Trend analysis is a technique used in technical analysis that attempts to predict future stock price movements based on recently observed trend data. Trend analysis uses historical data, such as price movements and trade volume, to forecast the long-term direction of market sentiment. Show
Trend analysis tries to predict a trend, such as a bull market run, and ride that trend until data suggests a trend reversal, such as a bull-to-bear market. Trend analysis is helpful because moving with trends, and not against them, will lead to profit for an investor. It is based on the idea that what has happened in the past gives traders an idea of what will happen in the future. There are three main types of trends: short-, intermediate- and long-term. A trend is a general direction the market is taking during a specified period of time. Trends can be both upward and downward, relating to bullish and bearish markets, respectively. While there is no specified minimum amount of time required for a direction to be considered a trend, the longer the direction is maintained, the more notable the trend. Trend analysis is the process of looking at current trends in order to predict future ones and is considered a form of comparative analysis. This can include attempting to determine whether a current market trend, such as gains in a particular market sector, is likely to continue, as well as whether a trend in one market area could result in a trend in another. Though a trend analysis may involve a large amount of data, there is no guarantee that the results will be correct. In order to begin analyzing applicable data, it is necessary to first determine which market segment will be analyzed. For instance, you could focus on a particular industry, such as the automotive or pharmaceuticals sector, as well as a particular type of investment, such as the bond market. Once the sector has been selected, it is possible to examine its general performance. This can include how the sector was affected by internal and external forces. For example, changes in a similar industry or the creation of a new governmental regulation would qualify as forces impacting the market. Analysts then take this data and attempt to predict the direction the market will take moving forward. Critics of trend analysis, and technical trading in general, argue that markets are efficient, and already price in all available information. That means that history does not necessarily need to repeat itself and that the past does not predict the future. Adherents of fundamental analysis, for example, analyze the financial condition of companies using financial statements and economic models to predict future prices. For these types of investors, day-to-day stock movements follow a random walk that cannot be interpreted as patterns or trends. Trend traders attempt to isolate and extract profit from trends. There are many different trend trading strategies using a variety of technical indicators:
Trend following is a trading system based on using trend analysis and following the recommendation produced to determine which investments to make. Often, the analysis is conducted via computer analysis and modeling of relevant data and is tied to market momentum. Indicators can simplify price information, as well as provide trend trade signals or warn of reversals. They may be used on all time frames, and have variables that can be adjusted to suit each trader's specific preferences. Usually, it is advisable to combine indicator strategies or come up with your own guidelines, so entry and exit criteria are clearly established for trades. Each indicator can be used in more ways than outlined. If you like an indicator, research it further, and most importantly, test it out before using it to make live trades.
A trend is the overall direction of a market during a specified period of time. Trends can be both upward and downward, relating to bullish and bearish markets, respectively. While there is no specified minimum amount of time required for a direction to be considered a trend, the longer the direction is maintained, the more notable the trend. Trends are identified by drawing lines, known as trendlines, that connect price action making higher highs and higher lows for an uptrend, or lower lows and lower highs for a downtrend.
Trend trading strategies attempt to isolate and extract profit from trends by combining a variety of technical indicators along with the financial instrument's price action. Typically, these include moving averages, momentum indicators, and trendlines, and chart patterns. Moving averages strategies involve entering into long, or short, positions when the short-term moving average crosses above, or below, a long-term moving average. Momentum indicator strategies involve entering into positions when a security is exhibiting strong momentum and exiting when that wanes. Trendlines and chart pattern strategies involve entering long, or short, positions when a security is trending higher, or lower, and placing a stop-loss below, or above, key trendline support levels to exit the trade.
