[MUSIC]. Welcome. Let's discuss business indicators for business forecasting. In our first two courses, whenever we attempted to forecast time series data, it was quite clear whether we had the systematic components of level, trend and seasonality. There is of course a fourth systematic component, the cyclical component. A time series data set that is related to economic activity, is likely to exhibit some cyclical behavior, related to the economic or business cycle. A complete business cycle would take a number of years. A business cycle is a type of fluctuation found in aggregate economic activity. Whereas the specific business forecast we want to make, maybe for something very specific like our product sales. A business cycle consists of an expansion in economic activity, followed by a high point in economic activity, a peak. Then the economy goes through a contraction, eventually reaching a low point of economic activity, a trough. And then the cycle repeats again, but each time with a different amplitude and a different length. All those cycles are systematic fluctuations in time series. Their periodicity is not constant unlike seasonal fluctuations, whose periodicity is constant. Each cycle from trough to peak has varied amplitude and varied length. Since product sales may fluctuate alongside the fluctuations in economic activity, how much we sell, depends on how the economy is going. Understanding business cycles may improve our sales forecasts. Typically, we would want to identify the current level of economic activity, and understand where we are in the business cycle. Then, we would project the level of economic activity over the coming months or periods. Using this information about the projected economic cycle, we can adjust and hopefully improve the business forecast of our target variable, such as product sales. Thus, there is a degree of subjectivity involved and hence why forecasting cyclical components comes under judgmental forecasting. The most important factor in predicting the future part of cycles, is understanding turning points when peaks and when troughs are reached. Turning points in economic activity are likely to cause major increases or major decreases in the level of the time series that we are trying to forecast. Analysis of cycles using real gross domestic product GDP, measures provided by a Bureau of Statistics is usually problematic due to the time lag of data collection. GDP figures for a given quarter are usually published 6-8 weeks after the end of that quarter. The data provided does not measure current economic activity but activity in the recent past. Thus the current cycles activity, which is needed to adjust our forecast by predicting turning points may not be available to us as forecasters. This problem can be overcome using leading indicators or leading indices. Leading indicators are time series, which tend to turn up or down in advance of the target series we're trying to forecast. Leading indicators go through similar cyclical fluctuations, as the target time series, but at slightly different points in time. Leading indicators tend to reach turning points before the target time series. The time lag between the leading indicator reaching its turning point and the target series reaching its turning point, can then be used in adjusting our forecasts of our target series. Why might leading indicators behave this way? One, due to time sequences of processes. Many of the processes relevant in business, follow logical time sequencing. Plans for investment occur before actual investment, and actual investment, changes before changes in production capacity. Two, due to market expectations as businesses and our consumer sentiment changes. This could in turn impact on actual production and / or actual sales. And three, due to prime movers. There are certain time series that actually drive economic activity by design. We've already mentioned investment and the same goes for interest rates and the money supply. By observing leading indicator cycle activity, we may be able to predict business cycle activity and adjust our forecasts accordingly. Various organizations have developed systems of leading indicator and business cycle analysis, to provide analysis of turning points in economic activity. One time series may not be sufficient to proxy the broad economic activity cycle. This is because broad economic activity, encompasses diverse sectors such as real goods, services, financial markets and international markets. No single measure is likely to be able to adequately measure changes in activity across such diverse sectors. Usually, several times series are combined in a composite index. Composite indices are weighted averages of several times series. Two commonly used composite indices produced by organizations are, the conference board leading economic index, the LEI, the OECD composite leading indicator, the CLI. Apart from these leading indices, there may also be leading indicators that can be applied at an organizational level. Inquiries logged are likely to be a leading indicator of customer orders and demand for the product we sell. Web or social media registrations and information search data are also likely to be a leading indicator of sales. Monitoring of related products and services may also be useful as leading indicators of the target organization sales. Components, manufacturers can monitor demand for related finished products. Tourism operators can monitor airline bookings and sales. Businesses can also create their own leading indicators using anticipatory surveys. Apart from leading indicators, there are also coincident indicators and lagging indicators. Coincident indicators are indices that reveal the current state of the business cycle. The turning points of coincident indicators are approximately at the same time as the turning points in the business cycle. They typically consist of time series that are published more regularly than economic activity data and are thus available, more readily. And finally, while leading indicators turn before the target series, lagging indicators turn after the target series. In the next video, we will work through an example in Excel that puts these ideas into practice. Don't forget to check out this week's tool box, for more resources and details about business indicators. Make sure that you attempt the quiz to practice what you've learned and receive some feedback on your learning. You're soon going to be an expert at business forecasting using business indicators, everyone say, wow. [MUSIC]
