This article explores Airbnb accommodation spatial distribution and it estimates the main determinants of its location choice. It employs spatial bivariate correlations and spatial econometrics to understand the heterogeneous spatial relationship between established hotels and Airbnb for three kinds of local tourism destinations: sun and beach, nature-based, and city. The case study concerns the Canary Islands where a good mixture of these attractions can be found. The main conclusion drawn is that Airbnb regulation needs to distinguish the kind of tourism. More precisely, Airbnb supply overlaps established hotels in city tourism, but it does not so clearly in sun and beach nor nature-based destinations. Airbnb supply matches tourist visits spatial distribution better than established hotels in city and nature-based destinations, but not in sun and beach destinations, where the incumbent hotels are closer to the tourism resources. Finally, the results from the spatial econometrics model shows that population size and the number of tourist visits matters as determinants of Airbnb location. However, the main determinant is price, which has got a much larger elasticity.

Finding the best trading rules is a well-known problem in the field of technical analysis of stock markets. One option is to employ genetic algorithms, as they offer valuable characteristics towards retrieving a “good enough” solution in a timely manner. However, depending on the problem size, their application might not be a viable option as the iterative search through a multitude of possible solutions does take considerable time. Even more so if a variety of stocks are to be analysed.In this paper we concentrate on the enhancement of a previously published genetic algorithm for the optimisation of technical trading rules, using example data from the Madrid Stock Exchange General Index (IGBM)

This paper investigates the profitability of a simple and very common technical trading rule applied to the General Index of the Madrid Stock Market. The optimal trading rule parameter values are found using a genetic algorithm. The results suggest that, for reasonable trading costs, the technical trading rule is always superior to a risk-adjusted buy-and-hold strategy.