orira.xyz › forex-forecasts. DailyFX is the leading portal for financial market news covering forex, commodities, and indices. Discover our charts, forecasts, analysis and more. You can't be confident that even the most accurate Forex forecast will be % correct. However, forecasts by forex analysts are a good. CURRENCY EXCHANGE RATES FOREX ANALYTICS Its popularity quickly provide the industry with a diverse. It also works users group to design agency behind. One way SQL is a bit knowledge to let to the server need not to. That's all there switch can lead Fri, May 6.
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Stock Market comprises of millions of public listed companies. For a trader, it is very difficult to monitor and focus on thousands of companies that run the index. In addition to it, news about the company also drives the prices of the company stock up and down. The forex market has a lot wider range of factors that influence the rates of currencies. The currency price is usually affected by the macroeconomic situation of the country, for example unemployment, inflation and gross domestic product GDP , as well as news and political events.
Since currency is traded in pairs, as an investor you need to be have complete information about both the currencies: the one you are buying and the one you are selling. Hence knowledge about both the currencies is important before trading. Forex indicators are constructive in making disciplined and informed decisions.
Accessibility — The forex market is the most accessible market because of its 24 hours trading period. Trading can be done online, through brokers, and they can access all the latest prices, news, price charts, tools and strategies. Quick Returns — The forex market is extremely liquid and very fast in term of trading. The waiting period for the asset to grow in value is very less enabling the traders to earn quick returns. Easy Short Selling — Currencies are always bought and sold in pairs.
Investors who foresee a particular currency declining can easily sell that currency and buy another currency in return. There is no borrowing involved. Ease of Analysis — Forex market can be easily predicted and analyzed using technical analysis.
Technical analysis requires price history and trends that gives enough information about supply, demand and market sentiments. Transparent Trading — Forex market is not much influenced by insider trading, as it is not centralized. This makes forex trading, very transparent. Simple Tax Rules and Calculations — Forex trading holds no complex tax rules. The tax calculation is very easy and simple.
Uncertain Price Determination — Price determination process in the forex market is considered very complex. Trading is done on the basis of technical knowledge and analysis. Limited number of teachers — Forex traders usually are at a loss when it comes to guidance for trading in the forex markets. The market has very limited advisors and managers for forex trading. High Volatility — High volatility is an advantage and a disadvantage both. Changes in government policies and economic situation can drastically affect the market making it difficult for investors to take risk.
Start with very low money — You can start trading with just one share also. Stock Market grows with the economy — Stock Market and the economy goes side by side. During times when the economy is booming, stock market is the best place to invest and earn returns. Profits can be earned in two ways — A stock trader can benefit from stock trading in two ways: 1 Appreciation in the value of the stock purchased 2 Profit from selling high and buying low.
Diversify your portfolio — stock trading is the best way to diversify your portfolio. You can easily invest in long tern stocks that will earn you profit in the long run and can invest in short term stocks which you can trade daily and earn profit. A lot of guidance and information is available — Stock traders can benefit from a lot of free knowledge that is available to them in order to start trading and search for good companies to invest in.
There are many firms and companies which give very accurate stock signals and stock recommendations. Here you can access of the best stock forecasts and prediction services. Earn through Dividends — Some of the companies regularly pay dividends to shareholders.
It becomes a regular stream of income for traders. Some of the best Monthly Dividend Stocks to Buy in are listed here. No guaranteed return — Stock Market is very unpredictable. There is no guarantee of any profit. Sometimes a stock can crash badly, making you lose your money.
You have to be a patient — In order to build a decent amount of wealth through trading, you need to wait for years. This becomes very difficult for majority of the people. Volatility — Stock Market is very volatile.
History have told many stories of stock market crashes stories and how people have lost their investments. Without decent knowledge you cannot be a stock trader — Stock trading requires a good amount of knowledge about the stock market, the companies and how the index runs. Also read: Best stocks to day trade. There is no simple answer to the above question. Choosing a financial instrument is not a simple task. Firstly, the amount of risk you are willing to take as in investor should be your top selection criteria.
Stock market holds a lot of companies which are low risk companies which grow with time. Whereas, Forex market is highly influenced by global politics and economy. These two factors are totally out of control and can make or break your investments. Secondly, your trading goals as an investor are also of prime importance.
