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as econometric approaches and artificial intelligence methods. The methods applied in the context of tourism demand forecasting are increasingly diverse and complex (Li, Song, & Witt 2005).
While in many cases, the increased complexity of many models has improved forecasting performance, this does not mean that forecasting strategies need to be complex. Martin and Witt (1989) improved forecasting accuracy by employing some simple time series models, such as the random walk model, rather than more sophisticated traditional econometric models, and Gustavsson and Nordstrom (2001) note that the ARIMA (a univariate model) outperformed the SARIMA (a multivariate model) in terms of predictive accuracy. In fact, in many cases, simple methods work more accurately and steadily than complex ones, for various countries and regions. Meanwhile, such models lessen the obstacles for people who do not have a background in statistical analysis. Such obstacles may be brought about by the intricacies of using econometric or other complex forecasting models. Thus, one goal of this chapter is to design a simple forecasting strategy.
To achieve this goal, we used the performance of the United States’
mainstream stock indexes (Nasdaq Composite, Dow Jones Industrial Average, and Standard & Poor’s 500) to predict international visitors’
spending (IVS) in the United States. The first advantage of this method is that stock market data is easy to obtain. Practitioners in the tourism industry can conveniently obtain stock market information from various sources, such as financial news reports on TV or the internet. Secondly, the stock market, which is the barometer of the modern economy, reacts to changes in economic trends in a timely manner. Moreover, the performance of the stock market reflects the impact of many other significant events that can affect tourism demand in many countries or regions, but are hard to quantify or to depict by means of a limited set of economic indexes. Many previous predictions have used sets of key features (variables), such as CPI (consumer price index) in a given destination, the number of hotel rooms in the destination, and the tourist population’s main places of origin. Although these features have played important roles in existing forecasting models, it is hard to guarantee that they cover all the major factors affecting tourism demand to a given destination. For example, using the CPI, it is difficult to capture the effects of terrorist attacks and political affairs (which can have a dramatic effect on tourism demand), or changes in the tourist population’s main places of origin. In contrast, these effects are reflected easily and quickly by stock market fluctuations in either the destination or the place of origin.
Figure 10.1 below illustrates the comparison between the change in the
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closing price of the Standard & Poor 500 Index (S&P 500) and the change in international visitors’ spending (IVS) in the United States, in the period from 1992 to 2013. It is easy to see that the fluctuations in the S&P 500 (specifically, the two dramatic decreases in 2000 and 2007 and the two dramatic increases in 2004 and 2010) were instantly followed (within one quarter, in most cases) by a similar fluctuation of IVS in the United States.
Similar discoveries could also be made by comparing the change of IVS in the United States and the change in the closing price of the NASDAQ Composite Index or the Dow Jones Industrial Average Index.
Figure 10.1 – S&P 500 Close Price vs. IVS (1992 - 2013)
According to the computation, quarterly based international visitors’
spending (IVS) in the United States is very sensitive to its mainstream stock markets, including the NASDAQ Composite Index (with a correlation coefficient equal to 0.700), the Dow Jones Industrial Average Index (with a correlation coefficient equal to 0.775), and the Standard &
Poor’s 500 Index (with a correlation coefficient equal to 0.698). In this paper, we demonstrate that an accurate prediction of quarterly IVS in the United States, whose mean absolute percentage error (MAPE) is around
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2.0, could easily be achieved by using the Linear Regression model, based on stock markets changes during the quarter that immediately precedes the target quarter.
This is good enough for the purpose of evaluating future tourism demand in the United States, as the method (Linear Regression [LR]) is easy to implement and the data (the change in the NASDAQ Composite Index, the Dow Jones Industrial Average Index, and the Standard & Poor’s 500 Index) is easy to obtain. However, like other research conducted using quarterly-based prediction, this strategy may lead to inaccuracies in some cases. For example, in some extreme cases, the MAPE could be greater than 10. This may be because the randomness of tourism demand is always greater in the short term than in the long term. Moreover, from an economic perspective, quarterly data usually has more outliers than annual data, because it is more likely that data across short time intervals will reflect reactions to periodic events rather than following a macroscopical trend (this will be discussed below). Thus, quarterly based predictions are likely to be more unstable than those that are annually based.
To overcome this deficiency, we designed a strategy based on the multi-period performance of the stock market. This did not simply involve adding new features to a previously existing model. This is because, first of all, the influence of macroeconomic policies or trends on the tourism industry can usually be detected only across a relatively long time period, as compared to detecting micro policies or trends. Secondly, because the time interval (a quarter) is relatively short, the performance of the target quarter’s IVS is, in most cases, not straightforwardly affected by a single policy or a single period. For example, after a great recession, though the stock market may rebound after a while, the tourism demand market cannot completely avoid earlier negative effects. Noticeably, our experiments illustrate that the predictions’ MAPE increased slightly as a result of using the multi-period strategy. The stability of predictions also improved. For example, the maximum absolute percentage error of predictions decreased from 23 to 19, and the ratio of bad cases, whose absolute percentage error is greater than 10, decreased from 13% to 10%.
In this chapter, the multi-period strategy used is the ordinary least square fit of IVS in the United States, based on the multi-period performance of the stock market. Despite the fact that this is an accurate and stable strategy, we aim to further improve the explanatory ability of this model. In fact, this model may have a multi-collinearity problem, which means that some of the information depicted by certain features may be duplicated. Although a multi-collinearity problem would not affect the accuracy of the model’s predictions, it would limit its explanatory
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ability. This is a problem shared with some previous studies. In order to enhance explanatory ability, the implementation of feature selection techniques in the process of establishing a model has been recommended.
Therefore, in the research presented in the chapter, we used Bestglm, a tool that produces the best subset of features in the generalized linear model (GLM). This will be discussed in full below. Key features indicated by this tool improved the explanatory ability of the previous model, as well as further improving the overall stability of prediction.