An econometric analysis of international tourism demand in Hong Kong : a cointegration and error correction approach

The purpose of this study is to develop econometric models to better understand the economic factors affecting inbound tourist flows from each of six origin countries that contribute to Hong Kong’s international tourism demand. To this end, we test alternative cointegration and error correction approaches to examine the economic determinants of tourist flows to Hong Kong, and to produce accurate econometric forecasts of inbound tourism demand. Our empirical findings show that permanent income is the most significant determinant of tourism demand in all models. The variables of own price, weighted substitute prices, trade volume, the share price index (as an indicator of changes in wealth in origin countries), and a dummy variable representing the Beijing incident (1989) are also found to be important determinants for some origin countries. The average long-run income and own price elasticity was measured at 2.66 and – 1.02, respectively. It was hypothesised that permanent income is a better explanatory variable of long-haul tourism demand than current income. A novel approach (grid search process) has been used to empirically derive the weights to be attached to the lagged income variable for estimating permanent income. The results indicate that permanent income, estimated with empirically determined relatively small weighting factors, was capable of producing better results than the current income variable in explaining long-haul tourism demand. This finding suggests that the use of current income in previous empirical tourism demand studies may have produced inaccurate results. The share price index, as a measure of wealth, was also found to be significant in two models. Studies of tourism demand rarely include wealth as an explanatory forecasting long-haul tourism demand. However, finding a satisfactory proxy for wealth common to different countries is problematic. This study indicates with the ECM (Error Correction Models) based on the Engle-Granger (1987) approach produce more accurate forecasts than ECM based on Pesaran and Shin (1998) and Johansen (1988, 1991, 1995) approaches for all of the long-haul markets and Japan. Overall, ECM produce better forecasts than the OLS, ARIMA and NAÏVE models, indicating the superiority of the application of a cointegration approach for tourism demand forecasting. The results show that permanent income is the most important explanatory variable for tourism demand from all countries but there are substantial variations between countries with the long-run elasticity ranging between 1.1 for the U.S. and 5.3 for U.K. Price is the next most important variable with the long-run elasticities ranging between -0.8 for Japan and -1.3 for Germany and short-run elasticities ranging between – 0.14 for Germany and -0.7 for Taiwan. The fastest growing market is Mainland China. The findings have implications for policies and strategies on investment, marketing promotion and pricing.

Assessing the value relevance of accounting data after the introduction of IFRS in Europe

Since 2005, European-listed companies have been required to prepare their consolidated financial statements in accordance with the International Financial Reporting Standards (IFRS). We examine whether value relevance increased following the introduction of IFRS, using a sample of 3,721 companies listed on five European stock exchanges: Frankfurt, Madrid, Paris, London, and Milan. We find mixed evidence of an increase in value relevance. However, the influence of earnings on share price increased following the introduction of IFRS in Germany, France, and the United Kingdom, while the influence of book value of equity decreased (except for the United Kingdom). © 2010 Blackwell Publishing Ltd.

The effect of timeliness and credit ratings on the information content of earnings announcements

Abstract: This paper investigates the impact of timeliness and credit ratings on the information content of the earnings announcements of Greek listed firms from 2001 to 2008. Using the classical event study methodology and regression analysis, we find that firms tend to release good news on time and are inclined to delay the release of bad news. We also provide evidence that the level of corporate risk differentiates the information content of earnings according to the credit rating category. Specifically, firms displaying high creditworthiness enjoy positive excess returns on earnings announcement dates. In contrast, firms with low creditworthiness undergo significant share price erosions on earnings announcement days. We also observe a substitution effect between timeliness and credit ratings in relation to the information content of earnings announcements. Specifically, we find that as the credit category of earnings-announcing firms improves, the informational role of timeliness is mitigated.

Stock repurchases and treasury share sales: Do they stabilize price and enhance liquidity?

The article studies the impact of a firm’s trading in its own shares on the volatility and market liquidity of the firm’s stock in the Italian stock market. In the study, both stock repurchases and treasury share sales executed on the open market are defined as trading in own shares. The study finds that Italian firms can reduce the volatility of their stock and boost market liquidity by trading their own shares.

Stock repurchases and treasury share sales:do they stabilize price and enhance liquidity?

Can companies reduce the volatility and increase the liquidity of their stocks by trading them? In the context of the Italian stock market, where companies have far more leeway to sell as well as buy their own stocks than in the U.S., the answer is yes. We examine the effects of trading (open-market share repurchases and treasury shares sales) on liquidity (bid–ask spread) and volatility (return variance). Further, we examine the impact of shareholder approvals of repurchase programs on liquidity and volatility. We find clear evidence that trading increases liquidity and reduces volatility. These results are consistent with our analysis of the motives Italian companies give for making share repurchases.

