Trade Information Research Articles

Openness, Institutions, and Policies: Determinants of Globalisation and Economic Growth in Developing Countries(Distinguished Lecture)

Globalisation describes the increasing integration of national economies through international trade, capital transfers, and the exchange of information or knowledge. This paper focuses exclusively on trade integration. Most economists hold that reducing trade barriers has a decisive positive effect on economic growth and poverty reduction (“openness hypothesis”). However, some economists diagnose deficient or weakly enforced non-market institutions as the major cause of slow growth. The main goal of this paper is to show that these two seemingly contrary points of view do not exclude each other. Openness is not only influenced by trade policy but also by other policies and the quality of institutions. Available data show a high negative correlation between the logarithm of tariff rates and indicators of the quality of institutions. Cross-national growth regressions demonstrate that the explanatory power of trade protection and a combined measure of openness, on the one hand, and institutional quality, on the other hand, is comparable. Therefore, it is concluded that liberal trade policies are recommendable, but must be complemented by sound macroeconomic management, micro-policy to strengthen domestic competition, and institutional improvements.

The Disappeared Outperformance of Post-Reorg Equity

Using trading information from a comprehensive sample of relisted Chapter 11 firms in the past few decades, we find that an equal-weighted calendar-time portfolio generates 7.2% annualized excess returns over a five-factor benchmark from 1992 to 2019. However, the outperformance concentrated in the 2000s, when institutional ownership of post-reorganization equity increased significantly. The positive post-emergence earnings announcement returns disappear in the most recent decade. The evidence suggests that the outperformance of post-reorg equity documented in earlier studies is most likely due to market expectation errors for future earnings and, thus, initial undervaluation, which has been corrected in the recent period by sophisticated institutional investors.

Information and Agreement in the Reputation Game Simulation.

Modern communication habits are largely shaped by the extensive use of social media and other online communication platforms. The enormous amount of available data and speed with which new information arises, however, often suffices to cause misunderstandings, false conclusions, or otherwise disturbed opinion formation processes. To investigate some of these effects we use an agent-based model on gossip and reputation dynamics with 50 agents, including Bayesian knowledge updates under bounded rationality and up to the second-order theory of mind effects. Thereby, we observe the occurrence of reputation boosts from fake images, as well as the advantage of hiding one's opinion in order to become a strong information trader. In addition, the simulations show fundamentally different mechanisms for reaching high agreement with others and becoming well-informed. Additionally, we investigate the robustness of our results with respect to different knowledge-update mechanisms and argue why it makes sense to especially emphasize the margins of distribution when judging a bounded quantity such as honesty in a reputation game simulation.

Volatility Spillovers Among the Three Places Across the Taiwan Strait: Evidence from a BEKK‐CARR Approach*

AbstractThis study introduces a new BEKK‐CARR model to explore the volatility spillover effects among mainland China, Hong Kong, and Taiwan stock markets during the COVID‐19 pandemic. We also extend the approach of Diebold and Yilmaz (2009, 2012) to infer a brand‐new volatility spillover index to discuss the bi‐directional volatility transmission. Our results show that the trading information flow among these three markets has changed significantly as a result of the COVID‐19 pandemic. The strength of volatility spillover is increasing during this momentous period. The Hong Kong stock market plays a pivotal role in volatility transmission. The values for half‐lives by exogenous shocks keep relatively low during the pandemic period. A reasonable explanation is that the trading information transmissions among stock markets are quicker than in the non‐pandemic period.

Overview and prospect of information and communication technology development in virtual power plants

AbstractAs a new energy‐supply service solution to address massive, distributed energy access to the power system, a virtual power plant has higher transmission reliability and real‐time communication requirements. To achieve collaborative optimisation, distributed load and energy resources must be aggregated using new information and communication technology. This study analyses underlying communication technologies for virtual power plant interaction from the perspective of standardisation, efficiency, reliability, and security, summarises the application of blockchain, cloud‐edge collaboration, machine learning, and other new information and communication technologies in virtual power plant energy trading, interaction, and scheduling, and proposes ideas for addressing shortcomings in interaction. To improve virtual power plant interaction, performance parameter mapping between communication and business technology, and multilevel virtual power plant interaction technology are proposed.

