恒生指数期货收盘价的预测分析

本文针对中国证券市场沪深300指数期货即将试行的情况，用多元统计分析的多元回归分析方法，探讨影响恒生指数期货走势的相关因素，并在此基础上，提出恒生指数期货（以下简称恒指期货）的回归预测模型，据此可对恒指期货的收盘价进行预测，供管理层和投资者在实际操作中参考。     

Understanding the impacts of product knowledge and product type on the accuracy of intentions-based new product predictions

New product development involves several critical decisions. A key decision making area in new product development is the evaluation of the viability and the market potentials of a new product. In the absence of any relevant historical data, companies ask the potential buyers of their products about their intentions to buy those products when assessing their viability. Despite the popularity of the use of behavioral intentions in predicting the market acceptance of new product ideas, both survey and empirical studies suggest that the accuracy of such predictions is usually very low. Although earlier case-based studies suggest that a number of factors can affect the quality of new product decisions, it is still empirically unclear how product knowledge and the type of new products might impact the predictive accuracy of intentions-based new product forecasting. This study utilized a longitudinal research design and empirically tested the hypotheses across two new products. The study first collected purchase intentions data about the new products. Second, it collected subsequent actual purchase data about the new products. The results of series of hierarchical regression analyses comparing the initial purchase intentions and subsequent actual behaviors showed that while product knowledge is positively related to the predictive accuracy and consistency of intentions-based new product forecasting, product type is negatively related to them.     

Forecasting the value effect of seasoned equity offering announcements

Seasoned Equity Offers (SEOs) by publicly listed firms generally result in unexpected negative share price returns, being often perceived as a signal of overvalued share prices and information asymmetries. Hence, forecasting the value effect of such announcements is of crucial importance for issuers, who wish to avoid share price dilution, but also for professional fund managers and individual investors alike. This study adopts the OR forecasting paradigm, where the latest part of the data is used as a holdout, on which a competition is performed unveiling the most effective forecasting techniques for the matter in question. We employ data from a European Market raising in total €8 billion through 149 SEOs. We compare economic and econometric models to forecasting techniques mostly applied in the OR literature such as Nearest Neighbour approaches, Artificial Neural Networks as well as human Judgment. Evaluation in terms of statistical accuracy metrics indicates the superiority of the econometric models, while economic evaluation based on trading strategies and simulated profits attests expert judgement and nearest-neighbour approaches as top performers.     

Long-term real dynamic investment planning

When long-term savers plan for retirement they need to know their investment prospects in terms of real income (Merton, 2014). While inflation has traditionally been considered as a complication in financial analysis and financial practise, we obtain enhanced predictability and model fit if the real returns are targeted in conjunction with earnings-by-price minus inflation as predictor. For this latter case, we propose an investment strategy of updating the simple classical Merton proportion as we go along. This simple strategy is very close to the complicated theoretically optimal solution but has comparably much lower parameter uncertainty.     

On the empirical performance of (T, s, S) heuristics

The periodic (T,s,S)$(T\text{,}s\text{,}S)$ policies have received considerable attention from the academic literature. Determination of the optimal parameters is computationally prohibitive, and a number of heuristic procedures have been put forward. However, these heuristics have never been compared in an extensive empirical study. Such an investigation on 3055 SKUs is carried out in this paper. Our study provides insights into the performance of (T,s,S)$(T\text{,}s\text{,}S)$ heuristics, also in relation to demand forecasting. The results show that Naddor’s heuristic is best able to minimize the total cost. However, the normal and power approximations achieve more efficient solutions in that backorder volumes are smaller at the same inventory levels, indicating the potentially superior performance of these methods if the balancing of holding and backorder costs can be improved. The results also show that, for all heuristics, the SBA variant of the Croston forecasting method significantly outperforms Croston as well as Single Exponential Smoothing (SES).     

Do forecasts expressed as prediction intervals improve production planning decisions?

A number of studies have shown that providing point forecasts to decision makers can lead to improved production planning decisions. However, point forecasts do not convey information about the level of uncertainty that is associated with forecasts. In theory, the provision of prediction intervals, in addition to point forecasts, should therefore lead to further enhancements in decision quality. To test whether this is the case in practice, participants in an experiment were asked to decide on the production levels that were needed to meet the following week’s demand for a series of products. Either underproduction cost twice as much per unit as overproduction or vice versa. The participants were supplied with either a point forecast, a 50% prediction interval, or a 95% prediction interval for the following week’s demand. The prediction intervals did not improve the quality of the decisions and also reduced the propensity of the decision makers to respond appropriately to the asymmetry in the loss function. A simple heuristic is suggested to allow people to make more effective use of prediction intervals. It is found that applying this heuristic to 85% prediction intervals would lead to nearly optimal decisions.     

Nursing care demand prediction based on a decomposed semi-markov population model

The semi-markovian population model introduced by Kao for the planning of progressive care hospitals is adapted to the prediction of nursing care demand at the level of a care unit in a general hospital. Assuming a feedback admission policy which refills the unit as soon as discharges occur, it is shown that the care unit can be decomposed into B independent subsystems corresponding to each of the B beds in the unit.For each bed the semi-Markov model permits the computation of the expected care demand and its variance for each of the seven forthcoming days. The model permits also the prediction of admissions of new patients. A prediction formula can thus be obtained where the expected care demand is expressed as a linear function of the expected number of admissions in the forthcoming days.Finally this methodology is illustrated on real data obtained in the gynaecology department of the Montreal Jewish General Hospital.     

一类不分明时间序列的回归预测

研究了一类不分明时间序列的线性回归预测问题,通过模糊数空间中的距离,建立了模糊环境中最小二乘回归模型,证明了回归模型解的存在性和唯一性,并给出了确定模型的模糊参数及检验模型拟合度的计算公式。     

A guide to using shadowing filters for forecasting and state estimation

Most traditional, and many modern, methods of state estimation have a number a failings when applied to high-dimensional and nonlinear systems. A promising new alternative that avoids many of these failings is a shadowing filter. An additional advantage of shadowing filters is that when combined with indistinguishable states they provide a consistent generalisation to the imperfect model scenario. This paper is a practical guide to implementing shadowing filters in nonlinear systems. It provides a detailed account of various tests and procedures to ensure successful implementation of a shadowing filter using gradient descent of indeterminism.     

Short-run electricity load forecasting with combinations of stationary wavelet transforms

Short-term forecasting of electricity load is an essential issue for the management of power systems and for energy trading. Specific modeling approaches are needed given the strong seasonality and volatility in load data. In this paper, we investigate the benefit of combining stationary wavelet transforms to produce one day-ahead forecasts of half-hourly electric load in France. First, we assess the advantage of decomposing the aggregate load into several subseries with a wavelet transform. Each component is predicted separately and aggregated to get the final forecast. One innovation of this paper is to propose several approaches to deal with the boundary problem which is particularly detrimental in electricity load forecasting. Second, we examine the benefit of combining forecasts over individual models. An extensive out-of-sample evaluation shows that a careful treatment of the border effect is required in the multiresolution analysis. Combinations including the wavelet predictions provide the most accurate forecasts. This result is valid with several assumptions about the forecast error in temperature and for different types of hours (peak, normal, off-peak), different days of the week and various forecasting periods.