Whether you are interested in long term investment or regular profits will further narrow down your selection of financial instrument. As an investor, you need to stay put and wait a while before you can benefit from your investment.
Investing in value stocks is a long-term investment. Thirdly, profit goals are also very important. They define your trading strategy and how to carry out your trades. Last but not the least, your investment time period also matters.
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Early in May, there was a similar test of the cloud, which resulted in a further downtrend Later, the m Today, the pair may correct with the target at 1. 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. Was the information useful for you?
Categories Forex Analysis. Most Popular Week. Now that we have established that there cannot be any universal benchmarks for when forecast accuracy can be considered satisfactory or unsatisfactory, how do we go about identifying the potential for improvement in forecast accuracy? As stated in the introduction, the first step is assessing your business results and the role forecasting plays in attaining them.
If forecasting turns out to be a main culprit explaining disappointing business results, you need to assess whether your forecasting performance is satisfying. Do your forecasts accurately capture systematic variation in demand?
There are usually many types of variation in demand that are somewhat systematic. There may be seasonality, such as demand for tea increasing in the winter time, or trends, such as an ongoing increase in demand of organic food, that can be detected by examining past sales data. In addition, especially at the store and product level, many products have distinct weekday-related variation in demand.
A good forecasting system that applies automatic optimization of forecast models should be able to identify this kind of systematic patterns without manual intervention. Do your forecasts accurately capture the impact of events known beforehand? Internal business decisions, such as promotions, price changes and assortment changes have a direct impact on demand. If these planned changes are not reflected in your forecast, you need to fix your planning process before you can start addressing forecast accuracy.
The next step then is to examine how you forecast for example the impact of promotions. Are you already taking advantage of all available data, such as promotion type, marketing activities, price discounts, in-store displays etc. You can read more about how we use causal models to forecast the impact of promotions here.
Some of these are known well in advance, such as holidays or local festivals. One-off events typically require manual planning, but for recurring events, such as Easter, for which past data is available, forecasting can be highly automated. Some external factors naturally take us by surprise, such as a specific product taking off in social media. In Finland, this happened recently with cauliflower, for which demand doubled in response to a social media campaign initiated by a few concerned citizens who wanted to help farmers move an exceptionally large crop.
Even when the information becomes available only after important business decisions have been made, it is important to use the information to cleanse the data used for forecasting to avoid errors in future forecasts. Does your forecast accuracy behave in a predictable way? It is often more important to understand in which situations and for which products forecasts can be expected to be good or bad, rather than to pour vast resources into perfecting forecasts that are by their nature unreliable.
Understanding when forecast accuracy is likely to be low, makes it possible to do a risk analysis of the consequences of over- and under forecasting and to make business decisions accordingly. As the products have limited shelf-life, the manufacturer does not want to risk potentially very inflated forecasts driving up inventory just in case, rather they make sure they have production capacity, raw materials and packaging supplies to be able to deal with a situation where the original forecast turns out to be too low.
The need for predictable forecast behavior is also the reason why we apply extreme care when taking new forecasting methods, such as different machine learning algorithms into use. For example, when testing different variants of machine learning on promotion data, we discarded one approach that was on average slightly more accurate than some others, but significantly less robust and more difficult for the average demand planner to understand.
Occasional extreme forecast errors can be very detrimental to your performance, when the planning process has been set up to tolerate a certain level of uncertainty. If demand changes in ways that cannot be explained or demand is affected by factors for which information is not available early enough to impact business decisions, you simply must find ways of making the process less dependent on forecast accuracy.
We already mentioned weather as one external factor having an impact on demand. In the short-term, weather forecasts can be used to drive replenishment to stores you can read more about how to use machine learning to benefit from weather data in your forecasting here. However, long-term weather forecasts are still too uncertain to provide value in demand planning that needs to be done months ahead of sales. In very weather-dependent businesses, such as winter sports gear, our recommendation is to make a business decision concerning what inventory levels to go for.
For high-margin items, the business impact of losing sales due to stock-outs is usually worse than the impact of needing to resort to clearance sales to get rid of excess stock, which is why it may make sense to plan in accordance with favorable weather. For low-margin items, rebates may quickly turn products unprofitable, which is why it may be wiser to have a more cautious inventory plan. In any case, setting your operations up so that final decisions on where to position stock are made as late as possible allow for collecting more information and improving forecast accuracy.
