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The main purpose of this paper is to contribute to the ongoing literature on the pertinence of TALC (tourism area life cycle) to model and analyse mature destinations, using quantitative data and alternative functional forms. With this purpose, this work analyses the recent data on tourist demand in Tenerife (Canary Islands), which is supposed to be playing the role of “refuge” with respect to other competing resorts, when the latter ones present political instability after the so-called Arab Springs, particularly the Middle East and North Africa.

To analyse the data, and to explore potential epiphenomenon, nonlinear competing regression analysis were performed (logistic, Gaussian and logarithmic), taking into account some insights from tourism life cycle theories.

Some suggestions are presented for political management of these destinations that are still working under a moratorium on tourist infrastructure.

Once the TALC is accepted – even under discussion – in the scholarly fore, certain incentives arise to identify, interpret and communicate signals of maturity. Public funds and specific policies (moratoria) can then be solicited to political instances. Further empirical research, complementary quantitative approaches, along with new data that confronts the evolution of demand in Tenerife with paulatine reactivation of competing destinations, will be strongly required to overcome the limitations of this first attempt and to properly determine the effectiveness of rejuvenation policies. Besides, additional quantitative data should be considered to explore the potential explanatory factors beyond the time series analysis into models that are more theoretical.

The results suggest that the diagnosis of maturity was at least premature in the first place, poorly based on data analysis and fast in promoting specific policies whose effectiveness is under discussion even after decades.

This work aims to explore the behavior of stock market prices according to the autoregressive fractional differencing integrated moving average model. This behavior will be compared with a measure of online presence, search engine results as measured by Google Trends.

The study sample is comprised by the companies listed at the STOXX® Global 3000 Travel and Leisure. Google Finance and Yahoo Finance, along with Google Trends, were used, respectively, to obtain the data of stock prices and search results, for a period of five years (October 2012 to October 2017). To guarantee certain comparability between the two data sets, weekly observations were collected, with a total figure of 118 firms, two time series each (price and search results), around 61,000 observations.

Relationships between the two data sets are explored, with theoretical implications for the fields of economics, finance and management. Tourist corporations were analyzed owing to their growing economic impact. The estimations are initially consistent with long memory; so, they suggest that both stock market prices and online search trends deserve further exploration for modeling and forecasting. Significant differences owing to country and sector effects are also shown.

This research contributes in two different ways: it demonstrate the potential of a new tool for the analysis of relevant time series to monitor the behavior of firms and markets, and it suggests several theoretical pathways for further research in the specific topics of asymmetry of information and corporate transparency, proposing pertinent bridges between the two fields.