Forecasting turning points in tourism growth

Tourism demand exhibits growth cycles, and it is important to forecast turning points in these growth cycles to minimise risks to destination management. This study estimates logistic models of Hong Kong tourism demand, which are then used to generate both short- and long-term forecasts of tourism growth. The performance of the models is evaluated using the quadratic probability score and hit rates. The results show that the ways in which this information is used are crucial to the models’ predictive power. Further, we investigate whether combining probability forecasts can improve predictive accuracy, and find that combination approaches, especially nonlinear combination approaches, are sensitive to the quality of forecasts in the pool. In addition, model screening can improve forecasting performance.     

China and international market integration: Evidence from the law of one price in the Middle East and Africa

We model market integration in the Middle East and Africa by analyzing price dispersion and testing the law of one price (LOP) on highly-comparable actual local retail prices of 135 goods and services across 23 countries in the region over the period of 1990–2016. Second-generation panel estimators are applied to four price benchmarks: Regional average, South Africa, China, and US prices. Cross-regional price dispersion diminishes considerably over time up to 2008, particularly for non-tradeables around China price. The test of LOP indicates the percentage of convergent prices is highest in China price benchmark, followed by US, South Africa, and regional average benchmarks. Direct estimation of the convergence speed confirms this order. Overall, the results show evidence of increasing market integration in Middle East and Africa but it appears to be driven by global forces and, especially, the rise of China as a new economic power. The results show that some emerging market economies, such as China, can step up and promote integration while traditional economic powerhouses, such as the USA and UK, disengage from international economic relations.     

Pricing used books on Amazon.com: a spatial approach to price dispersion

This paper estimates a spatial autoregressive (SAR) model of price dispersion using publicly available internet bookselling data. It uses a semiparametric adaptive estimator that does not require the usual Gaussian assumption of maximum likelihood (ML) estimators. The results suggest that both price competition and seller heterogeneity are key drivers of the observed price dispersion. The paper finds that sellers with large sales volume, newly established sellers and US mainland states-based sellers tend to price lower. The identified significant spatial interaction is evidence of spatial price competition. Controlling for everything else, a seller asks a lower price when large sellers charge relatively high prices, which is also evidence of price-based selling and undercutting.     

Analyst earnings forecast precision and local advantage: evidence from American depositary receipt firms

Prior research has documented a finding that local analysts provide more accurate earnings forecasts than nonlocal analysts in many settings. However, little is known about local and nonlocal analysts’ relative earnings forecast abilities for ADR stocks. In this study, we find that the local advantage disappears for ADR stocks and that nonlocal analysts in fact outperform local ones in this case. We investigate the source of this ‘local disadvantage’ and find evidence against hypotheses based on accounting standards and exchange rates. We document that the local advantage decreases with increased investor interest in foreign firms, consistent with our nonlocal investors’ interest hypothesis.     

信息分享对原始设备制造商与其合同制造商竞争的影响

研究了原始设备制造商的预测信息分享对一个原始设备制造商和一个与其同时有合作和竞争的合同制造商组成的供应链系统的影响,建立制造商间信息分享的模型,该模型包括一个原始设备制造商和一个合同制造商。研究发现,原始设备制造商关于市场潜在需求预测信息的分享对其预期利润是不利的,同时需求信息预测的精度对原始设备制造商信息分享的决策也有影响,原始设备制造商没有动机与其供应链成员进行信息分享,但信息分享使得供应链整体利润增加。最后,建立一个信息分享补偿机制分享供应链利润的增加量,以期通过信息分享补偿机制促使原始设备制造商有动机进行信息分享,从而实现其与合同制造商的“双赢”。     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

Forecasting upon a star: Forecasting or wishful thinking?

The purpose of this paper was to perform a detailed analysis of the challenges faced while developing and using econometric models to forecast future transportation demand. To this end, a comparative analysis of the state of practice and state of the art was undertaken on a concrete example – Viracopos Airport in Brazil. A review of relevant technical and scientific literature identified a number of approaches and each representative example was synthesized into a specification “template”. We then compared the performance of each “template” with the observed demand, through an intra-series forecast. A general finding was that econometric model specifications proved to be somewhat homogenous and simpler in nature, with results indicating a relatively small difference in fit and forecast capability across models. Even with the elimination of what is typically considered the main culprit for deviations – the forecast uncertainty of the explanatory (input) variables – the forecast is still subject to sizeable deviation. To address this issue, we proposed developing some sanity check indexes, particularly relevant for long-term forecasts. We conclude that the challenges faced at the Viracopos Airport Concession were far from econometric ones, that the success of the demand forecast and the concession itself required more than a well estimated econometric model. Finally, regarding investment obligations within the concession agreement, we strongly recommend making them conditional to meeting demand milestones, given the inherent unpredictability in forecasts.     

Affiliated block shareholders and analyst optimism

We investigate several previously under-documented conflicts of interest that may result in analyst optimism by utilizing two unique features of brokerage firms in China, namely, the dominant ownership of large shareholders within the brokerage firms and the mandatory disclosure of brokerage firms’ commission income derived from each mutual fund client. We show that controlling shareholders of an analyst’s brokerage-firm put pressure on the analyst to report more optimistically biased earnings forecasts and recommendations to the stocks they hold larger positions in. We also find that the magnitude of analyst optimism increases with the shareholdings of the mutual funds that contribute commission fees to the analyst’s brokerage firm. These findings remain robust after incorporating a regulation change that reduces conflicts of interest in the brokerage industry and higher dimensional fixed effects, and thus are unlikely to be driven by reverse causality or omitted variable bias.     

What do professional forecasters actually predict?

This paper studies what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into trend, business cycle, and irregular components. We examine which components are captured by professional forecasters by regressing their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods, we find that, in the short run, the Survey of Professional Forecasters can predict almost all of the variation in the time series due to the trend and the business cycle, but that the forecasts contain little or no significant information about the variation in the irregular component.     

Yen or Yuan? The law of one price and economic integration in Asia

Using highly comparable local retail prices of 146 goods and services across 18 Asian countries over 1990–2014, we analyse price dispersion and test convergence to the law of one price (LOP ) for these prices around three price benchmarks—Asia‐average, Japan and China prices—to gain insight about market integration in overall Asia as well relative integration of Asian economies to Japan and China. Cross‐Asia price dispersion around China‐price benchmark, for both tradables and non‐tradables, diminishes significantly over the sample period whereas that around Japan‐price benchmark increases considerably, particularly after the 2008 crisis. There is convergence to the LOP for about half of goods and services in China‐ and Asia‐average price benchmarks. The percentage of convergent prices is significantly smaller in Japan‐price benchmark. Direct estimates of the convergence speed parameter also confirm these observations. Overall, our results show evidence of increasing economic integration in Asia in the last two decades. The process of price convergence appears to be driven by the emergence of China as the centre of economic gravity in the region. There is much room for improvement as economic integration in Asia is still far below that in Europe in the 1990s or USA in the 1980s.