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In the process of discussing the relationship between volume and price in the stock market, the purpose of this paper is to consider how to take the flow of foreign capital into consideration, to determine whether the inclusion of volume information really contributes to the prediction of the volatility of the stock price.

By comparing the relative advantages and disadvantages of the two main non-parametric methods mainstream, and taking the characteristics of the time series of the volume into consideration, the stochastic volatility with Volume (SV-VOL) model based on the APF-LW simulation method is used in the end, to explore and implement a more efficient estimation algorithm. And the volume is incorporated into the model for submersible quantization, by which the problem of insufficient use of volume information in previous research has been solved, which means that the development of the SV model is realized.

Through the Sequential Monte Carlo (SMC) algorithm, the effective estimation of the SV-VOL model is realized by programming. It is found that the stock market volume information is helpful to the prediction of the volatility of the stock price. The exchange market volume information affects the stock returns and the price-volume relationship, which is achieved indirectly through the net capital into stock market. The current exchange devaluation and fluctuation are not conducive to the restoration and recovery of the stock market.

It is still in the exploratory stage that whether the inclusion of volume information really contributes to the prediction of the volatility of the stock price, and how to incorporate the exchange market volume information. This paper tries to determine the information weight of the exchange market volume according to the direct and indirect channels from the perspective of causality. The relevant practices and conclusions need to be tested and perfected.

Previous studies have neglected the influence of the information contained in the exchange market volume on the volatility of stock prices. To a certain extent, this research makes a useful supplement to the existing research, especially in the aspects of research problems, research paradigms, research methods and research conclusion.

SV model with volume information can not only effectively solve the inefficiency of information use problem contained in volume in traditional practice, but also further improve the estimation accuracy of the model by introducing the exchange market volume information into the model through weighted processing, which is a useful supplement to the existing literature. The SMC algorithm realized by programming is helpful to the further advancement and development of non-parametric algorithms. And this paper has made a useful attempt to determine the weight of the exchange market volume information, and some useful conclusions are drawn.

This paper aims to identify the direct impact of fund style drift on the risk of stock price collapse and the intermediary mechanism of financial risk, so as to better protect the interests of minority investors.

This paper takes all the non-financial companies on the Chinese Growth Enterprise Market from 2011 to 2020 as study object and selects securities investment funds of their top ten circulation stocks to study the relationship between fund style drift and stock price crash risk.

Fund style drift is likely to add stock price crash risk. Financial risk is positively correlated with stock price crash risk. Fund style drift affects stock price crash risk via the mediating effect of financial risk, and fund style drift and financial risk have a marked impact on the stock price crash risk of non-state enterprises, yet a non-significant impact on that of state-owned enterprises.

This paper links fund style drift with stock price crash risk in an exploratory manner and enriches the study perspectives of relationship between institutional investors’ behaviors and stock price crash risk, thus enjoying certain academic value. On the one hand, it furnishes a new approach to the academic frontier issue concerning financial risk and stock price crash risk, and proves that financial risk is positively correlated with stock price crash risk. On the other hand, it regards financial risk as a mediating variable of fund style drift for stock price crash risk and further explores different influencing mechanism of institutional investors’ behaviors.

The purpose of this paper is to measure a single financial institution's contribution to systemic risk by using extremal quantile regression and analyzing the influential factors of systemic risk.

Extreme value theory is applied when measuring the systemic risk of financial institutions. Extremal quantile regression, where extreme value distribution is assumed for the tail, is used to measure the extreme risk and analyze the changes in and dependencies of risk. Furthermore, influential factors of systemic risk are analyzed using panel regression.

The key findings of the paper are that value at risk and contribution to systemic risk are very different when measuring the risk of a financial institution; banks’ contributions to systemic risk are much higher; and size and leverage ratio are two significant and important factors influencing an institution's systemic risk.

Characterizing variables of financial institutions such as size, leverage ratio and market beta should be considered together when regulating and constraining financial institutions.

To take extreme risk into account, this paper measures systemic financial risk using extremal quantile regression for the first time.

The purpose of this paper is to empirically analyze the transmission mechanism between benchmark interest rate of financial market, money market interest rate and capital market yields in order to reveal the dynamic evolution characters and core influential structure between different market interest rates.

Using Dirichlet-VAR (DVAR) model, this study analyze the relationship between markets rates according to the equilibrium model in money market and capital market.

Empirical results show that the interest rate transmission mechanism functions smoothly between interest rates of different levels. Interest rate of bills issued by the central bank can effectively reflect changes in monetary policy and guide the fluidity of market, playing the anchor role in interest rate pricing. There exists a closed loop feedback between interest rate of bills issued by the central bank, and money market interest rate, as well as between money market interest rate and bond market interest rate. The former is a loop by administrative means while the latter is the one mainly affected by market-oriented means. The response by money market and bond market toward the change of benchmark interest rate is unsymmetrical as money market is more sensitive to a loose monetary policy while bond market is more sensitive to a tight monetary policy. Stock market is strongly affected by uncertainty of benchmark interest rate.

DVAR model is the extension of research on instable data and multiple variable causality test, which expands the causality analysis between two variables to multiple variables causality impact analysis which contains non-stable and structurally instable economic data.

This case provides detailed information about digital technologies and business practices that may help offline retailers catch up with the trend of new retail. After studying the case and working on the assignment questions, students will be able to:▪ Understand new features of smart cash registers, including facial-recognition payment, purchase-sales-inventory management, customer profile and store management, which all are important for the long-term development of the retail business in the age of “new retail”.▪ Identify opportunities, practices and impacts of digital technologies, such as big data and artificial intelligence, on contemporary retail businesses.▪ Identify problems of traditional retail and suggest solutions by applying the concepts and tools learned above.▪ Apply digital marketing approaches and tools (e.g., social media, livestreaming and online word-of-mouth) to design marketing campaigns; students should include basic elements such as the 6Ms for effective marketing communications (market, mission, message, media, money and measure).