Critics of trend analysis, and technical trading in general, argue that markets are efficient, and already price in all available information. That means that history does not necessarily need to repeat itself and that the past does not predict the future. Adherents of fundamental analysis, for example, analyze the financial condition of companies using financial statements and economic models to predict future prices. For these types of investors, day-to-day stock movements follow a random walk that cannot be interpreted as patterns or trends. Reading time: 11 minutes What is the top pain point for business executives? Gartner, the world’s largest IT research firm, gives a clear answer: demand volatility. Too many factors — from weather fluctuations to posts by social media influencers — impact buyers, causing them to frequently change their minds. Worse still, things reshaping customer intentions happen quite unexpectedly – from a competitor’s shop opening next door to the global lockdown due to the COVID-19 pandemic. Or consider the teenage climate activist Greta Thunberg. Her refusal to fly for environmental reasons kick-started the “flight shame” movement, which caused a five-percent decrease in air passenger numbers in Sweden. There is no magic wand to predict scenarios like the “Thunberg effect”. But there are technologies to improve the accuracy of demand forecasting. Honestly, it will never be 100 percent precise, yet it can be precise enough to help you achieve your business goals. In this article, we will look at the capabilities of advanced forecasting methods and outline their current limitations. What is demand forecasting?Demand forecasting is the estimation of a probable future demand for a product or service. The term is often used interchangeably with demand planning and demand sensing, but there’s a difference between the three. Let’s clear it up. Watch our video for a quick overview of demand forecasting strategies Demand planning — understanding market needsDemand planning is a broader process that begins with forecasting but is not limited to it. According to the Institute of Business Forecasting and Planning (IBF), it applies “forecasts and experience to estimate demand for various items at various points in the supply chain.” In addition to making estimations, demand planners take part in inventory optimization, ensure the availability of products needed, and monitor the difference between forecasts and actual sales. Demand planning serves as the starting point for many other activities, such as warehousing, shipping, price forecasting, financial planning, and, especially, supply planning that aims at fulfilling the demand and requires data on the anticipated needs of customers. Demand sensing – creating short-term predictionsA relatively new concept in the planning process, demand sensing is a forecasting method that employs advanced analytical techniques to capture real-time fluctuations in purchase behavior. The technology can be of great help for companies, operating in fast-changing markets. Demand sensing solutions extract daily data from POS systems, warehouses, and external sources to detect an increase or decrease in sales by comparison with historical patterns. The system automatically evaluates the significance of each divergence, analyzes influence factors, and offers adjustments to short-term plans. Adopting demand sensing reportedly reduces near-time forecast errors by 30 to 40 percent. It empowers companies to rapidly address sudden changes in customer needs and facilitates building a data-driven supply chain. Of course, you can’t make all decisions based on this technique alone, as it doesn’t work for mid- or long-term planning. But it may serve as a valuable complement to traditional forecasting methods. A demand sensing software dashboard, capturing a change in demand in the short term, and showing factors that cause the fluctuation. Source: E2Open Here, again, we return to forecasting. Getting as close to reality as possible is the key to improving efficiency across the entire supply chain. How do you reach the uppermost accuracy possible? The answer depends on business type, available resources, and objectives. Let’s compare the existing options: traditional statistical forecasting and machine learning algorithms. Traditional statistical forecasting — good for stable markets, ill-disposed to changesTraditional statistical methods (TSM) have been here for ages and remain a staple of forecasting processes. The only difference if compared with the previous century is that all calculations are performed automatically, by modern software. For example, you can create time-series forecasts for sales and trends in Excel. Data sources. To predict the future, statistics utilizes data from the past. That’s why statistical forecasting is often called historical. The common recommendation is collecting data on sales for at least two years. Why use it. Traditional forecasting is still the most popular approach to predict sales, and for a reason. As a rule, demand planning solutions based on statistical techniques seamlessly integrate with Excel and existing Enterprise Resource Planning (ERP) systems without requiring additional tech expertise. The most advanced systems can consider seasonality and market trends as well as apply numerous methods to finetune results. Things to consider. An important prerequisite of statistical forecasting accuracy is stability. We assume that history repeats itself: Situations that occurred two or three years ago will reoccur. Which is far from being true. Flawless in an ideal world, statistical methods often fail to foresee illogical alterations in customer preferences or predict when market saturation will occur. A statistical forecasting software dashboard. Source: Streamline Best fit. All in all, automated statistical forecasting offers a satisfying level of accuracy for:
