Which of the following quantitative techniques responds the most quickly to trends?

High alpha factor exponential smoothing is a quantitative forecasting technique that places greater emphasis on recent data, allowing it to respond quickly to changes or trends in the dataset. The alpha factor, a smoothing constant between 0 and 1, determines the weight given to the most recent observation compared to previous observations. A higher alpha value will result in more weight being placed on recent data, which means that the forecast will adjust rapidly as new data becomes available. This is particularly useful in environments where trends may shift frequently, as it allows forecasts to be more aligned with current realities.

In contrast, a moving average tends to smooth out fluctuations over a defined period, which can lead to slower responses to recent changes since it averages several past observations. Seasonal indices are used to account for seasonal variations in data and do not directly react to trends; they serve to adjust forecasts based on expected seasonal changes rather than immediate trends. Expert opinion relies on subjective insights rather than a quantitative response to data patterns, hence it does not react as quickly to quantitative changes in trends as exponential smoothing does. This makes high alpha factor exponential smoothing the most suitable technique for quickly adapting to emerging trends.