Which of the following models is more adaptable to sudden market changes?

Exponential smoothing is recognized for its adaptability to sudden market changes due to how it applies weighted averages to past data, giving more importance to recent observations. This characteristic allows it to respond more quickly to fluctuations and trends in data compared to other models.

In exponential smoothing, the forecast is adjusted with a smoothing constant that determines the weight given to the most recent data points versus older data. This means that if a sudden market change occurs, the model can quickly reflect that change in its predictions by adjusting the weights of the data accordingly.

In contrast, moving averages treat all historical data with equal importance, making them less responsive to recent shifts. Causal models rely on relationships between variables and can be less flexible if unexpected changes occur, as they assume a stable relationship based on historical data. Time series models are similar in that they analyze data trends over time but may use formulaic approaches that can lag behind sudden changes compared to exponential smoothing’s immediate adjustment to recent data. Thus, exponential smoothing stands out as the most adaptable choice for managing unpredictable market conditions.