Let's think about all the different decisions you can make in running ARIMA models. If you make a slightly different decision, you can get different results. In the end, these differences affect your forecast. This forecast affect your trading. How? When you run ARIMA models, you are modeling a mean-reverting series. Recall that you need a stationary series to create a model. Stationary series have a constant mean. ARIMA is a way of describing how you can expect past-observed and unobserved values to depart from the mean, but eventually they return. ARIMA represents a series with noise but one that returns to its long-term average. You made six key decisions, each of the decisions can affect that average. Suppose you work with equity prices. Suppose you choose your data exclusively from bull markets. If your choice of data excluded periods of bearish markets, then perhaps your mean is too high. You can choose the p and the q. This gives you the form of the model. You estimated the slopes and intercept accordingly. This gives you the value of the estimates. You can see that using the values of the coefficients. You can determine the long-term average, you can also determine how far the series typically deviates from the mean. You can observe the current value so the model can tell you where you are in relative terms between the average and the expected deviation. This is good news. This tells you how long you can expect to wait to get back to the mean. This helps you trade. This can determine how long you should hold a trade. This can help you set realistic profit objectives. This can help you set stop losses. You chose a p and a q. This gives you the form of the model. You estimated the slopes and intercept accordingly. This gives the value of the estimates. Let's think about the values of the AR coefficients. Recall that these AR coefficients are less than one. A high value for the AR coefficient is 80 percent. This high coefficient tells you that the market has a long-lasting memory. If the first AR term had a coefficient of 80 percent, then its value is highly influential in determining the next value. This could indicate significant market impact. Suppose you trade a small cap stock. You run an AR model on the returns and you get a high coefficient. This says that the market has a memory, the past influence is what comes next. Suppose you use trade in extremely liquid exchange-traded fund. You run an AR(1) model on the returns, you get a low coefficient. This says that the market is more resilient. The past doesn't influence what comes next. This could indicate very little market impact. The AR coefficients in particular are easy to interpret. They tell you how important the past is and waiting what comes next. Finally, the results of the model also tell you how much explanatory power there is in the model itself. What is your model best fit is in ARIMA(0,0)? Then you have a random walk. This tells you that the market is efficient. But what if your model is best fit by an AR(1)? Then there's structure. However your choices of data; p, d, q, and ARIMA form, estimation method and software affects the outcomes. Some combinations of these choices may show ARIMA(0,0) is better, others may show AR(1) is better. These two models result in two different trading strategies. Very different. We now see why trading is sensitive to model estimates. You make modeling choices throughout the process. These decisions affect parameter values. Parameter values describe the ebb and flow of the series, where it is expected to be, how far it deviates, and how long it takes to return. These averages, volatilities, and expected reversion time affect your trading. Different decisions create different perspectives about how you should trade, therein lies the sensitivity.