A Secure Intra-Regional-Inter-Regional Peer-to-Peer Electricity Trading System for Electric Vehicles

Peer-to-peer (P2P) trading is becoming a prominent topic and demonstrating the development trend of integration with other theories in order to achieve an efficient allocation of electricity resources in the electric vehicle (EV) market. In this article, we present a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> ecure i <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> tra-regional- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> nter-regional <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> 2 <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> lectricity <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> rading <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> ystem (SNIPPETS) for EVs. A trading information prediction model is constructed based on Ensemble Learning, upon which an intra-regional-inter-regional trading mechanism is proposed to find the optimal electricity allocation strategy, including the price and volume of electricity traded between EVs, in order to maximize the regional overall social welfare. In the intra-regional-inter-regional trading mechanism, multi-objective optimization is performed within each region to coordinately maximize the benefits of different types of EVs, followed by an investigation of pricing competition among neighboring regions based on a supermodular game. Furthermore, blockchain is introduced to support transaction payments and improve data security and privacy. Finally, the proposed SNIPPETS is validated through case studies. Compared to the traditional energy trading system and representative existing trading systems, SNIPPETS can effectively improve the regional overall social welfare and has higher computational efficiency.

Distributed operation optimization of active distribution network with P2P electricity trading in blockchain environment

With the increasing integration of distributed generators and the rapid development of electricity markets, active distribution networks (ADNs) confront new challenges in operation optimization. Centralized optimization methods usually aggravate the computational burden when applied to large-scale distribution networks (DNs). A distributed operation optimization model incorporating peer-to-peer (P2P) electricity trading in a blockchain environment is proposed in this paper, where network usage fees considering electrical distance are specially addressed. Meanwhile, the private information security of prosumers is ensured by the Proof-of-Authority (PoA) consensus blockchain during P2P electricity trading. The trading information of adjustable prosumers should be sent to the proxy entity authorized by the DN using the blockchain platform. In addition, a network partitioning method via dynamic reconfiguration considering multi-level switching is offered to adapt to the required distributed manner. The operation network, as well as the corresponding market structure can be automatically updated according to the results of dynamic reconfiguration. Moreover, the unit price of network usage fees can be adjusted to derive a higher amount of P2P electricity trading with security constraints. Case studies on a practical 148-bus system and a 297-bus system verify that the proposed model maximizes the profits and reduces the computational time in the distributed operation optimization.

Do Heterogeneous Beliefs Matter to Post‐announcement Informed Trading?

We examine whether heterogeneous beliefs among investors affect post‐announcement informed trading. Using a global transaction‐level dataset, we find that post‐announcement informed trading increases with heterogeneous beliefs developed at the event window. Such an inference survives from several robustness checks. As additional analyses indicate, shrinking liquidity at the earnings announcement serves as a transmission mechanism to rationalize the documented association. Eventually, we reveal that informed trading driven by belief heterogeneity plays a dominant role in explaining post‐announcement returns.

Trade Informativeness in Modern Markets

Using transactions-based calendar time (TBCT) portfolio analysis, we investigate informativeness of trades of investor categories, namely institutions, proprietary traders, and retail clients. We find that trade informativeness is positive for institutional and negative for retail-client investors. The informativeness of liquidity-demanding trades are less than the informativeness of liquidity-supplying trades for all trading groups, over both long and short horizons. We also find that institutions are benefitted by algorithmic executions compared to manual executions and this benefit is elevated on days of high volume and volatility. Proprietary algorithmic traders (high-frequency traders) generate positive alpha for their trades only from their liquidity-supplying trades.

Shopping and Patronage of Online Customers

The advent of the internet has given rise to the new era of e-commerce which is the trading of information, goods, and services via the internet leading to the growth of online shopping or e-shopping (Klakota &amp; Whinston, 2008). Online business has grown very fast due to numerous preferences related to purchasing on the web due to lower transactions and inquiry costs when contrasted with different types of shopping. Through online shopping, consumers can purchase more quickly, have more alternatives, and order products and services with relatively minimal value (Cuneyt &amp; Gautam, 2004). Along these lines, Marketers have painstakingly examined the consumers' behavior towards online shopping and spent billions of dollars to facilitate all the demographics of online customers.This study aimed to determine the effectiveness of online shopping and patronage for online stores, specifically among customers of the Philippines e-commerce giant website- lazada.com.ph, and to identify those variables that satisfy them and increase their patronage. This should enable online store merchants to identify the most effective ways of closing gaps between effectiveness and patronage of online customers and choose which gaps to focus on.The outcome of this study will be necessary to online marketers, sellers, and investors in that it will help them formulate policies, frameworks, and marketing strategies that will enhance online shopping penetration and its effectiveness in the Philippines. Additionally, findings from this study may also help retailers improve the quality of their services to consumers. The study may inevitably add knowledge to the research area of online shopping and e-commerce in the country by providing an up-to-date assessment of the sector.

