How Smooth Is Your Data Smoother? Traders use moving averages to remove random fluctuations from price data. Some moving averages work better than others. Take a look. In 1995, I wrote an article for STOCKS & COMMODITIES presenting some of the properties of three methods for removing random noise from financial time series. I discussed the lags between the turning point of a simple test data sequence and the turning points of three types of moving averages. I showed that for equal moving average periods, the three moving averages had markedly different lag characteristics. Specifically, for each period, an endpoint moving average (EPMA) had a smaller lag than an exponential moving average (EMA), and both the EPMA and EMA had smaller lags than the corresponding simple moving average (SMA). While it is important to define the lag characteristics of a data smoother, there is another, even more basic, question that must be addressed about the performance of the smoothing function. How smoothly does the smoothing function smooth the data?