A novel time-series model based on empirical mode decomposition for forecasting TAIEX

Stock price prediction is regarded as a challenging task of the financial time series prediction process. Time series models have successfully solved prediction problems in many domains, including the stock market. Unfortunately, there are two major drawbacks in stock market by time-series model: (1) some models cannot be applied to the datasets that do not follow the statistical assumptions; and (2) most time-series models which use stock data with many noises involutedly (caused by changes in market conditions and environments) would reduce the forecasting performance. For solving the above problems and promoting the forecasting performance of time-series models, this paper proposes a hybrid time-series support vector regression (SVR) model based on empirical mode decomposition (EMD) to forecast stock price for Taiwan stock exchange capitalization weighted stock index (TAIEX). In order to evaluate the forecasting performances, the proposed model is compared with autoregressive (AR) model and SVR model. The experimental results show that the proposed model is superior to the listing models in terms of root mean squared error (RMSE). And the more fluctuation year (2000–2001) occurs, the better accuracy of proposed model will be obtained.     

基于ANFIS和GA的多级分销链预测模型

为提高分销链企业的预测水平,优化分销链中各级企业库存管理,提出了基于自适应神经模糊推理系统(ANFIS)的单级预测模型和成本目标函数的多级预测模型。利用ANFIS模糊推理机制实现了其输入层与输出层间的非线性映射及该神经网络的信息存储和学习能力。从分销链整体成本优化的角度建立了多级预测模型并应用遗传算法(GA)进行求解。仿真结果表明,ANFIS与BP神经网络相比具有较高的准确性;在一定程度上改善了分销链中订货信息多级放大的现象。     

中国货币政策与股票市场的关系

本文提出的综合理论框架全面分析描述了以稳定物价水平、促进国民经济持续增长为目的的货币政策与股票市场的关系 ,着重对中央银行干预股票市场的必要性和有效性进行理论分析和实证检验。本文应用的动态滚动式的计量检验方法适应中国经济体制不断调整的特征 ,不但可以完成我们的理论分析 ,更可以检测中央银行对股票市场干预的机制及干预的有效性 ,从而分析进一步的政策含义 ,为中央银行的货币政策制订和预期效果提供一个前瞻性的预测分析框架。     

Tracking technology trajectory through regression modelling: a retrospective techno-analysis

A significant number of studies have been conducted to forecast the expanding market and evaluate new generation smartphone technologies. However, no such study has been witnessed so far that could forecast the release time of these technologies. The purpose of the paper is to test the forecasting capabilities of stepwise regression in forecasting the smartphones commercialisation time. This technique predicts the release time of smartphones released in 2006 (belonging to the second generation of smartphones) and 2007 (belonging to the third generation of smartphones). The stepwise regression approach based on 12 year data set from 1994 to 2005 determines whether it provides a superior fitting and forecasting performance. The validation approach applied for the first- and second-generation smartphones will benefit future researchers and practitioners in understanding that a regression model developed on the basis of one generation may not give accurate results for the next generation, owing to the fact that technological developments are multi-folded.     

Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory

This paper employs a multi-equation model approach to consider three statistic problems (heteroskedasticity, endogeneity and persistency), which are sources of bias and inefficiency in the predictive regression models. This paper applied the residual income valuation model (RIM) proposed by Ohlson (1995) to forecast stock prices for Taiwan three sectors. We compare relative forecasting accuracy of vector error correction model (VECM) with the vector autoregressive model (VAR) as well as OLS and RW models used in the prior studies. We conduct out-of-sample forecasting and employ two instruments to assess forecasting performance. Our empirical results suggest that the VECM statistically outperforms other three models in forecasting stock prices. When forecasting horizons extend longer, VECM produces smaller forecasting errors and performs substantially better than VAR, suggesting that the ability of VECM to improve VAR forecast accuracy is stronger with longer horizons. These findings imply that an error correction term (ECT) of the VECM contributes to improving forecast accuracy of stock prices. Our economic significance analyses and robustness tests for different data frequency are in favor of the superiority of VECM estimator.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.     