IoT Network for International Trade Cold Chain Logistics Tracking Based on Kalman Algorithm.

The application of Internet of Things technology in cold chain transportation can greatly strengthen the monitoring of all aspects of cold chain logistics, so as to promote the progress of cold chain logistics industry. In order to study this aspect, this paper chooses the key link of the Internet of Things as a breakthrough. By analyzing various aspects of the transportation process, an Internet of Things middleware framework serving cold chain transportation tracking is designed. The main products of logistics cold chain transportation are fresh agricultural products, so in order to further make the research fit the actual situation, this paper collects the logistics data and information of agricultural products through investigation and combines the core technology of agricultural products logistics with the Internet and the key technology of cold chain transportation to process the obtained information. The analytic hierarchy process and fuzzy comprehensive evaluation method are used in this process. The processing results prove the validity and rationality of the established index system. The purpose of developing the international trade management system is to enable the company to optimize its international trade management process, reduce some tedious and inconvenient manual operations, make the recording and statistics of international trade information very simple, improve work efficiency, and satisfy the information needs of various departments to enable enterprises to minimize costs, thereby enabling enterprises to obtain better economic benefits.

Identifying kidney trade networks using web scraping data

Kidney trade has been on the rise despite the domestic and international law enforcement aiming to protect the vulnerable population from potential exploitation. Regional hubs are emerging in several parts...

Exploring preservice mathematics and social studies teachers’ internet addiction as connected with gender and grade point average

"No doubt the internet has become a globally recognized conduit for information trading and schmoozing. However, there is a rising case of internet addiction among young adults of which preservice mathematics and social studies teachers are implicated. This study investigated the correlation between preservice mathematics and social studies teachers’ internet addiction and their grade point average (GPA). Likewise, the study examined the influence of gender on internet addiction. A descriptive survey research design was implemented in the study with 300 preservice mathematics and social studies teachers and primary data collected with Internet Addiction Test (IAT, Cronbach alpha coefficient=0.92) were analysed using mean, standard deviation, independent samples t-test, Pearson product moment correlation coefficient and simple linear regression analysis. The results showed that preservice mathematics and social studies teachers’ internet addiction had statistically significant negative relationship with their GPA. More so, internet addiction contributed significantly to the prediction of preservice mathematics and social studies teachers’ GPA. Gender contributed significantly to the prediction of preservice mathematics and social studies teachers’ internet addiction. In addition, there was a significant influence of gender on preservice mathematics and social studies teachers’ internet addiction with males showing higher internet addiction than their female counterparts. In line with the above findings, it was recommended that preservice mathematics and social studies teachers should lower their usage of internet and follow examples of good usage of internet technology to improve their scholastic achievement in the university. "

Peramalan Harga Saham Serentak Menggunakan Model Multivariate Singular Spectrum Analysis

Stock price fluctuations in the stock market are widely influenced by financial environment changes in both micro and macro that are usually unpredictable and can not be controlled by stock players. On the other side, stock price information is very essential and much needed for both buyers and traders. Stock price forecasting is one of the analytical techniques to obtain stock price prediction based on the previous historical stock prices. The open and close prices are important information in stock trading. The opening price can influence the movement towards the closing price, and the closing price becomes important for the next day's opening price. In technical analysis, the relationship between the two stock prices can be parametric or non-parametric. This study discusses the stock price prediction or forecasting through the non-parametric approach using a multivariate singular spectrum analysis method with the consideration that open and close prices are simultaneously working in the same system and time. Performance evaluation using Mean Absolute Percentage Error shows that the multivariate singular spectrum analysis method can produce predicted open and close prices with an error rate of 3.18% and 3.21%, respectively. Hence, this method can be used as an alternative for stock price forecasting simultaneously.