Does it make business sense to invest in more sophisticated technologies? We’ll try to clear things up in the next section. Machine learning for demand planning — advanced accuracy at the price of added complexityIncreased computer power on the one hand and increased demand volatility on the other created prerequisites for wider use of machine learning (ML) to design predictions. Say, demand sensing that we mentioned above solely relies on ML techniques to generate short-term predictions in response to diverse market changes. ML also drives predictive analytics beyond just estimating demand. It combines historical and current data to generate insights in trends and custom behavior under certain conditions. Data sources. Built upon statistical models, machine learning utilizes additional internal and external sources of information to make more accurate, data-driven predictions. ML engines can work with both structured and unstructured data including
Data sources for demand forecasting with machine learning. Source: IBF (Institute of Business Forecasting and Planning ). Why use it. Machine learning applies complex mathematical algorithms to automatically recognize patterns, capture demand signals, and spot complicated relationships in large datasets. Apart from analyzing huge volumes of information, smart systems continuously retrain models, adapting them to changing conditions thus addressing volatility. These capabilities enable ML-based software to produce more accurate and reliable forecasts in complex scenarios. What does more accurate really mean? Companies that added machine learning to their existing systems report an increase of 5 to 15 percent in forecast reliability (up to 85 and even 95 percent). In addition to this, your team gets rid of time-consuming manual adjustments and recalibrations. Things to consider. To take advantage of the machine learning solution, you need sufficient processing power and really large batches of high-quality data. Otherwise, the system won’t be able to learn and generate valuable predictions. Also, bear in mind the additional complexity in terms of software maintenance and result interpretation. While ML mechanisms come to conclusions without human intervention, it’s up to a live tech expert to determine what features should be fed to the model, which of them have the largest impact on the output, and why the model generates a certain prediction. Check our detailed article about roles in a data science team to get a picture of which specialists have to be involved. Best fit. The list of situations in which machine learning definitely works better than traditional statistics includes:
Comparison between traditional and machine learning approaches to demand forecasting. As you can see, employing machine learning comes with some tradeoffs. Depending on the planning horizon, data availability, and task complexity, you can use a combination of different statistical and ML solutions. Whichever methods you choose, you’ll need specialized software that can help you with your predictions. Demand forecasting software: how to chooseToday, there are a lot of solutions on the market that can support your demand planning activities. They have different capabilities, and the choice depends on your business needs. Here are several things you should consider if you’re thinking of acquiring an off-the-shelf tool. FunctionalityObviously, the first thing you have to look at is whether it fits your business requirements. Depending on your industry and business model, you might need
How promotion influences demand. Source: Relex It’s crucial to connect your internal systems (like ERP or sales management software) to a demand forecasting solution to enable data sharing, collecting complete historic information, and building demand trends. Besides, smooth integrations with your inventory management system or warehouse management system (WMS) will allow you to streamline procurement and capacity management. Many vendors of demand forecasting software offer out-of-the-box integrations with the most popular ERP providers, Excel, and other business tools, so check if the chosen provider can assist you with system connections. If not, you’ll have to engage IT specialists to build internal integrations. Tech support and trainingLogically connected to the previous recommendation, make sure the software provider offers all the necessary support during and after implementation. Such tools aren’t something you download and just start using – they need a lot of data for analysis that has to be imported properly. Also, remember that you’ll have to conduct sufficient training for your staff. Data sources and external factorsDepending on your industry, you might need to consider external factors to increase the accuracy of your predictions, e.g., weather, macroeconomic trends, and others. Contact your provider to find out which data sources they use. Typically, the more data you have, the more impacting factors would be considered, and the more accurate your forecasts will be. But building such complex, custom analytical infrastructure requires investment and engagement of ML engineers, data scientists, and other specialists. However, it pays off. Let’s talk about some real-world examples of successful ML-based demand forecasting. When machine learning works best for demand planning: successful use casesHighly variable environment, dozens of factors driving buying behaviors, many types of data involved — all these often make demand planning too complex to be successfully performed with simple tools. So, big companies choose to invest in smart technologies to optimize their inventory management. Nestlé implements demand-driven forecastingNestlé used to create 80 percent of their forecasts with human intervention. But they wanted to better understand their customer motives. SAS forecasting and analytics technology allowed them to sense and analyze demand signals associated with sales promotions, price, advertising, in-store merchandising, and economic factors. Charles Chase, an Industry consultant, reports, “Today, 80 percent of Nestlé’s forecasts are driven right out of the