Environmental Policy in the Presence of Induced Technological Change

We examine the hypothesis that induced technological change (ITC) can dramatically lower the cost of a carbon tax in a static optimal tax model. The research and development sector is represented by an aggregate stock of energy-saving technology, which acts as a weak substitute with a polluting resource in the energy generation sector. Using this model, we analytically show how ITC occurs and affects the cost of a carbon tax. Applying quantitative estimates of the size of ITC to numerical simulations calibrated to the US economy, we find that existing empirical evidence can reduce the welfare cost of environmental tax reform by 12%. Our tests of alternative parameters show that this result is highly sensitive to the assumptions used, suggesting that ITC could result in much larger reductions in cost.     

OWA-based ANFIS model for TAIEX forecasting

In stock market forecasting, high-order time-series models that use previous several periods of stock prices as forecast factors are more reasonable to provide a superior investment portfolio for investors than one-order time-series models using one previous period of stock prices. However, in forecasting processes, it is difficult to deal with high-order stock data, because it is hard to give a proper weight to each period of past stock price, reduce data dimensions without losing stock information, and produce a comprehensive forecasting result based on stock data with nonlinear relationships.Additionally, there are two more drawbacks to past time-series models: (1) some assumptions (Bollerslev, 1986; Engle, 1982) about stock variables are required for statistical methods, such as the autoregressive model (AR) and autoregressive moving average (ARMA); (2) numeric time-series models have been presented to deal with forecasting problems for stock markets, but they can not handle the nonlinear relationships within the stock prices.To address these shortcomings, this paper proposes a new time series model, which employs the ordered weighted averaging (OWA) operator to fuse high-order data into the aggregated values of single attributes, a fusion adaptive network-based fuzzy inference system (ANFIS) procedure, for forecasting stock price in Taiwanese stock markets.In verification, this paper employs a seven-year period of the TAIEX stock index, from 1997 to 2003, as experimental datasets and the root mean square error (RMSE) as evaluation criterion. The experimental results indicate that the proposed model is superior to the listing methods in terms of root mean squared error.     

Cutting costs of catching carbon—Intertemporal effects under imperfect climate policy

We use a two-period model to investigate intertemporal effects of cost reductions in climate change mitigation technologies for the power sector. The effect of cost reductions for CCS depends on how carbon taxes are set. If there is no carbon tax in period 1, but an optimally set carbon tax in period 2, a CCS cost reduction may reduce early emissions. Such an innovation may therefore be more desirable than comparable cost cuts related to renewable energy. The finding rests on the incentives fossil fuel owners face. If future profitability is reduced, they speed up extraction (the ‘green paradox’), and vice versa.     

敏捷供应链的采购管理系统模型研究

基于敏捷供应链的采购管理是现代企业资源决策和提高企业核心竞争力的重要手段。该管理模式的有效运作有助于企业与供应商建立良好的协同与合作关系,降低库存和成本,提高采购预测的准确性和整个供应链的效率。     

Does non-linearity help us understand,model and forecast UK stock and bond returns: evidence from the BEYR

The usefulness of non-linear models to provide accurate estimates and forecasts remains an open empirical debate. This paper examines the nature of the estimated relationships and forecasting power of smooth-transition models for UK stock and bond returns using a range of financial and macroeconomic variables as predictors. Notably, evidence of non-linearity is stronger when the bond-equity yield ratio is used as the transition variable. This ratio measures whether stocks are over (under)-valued relative to bonds and can act as a signal for portfolio managers. In-sample results reveal noticeable differences regarding the nature of relationships between the linear and non-linear setting, while results of a recursive forecasting exercise reveal both statistical and economic improvement over a linear model. Overall, these results support the view that non-linear estimates and forecasts can provide useful information for stock market traders, portfolio managers and policy-makers.     