In practice, this can mean holding back a proportion of inventory at your distribution centers to be allocated to the regions that have the most favorable conditions and the best chance of selling the goods at full price. You can read more about managing seasonal products here. Depending on the chosen metric, level of aggregation and forecasting horizon, you can get very different results on forecast accuracy for the exact same data set.
To be able to analyze forecasts and track the development of forecasts accuracy over time, it is necessary to understand the basic characteristics of the most commonly used forecast accuracy metrics. There is probably an infinite number of forecast accuracy metrics, but most of them are variations of the following three: forecast bias, mean average deviation MAD , and mean average percentage error MAPE.
We will have a closer look at these next. Do not let the simple appearance of these metrics fool you. After explaining the basics, we will delve into the intricacies of how the metrics are calculated in practice and show how simple and completely justifiable changes in the calculation logic has the power of radically altering the forecast accuracy results. Forecast bias is the difference between forecast and sales. If the forecast over-estimates sales, the forecast bias is considered positive.
If the forecast under-estimates sales, the forecast bias is considered negative. In many cases it is useful to know if demand is systematically over- or under-estimated. For example, even if a slight forecast bias would not have notable effect on store replenishment, it can lead to over- or under-supply at the central warehouse or distribution centers if this kind of systematic error concerns many stores.
A word of caution: When looking at aggregations over several products or long periods of time, the bias metric does not give you much information on the quality of the detailed forecasts. The bias metric only tells you whether the overall forecast was good or not. It can easily disguise very large errors. You can find an example of this in Table 1. Mean absolute deviation MAD is another commonly used forecasting metric.
This metric shows how large an error, on average, you have in your forecast. However, as the MAD metric gives you the average error in units, it is not very useful for comparisons. An average error of 1, units may be very large when looking at a product that sells only 5, units per period, but marginal for an item that sells , units in the same time.
Basically, it tells you by how many percentage points your forecasts are off, on average. This is probably the single most commonly used forecasting metric in demand planning. As the MAPE calculations gives equal weight to all items, be it products or time periods, it quickly gives you very large error percentages if you include lots of slow-sellers in the data set, as relative errors amongst slow sellers can appear rather large even when the absolute errors are not see Table 2 for an example of this.
In fact, a typical problem when using the MAPE metric for slow-sellers on the day-level are sales being zero, making it impossible to calculate a MAPE score. Measuring forecast accuracy is not only about selecting the right metric or metrics. There are a few more things to consider when deciding how you should calculate your forecast accuracy:.
Measuring accuracy or measuring error: This may seem obvious, but we will mention it anyway, as over the years we have seen some very smart people get confused over this. Aggregating data or aggregating metrics: One of the biggest factors affecting what results your forecast accuracy metrics produce is the selected level of aggregation in terms of number of products or over time. As discussed earlier, forecast accuracies are typically better when viewed on the aggregated level.
However, when measuring forecast accuracy at aggregate levels, you also need to be careful about how you perform the calculations. As we will demonstrate below, it can make a huge difference whether you apply the metrics to aggregated data or calculate averages of the detailed metrics. In the example see Table 3 , we have a group of three products, their sales and forecasts from a single week as well as their respective MAPEs. The bottom row shows sales, forecasts, and the MAPE calculated at a product group level, based on the aggregated numbers.
Which number is correct? The answer is that both are, but they should be used in different situations and never be compared to one another. The same dynamics are at play when aggregating over periods of time. The data in the previous examples were on a weekly level, but the results would look quite different if we calculated the MAPE for each weekday separately and then took the average of those metrics. Which metric is the most relevant? If these were forecasts for a manufacturer that applies weekly or longer planning cycles, measuring accuracy on the week level makes sense.
But if we are dealing with a grocery store receiving six deliveries a week and demonstrating a clear weekday-related pattern in sales, keeping track of daily forecast accuracy is much more important, especially if the items in question have a short shelf-life. After all, Product C represents over two thirds of total sales and its forecast error is much smaller than for the low-volume products. Should not the forecast metric somehow reflect the importance of the different products?
This can be resolved by weighting the forecast error by sales, as we have done for the MAPE metric in Table 5 below. This is because the MAPE for each day is weighted by the sales for that day. On the group level, the volume-weighted MAPE is now much smaller, demonstrating the impact on placing more importance on the more stable high-volume product.
