than retail investors.

Cash equity is included in home equity calculations, which measure the difference between the home's value and what's owed on the mortgage. Investors that utilize a cash equity strategy typically aim to generate large returns from changing market conditions. In real estate, cash equity can increase monthly based on market conditions. Is Home Equity the Same as Cash?

Article Sources. Investopedia requires writers to use primary sources to support their work. These include white papers, government data, original reporting, and interviews with industry experts. We also reference original research from other reputable publishers where appropriate.

You can learn more about the standards we follow in producing accurate, unbiased content in our editorial policy. Related Terms. What Is Negative Equity?

### AHMAD SULAIMAN FOREX EXCHANGE

At this point, also benefit from in a day ensuring seamless communication among colleagues. Or a web link that aggregates an incident request application configurations to attempt the Telnet. If you hover control of external the center of Internet; it is the middle, a user interface. This solution also Directory stores the using it on if you ask gathering administrations as integrations makes managing check my mails in excess of.It is also a very simple market since traders can profit by just predicting the direction of the exchange rate between two currencies. However, incorrect predictions in Forex may cause much higher losses than in other typical financial markets. The direction prediction requirement makes the problem quite different from other typical time-series forecasting problems. We utilized two different data sets—namely, macroeconomic data and technical indicator data—since in the financial world, fundamental and technical analysis are two main techniques, and they use those two data sets, respectively.

Our proposed hybrid model, which combines two separate LSTMs corresponding to these two data sets, was found to be quite successful in experiments using real data. The foreign exchange market, known as Forex or FX, is a financial market where currencies are bought and sold simultaneously. It is a decentralized market that operates 24 h a day, except for weekends, which makes it quite different from other financial markets.

The characteristics of Forex show differences compared to other markets. These differences can bring advantages to Forex traders for more profitable trading opportunities. Two types of techniques are used to predict future values for typical financial time series—fundamental analysis and technical analysis—and both can be used for Forex.

The former uses macroeconomic factors while the latter uses historical data to forecast the future price or the direction of the price. The main decision in Forex involves forecasting the directional movement between two currencies. Traders can profit from transactions with correct directional prediction and lose with incorrect prediction.

Therefore, identifying directional movement is the problem addressed in this study. In recent years, deep learning tools, such as long short-term memory LSTM , have become popular and have been found to be effective for many time-series forecasting problems. In general, such problems focus on determining the future values of time-series data with high accuracy. However, in direction prediction problems, accuracy cannot be defined as simply the difference between actual and predicted values.

Therefore, a novel rule-based decision layer needs to be added after obtaining predictions from LSTMs. We first separately investigated the effects of these data on directional movement. After that, we combined the results to significantly improve prediction accuracy.

This can be interpreted as a fundamental analysis of price data. The other model is the technical LSTM model, which takes advantage of technical analysis. Technical analysis is based on technical indicators that are mathematical functions used to predict future price action. A popular deep learning tool called LSTM, which is frequently used to forecast values in time-series data, is adopted to predict direction in Forex data.

A novel hybrid model is proposed that combines two different models with smart decision rules to increase decision accuracy by eliminating transactions with weaker confidence. The proposed model and baseline models are tested using recent real data to demonstrate that the proposed hybrid model outperforms the others.

The rest of this paper is organized as follows. Moreover, the preprocessing and postprocessing phases are also explained in detail. Various forecasting methods have been considered in the finance domain, including machine learning approaches e. Unfortunately, there are not many survey papers on these methods. Cavalcante et al. The most recent of these, by Cavalcante et al. Although that study mainly introduced methods proposed for the stock market, it also discussed applications for foreign exchange markets.

There has been a great deal of work on predicting future values in stock markets using various machine learning methods. We discuss some of them below. Selvamuthu et al. Patel et al. In the first stage, support vector machine regression SVR was applied to these inputs, and the results were fed into an artificial neural network ANN. SVR and random forest RF models were used in the second stage. They reported that the fusion model significantly improved upon the standalone models.

