Return Volatility,Trading Imbalance and the Information Content of Volume

In this paper, we examine the relationship between volume and return volatility using the transaction data. We introduce transaction and volume imbalance measures to capture the information content of trades. These two information measures are shown to have a strong explanatory power for return volatility and contain incremental information about the asset values over and above that conveyed by the size and frequency of trades. Also, return volatility is significantly correlated with the percentage of trading volume taking place at NYSE. This result suggests that NYSE trades are more informative and contribute more to price discovery. There is evidence that price discovery concentrates in more heavily traded stocks, particularly the Dow Jones Stocks. Finally, return volatility is found to be persistent at the intraday level. The persistence level is higher for less frequently traded stocks. Return volatility also exhibits temporal variations. In particular, return volatility is significantly higher in the opening half-hour for less frequently traded stocks. Thus, stocks with different frequencies of trades may follow different volatility processes.     

计算机在控制理论频率法中的应用

介绍了利用计算机在输入系统参数后,能迅速显示和打印出开环幅相频率特性曲线,同时计算相位裕量和增益裕量及判定闭环控制系统稳定性的方法。此课题的研究促进了反馈控制理论中频率法的应用。     

射频识别(RFID)产业发展格局研究

近年来,在沃尔玛、麦德龙等跨国零售巨头的推动下,射频识别(RFID)技术受到了越来越多的厂商的关注。从射频识别技术的基本概念、分类和产业链结构出发,结合射频识别产业的标准现状,对近年来国内外射频识别产业的发展格局进行分析研究,并对中国如何发展射频识别产业提出自己的一些建议。     

一种OFDM系统中频率同步的改进算法

针对传统的频率同步方法中估计精度与估计范围的矛盾,提出了一种改进的频率同步算法。该算法通过两次频率偏移估计,可以同时获得大的估计范围和高的估计精度,而且对训练序列没有其他限制,可以提高系统数据传输效率。通过仿真,验证了该算法的有效性。     

How does the retailing industry decide the best replenishment strategy by utilizing technological support through blockchain?

Practitioners face two significant issues: product inaccuracy and transparency in supply chain management. Blockchain is a highly secure and trustworthy means of storing data. Radio frequency identification incorporation is essential if reliability is at a low level. Incorporating radio frequency identification can improve supply chain management in terms of product's visibility for the best replenishment strategy. A production and replenishment coordination via mathematical modeling is visualized through a three-echelon supply chain with a non-reliable production process, and the retailer deals with misplacement issues. The manufacturer handles the inventory flowing reversely and is responsible for proper end-of-life treatment, either repairing or remanufacturing. Repairs are sold in bulk on the secondary market, and remanufactured items are used to prevent retailers' shortages. In this model, radio frequency identification technology on the physical surface is combined with a blockchain on the cyber surface, containing all the information about the product, including its location and attributes. A comparative study is provided for the traditional supply chain with misplacement versus a blockchain-based supply chain with radio frequency identification. An analytical approach is used to arrive at the optimum policy for the practitioners, and numerical analysis illustrates the problem. Numerical experiments indicate that the technology is highly profitable for supply chain management. Radio frequency identification technology can increase profit by up to 61%. After discrepancy, holding cost is the second most sensitive parameter for the profit function. If the holding cost is higher, profit can be increased by 40% using radio frequency identification and blockchain. The negative effect of misplacement is reduced with an increasing demand rate, but the reduction rate is very slow. The choice of not adopting radio frequency identification can only be successful if demand is so high that it can reduce the effect of misplacement.     

Forecasting economic time series using score-driven dynamic models with mixed-data sampling

We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for the forecasting of low-frequency time series variables through the use of timely information from high-frequency variables. We verify the in-sample and out-of-sample performances of the model in an empirical study on the forecasting of U.S. headline inflation and GDP growth. In particular, we forecast monthly headline inflation using daily oil prices and quarterly GDP growth using a measure of financial risk. The forecasting results and other findings are promising. Our proposed score-driven dynamic model with mixed-data sampling weighting outperforms competing models in terms of both point and density forecasts.     

The impact of mandatory IFRS reporting on institutional trading costs: Evidence from Australia

This study examines the impact of mandatory International Financial Reporting Standards (IFRS) on the market quality of the Australian Securities Exchange (ASX) 200 constituent stocks. Using traditional metrics that are consistent with prior literature (i.e., bid‐ask spreads), the first stage analysis confirms that stock liquidity has improved. However, when the analysis is extended to consider the trading costs incurred by market participants (i.e., execution shortfall), results suggest liquidity has not changed significantly. The paper utilizes rich unique datasets that contain detailed trade information, and findings are robust after controlling for trade difficulty and market conditions. In the era of High Frequency Trading (HFT) and occurrences of ‘fleeting’ liquidity, this paper provides some evidence that while IFRS may have enhanced ‘visible’ bid‐ask spreads, tangible liquidity for market participants, particularly global institutional investors, has not improved significantly.     

Costs of complying with quality requirements in meal services for older adults

ABSTRACT

Care of older adults, including meal service to home-dwelling individuals is under pressure for improving its resource utilization. There is concern for potential meal quality impacts of this development – and subsequently for the users’ food intake and nutritional status. The objective of the study is to examine the municipalities’ additional costs of initiatives to improve quality in the supply of meal services for the home-dwelling elderly. An economic optimization model was developed and used to determine the cost-minimizing meal production scheme under alternative specifications regarding production methods, ingredient sourcing, composition of dishes, menu variation, and delivery frequency for each municipality in Denmark, while taking heterogeneity of users into account. Alternative modifications to the quality standards within these dimensions have been analyzed. Results suggest that except for higher delivery frequency, the additional costs of the considered quality improvements are modest (1–5% of the current cost), whereas daily delivery would increase the cost by 10–15% in several municipalities. Despite relatively low additional costs, Danish municipalities may lack the economic incentive to undertake such quality improvements, because the additional costs cannot be passed on to the users; thus, municipalities will need to finance these costs by savings on other municipal services.     

Ways of shopping & retail mix at the Greengrocer's

The aim of this research is to relate the way in which consumers perceive value creation when shopping, reflected through the Ways of shopping concept, with the perception that they have of the characteristics of the retail mix in the greengrocery context. Different types of consumers are considered. To understand these relationships is the basis for the greengrocery's development of successful retail-mix strategies. To do so, we have developed an investigation with a sample of 1200 consumers of fresh food products. Our results include indicating the importance of managing the product portfolio and the shopping list of consumers, who see this purchase as a task. This is one of the factors which most affect their perception of the retail mix. These aspects must be the greengrocery's basis for the creating successful strategies.     

Mixed data sampling expectile regression with applications to measuring financial risk

To avoid information loss or measurement error in traditional methods dealing with mixed frequency data, we develop a novel mixed data sampling expectile regression (MIDAS-ER) model to measure financial risk. We construct the MIDAS-ER model by introducing a MIDAS structure into expectile regressions. This enables us to perform an expectile regression on raw mixed frequency data directly. We apply the proposed MIDAS-ER model to estimate two popular financial risk measures, namely, Value at Risk and Expected Shortfall, with both simulated data and four stock indices, and compare the model's performance with those of several popular models. The outstanding performance of our model demonstrates that high-frequency information helps to improve the accuracy of risk measurement. In addition, the numerical results also imply that our model can be a significant tool for risk-averse investors to control risk losses and for financial institutions to implement robust risk management.