Epistemological foundations for the assessment of risks in banking and finance

This article presents the epistemological and conceptual foundations on which current attempts to model crises and assess financial risks are based. It draws a distinction between two research programs, in Lakatos' sense: on the one hand, crises understood as structural events within a cycle; on the other hand, crises seen as statistical tail events. The methodological, theoretical and practical consequences of such a dichotomy are exposed. A crucial difference lies in the assumptions about change in the causal processes generating economic outcomes, especially asset returns. Furthermore, this article insists on providing conceptual definitions of key terms that have distinct meanings within the two research programs.     

Return prediction and stock selection from unidentified historical data

The experimental approach was applied to test the value of historical return series in technical prediction. Return sequences were randomly drawn cross-sectionally and over time from S&P500 records and participants were asked to predict the 13th realization from 12 preceding returns. The hypothesis that predictions (nominal or real) are randomly assigned to historical sequences is rejected in permutation tests, and the best-stock portfolios by experimental predictions significantly outperform the worst-stock portfolios in joint examination of mean return and volatility. The participants dynamically adjust their predictions to the observed series and switch from momentum riding to contrarian extrapolation when recent trends get extreme. The implicit tuning of predictions to specific series captures variabilities that could not be inferred by schematic statistical forecasting.     

Simple averaging of direct and recursive forecasts via partial pooling using machine learning

This article introduces the winning method at the M5 Accuracy competition. The presented method takes a simple manner of averaging the results of multiple base forecasting models that have been constructed via partial pooling of multi-level data. All base forecasting models of adopting direct or recursive multi-step forecasting methods are trained by the machine learning technique, LightGBM, from three different levels of data pools. At the competition, the simple averaging of the multiple direct and recursive forecasting models, called DRFAM, obtained the complementary effects between direct and recursive multi-step forecasting of the multi-level product sales to improve the accuracy and the robustness.     

基于小波神经网络的鱼类价格预测研究

鱼类市场价格是影响渔民收益的重要因素,因此如何能够准确分析和预测鱼类的价格是一个十分重要的问题.论文在考虑了BP网络原形的一些缺点和不足,尝试在传统BP算法的基础上将网络神经元中的激励函数换成小波子函数,组建成的小波神经网络.通过对鲈鱼价格的预测,验证了小波神经网络的可行性,然后基于阿里巴巴的鲈鱼价格的历史数据,验证了该方法的合理性.最后开展对新疆乌伦古湖水产综合基地三类主要鱼类价格预测的实证研究,研究结果表明河鲈和梭鲈的价格会有小幅的波动,但高白鲑的价格会有大幅的提升.因此,小波神经网络作为一种传统神经网络的改进,可用于预测鱼类价格的短期预测,为水产基地的决策提供依据.     

基于相空间重构及自适应支持向量机的短期风速预测

风速具有较强的随机性和间歇性,导致大规模风电接入电网会严重影响电力系统的安全稳定运行以及电能质量。较为准确的风速预测可以降低风能对电网的不利影响,为电网运行调度提供可靠的依据。在对风速进行混沌属性分析及相空间重构的基础上,采用自适应支持向量机进行短期风速预测,结果表明该方法的预测精度高于BP、RBF等预测模型。     

Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data

We utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.     

The importance of urban milieu in predicting recreation participation: The case of day hiking

Abstract

Numerous outdoor recreation forecasting studies have included household characteristics as well as aspects of the recreation facilities and accessibility. Only a few studies, however, have included in the prediction equation any measure of the effects of urban milieu on behavior. Four commonly cited surrogates of urban milieu are used to cluster the cities of Illinois into three relatively homogeneous groups of environments. Regression analyses are then undertaken using individual household data, aspects of the recreation facilities patronized by the households, participation, and distance and travel time estimates. Comparisons of the regression analyses indicate that, as a concept, milieu is an important predictor of both the volume of recreation participation and salient features of recreation facilities.     

The impact of the Asian financial crisis on Japanese demand for travel to Hong Kong: A study of various forecasting techniques

The Asian financial crisis has drawn worldwide attention because of its significant economic impact on local economics, especially on the economy of a tourism‐dependent destination. Unfortunately, there have been very few articles about the relationship of the Asian financial crisis and tourism demand forecasting. This relative lack of prior studies on the Asian financial crisis and tourism demand forecasting is particularly true in the context of Hong Kong. This article reports on a study that utilized officially published data to test the accuracy of forecasts of Japanese demand for travel to Hong Kong, measured in terms of the number of Japanese tourist arrivals. Seven commonly‐used tourism forecasting techniques were used to determine the forecasting accuracy. The quality of forecasting accuracy was measured in five dimensions. Experimental results indicated mixed results in terms of forecasting accuracy. Overall, artificial neural network outperformed other techniques in three of the five dimensions.     

Forecasting in Short-Term Planning and Management for a Casino Buffet Restaurant

SUMMARY

Demand fluctuation accounts for an important consideration in a restaurant's daily operational decisions. Good short-term planning and management require accurate forecasts of daily demand. The objective of this study is three-fold: (1) to apply, evaluate, and compare different methods of forecasting customer counts for an on-premises buffet restaurant of a local casino in Las Vegas, (2) to describe and propose a combined forecasting approach for this casino buffet restaurant, and (3) to explore the concept of revenue and capacity management for this buffet restaurant. Eight forecasting models were tested and evaluated by two common error measures. The results suggest that a double moving average model was the most accurate model with the smallest MAPE and RMSPE. Extensive discussions on forecasting and planning/management in buffet operations are provided along with recommended future research.     

Forecasting China's Monthly Inbound Travel Demand

Summary

China is currently expecting a growth in inbound travel demand as the result of China's “open door policy,” participation in World Trade Organization (WTO), success in hosting the Olympics in Beijing in the year 2008 and political stability. This paper focused on two issues: (1) forecasting China's monthly inbound travel demand and (2) seasonally and seasonal ARIMA model selection for monthly tourism time-series. In this paper following seasonal ARIMA models were considered: the seasonal ARIMA model with first differences and 11 seasonal dummy variables, the conventional seasonal ARIMA model with first and the fourth differences. In order to select the best forecasting model, finally both seasonal ARIMA models were compared with the AR model with fourth differences, the basic structural model (BSM) and the naive “No Change” model. In the one-step ahead forecasting comparison, the conventional seasonal ARIMA model with first and the fourth differences becomes the best forecasting model for both inbound foreign visitor demand and total visitor demand. This may be due to the nature of monthly seasonal variations in visitor arrivals, which is less marked. Our forecasts indicate that China foreign visitor arrivals and total visitor arrivals are expected to grow by 14% and 27% respectively from 2002 to 2005.