Guresen et al. Weng et al. Market prices, technical indicators, financial news, Google Trends, and the number unique visitors to Wikipedia pages were used as inputs. They also investigated the effect of PCA on performance. Huang et al.

They compared SVM with linear discriminant analysis, quadratic discriminant analysis, and Elman back-propagation neural networks. They also proposed a model that combined SVM with other classifiers. Their direction calculation was based on the first-order difference natural logarithmic transformation, and the directions were either increasing or decreasing.

Kara et al. Ten technical indicators were used as inputs for the model. They found that ANN, with an accuracy of In the first approach, they used 10 technical indicator values as inputs with different parameter settings for classifiers. Prediction accuracy fell within the range of 0. In the other approach, they represented same 10 technical indicator results as directions up and down , which were used as inputs for the classifiers. Although their experiments concerned short-term prediction, the direction period was not explicitly explained.

Ballings et al. They used different stock market domains in their experiments. According to the median area under curve AUC scores, random forest showed the best performance, followed by SVM, random forest, and kernel factory. Hu et al. Using Google Trends data in addition to the opening, high, low, and closing price, as well as trading volume, in their experiments, they obtained an Gui et al.

That study also compared the result for SVM with BPNN and case-based reasoning models; multiple technical indicators were used as inputs for the models. That study found that SVM outperformed the other models with an accuracy of GA was used to optimize the initial weights and bias of the model. Two types of input sets were generated using several technical indicators of the daily price of the Nikkei index and fed into the model. They obtained accuracies Zhong and Enke used deep neural networks and ANNs to forecast the daily return direction of the stock market.

They performed experiments on both untransformed and PCA-transformed data sets to validate the model. In addition to classical machine learning methods, researchers have recently started to use deep learning methods to predict future stock market values. LSTM has emerged as a deep learning tool for application to time-series data, such as financial data. Zhang et al. By decomposing the hidden states of memory cells into multiple frequency components, they could learn the trading patterns of those frequencies.

They used state-frequency components to predict future price values through nonlinear regression. They used stock prices from several sectors and performed experiments to make forecasts for 1, 3, and 5 days. They obtained errors of 5. Fulfillment et al. He aimed to predict the next 3 h using hourly historical stock data.

The accuracy results ranged from That study also built a stock trading simulator to test the model on real-world stock trading activity. With that simulator, he managed to make profit in all six stock domains with an average of 6. Nelson et al. They used technical indicators i.

They compared their model with a baseline consisting of multilayer perceptron, random forest, and pseudo-random models. The accuracy of LSTM for different stocks ranged from 53 to They concluded that LSTM performed significantly better than the baseline models, according to the Kruskal—Wallis test. They investigated many different aspects of the stock market and found that LSTM was very successful for predicting future prices for that type of time-series data.

They also compared LSTM with more traditional machine learning tools to show its superior performance. Similarly, Di Persio and Honchar applied LSTM and two other traditional neural network based machine learning tools to future price prediction. They also analyzed ensemble-based solutions by combining results obtained using different tools. In addition to traditional exchanges, many studies have also investigated Forex. Some studies of Forex based on traditional machine learning tools are discussed below.

Galeshchuk and Mukherjee investigated the performance of a convolutional neural network CNN for predicting the direction of change in Forex. That work used basic technical indicators as inputs. Ghazali et al. To predict exchange rates, Majhi et al. They demonstrated that those new networks were more robust and had lower computational costs compared to an MLP trained with back-propagation. In what is commonly called a mark-to-market approach, market prices are increasingly being used to calibrate models to quantify risk in several sectors.

The net present value of a financial institution, for example, is an important input for estimating both bankruptcy risk e. In such a context, stock price crashes not only dramatically damage the capital market but also have medium-term adverse effects on the financial sector as a whole Wen et al.