The choice between arithmetic and weighted averages is a matter of judgment and preference. On the on hand, it makes sense to give more weight to products with higher sales, but on the other hand, this way you may lose sight of under-performing slow-movers. The final or earlier versions of the forecast: As discussed earlier, the longer into the future one forecasts, the less accurate the forecast is going to be.
Typically, forecasts are calculated several months into the future and then updated, for example, on a weekly basis. So, for a given week you normally calculate multiple forecasts over time, meaning you have several different forecasts with different time lags.
The forecasts should get more accurate when you get closer to the week that you are forecasting, meaning that your forecast accuracy will look very different depending on which forecast version you use in calculating it. The forecast version you should use when measuring forecast accuracy is the forecast for which the time lag matches when important business decisions are made.
In retail distribution and inventory management, the relevant lag is usually the lead time for a product. If a supplier delivers from the Far East with a lead time of 12 weeks, what matters is what your forecast quality was when the order was created, not what the forecast was when the products arrived.
In terms of assessing forecast accuracy, no metric is universally better than another. It is all a question of what you want to use the metric for:. The forecast accuracy metric should also be selected to match the relevant levels of aggregation and the relevant planning horizon.
In Table 6 we present a few examples of different planning processes utilizing forecasts and typical levels of aggregation over products and time as well as the time spans associated with those planning tasks. To make things even more complicated, the same forecast is often used for several different purposes, meaning that several metrics for with different levels of aggregation and different time spans are commonly required.
A good example is store replenishment and inventory management at the supplying distribution center. Our recommendation is to use the same forecast that drives store replenishment translated into projected store orders to drive inventory management at the distribution center DC. The requirements for the store forecasts and the DC forecast are, however, not the same. The store-level forecast need to be accurate on the store and product level whereas the DC-level forecast needs to be accurate for the full order volume per product and all stores.
On the DC level, aggregation typically reduces the forecast error per product. However, we need to be careful about systematic bias in the forecasts, as a tendency to over- or under-forecast store demand may become aggravated through aggregation. The number of forecasts in a retail or supply chain planning context is typically very large to begin with and dealing with multiple metrics means that the number is increased even further.
This means that you need an exception-based process for monitoring accuracy. Otherwise, your demand planners will either be completely swamped or risk losing valuable demand signals in the averages. To be able to effectively identify relevant exceptions, it usually makes sense to classify products based on their importance and predictability.
This can be done in many ways, but a simple starting point is to classify products based on sales value ABC classification , which reflects economic impact, and sales frequency XYZ classification , which tends to correlate with more accurate forecasting. For high sales value and sales frequency AX products, for example, a high forecast accuracy is realistic and the consequences of deviations quite significant, which is why the exception threshold should be kept low and reactions to forecast errors be quick.
For low sales frequency products, your process needs to be more tolerant to forecast errors and exception thresholds should be set accordingly. Another good approach, which we recommend using in combination with the above, is singling out products or situations where forecast accuracy is known to be a challenge or of crucial importance.
A typical example is fresh or other short shelf-life products, which should be monitored very carefully as forecast errors quickly translate into waste or lost sales. Special situations, such as new kinds of promotions or product introductions can require special attention even when the products have longer shelf-life. Of course, to get value out of monitoring forecast accuracy you need to be able to react to exceptions. Simply addressing exceptions by manually correcting erroneous forecasts will not help you in the long run as it does nothing to improve the forecasting process.
Therefore, you need to make sure your forecasting system 1 is transparent enough for your demand planners to understand how any given forecast was formed and 2 allows your demand planners to control how forecasts are calculated see Exhibit 2. Sophisticated forecasting involves using a multitude of forecasting methods considering many different demand-influencing factors. To be able to adjust forecasts that do not meet your business requirements, you need to understand where the forecast errors come from.
To efficiently debug forecasts, you need to be able to separate the different forecast components. In simple terms, this means visibility into baseline forecast, forecasted impact of promotions and events, as well as manual adjustments to the forecast separately see Figure 7. Especially when forecasts are adjusted manually, it is very important to continuously monitor the added value of these changes. Several studies indicate that the human brain is not well suited for forecasting and that many of the changes made, especially small increases to forecasts, are not well grounded.