Searching for informed traders in stock markets: The case of Banco Popular

In this paper, we use several indicators of trade informativeness to search for informed traders on the final trading days of Banco Popular, the first and only bank resolution case to date in the euro area. In particular, we use the model proposed by Preve and Tse (2013) to estimate the adjusted daily probability of informed trading and the probability of symmetric order-flow shock using high-frequency transaction data. Our empirical results indicate that upon the anticipation of a possible liquidation of the bank, informed investors reacted to the bad news by placing more weight on it and that Banco Popular experienced large increases in both buy- and sell-orders during the last days of trading when the bank registered a significant depletion of its deposit base. Moreover, we find evidence supporting the presence of inside trading and illiquidity, especially after speculation in the media that the bank could face a liquidation. Our study has important implications for market participants and regulatory authorities.

Characterization of avoided crossings in acoustic superlattices: The Shannon entropy in acoustics

We show that Shannon’s information entropy provides a correct physical insight of localization effects taking place in structured fields fashioned by eigenmodes upon substrate. In particular, we find that the localization exchange among levels when an avoided crossing occurs is explainable in terms of an informational trade among those levels. We use it to characterize the resonant Zener-like effect in two types of ultrasonic superlattices, one made of metamaterial slabs and the other made of Plexiglas and water cavities. When the gradient of the layer cavities is varied along the narrow region where the avoided crossing appears, it is found that Shannon’s entropy of both levels maximizes at the critical gradient showing the levels’ anti-crossing.

Retail investor attention and IPO prices with a pre-IPO market

This study investigates whether retail investor attention influences initial public offering (IPO) subscription and prices in Taiwan's economy, which features little information asymmetry because of its pre-IPO market. We find that IPO trading prices in a pre-IPO market increase with investor attention before IPO filing date. Our results support attention theory and indicate that retail investors naïvely subscribe to IPOs that attract their attention, whereas underwriters employ pre-IPO market trading information rather than investor attention to price IPOs. The trading price in a premarket is more valuable than is retail investor attention for IPO pricing.

Futures volatility forecasting based on big data analytics with incorporating an order imbalance effect

Future markets play vital roles in supporting economic activities in modern society. For example, crude oil and electricity futures markets have heavy effects on a nation’s energy operation management. Thus, volatility forecasting of the futures market is an emerging but increasingly influential field of financial research. In this paper, we adopt big data analytics, called Extreme Gradient Boosting (XGBoost) from computer science, in an attempt to improve the forecasting accuracy of futures volatility and to demonstrate the application of big data analytics in the financial spectrum in terms of volatility forecasting. We further unveil that order imbalance estimation might incorporate abundant information to reflect price jumps and other trading information in the futures market. Including order imbalance information helps our model capture underpinned market rules such as supply and demand, which lightens the information loss during the model formation. Our empirical results suggest that the volatility forecasting accuracy of the XGBoost method considerably beats the GARCH-jump and HAR-jump models in both crude oil futures market and electricity futures market. Our results could also produce plentiful research implications for both policy makers and energy futures market participants.

Advancing Privacy and Security of Internet of Things to Find Integrated Solutions

Internet of things is the part of software engineering where the different items are associated with one another to trade information between them by means of web in an organization. IoT is the concept that vigorously depends on the framework of the organisation to associate large number of gadgets whose aim is to gather information and impart among each other in the bid to improve their dynamic capacities. Notwithstanding, with this incredible development comes different difficulties that compromises the data innovation industry; these incorporate security and protection issues. The presence of an enormous number of interconnected innovations creates a situation in the IT field which has been deteriorated by the absence of limit of different organizations to distinguish, survey and screen significant parts to guarantee consistence with security strategies. In this way, there is need for an unmistakable comprehension of the main things and how they can be settled to saddle completely. This paper will explain the solutions that can be provided to enhance the security and privacy issues in the field of internet of things.

Knowledge Sharing via Domain Adaptation in Customs Fraud Detection

Knowledge of the changing traffic is critical in risk management. Customs offices worldwide have traditionally relied on local resources to accumulate such knowledge and detect tax frauds. This naturally poses countries with weak infrastructure to become tax havens of potentially illicit trades. The current paper proposes DAS, a memory bank platform to facilitate knowledge sharing across multi-national customs administrations to support each other. We propose a domain adaptation method to share transferable knowledge of frauds as prototypes while safeguarding the local trade information. Data encompassing over 8 million import declarations have been used to test the feasibility of this new system, which shows that participating countries may benefit up to 2-11 times in fraud detection with the help of shared knowledge. We discuss implications for substantial tax revenue potential and strengthened policy against illicit trades.