Time period bias is a sampling error caused by selecting observations that only cover a certain time period (i.e., a certain set of circumstances or factors). Time period bias may lead to inaccurate results since the conclusions obtained from using a sample suffering from the bias may be uniquely specific to observations sampled for the study and thus, are not representative of the population as a whole.
Examples of Time Period Bias
An example of time period bias in finance is calculating average profits generated by companies in a given industry during a time period of higher or lower than normal profitability. If the calculation of average profits looks at a 5-year window where economic prosperity was particularly high due to, for example, temporarily reduced raw material costs for the industry, then the average is not representative of the industry as a whole over a wide range of situations.
Another example of time period bias is when looking at a 12-month trailing average of stock market returns during the 2008 Global Financial Crisis. Such an analysis would paint a very bleak picture of the market returns as the only data used would be negatively skewed by the crisis. Thus, the conclusions drawn would not necessarily be applicable to financial markets as a whole. We know this is true as we’ve seen the financial markets recover, and the long-term return on the S&P 500 remains pegged at almost 10%.
How to Prevent Time Period Bias
Time period bias can skew study results and make conclusions specific to a particular set of circumstances. To avoid such situations, analysts should make sure to use a large number of observations that span a wide time range. This will allow the blending out of short-term observations and enable us to draw very general conclusions that will hold true under a wide range of situations.
Alternatively, analysts can protect their work from time period bias by keeping moving averages or trailing averages of securities that they may be interested in. Doing so will preserve the long-term trend that the security in question may seem to be going in, and the analysts will be able to access a large data set to compare results from period to period. Such an approach will allow them to distinguish between periods of particularly high or low economic prosperity since the fluctuations will stick out from the overall observed trends.
Practical Example: Time Period Bias
Consider the following market returns for a given stock market:
In the table above, we see the monthly returns of the stock market, as well as the 3-month and 5-month trailing averages. The far-right column also shows the difference between the two trailing averages.
The difference observed is due to time period bias, where the 5-month trailing averages take into account more data points and thus can be said to be a better average measure than the 3-month average. Due to the greater number of data points in the 5-month trailing average, no single data point has as much weight over what the trailing average will be. The measure is not easily affected by abnormally high or low monthly returns; thus, making the measure more resistant to time period bias.
Nonetheless, the 3-month measure would still be useful for analysts analyzing the short-term outlook for some of their positions in the market. Day traders often take it to the extreme and rely on abnormal highs and lows that will occur in a single day in order to make profitable trades. In such scenarios, time period bias is not a concern since the parties are mostly interested in short-term trends rather than all-encompassing conclusions.
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