Agent-orientated auction mechanism and strategy design

Agent-based technology is playing an increasingly important role in today’s economy. Usually a multi-agent system is needed to model an economic system such as a market system, in which heterogeneous trading agents interact with each other autonomously. Two questions often need to be answered regarding such systems: 1) How to design an interacting mechanism that facilitates efficient resource allocation among usually self-interested trading agents? 2) How to design an effective strategy in some specific market mechanisms for an agent to maximise its economic returns? For automated market systems, auction is the most popular mechanism to solve resource allocation problems among their participants. However, auction comes in hundreds of different formats, in which some are better than others in terms of not only the allocative efficiency but also other properties e.g., whether it generates high revenue for the auctioneer, whether it induces stable behaviour of the bidders. In addition, different strategies result in very different performance under the same auction rules. With this background, we are inevitably intrigued to investigate auction mechanism and strategy designs for agent-based economics. The international Trading Agent Competition (TAC) Ad Auction (AA) competition provides a very useful platform to develop and test agent strategies in Generalised Second Price auction (GSP). AstonTAC, the runner-up of TAC AA 2009, is a successful advertiser agent designed for GSP-based keyword auction. In particular, AstonTAC generates adaptive bid prices according to the Market-based Value Per Click and selects a set of keyword queries with highest expected profit to bid on to maximise its expected profit under the limit of conversion capacity. Through evaluation experiments, we show that AstonTAC performs well and stably not only in the competition but also across a broad range of environments. The TAC CAT tournament provides an environment for investigating the optimal design of mechanisms for double auction markets. AstonCAT-Plus is the post-tournament version of the specialist developed for CAT 2010. In our experiments, AstonCAT-Plus not only outperforms most specialist agents designed by other institutions but also achieves high allocative efficiencies, transaction success rates and average trader profits. Moreover, we reveal some insights of the CAT: 1) successful markets should maintain a stable and high market share of intra-marginal traders; 2) a specialist’s performance is dependent on the distribution of trading strategies. However, typical double auction models assume trading agents have a fixed trading direction of either buy or sell. With this limitation they cannot directly reflect the fact that traders in financial markets (the most popular application of double auction) decide their trading directions dynamically. To address this issue, we introduce the Bi-directional Double Auction (BDA) market which is populated by two-way traders. Experiments are conducted under both dynamic and static settings of the continuous BDA market. We find that the allocative efficiency of a continuous BDA market mainly comes from rational selection of trading directions. Furthermore, we introduce a high-performance Kernel trading strategy in the BDA market which uses kernel probability density estimator built on historical transaction data to decide optimal order prices. Kernel trading strategy outperforms some popular intelligent double auction trading strategies including ZIP, GD and RE in the continuous BDA market by making the highest profit in static games and obtaining the best wealth in dynamic games.

Price discovery for Chinese shares cross-listed in multiple markets

In this article we study the relationship between security returns cross-listed on the A share market of China and the H share market at the Stock Exchange of Hong Kong (SEHK). Most of these securities are also cross-listed on other markets. An important feature of this article is that we focus on the multilateral relationships between all cross-listed markets rather than concentrating only on the bi-lateral relationship between A and Hong Kong H shares. Using the impulse response functions and the variance decompositions from a Vector Autoregressive (VAR) process we show that the returns to the A share market are almost exclusively determined by domestic factors. In contrast, we find that the H share market is influenced by both the A share market within China and foreign stock markets elsewhere in the world. Impulse response functions suggest that innovations to the A share market and the Hong Kong H share market are partly transmitted to each other and to stock markets outside China. We show that liquidity has an important role to play in determining the impact that the home market has on cross-listed variance decompositions. © 2012 Copyright Taylor and Francis Group, LLC.

Optimising market share and profit margin:SMDP-based tariff pricing under the smart grid paradigm

Smart grid technologies have given rise to a liberalised and decentralised electricity market, enabling energy providers and retailers to have a better understanding of the demand side and its response to pricing signals. This paper puts forward a reinforcement-learning-powered tool aiding an electricity retailer to define the tariff prices it offers, in a bid to optimise its retail strategy. In a competitive market, an energy retailer aims to simultaneously increase the number of contracted customers and its profit margin. We have abstracted the problem of deciding on a tariff price as faced by a retailer, as a semi-Markov decision problem (SMDP). A hierarchical reinforcement learning approach, MaxQ value function decomposition, is applied to solve the SMDP through interactions with the market. To evaluate our trading strategy, we developed a retailer agent (termed AstonTAC) that uses the proposed SMDP framework to act in an open multi-agent simulation environment, the Power Trading Agent Competition (Power TAC). An evaluation and analysis of the 2013 Power TAC finals show that AstonTAC successfully selects sell prices that attract as many customers as necessary to maximise the profit margin. Moreover, during the competition, AstonTAC was the only retailer agent performing well across all retail market settings.

Asymmetric price transmission in EU petroleum markets

This research investigates the determinants of asymmetric price transmission (APT) in European petroleum markets. APT is the faster response of retail prices to cost increases than to cost decreases; resulting in a welfare transfer from consumers to fuel retailers. I investigate APT at 3 different levels: the EU, the UK and at the Birmingham level. First, I examine the incidence of asymmetries in the retail markets of six major EU countries; significant asymmetries are found in all countries except from the UK. The market share data suggest that asymmetries are more important in more concentrated markets; this finding supports the collusion theory. I extend the investigation to 12 EU countries and note that APT is greater in diesel markets. The cross-country analysis suggests that vertical and horizontal concentration at least partly explains the degree of asymmetry. I provide evidence justifying scrutiny over retail markets’ pricing and structure. Second daily data unveil the presence of APT in the UK fuel markets. I use break tests to identify segments with different pricing regimes. Two main types of periods are identified: periods of rising oil price exhibit significant asymmetries whilst periods of recession do not. Our results suggest that oligopolistic coordination between retailers generate excess rents during periods of rising oil price whilst the coordination fails due to price wars when oil prices are going downwards. Finally I investigate the pricing behaviour of petroleum retailers in the Birmingham (UK) area for 2008. Whilst the market structure data reveals that the horizontal concentration is higher than the national UK average, I find no evidence of APT. In contrast, I find that retail prices are sticky upwards and downwards and that firms with market power (majors and supermarkets) adjust their prices slower than other firms.