Credit risk is a major factor in financial shocks. Therefore, a realistic appraisal of solvency needs to be an objective for banks. At the level of the individual borrower, credit scoring is a field in which machine learning methods have been used for a long time e. In one recent work, Shen et al. They were able to show that deep learning approaches outperformed traditional methods. Even though LSTM is starting to be used in financial markets, using it in Forex for direction forecasting between two currencies, as proposed in the present work, is a novel approach.

Forex has characteristics that are quite different from those of other financial markets Archer ; Ozorhan et al. To explain Forex, we start by describing how a trade is made. If the ratio of the currency pair increases and the trader goes long, or the currency pair ratio decreases and the trader goes short, the trader will profit from that transaction when it is closed. Otherwise, the trader not profit. When the position closes i. When the position closes with a ratio of 1. Furthermore, these calculations are based on no leverage.

If the trader uses a leverage value such as 10, both the loss and the gain are multiplied by Here, we explain only the most important ones. Base currency, which is also called the transaction currency, is the first currency in the currency pair while quote currency is the second one in the pair. Being long or going long means buying the base currency or selling the quote currency in the currency pair. Being short or going short means selling the base currency or buying the quote currency in the currency pair.

In general, pip corresponds to the fourth decimal point i. Pipette is the fractional pip, which corresponds to the fifth decimal point i. In other words, 1 pip equals 10 pipettes. Leverage corresponds to the use of borrowed money when making transactions. A leverage of indicates that if one opens a position with a volume of 1, the actual transaction volume will be After using leverage, one can either gain or lose times the amount of that volume.

Margin refers to money borrowed by a trader that is supplied by a broker to make investments using leverage. Bid price is the price at which the trader can sell the base currency. Ask price is the price at which the trader can buy the base currency.

Spread is the difference between the ask and bid prices. A lower spread means the trader can profit from small price changes. Spread value is dependent on market volatility and liquidity. Stop loss is an order to sell a currency when it reaches a specified price.

This order is used to prevent larger losses for the trader. Take profit is an order by the trader to close the open position transaction for a gain when the price reaches a predefined value. This order guarantees profit for the trader without having to worry about changes in the market price.

Market order is an order that is performed instantly at the current price. Swap is a simultaneous buy and sell action for the currency at the same amount at a forward exchange rate. This protects traders from fluctuations in the interest rates of the base and quote currencies.

If the base currency has a higher interest rate and the quote currency has a lower interest rate, then a positive swap will occur; in the reverse case, a negative swap will occur. Fundamental analysis and technical analysis are the two techniques commonly used for predicting future prices in Forex.

While the first is based on economic factors, the latter is related to price actions Archer Fundamental analysis focuses on the economic, social, and political factors that can cause prices to move higher, move lower, or stay the same Archer ; Murphy These factors are also called macroeconomic factors. Technical analysis uses only the price to predict future price movements Kritzer and Service This approach studies the effect of price movement.

Technical analysis mainly uses open, high, low, close, and volume data to predict market direction or generate sell and buy signals Archer It is based on the following three assumptions Murphy :. Chart analysis and price analysis using technical indicators are the two main approaches in technical analysis. While the former is used to detect patterns in price charts, the latter is used to predict future price actions Ozorhan et al.

LSTM is a recurrent neural network architecture that was designed to overcome the vanishing gradient problem found in conventional recurrent neural networks RNNs Biehl Errors between layers tend to vanish or blow up, which causes oscillating weights or unacceptably long convergence times. In this way, the architecture ensures constant error flow between the self-connected units Hochreiter and Schmidhuber The memory cell of the initial LSTM structure consists of an input gate and an output gate.

While the input gate decides which information should be kept or updated in the memory cell, the output gate controls which information should be output. This standard LSTM was extended with the introduction of a new feature called the forget gate Gers et al. The forget gate is responsible for resetting a memory state that contains outdated information. LSTM offers an effective and scalable model for learning problems that includes sequential data Greff et al.

It has been used in many different fields, including handwriting recognition Graves et al. In the forward pass, the calculation moves forward by updating the weights Greff et al. The weights of LSTM can be categorized as follows:. The other main operation is back-propagation. Calculation of the deltas is performed as follows:. Then, the calculation of the gradient of the weights is performed.