This case describes difficult situations facing Leo Shoudong Pan, the founder and CEO of Yun Dong Jia Technologies Co Ltd (YDJ), in marketing communications. With a motto of “Making it easy to open stores anywhere”, YDJ develops and sells smart cash registers, which provide a self-developed operating system and cloud computing services. Pan targets small and micro retailers, who are technology laggards when digital transitions had swept the world. His goal is to build a network of 100,000 pieces of smart cash registers across China, but he has only sold 8,000 pieces since he founded YDJ in 2016. He must make a breakthrough in the business. To drive leads and sales, he feels the urgency of conducting effective marketing communications with target customers and enhance their understanding on the value that YDJ creates for them. Monetary incentives are tangible but not yet fully demonstrated YDJ’s value. With the traditional retail approach, brick-and-mortar stores, especially those small-scaled ones, are not able to meet the market change; instead, they must adopt digital techniques to catch up with the trend of new retail, which is necessary for a long-term business development rather than just a temporary measure during the Covid-19 pandemic. Pan must craft more compelling messages. What customer value should be chosen as incentives to motivate the target market? How to conduct effective marketing communications correspondingly?

Senior undergraduate; Postgraduate; MBA; EMBA.

Teaching notes are available for educators only.

CSS 8: Marketing.

Intelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment. At present, the diagnosis of various kinds of bearing fault information, such as the occurrence, location and degree of fault, can be carried out by machine learning and deep learning and realized through the multiclassification method. However, the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information. To improve the above shortcomings, an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.

In this model, the labels of each bearing are binarized by using the binary relevance method. Then, the integrated convolutional neural network and gated recurrent unit (CNN-GRU) is employed to classify faults. Different from the general CNN networks, the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.

The Paderborn University bearing dataset is utilized to demonstrate the practicability of the model. The experimental results show that the average accuracy in test set is 99.7%, and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing, and the multilabel classification method is superior to the multiclassification method. Consequently, the model can intuitively classify faults with higher accuracy.

The fault labels of each bearing are labeled according to the failure or not, the fault location, the damage mode and the damage degree, and then the binary value is obtained. The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method, and the predicted probability value of each fault label is directly output in the output layer, which visually distinguishes different fault conditions.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

Automated technologies have been applied to facility management (FM) practices to address labour demands of, and time consumed by, inputting and processing manual data. Less attention has been focussed on automation of visual information, such as images, when improving timely maintenance decisions. This study aims to develop image classification algorithms to improve information flow in the inspection-repair process through building information modelling (BIM).

To improve and automate the inspection-repair process, image classification algorithms were used to connect images with a corresponding image database in a BIM knowledge repository. Quick response (QR) code decoding and Bag of Words were chosen to classify images in the system. Graphical user interfaces (GUIs) were developed to facilitate activity collaboration and communication. A pilot case study in an inspection-repair process was applied to demonstrate the applications of this system.

The system developed in this study associates the inspection-repair process with a digital three-dimensional (3D) model, GUIs, a BIM knowledge repository and image classification algorithms. By implementing the proposed application in a case study, the authors found that improvement of the inspection-repair process and automated image classification with a BIM knowledge repository (such as the one developed in this study) can enhance FM practices by increasing productivity and reducing time and costs associated with ecision-making.

This study introduces an innovative approach that applies image classification and leverages a BIM knowledge repository to enhance the inspection-repair process in FM practice. The system designed provides automated image-classifying data from a smart phone, eliminates time required to input image data manually and improves communication and collaboration between FM personnel for maintenance in the decision-making process.

The purpose of this paper is to derive the optimal procurement policy of an item for a buyer and reduce the total cost to the buyer.

In a multi‐supplier setting and from the perspective of the buyer, the paper addresses long‐term supply contracts for a single item with total minimum commitments and order constraints each period. Under the conditions, the buyer agrees to procure at least a certain quantity of an item from every selected supplier over the predetermined plan horizon and the order constraints specify the minimum and maximum of the quantity purchased each period. An optimization model is developed minimizing the total cost to the buyer, including purchase, transportation, and storage cost of all periods. To derive the optimal procurement policy for the buyer, a two‐phase solution is proposed integrating multidimensional dynamic programming with heuristic method.

The optimal procurement policies can be computed easily and result in a certain decrease on the total cost to the buyer. There may be multiple optimal procurement strategies resulting in the same total cost to the buyer. The commitments to the suppliers result in an increase on the total cost to the buyer.

Sensitivity analysis should be provided and uncertain demands should be considered.

This paper presents a very useful approach to derive optimal procurement strategy for such buyers as project owners.

The paper extends the total minimum commitment to a multi‐supplier setting.

This study aims to investigate the spectral illumination characteristics and geometric features of bicycle parts and proposes an image stitching method for their automatic visual inspection.

The unrealistic color casts of feature inspection is removed using white balance for global adjustment. The scale-invariant feature transforms (SIFT) is used to extract and detect the image features of image stitching. The Hough transform is used to detect the parameters of a circle for roundness of bicycle parts.

Results showed that maximum errors of 0°, 10°, 20°, 30°, 40° and 50° for the spectral illumination of white light light-emitting diode arrays with differential shift displacements are 4.4, 4.2, 7.8, 6.8, 8.1 and 3.5 per cent, respectively. The deviation error of image stitching for the stem accessory in x and y coordinates are 2 pixels. The SIFT and RANSAC enable to transform the stem image into local feature coordinates that are invariant to the illumination change.

This study can be applied to many fields of modern industrial manufacturing and provide useful information for automatic inspection and image stitching.