Baidu index and predictability of Chinese stock returns

A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.

Credit Rationing and the Simulation of Bank-Small and Medium Sized Firm Artificial Credit Market

By analyzing the financing difficulties faced by the small and medium-sized firms, the paper built an artificial credit markets with the agent-based computational modeling to simulate the real world credit transactions. There are firms, banks, different risk-type projects as well as legal and supervision environments in which debt contracts constitute the financial instruments. The simulation results show that the number of collateral, average success probability of projects, and the prime interest rate have materially impact on bank's average profit, bank's capital, the overall interest rate,the number of borrowing firms, loan size, and the degree of credit rationing. These results in line with those of the classical S-W model in the sense that the relationship between bank profits and interest rates is non-monotonic as well as the relationship between credit rationing and interest rates. And thus there is an adverse selection effect in credit rationing theory.

Digital Influencer's choice of product endorsement:A perspective of congruence

To infer product fitness information,consumers often consult digital influencers who exert effort to learn and evaluate product features and then propagate information to their followers.However,influencers'recommendations are not limited to products that exactly match their expertise,which may lead followers to unfollow them.This study investigates which product(high or low congruence)an influencer should endorse and how much endorsement effort should be exerted.Using a theoretical model,we obtain several interesting results.First,endorsing a low-congruence product can surprisingly benefit the influencer if the penalty per lost follower is moderate or if the influencer has sufficiently high efficiency in reducing the perception error of this product.Second,although the influencer has high expertise in the high-congruence product,she will lose more followers when endorsing it than when endorsing the low-congruence product if a particularly small or large proportion of her followers are potential consumers of the low-congruence product.Third,when the influencer endorses the low-congruence product,the number of new followers increases with the size of the influencer's follower base only when the follower base is small and a large proportion of followers are potential consumers of the low-congruence product.Interestingly,as the size of the follower base increases,the influencer always exerts more effort when endorsing the high-congruence product but will exert less effort in learning about the low-congruence product if a sufficiently small proportion of followers are potential consumers.Finally,after taking consumers'knowledge about product features into account,the main results still hold,and when considering the competition between influencers,we uncover the conditions under which both influencers will choose to endorse the high-congruence product.

Mobile telecommunication companies’investment and pricing strategies for content service

Several mobile telecommunication companies(MTCs),such as AT&T,Verizon,and China Mobile,are investing in content services to drive continued growth as the telecom industry has become saturated.In this study,we investigate the competition between two MTCs,one of which(MTC 1)provides both data and content services,while the other(MTC 2)provides only data services.We propose a two-stage game-theoretic model to analyze the two MTCs'optimal pricing strategies for data service and content service as well as the investment strategy for the content service.Our analysis reveals that a higher value of value-added service offered by MTC 1 will incentivize it to raise its subscription fee for the content service and induce both MTCs to reduce the prices of their data services,which results in an increased demand for the content service and a decreased demand for MTC 2's data service.As MTC 1 invests further in the quality of the content service,it would benefit from increasing the subscription fee for the content service,and the two MTCs should increase the subscription fee for the data service.However,compared to the decisions in the scenario where MTC 1 does not provide content service,the provision of this service by MTC 1 leads to a lower subscription fee for the two MTCs'data services,an increase in the demand for MTC 1's data service,and a decrease in the demand for MTC 2's data service.These findings reveal that regularly monitoring market competition,as well as adjusting pricing strategies based on market scenarios and the value of services,can enhance competitiveness and long-term profitability for MTCs.

Joint optimization of production,maintenance,and quality control considering the product quality variance of a degraded system

The joint optimization of production,maintenance,and quality control has shown effectiveness in reducing long-term operational costs in production systems.However,existing studies often assume that changes in the mean value of product quality characteristics in a deteriorating system follow a specific distribution while keeping variance constant.To address this limitation,we propose an innovative method based on the continuous ranking probability score(CRPS).This method enables the simultaneous detection of changes in mean and variance in nonconformities,thus removing the assumption of a specific distribution for quality characteristics.Our approach focuses on developing optimal strategies for production,maintenance,and quality control to minimize cost per unit of time.Additionally,we employ a stochastic model to optimize the production time allocated to the inventory buffer,resulting in significant cost reductions.The effectiveness of our proposed joint optimization method is demonstrated through comprehensive numerical experiments,sensitivity analysis,and a comparative study.The results show that our method can achieve cost reductions compared to several other related methods,highlighting its practical applicability for manufacturing companies aiming to reduce costs.

The Relationship between Firm-Specific Return Variation and Price Informativeness:Some Cross-Sectional Evidence

The progressive removal of short-selling constraints in the Chinese stock market provides us with a natural experiment to investigate the relationship between firm-specific return variation(FSRV)and price informativeness.Based on the empirical finding that idiosyncratic volatility is a satisfied proxy for FSRV when the information environment for individual firms improves,we mainly find that the FSRV is negatively related to price informativeness.This negative relationship is robust to alternative model specifications,alternative proxies for price informativeness,and alternative estimation windows.Generally speaking,our results complement the extant literature on the mixed relationships between FSRV and price informativeness by providing cross-sectional evidence.