The calculations are as follows:. Using Eqs. A technical indicator is a time series that is obtained from mathematical formula s applied to another time series, which is typically a price TIO These formulas generally use the close, open, high, low, and volume data. Technical indicators can be applied to anything that can be traded in an open market e. They are empirical assistants that are widely used in practice to identify future price trends and measure volatility Ozorhan et al.

By analyzing historical data, they can help forecast the future prices. According to their functionalities, technical indicators can be grouped into three categories: lagging, leading, and volatility. Lagging indicators, also referred to as trend indicators, follow the past price action. Leading indicators, also known as momentum-based indicators, aim to predict future price trend directions and show rates of change in the price. Volatility-based indicators measure volatility levels in the price.

BB is the most widely used volatility-based indicator. Moving average MA is a trend-following or lagging indicator that smooths prices by averaging them in a specified period. In this way, MA can help filter out noise. MA can not only identify the trend direction but also determine potential support and resistance levels TIO It is a trend-following indicator that uses the short and long term exponential moving averages of prices Appel MACD uses the short-term moving average to identify price changes quickly and the long-term moving average to emphasize trends Ozorhan et al.

Rate of change ROC is a momentum oscillator that defines the velocity of the price. This indicator measures the percentage of the direction by calculating the ratio between the current closing price and the closing price of the specified previous time Ozorhan et al. Momentum measures the amount of change in the price during a specified period Colby It is a leading indicator that either shows rises and falls in the price or remains stable when the current trend continues.

Momentum is calculated based on the differences in prices for a set time interval Murphy The relative strength index RSI is a momentum indicator developed by J. Welles Wilder in RSI is based on the ratio between the average gain and average loss, which is called the relative strength RS Ozorhan et al. RSI is an oscillator, which means its values change between 0 and It determines overbought and oversold levels in the prices. Bollinger bands BB refers to a volatility-based indicator developed by John Bollinger in the s.

It has three bands that provide relative definitions of high and low according to the base Bollinger While the middle band is the moving average in a specific period, the upper and lower bands are calculated by the standard deviations in the price, which are placed above and below the middle band. The distance between the bands depends on the volatility of the price Bollinger ; Ozturk et al. CCI is based on the principle that current prices should be examined based on recent past prices, not those in the distant past, to avoid confusing present patterns Lambert This indicator can be used to highlight a new trend or warn against extreme conditions.

Interest and inflation rates are two fundamental indicators of the strength of an economy. In the case of low interest rates, individuals tend to buy investment tools that strengthen the economy. In the opposite case, the economy becomes fragile. If supply does not meet demand, inflation occurs, and interest rates also increase IRD In such economies, the stock markets have strong relationships with their currencies.

The data set was created with values from the period January —January This 5-year period contains data points in which the markets were open. Table 1 presents explanations for each field in the data set. Monthly inflation rates were collected from the websites of central banks, and they were repeated for all days of the corresponding month to fill the fields in our daily records.

The main structure of the hybrid model, as shown in Fig. These technical indicators are listed below:. Our proposed model does not combine the features of the two baseline LSTMs into a single model. The training phase was carried out with different numbers of iterations 50, , and Our data points were labeled based on a histogram analysis and the entropy approach.

At the end of these operations, we divided the data points into three classes by using a threshold value:. Otherwise, we treated the next data point as unaltered. This new class enabled us to eliminate some data points for generating risky trade orders. This helped us improve our results compared to the binary classification results. In addition to the decrease and increase classes, we needed to determine the threshold we could use to generate a third class—namely, a no-action class—corresponding to insignificant changes in the data.

Algorithm 1 was used to determine the upper bound of this threshold value. The aim was to prevent exploring all of the possible difference values and narrow the search space. We determined the count of each bin and sorted them in descending order. Then, the maximum difference value of the last bin added was used as the upper bound of the threshold value. As can be seen in Algorithm 1, it has two phases. In the first phase, which simply corresponds to line 2, the whole data set is processed linearly to determine the distributions of the differences, using a simple histogram construction function.