焦点企业知识存量对联盟组合分裂断层的影响：知识转移效率的中介作用

运用断层理论，从知识获取和关系治理角度，分析焦点企业知识存量如何影响联盟组合分裂断层及分裂断层形成机理。基于焦点企业知识存量、分裂断层、知识转移效率、情景嵌入性之间的关系理论模型框架，认为焦点企业知识存量能够影响联盟组合分裂断层，其中，知识转移效率在这一过程中发挥中介效应，情景嵌入性发挥调节效应。结果发现：焦点企业知识存量与分裂断层之间存在显著负相关关系；知识转移效率能够部分中介焦点企业知识存量与分裂断层之间的关系；情景嵌入性能够正向调节焦点企业知识存量与知识转移效率之间的关系。     

Policy implications of potential conflicts between short-term and long-term efficiency in CO2 emissions abatement

This paper shows that, under certain conditions (including path dependence and lock-in), policies and measures leading to a cost-effective GHG emissions mitigation in the short term may not allow reaching long-term emissions targets at the lowest possible cost, that is, they might not be cost-effective in the long term. The reason is that, in a situation where currently expensive technologies have a large potential for cost reductions through learning effects and R&D investments, the implementation of incentive-based mitigation policies such as taxes or tradable permits will encourage the adoption and diffusion of currently low-cost abatement technologies, but might not be enough to make attractive the diffusion of expensive ones, which is a necessary condition for these technologies to realise their cost-reduction potential through the aforementioned effects. A simple model and a numerical simulation are provided to show this possible conflict between static and dynamic efficiency, which points out to the need to combine different instruments, some aiming at short-term cost-efficiency (such as incentive-based environmental policy) and others at encouraging dynamic cost reductions (such as technology/innovation policy).     

Predicting European Union Recessions in the Euro Era: The Yield Curve as a Forecasting Tool of Economic Activity

Several studies have established the predictive power of the yield curve for the U.S. and various European countries. In this paper we use data from the European Union (EU15), from 1994:Q1 to 2008:Q3. We use the European Central Bank’s euro area yield spreads to predict European real GDP deviations from the long-run trend. We also augment the models tested with non monetary policy variables: the unemployment and a composite European stock price index. The methodology employed is a probit model of the inverse cumulative distribution function of the standard distribution using several formal forecasting and goodness of fit evaluation tests. The results show that the yield curve augmented with the composite stock index has significant forecasting power in terms of the EU15 real output.     

Forecasting the realized volatility in the Chinese stock market: further evidence

In this study, the impact of noise and jump on the forecasting ability of volatility models with high-frequency data is investigated. A signed jump variation is added as an additional explanatory variable in the volatility equation according to the sign of return. These forecasting performances of models with jumps are compared with those without jumps. Being applied to the Chinese stock market, we find that the jump variation has a significant in-sample predictive power to volatility and the predictive power of the negative one is greater than the positive one. Furthermore, out-of-sample evidence based on the fresh model confidence set (MCS) test indicates that the incorporation of singed jumps in volatility models can significantly improve their forecasting ability. In particular, among the realized variance (RV)-based volatility models and generalized autoregressive conditional heteroscedasticity (GARCH) class models, the heterogeneous autoregressive model of realized volatility (HAR-RV) model with the jump test and a decomposed signed jump variation have better out-of-sample forecasting performance. Finally, the use of the decomposed signed jump variations in predictive regressions can improve the economic value of realized volatility forecasts.     

Technological change in energy systems: Learning curves, logistic curves and input-output coefficients

Learning curves have recently been widely adopted in climate-economy models to incorporate endogenous change of energy technologies, replacing the conventional assumption of an autonomous energy efficiency improvement. However, there has been little consideration of the credibility of the learning curve. The current trend that many important energy and climate change policy analyses rely on the learning curve means that it is of great importance to critically examine the basis for learning curves. Here, we analyse the use of learning curves in energy technology, usually implemented as a simple power function. We find that the learning curve cannot separate the effects of price and technological change, cannot reflect continuous and qualitative change of both conventional and emerging energy technologies, cannot help to determine the time paths of technological investment, and misses the central role of R&D activity in driving technological change. We argue that a logistic curve of improving performance modified to include R&D activity as a driving variable can better describe the cost reductions in energy technologies. Furthermore, we demonstrate that the top-down Leontief technology can incorporate the bottom-up technologies that improve along either the learning curve or the logistic curve, through changing input-output coefficients. An application to UK wind power illustrates that the logistic curve fits the observed data better and implies greater potential for cost reduction than the learning curve does.     

