Structural change shifts the assumptions used to determine courses of action, for instance, changing the way market orders are processed. A major driver of structural change is innovation. For example, the advent of the smartphone was a huge change for both businesses and consumers as products, such as flashlights and cameras, saw demand wane as their functionality was readily available to everyone as part of a compact device whose primary use was something else.
This led to the development of "apps" applications for everything, including monitoring a bank or commercial account, finding information, and making purchases. Other factors that can often spark structural change include new economic developments, global shifts in the pools of capital and labor, changes in resource availability due to war or natural disaster, changes due to the supply and demand of all resources, and changes in the political landscape with either a new regime coming to power or major overhauls in existing laws, especially with regard to business regulation and taxation.
Not only will businesses have to adapt to the new order, so will markets. For example, in the futures market , crude oil is usually in contango , which means that oil for delivery in the future is more valued than spot oil is today.
If there are production cutbacks, either by decree from producing countries or political instability in the producing regions of the word, fears of scarce reserves will arise. The oil market may then undergo a structural change. Demand for near-term oil may increase, as people would fear lower supply levels in the future. Consequently, the market may shift to backwardation , where oil today is more valuable than future oil.
Agricultural advancements led to the rise of factory farming. Even labor unions caused changes in the workplace forcing companies to adapt. Technological proliferation is causing a structural change in service industries with online shopping, self-ordering kiosks in fast food restaurants, and voice operated devices to access information and order products without using a phone call or, even, a computer.
On a country level, structural changes in productivity could transform an economy from a developing nation to an emerging and, eventually, a developed nation. Technical progress is seen as crucial in bringing about structural change as it involves the obsolescence of skills, vocations, and permanent changes in spending and production. The key to effect structural change is the dynamism that is inherent in that system. Currently, globalization is driving the structural change that is causing the economies of the world to adapt, and that is possible solely due to the dynamic nature of the global economic system.
Your Money. Personal Finance. Your Practice. Popular Courses. Currency devaluation may lower productivity in the long term since imports of capital equipment and machinery become too expensive for local businesses. If currency depreciation is not accompanied by genuine structural reforms, productivity will eventually suffer. Among the hazards:. It does not appear that the world is currently in the grips of a currency war. Recent rounds of easy money policies by numerous countries represent efforts to combat the challenges of a low-growth, deflationary environment, rather than an attempt to steal a march on the competition through overt or surreptitious currency depreciation.
A currency devaluation, deliberate or not, can damage a nation's economy by causing inflation. If its imports rise in price. If it cannot replace those imports with locally-sourced products, the country's consumers simply get stuck with the bill for higher-priced products. A currency devaluation becomes a currency war when other countries respond with their own devaluations, or with protectionist policies that have a similar effect on prices.
By forcing up prices on imports, each participating country may be worsening their trade imbalances instead of improving them. The United States has an enormous trade gap with China. That is, the U. In , then-President Donald Trump tried to correct that imbalance by imposing a raft of tariffs on Chinese goods entering the U.
This protectionist policy was aimed at increasing the prices of Chinese goods and therefore making them less attractive to U. One effect was an apparent shift in U. Another effect was a weakening of the Chinese currency, the renminbi. Less demand for Chinese products led to less demand for the Chinese currency. A country devalues its currency in order to decrease its trade deficit. The goods it exports become cheaper, so sales rise.
The goods it imports become more expensive, so their sales decline in favor of domestic products. The end result is a better trade balance. The problem is, other nations may respond by devaluing their own currencies or imposing tariffs and other barriers to trade. The advantage is lost. Treasury Department placed India on its watchlist of currency manipulators in April It cited India's outsized purchase of U.
India's rupee hit a record low of 1 U. The rupee has had a tumultuous history since its introduction in when the nation achieved its independence. The nation moved from a dollar peg to a floating currency in and, at the same time, devalued the currency to about 1 U.
The rupee's value remained relatively high through the first years of its remarkable economic growth but faltered during the economic crisis of National Bureau of Economic Research. Accessed Dec. BBC News. Dollar in Against the Major Currencies. The New York Times. Economic Times. Compare Remit. Monetary Policy. Federal Reserve. Your Money. Personal Finance. Your Practice. Popular Courses. Table of Contents Expand. Table of Contents. What Is a Currency War?
Are We in a Currency War Now? Why Depreciate a Currency? Strong Dollar Policy. Negative Effects. The Bottom Line. Is India in a Currency War? Economy Economics. Part of. Global Trade Guide. Part Of. Global Players. Cryptocurrencies and Global Trade. Key Takeaways A currency war is a tit-for-tat policy of official currency devaluation aimed at improving each nation's foreign trade competitiveness at the expense of other nations. A currency devaluation is a deliberate move to reduce the purchasing power of a nation's own currency.
Countries may pursue such a strategy to gain a competitive edge in global trade and reduce their sovereign debt burden. Devaluation can have unintended consequences that are self-defeating. The worst of these is inflation. The nation's consumers bear the burden of higher prices on imports. It may be the reverse: a trade war damages the currency of the country it targets.
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Since inflation-protected securities are issued by sovereign governments, there is no or minimal credit risk and, therefore, limited benefit in diversifying any further. Inflation can be fixed income's worst enemy, but an IPS can make inflation a friend. This is a comfort, especially to those who recall how inflation ravaged fixed income during the high inflationary period of the s and early s. While the benefits are clear, inflation-protected securities do come with some risk.
First, to realize fully the guaranteed real rate of return, you have to hold the IPS to maturity. Otherwise, the short-term swings in the real yield could negatively affect the short-term return of the IPS.
For example, some sovereign governments issue a year IPS, and although an IPS of this length can be quite volatile in the short term , it is still not as volatile as a regular year bond from the same issuer. A second risk associated with inflation-protected securities is that, since the accrued interest on the principal tends to be taxed immediately, inflation-protected securities tend to be better held within tax-sheltered portfolios.
Third, they are not well understood and the pricing can be both difficult to understand and calculate. Ironically, inflation-protected securities are one of the easiest asset classes to invest in, but they are also one of the most overlooked. Their poor correlation with other asset classes and unique tax treatment make them a perfect fit for any tax-sheltered, balanced portfolio.
Default risk is of little concern as sovereign government issuers dominate the IPS market. Investors should be aware that this asset class does come with its own sets of risks. Longer-term issues can bring high short-term volatility that jeopardizes the guaranteed rate of return. As well, their complex structure can make them difficult to understand. However, for those who are willing to do their homework, there truly is a nearly "free lunch" out there in the investment world. Dig in!
Portfolio Management. Fixed Income. Investing Essentials. Treasury Bonds. Your Money. Personal Finance. Your Practice. Popular Courses. Financial Advisor Portfolio Construction. 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. Bond A bond is a fixed-income investment that represents a loan made by an investor to a borrower, ususally corporate or governmental. Inflation-Indexed Security An inflation-indexed security is a security that guarantees a return higher than the rate of inflation if it is held to maturity. 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. 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.
Deflation is when prices drop significantly, due to too large a money supply or a slump in consumer spending; lower costs mean companies earn less and may. Typically, a country with a consistently lower inflation rate exhibits a rising currency value, as its purchasing power increases relative to other currencies. Demand-pull inflation is the upward pressure on prices that follows a shortage in supply where too much money is chasing too few goods.