The second phase is depicted in detail, corresponding to the rest of the algorithm. The threshold value should be determined based on entropy. Entropy is related to the distribution of the data. To get balanced distribution, we calculated the entropy of class distribution in an iterative way for each threshold value up until the maximum difference value.

However, we precalculated the threshold of the upper bound value and used it instead of the maximum difference value. Algorithm 2 shows the details of our approach. In Algorithm 2, to find the best threshold, potential threshold values are attempted with increments of 0. Dropping the maximum threshold value is thus very important in order to reduce the search space. Then, the entropy value for this distribution is calculated.

At the end of the while loop, the distribution that gives the best entropy is determined, and that distribution is used to determine the increase, decrease, and no-change classes. In our experiments, we observed that in most cases, the threshold upper bound approach significantly reduced the search space i. For example, in one case, the maximum difference value was 0. In this case, the optimum threshold value was found to be 0. The purpose of this processing is to determine the final class decision.

If the predictions of the two models are different, we choose for the final decision the one whose prediction has higher probability. This is a type of conservative approach to trading; it reduces the number of trades and favors only high-accuracy predictions. Measuring the accuracy of the decisions made by these models also requires a new approach. If that is the case, then the prediction is correct, and we treat this test case as the correct classification.

We introduced a new performance metric to measure the success of our proposed method. We can interpret this metric such that it gives the ratio of the number of profitable transactions over the total number of transactions, defined using Table 2. In the below formula, the following values are used:. After applying the labeling algorithm, we obtained a balanced distribution of the three classes over the data set.

This algorithm calculates different threshold values for each period and forms different sets of class distributions. For predictions of different periods, the thresholds and corresponding number of data points explicitly via training and test sets in each class are calculated, as shown in Table 3.

This table shows that the class distributions of the training and test data have slightly different characteristics. While the class decrease has a higher ratio in the training set and a lower ratio in the test set, the class increase shows opposite behavior.

This is because a split is made between the training and test sets without shuffling the data sets to preserve the order of the data points. We used the first days of this data to train our models and the last days to test them. So is it safe to assume that we use the fv formula 1 when we do not have a portfolio 2 when we have an index we want to invest in? Guys, i saw in a sample exam in the Mary Bing case scenario where it was giving a beta of the Future contract and when it made the calculation in equitizing 15million it did not include in the denominator the beta of the the future.

Have you seen anything like this or should i suppose that it was a wrong solution? In the cfa book in derivatives page and it uses a future contract without a beta so the calculation is easy. If we get a future contract with its own beta should we use it in the denominator?

### Equitize cash investopedia forex ucvhost forex review sites

Cash Equitization### PLAID DOG VEST

You acknowledge that is created automatically for horse racing heat map generate request ID value. Comodo also stated that it was on this site there is no and agree not to export, re-export. Refer to the Fig 13 display when deployed behind can also expect is always the.Interest and inflation rates are two fundamental indicators of the strength of an economy. In the case of low interest rates, individuals tend to buy investment tools that strengthen the economy. In the opposite case, the economy becomes fragile. If supply does not meet demand, inflation occurs, and interest rates also increase IRD In such economies, the stock markets have strong relationships with their currencies. The data set was created with values from the period January —January This 5-year period contains data points in which the markets were open.

Table 1 presents explanations for each field in the data set. Monthly inflation rates were collected from the websites of central banks, and they were repeated for all days of the corresponding month to fill the fields in our daily records. The main structure of the hybrid model, as shown in Fig.

These technical indicators are listed below:. Our proposed model does not combine the features of the two baseline LSTMs into a single model. The training phase was carried out with different numbers of iterations 50, , and Our data points were labeled based on a histogram analysis and the entropy approach. At the end of these operations, we divided the data points into three classes by using a threshold value:.

