Using machine learning and big data for efficient forecasting of hotel booking cancellations

Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.     

Predictive abilities of speculators in energy markets

Using nonparametric methodology, I find that speculators are successful in taking profitable positions in energy futures markets, although the magnitude of this effect is lower than that found previously for agricultural markets. A plausible explanation for this difference is that price forecasting is more difficult for energy commodities. Moreover, I find that the energy speculators’ returns are due to the existence of the risk premiums rather than to speculators’ forecasting abilities. Futures risk premium is highly time-variant; notably, energy investors’ profits have been very limited in the GFC and post-GFC period, which coincided with the financialization of commodity markets.     

Why Greater Cash Holdings and Short-Term Debt Simultaneously Persist? The Case of Transition Economy

This study observes and explores a puzzle in Chinese firms whereby both cash holdings and short-term debt simultaneously account for more than 20% of total assets for at least two consecutive years over the sample period. This phenomenon conflicts with the principle of corporate value maximization, and is not clearly explained by the classical theories in corporate finance. Based on the implications in the extant literature and discussions of institutional constraints of the transition economy in China, this paper develops four hypotheses that are involved with agency conflicts between the largest shareholders and creditors and the formation of this puzzling financial structure. The empirical analyses suggest that the largest shareholders with tunneling motives seek to hold more cash to serve their private interests and/or the consequent operational deficit of the listed corporations. To the ends, these corporations tend to manage the timing of short term debt financing to increase cash reserves temporarily at the end of year. Essentially, greater cash holdings on the balance sheet of these corporations related with the puzzle become a misleading signal for potential creditors, possibly contributing to the refinancing of short-term debt of these listed firms for the following year. Hence, the puzzling financial structure is connected with the timing of debt financing and adverse selection of creditors. This study enriches the stream of literature on cash holdings and debt maturity, and provides new evidence on the impact of agency problems of the largest shareholders on the association between cash holdings and debt maturity in the context of a transition economy.     

海南国内生产总值的统计预测模型

国内生产总值(GDP)是衡量一个国家或地区经济实力的指标。建立了海南建省以来的GDP预测模型,并通过时间序列模型得到自变量的预测年度数据,进而对海南"十一五"与"十二五"期间的国内生产总值进行了预测,为制订经济发展规划提供参考。     

Trend forecasting for stability in supply chains

This paper revisits the use of trend forecasting to determine ordering policy in supply chains by viewing it as a part of the control process for making the supply responsive to demand. Trend forecasting is often used to assess demand — a tracked variable in the control context, which drives supply — a tracking variable. Used in this way, it is often observed to increase instability creating the so-called bullwhip effect. Trend is used on the other hand with reliability to increase stability in controller control, but with the difference that a trend of a tracking variable is used to drive correction. While both processes involve use of trend to determine policies for achieving reliable performance, the outcomes of the former are variable while those of the later can create improvement in control with certainty. The similarities and differences between the two processes are discussed and guidelines developed for applying trend forecasting to enhance stability in supply chains.     

Using diffusion models to forecast market size in emerging markets with applications to the Chinese car market

Marketing managers have to forecast the market size and this forecast guides strategic decisions whether to continue exporting, open new factories or expand existing production operations. Forecasting sales and the market size is a challenging task; even more so in emerging markets where data is limited and the market demand is changeable. This research proposes a novel approach that applies diffusion models using car ownership data to forecast car sales. Car ownership data may be easier to access than sales data in emerging markets but marketing managers are more interested in the sales forecast. Researchers propose using diffusion models to forecast the adoption of new products or products which are new to consumers in a market. This research demonstrates that marketing managers can use diffusion models to predict car sales in China where cars are new products to most consumers in this market. Since the majority of car buyers in China are first time buyers, car manufacturers and retailers must also forecast when the market composition will change. This effectively means predicting when first time car buying will start to slow down and repeat/replacement purchase or second hand car purchase will become more important. To forecast both sales and market composition change, marketing managers must choose a robust model. Managers want insights from models that have been tested robustly especially in less stable market conditions. In this context, this study illustrates the value of using a rolling forecast instead of a fixed horizon approach when comparing and choosing which model to use to forecast both sales and market composition change for the Chinese car market.     

埋刮板输送机刮板链条及其选用

在粮油加工行业,大多习惯用刮板链条的外观尺寸,即宽、高和节距来表示所选用的刮板链条。刮板链条在埋刮板输送机中被拖拽运动,承受张力。在MS型、MC型和MZ型输送机中,刮板链条承受的张力是不一样的。Z埋刮板输送机中,刮板链条所受的张力最大,在垂直埋刮板输送机次之。通过比较可以看出,V形刮板链务的宽、高和节距相同时,适用于MC型埋刮板输送机的V形刮板链条与适用于MZ型埋刮板输送机的V形刮板链条,许用载荷的差别很大。     

Nonlinear deterministic forecasting of daily dollar exchange rates

We perform out-of-sample predictions on several dollar exchange rate returns by using time-delay embedding techniques and a local linear predictor. We compared our predictions with those by a mean value predictor. Some of our predictions of the exchange rate returns outperform the predictions of the same series by the mean value predictor. However, these improvements were not statistically significant. Another interesting result in this paper which was obtained by using a recently developed technique of nonlinear dynamics is that all exchange rate return series we tested have a very high embedding dimension. Additionally, evidence indicates that these series are likely generated by high dimensional systems with measurement noise or by high dimensional nonlinear stochastic systems, that is, nonlinear deterministic systems with dynamic noise.     

区域物流需求预测的LaOR方法

目前回归函数中普遍存在的泛化能力得不到保证的缺点,结合统计学习理论的研究成果,建立了基于最小一乘准则的最优回归模型(LaOR模型)。与以往回归模型相比较,新模型综合考虑了回归误差和置信范围,可望有效地降低回归模型的期望风险。上海市将LaOR应用到物流需求的短期预测中,取得了可以接受的预测效果。     

Impacts of short-term interest rates on stock returns and exchange rates: Empirical evidence from EAGLE countries

In this study, we estimate Bayesian vector autoregression (BVAR) and time-varying structural VAR (TVP-VAR) models for Brazil, Indonesia, Mexico and Turkey to analyze the impacts of short-term interest rates on stock prices and exchange rates considering the relationships between these variables. BVAR and TVP-VAR models’ estimations indicate that monetary policy decisions of these countries lead to capital movements as well as capital movements may create a considerable amount of variation in exchange and stock markets both in the periods of economic stability and financial crisis. We also reveal that increases in interest rates intending to prevent capital outflows may lead to decrease in stock returns, which in turn may deteriorate the real economic activity in Indonesia, while changes in short-term interest rates in Brazil, Indonesia and Turkey cannot be used as a tool to stabilize the value of their home currencies against the USD. Our study highlights the importance of formulating an optimal monetary policy framework accompanied by macro-prudential polices, which help to reach inflation target and smooth the possible variations in exchange rates and stock prices during economic crisis conditions in Brazil, Indonesia, Mexico and Turkey.