Purpose The purpose of this study is to provide new insights into the factors that influence cancellation behaviour with respect to hotel bookings. The data are based on individual bookings drawn from a hotel reservation system database comprising nine hotels. Design/methodology/approach The determinants of cancellation probability are estimated using a probit model with cluster adjusted standard errors at the hotel level. Separate estimates are provided for rooms booked offline, through online travel agencies and through traditional travel agencies. Findings Evidence based on 233,000 bookings shows that the overall cancellation rate is 8 per cent. Cancellation rates are highest for online bookings (17 per cent), followed by offline bookings (12 per cent) and travel agency bookings (4 per cent). Probit estimations show that the probability of cancelling a booking is significantly higher for early bookings, large groups that book offline, offline bookings during high seasons, bookings not involving children and bookings made by guests from specific countries (e.g. China and Russia). Among the factors, booking lead time and country of residence play the largest role, particularly for online bookings. Research limitations/implications The analysis is based on individual-level booking data from one hotel chain in Finland, and therefore cannot be generalised for the total population of hotels in the country under observation. Originality/value The main contribution of this paper is a thorough investigation of the factors that influence cancellation behaviour at both the theoretical and empirical levels. Detailed and unique data from a hotel reservation system allow for new empirical insights into this behaviour.

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This study focuses on non-institutional trading behaviour around interim earnings announcements in the emerging market. We separate the stock trading activity of Finnish households into five trading classes and compare the results to institutional trading. Data covering the years 1996-2000 shows that earnings news triggers trading in every trading class. We also find some evidence that actively trading individuals especially (compared to passively trading ones) show increased buying and selling activity before the event compared to the non-event period. After the event we find that Finnish households in the most active investor class tend to follow a contrarian strategy, especially selling after good news. This adds to previous evidence by Grinblatt and Keloharju (2000b) . Furthermore, the performance of the active investor classes is superior to that of passive ones. Finally, the institutional trading class is clearly less affected by the announcement than the active investor classes, suggesting that institutions utilize a broader information set than individual investors. Copyright Blackwell Publishers Ltd, 2006.

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This study analyzes the role of personal guarantees and collateral in the context of two different lending structures: one transaction and the other relationship based. The Finnish bank data, which were uniquely accessible for the study, enabled an exploration of credit files with specific details of the characteristics of the lending relationship during the period 1995–2001. According to the empirical results, the use of personal guarantees is an indication of transaction‐based lending. Personal guarantees seem to increase the loan premium in transaction‐based loans more than in relationship‐based loans. Close ties between a bank and a firm seem to be a desirable basis for small and medium‐sized enterprise bank lending.

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A difference-in-differences analysis is used to investigate the short-term price effects of eight sporting events in Finnish Lapland. Data consist of 220,000 room bookings from the reservation system of a nine-hotels chain. Treated hotels are those located within an area where sporting events are regularly held. The control group consists of hotels further away that are not affected by the event. Robust regressions show that hotel room prices rise by 14% on average during the event, when booking and guest specific factors are held constant. For the pre-event period, no significant positive price effect can be detected, and for the post-event period, there is even a significant negative effect of 6%, on average. In addition, there is a large variation in the price effects across the different sporting events, with the highest for the Levi FIS Alpine Ski World Cup competition (60%) and no effect for some small-scale events. Quantile regressions show that price effects are slightly higher for high-priced than for low-priced rooms.