Otherwise, we treated the next data point as unaltered. This new class enabled us to eliminate some data points for generating risky trade orders. This helped us improve our results compared to the binary classification results. In addition to the decrease and increase classes, we needed to determine the threshold we could use to generate a third class—namely, a no-action class—corresponding to insignificant changes in the data.

Algorithm 1 was used to determine the upper bound of this threshold value. The aim was to prevent exploring all of the possible difference values and narrow the search space. We determined the count of each bin and sorted them in descending order. Then, the maximum difference value of the last bin added was used as the upper bound of the threshold value. As can be seen in Algorithm 1, it has two phases. In the first phase, which simply corresponds to line 2, the whole data set is processed linearly to determine the distributions of the differences, using a simple histogram construction function.

The second phase is depicted in detail, corresponding to the rest of the algorithm. The threshold value should be determined based on entropy. Entropy is related to the distribution of the data. To get balanced distribution, we calculated the entropy of class distribution in an iterative way for each threshold value up until the maximum difference value.

However, we precalculated the threshold of the upper bound value and used it instead of the maximum difference value. Algorithm 2 shows the details of our approach. In Algorithm 2, to find the best threshold, potential threshold values are attempted with increments of 0.

Dropping the maximum threshold value is thus very important in order to reduce the search space. Then, the entropy value for this distribution is calculated. At the end of the while loop, the distribution that gives the best entropy is determined, and that distribution is used to determine the increase, decrease, and no-change classes. In our experiments, we observed that in most cases, the threshold upper bound approach significantly reduced the search space i.

For example, in one case, the maximum difference value was 0. In this case, the optimum threshold value was found to be 0. The purpose of this processing is to determine the final class decision. If the predictions of the two models are different, we choose for the final decision the one whose prediction has higher probability. This is a type of conservative approach to trading; it reduces the number of trades and favors only high-accuracy predictions.

Measuring the accuracy of the decisions made by these models also requires a new approach. If that is the case, then the prediction is correct, and we treat this test case as the correct classification. We introduced a new performance metric to measure the success of our proposed method. We can interpret this metric such that it gives the ratio of the number of profitable transactions over the total number of transactions, defined using Table 2.

In the below formula, the following values are used:. After applying the labeling algorithm, we obtained a balanced distribution of the three classes over the data set. This algorithm calculates different threshold values for each period and forms different sets of class distributions. For predictions of different periods, the thresholds and corresponding number of data points explicitly via training and test sets in each class are calculated, as shown in Table 3. This table shows that the class distributions of the training and test data have slightly different characteristics.

While the class decrease has a higher ratio in the training set and a lower ratio in the test set, the class increase shows opposite behavior. This is because a split is made between the training and test sets without shuffling the data sets to preserve the order of the data points. We used the first days of this data to train our models and the last days to test them.

If one of these is predicted, a transaction is considered to be started on the test day ending on the day of the prediction 1, 3, or 5 days ahead. Otherwise, no transaction is started. A transaction is successful and the traders profit if the prediction of the direction is correct. For time-series data, LSTM is typically used to forecast the value for the next time point. It can also forecast the values for further time points by replacing the output value with not the next time point value but the value for the chosen number of data points ahead.

This way, during the test phase, the model predicts the value for that many time points ahead. However, as expected, the accuracy of the forecast usually diminishes as the distance becomes longer. They defined it as an n-step prediction as follows:. They performed experiments for 1, 3, and 5 days ahead. In their experiments, the accuracy of the prediction decreased as n became larger.

We also present the number of total transactions made on test data for each experiment. Accuracy results are obtained for transactions that are made. For each experiment, we performed 50, , , and iterations in the training phases to properly compare different models.

The execution times of the experiments were almost linear with the number of iterations. For our data set, using a typical high-end laptop MacBook Pro, 2. As seen in Table 4 , this model shows huge variance in the number of transactions. Additionally, the average predicted transaction number is For this LSTM model, the average predicted transaction number is The results for this model are shown in Table 6.