solution with no human judgment at all. …Every one percent improvement in forecast accuracy translated into a two percent reduction in inventory safety stock. They were eventually able to take out anywhere between 14- 20 percent of their inventory safety stock, reduce it and still meet consumer demand with this improved forecasting capability. If you have US $100 million in inventory that’s a US $20 million reduction.” PUMA adopts an integrated approach to inventory managementPUMA experienced losses and had a gap between supply and demand due to disconnected business systems and fragmented tools. After implementing a comprehensive solution with data management, forecasting, and simulation capabilities, they managed to fix and standardize their planning and analytics processes. As a result, they streamlined procurement, mitigated shortages and residual stock, and gained fuller visibility into both external market conditions and internal operations. UK hospitals reduce waste from blood overstocks with MLThe significant complexity of supply chain, short-term demand spikes, and the high cost of errors (with human lives at stake) prompted the Blood and Transport department of the UK’s National Health System (NHS) to transfer from spreadsheets and manual databases to ML-fueled planning system with enhanced predictive capabilities. It allowed hospitals to reduce waste from blood overstocks by 30 percent without any drop in service quality and enabled rapid responding to potential shortages. “If there’s no yogurt on the supermarket shelf — well, that’s unfortunate. If there’s no blood in the hospital, the consequences are very different,” an NHS executive explained as the reason to invest heavily in the advanced solution. How to approach demand forecasting?If you want to implement any kind of demand forecasting solution to enhance your supply chain and planning operations, there are several important considerations. Buy or build?As we said, the answer depends on your needs and resources. Use a demand forecasting module. The easiest (not the cheapest) thing to do is get comprehensive business software such as ERP or WMS that already has an in-built demand forecasting module and manage all your business operations from a single system. Pros and cons: you don’t need to build internal integrations, but the functionality might be limited and hard to customize. Buy a separate tool. If you’re big enough to track and forecast demand, but still not ready for a full-blown ERP, get a specialized solution that fits your needs. Pros and cons: It’s cheaper and you’re more flexible with choosing the functionality, but you’ll have to build bespoke integrations. Build a custom system. If you have unique business needs and want to have a system tailored for them, it will require custom development. Pros and cons: Maximum customization and accuracy, but takes a lot of time and investment. Test the waters with MLWhile adoption of machine learning tools can somewhat narrow the gap between anticipation and reality, it doesn’t mean that every company should immediately jump to complex intelligent technology. You can start with small enhancements to your existing system that will address those problems that are difficult to solve by traditional methods. For example, use a machine learning module to make data-driven changes in planning for the short term and leave long-term forecasting to old-school statistics. Remember about cost-effectivenessSometimes it turns out that dealing with forecast errors (such as excessive stock) is cheaper than fine-tuning your ML models to obtain maximum accuracy. Don’t get carried away with perfectionism; better calculate the ROI and think if further investment is worth it. Besides, sometimes forecasts are erroneous because of random or completely unpredictable factors, so any efforts to increase prediction accuracy are pointless. Who could’ve predicted those notorious toilet paper shortages? Source: USA Today Integrations and testing are key to successSome 20 years ago, the retail giant Nike invested $400 million in their supply chain software and demand planning system. However, instead of having the right inventory to fulfill customer demand, they ended up losing $100 million because the predictions were wrong. It happened because the system wasn’t tested sufficiently. It turned out that it had some bugs and wasn’t properly integrated with data sources. The lesson learned: Build seamless integrations to establish smooth data exchange and give sufficient attention to your testing activities to make sure your system functions properly and forecasting results are reliable. Short-term forecasts are typically more accurateIt’s simply easier to predict what happens in a day or two than trying to imagine what life will be like in a year. Starting from simple factors like weather and ending with ever-changing customer preferences and other unstable market and macroeconomic conditions, the shorter the time frame the more accurate the prediction is likely to be. Human brains still matterForecasting demand is a challenging task, and it still has much room for improvement. Upland, the U.S. provider of business management software, claims that sales forecasts are less than 75% accurate and calculates that the US economy spends around $50 billion on predictions that are pretty much worthless. Sounds scary, doesn’t it? However smart your forecasting solution is, the key decisions still rest with human capital. You need industry specialists to define which factors should be considered in your predictive models. Human logic is still required to evaluate the relevance of outcomes produced by digital brains and to make final conclusions based on common sense and deep domain expertise. That’s why even ML-powered demand planning systems often include a collaborative platform that allows for engaging different specialists in a forecasting process. Only by taking the best of what both artificial and human intelligence offer can you see and plan a better future for your business. |