Evaluating new CO2 reduction technologies in Japan up to 2030

A mathematical model was developed for evaluating CO₂-reduction technologies in power generation, residential, commercial and road transport sectors in Japan. The existing and new power generation technologies evaluated included 34 centralized and 8 dispersed power generation technologies in the residential and commercial energy demand sectors. To take into account the varieties of useful energy and of its demand duration patterns among entities in the demand sectors, the hourly mean power and heating and cooling demand–supply balances in one residential and four commercial representative entities were considered for each month. The road transport sector addressed five types of automotive use. The useful-energy demands are exogenously given; the model calculates the technology installations that satisfy the demands to minimize the total systems cost for each year up to 2030. The availability of the new technologies, i.e., the first years they are installable, is derived from research and development (R&D) process analyses on the basis of surveys to experts. As a result of the model calculation, dispersed molten carbonate and solid oxide fuel cells and onboard gasoline reforming-type fuel cell vehicle (FCV) technologies are expected to have the largest economic values, approximately 60–120 billion constant 1998 yen [460–920 million U.S. dollars (USD)] among the evaluated new CO₂-reduction technologies. One of the implications from our calculations is that extending electric power corporations' commercial coverage to dispersed power generation, in addition to centralized power generation, is desirable to help lower overall costs in society, as well as to secure industry profits.     

Forecasting house prices using dynamic model averaging approach: Evidence from China

Forecasting house price has been of great interests for macroeconomists, policy makers and investors in recent years. To improve the forecasting accuracy, this paper introduces a dynamic model averaging (DMA) method to forecast the growth rate of house prices in 30 major Chinese cities. The advantage of DMA is that this method allows both the sets of predictors (forecasting models) as well as their coefficients to change over time. Both recursive and rolling forecasting modes are applied to compare the performance of DMA with other traditional forecasting models. Furthermore, a model confidence set (MCS) test is used to statistically evaluate the forecasting efficiency of different models. The empirical results reveal that DMA generally outperforms other models, such as Bayesian model averaging (BMA), information-theoretic model averaging (ITMA) and equal-weighted averaging (EW), in both recursive and rolling forecasting modes. In addition, in recent years it is found that the Google search index, instead of fundamental macroeconomic or monetary indicators, has developed greater predictive power for house price in China.     

Wind Power in Europe: A Simultaneous Innovation–Diffusion Model

The purpose of this paper is to provide a quantitative analysis of innovation and diffusion in the European wind power sector. We derive a simultaneous model of wind power innovation and diffusion, which combines a rational choice model of technological diffusion and a learning curve model of dynamic cost reductions. These models are estimated using pooled annual time series data for four European countries (Denmark, Germany, Spain and the United Kingdom) over the time period 1986–2000. The empirical results indicate that reductions in investment costs have been important determinants of increased diffusion of wind power, and these cost reductions can in turn be explained by learning activities and public R&D support. Feed-in tariffs also play an important role in the innovation and diffusion processes. The higher is the feed-in price the higher is, ceteris paribus, the rate of diffusion, and we present some preliminary empirical support for the notion that the impact on diffusion of a marginal increase in the feed-in tariff will differ depending on the support system used. High feed-in tariffs, though, also have a negative effect on cost reductions as they induce wind generators to choose high-cost sites and provide fewer incentives for cost cuts. This illustrates the importance of designing an efficient wind energy support system, which not only promotes diffusion but also provides continuous incentives for cost-reducing innovations.