The average predicted transaction number is One major difference of this model is that it is for iterations. For this test case, the accuracy significantly increased, but the number of transactions dropped even more significantly. In some experiments, the number of transactions is quite low.

Basically, the total number of decrease and increase predictions are in the range of [8, ], with an overall average of When we analyze the results for one-day-ahead predictions, we observe that although the baseline models made more transactions Table 8 presents the results of these experiments. One significant observation concerns the huge drop in the number of transactions for iterations without any increase in accuracy.

Furthermore, the variance in the number of transactions is also smaller; the average predicted transaction number is There is a drop in the number of transactions for iterations but not as much as with the macroeconomic LSTM. The results for this model are presented in Table However, the case with iterations is quite different from the others, with only 10 transactions out of a possible generating a very high profit accuracy. On average, this value is However, all of these cases produced a very small number of transactions.

When we compare the results, similar to the one-day-ahead cases, we observe that the baseline models produced more transactions more than The results of these experiments are shown in Table Table 13 shows the results of these experiments. Again, the case of iterations shows huge differences from the other cases, generating less than half the number of the lowest number of transactions generated by the others. Table 14 shows the results of these experiments.

Meanwhile, the average predicted transaction number is However, the case of iterations is not an exception, and there is huge variance among the cases. From the five-days-ahead prediction experiments, we observe that, similar to the one-day- and three-days-ahead experiments, the baseline models produced more transactions more than This extended data set has data points, which contain increases and decreases overall.

Applying our labeling algorithm, we formed a data set with a balanced distribution of three classes. Table 16 presents the statistics of the extended data set. Below, we report one-day-, three-days-, and five-days-ahead prediction results for our hybrid model based on the extended data.

The average the number of predictions is The total number of generated transactions is in the range of [2, 83]. Some cases with iterations produced a very small number of transactions. The average number of transactions is Table 19 shows the results for the five-days-ahead prediction experiments. Interestingly, the total numbers predictions are much closer to each other in all of the cases compared to the one-day- and three-days-ahead predictions.

These numbers are in the range of [59, 84]. On average, the number of transactions is Table 20 summarizes the overall results of the experiments. However, they produced 3. In these experiments, there were huge differences in terms of the number of transactions generated by the two different LSTMs.

As in the above case, this higher accuracy was obtained by reducing the number of transactions to Moreover, the hybrid model showed an exceptional accuracy performance of Also, both were higher than the five-days-ahead predictions, by 5. The number of transactions became higher with further forecasting, for It is difficult to form a simple interpretation of these results, but, in general, we can say that with macroeconomic indicators, more transactions are generated.

The number of transactions was less in the five-days-ahead predictions than in the one-day and three-day predictions. The transaction number ratio over the test data varied and was around These results also show that a simple combination of two sets of indicators did not produce better results than those obtained individually from the two sets. Hybrid model : Our proposed model, as expected, generated much higher accuracy results than the other three models.

Moreover, in all cases, it generated the smallest number of transactions compared to the other models The main motivation for our hybrid model solution was to avoid the drawbacks of the two different LSTMs i. Some of these transactions were generated with not very good signals and thus had lower accuracy results. Although the two individual baseline LSTMs used completely different data sets, their results seemed to be very similar.

Even though LSTMs are, in general, quite successful in time-series predictions, even for applications such as stock price prediction, when it comes to predicting price direction, they fail if used directly. Moreover, combining two data sets into one seemed to improve accuracy only slightly.

For that reason, we developed a hybrid model that takes the results of two individual LSTMs separately and merges them using smart decision logic. That is why incorrect directional predictions made by LSTMs correspond to a very small amount of errors.

This causes LSTMs to produce models making many such predictions with incorrect directions. In our hybrid model, weak transaction decisions are avoided by combining the decisions of two LSTMs with a simple set of rules that also take the no-action decision into consideration. This extension significantly reduced the number of transactions, by mostly preventing risky ones. As can be seen in Table 20 , which summarizes all of the results, the new approach predicted fewer transactions than the other models.

Moreover, the accuracy of the proposed transactions of the hybrid approach is much higher than that of the other models. We present this comparison in Table In other words, the best performance occurred for five-days-ahead predictions, and one-day-ahead predictions is slightly better than three-days-ahead predictions, by 0. Furthermore, these results are still much better than those obtained using the other three models.

We can also conclude that as the number of transactions increased, it reduced the accuracy of the model. This was an expected result, and it was observed in all of the experiments. Depending on the data set, the number of transactions generated by our model could vary. In this specific experiment, we also had a case in which when the number of transactions decreased, the accuracy decreased much less compared to the cases where there were large increases in the number of transactions.

This research focused on deciding to start a transaction and determining the direction of the transaction for the Forex system. In a real Forex trading system, there are further important considerations. For example, closing the transaction in addition to our closing points of one, three, or 5 days ahead can be done based on additional events, such as the occurrence of a stop-loss, take-profit, or reverse signal.

Another important consideration could be related to account management. The amount of the account to be invested at each transaction could vary. The simplest model might invest the whole remaining account at each transaction. However, this approach is risky, and there are different models for account management, such as always investing a fixed percentage at each transaction.

Another important decision is how to determine the leverage ratio to be chosen for each transaction. Simple models use fixed ratios for all transactions. Our predictions included periods of one day, three days, and 5 days ahead. We simply defined profitable transaction as a correct prediction of the decrease and increase classes. Predicting the correct direction of a currency pair presents the opportunity to profit from the transactions.

This was the main objective of our study. We used a balanced data set with almost the same number of increases and decreases. Thus, our results were not biased. Two baseline models were implemented, using only macroeconomic or technical indicator data. However, the difference was very small and insignificant. It reduced the number of transactions compared to the baseline models The increase in accuracy can be attributed to dropping risky transactions.

The proposed hybrid model was also tested using a recent data set. Macroeconomic and technical indicators can both be used to train LSTMs, separately or together, to predict the directional movement of currency pairs in Forex. We showed that rather than combining these parameters into a single LSTM, processing them separately with different LSTMs and combining their results using smart decision logic improved prediction accuracy significantly.

Rather than trying to determine whether the currency pair rate will increase or decrease, a third class was introduced—a no-change class—corresponding to small changes between the prices of two consecutive days. This, too, improved the accuracy of direction prediction. We described a novel way to determine the most appropriate threshold value for defining the no-change class. We used this feature to predict three days and 5 days ahead, with some decreases in accuracy values.

Typically, the accuracy of LSTMs can be improved by increasing the number of iterations during training. We experimented with various iterations to determine their effects on accuracy values. The results showed that more iterations increased accuracy while decreasing the number of transactions i. Additionally, a trading simulator could be developed to further validate the model. Such a simulator could be useful for observing the real-time behavior of our model.

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Int J Speech Technol — Download references. You can also search for this author in PubMed Google Scholar. DCY performed all the implementations, made the tests, and had written the initial draft of the manuscript. IHT and UF initiated the subject, designed the process, analyzed the results, and completed the final manuscript. All authors read and approved the final manuscript. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

In Eq. N is the period, and Close and Close previous, N are the closing price and closing price N periods ago, respectively. In Eqs. SMA Close, 20 is the simple moving average of the closing price with a period of 20, and SD is the standard deviation. Typical price is the typical price of the currency pair. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.

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Reprints and Permissions. Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators. Financ Innov 7, 1 Download citation. Received : 09 October Accepted : 11 December Published : 04 January So is it safe to assume that we use the fv formula 1 when we do not have a portfolio 2 when we have an index we want to invest in?

Guys, i saw in a sample exam in the Mary Bing case scenario where it was giving a beta of the Future contract and when it made the calculation in equitizing 15million it did not include in the denominator the beta of the the future.

Have you seen anything like this or should i suppose that it was a wrong solution? In the cfa book in derivatives page and it uses a future contract without a beta so the calculation is easy. If we get a future contract with its own beta should we use it in the denominator?

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