Market Order Research Articles

A stock price manipulation detecting model with ensemble learning

Stock price manipulation has a serious negative impact on the normal market trading environment and order, and among them, trade-based manipulation is particularly common and hidden, which makes it difficult to be detected by regulatory agencies. A variety of algorithms have been applied onto solving this problem, among which RNN-based models perform well due to their adeptness at processing time-series data. Several works have been conducted to integrate RNNs into ensemble models by treating RNNs as traditional classifiers, but the training process and the sub-model connection of traditional ensemble models could drop potentially informative features learned by RNNs. In this paper, we propose a model utilizing the stacking generalization technique that combines predictions of multiple submodules to give the final result while keeping the structure of neural networks. To validate the performance of our model, we collected public data from China Securities Regulatory Commission and created a dataset based on these data. The results show that our model performs better than the baseline in multiple metrics.

Optimal trade execution in cryptocurrency markets

Novel technologies allow cryptocurrency exchanges to offer innovative services that set them apart from other exchanges. In this paper we study the distinct features of cryptocurrency fee schedules and the implications for optimal trade execution. We formulate an optimal execution strategy that minimizes the trading fees charged by the exchange. We further provide a proof for the existence of an optimal execution strategy for this type of fee schedule. The optimal strategy involves both market and limit orders on various price levels. The optimal order distribution scheme depends on the market conditions expressed in terms of the distribution of limit order execution probabilities and the exchange’s specific configuration of the fee schedule. Our results indicate that a strategy kernel with an exponentially decaying allocation of trade volume to price levels further away from the best price provides superior performance and potential reduction of trade execution cost of more than 60%. The robustness of these results is confirmed in an empirical study. To our knowledge, this is the first study of optimal trade execution that takes into consideration the full fee schedule of exchanges in general.

Social influence in the darknet market: The impact of product descriptions on cocaine sales

BackgroundThe rise of the darknet market, supported by technologies such as the Tor Browser and cryptocurrencies, has created a secure environment in which illicit transactions can occur. However, due to the lack of government oversight in this hidden online domain, darknet markets face significant challenges in upholding social order. Hence, this study explores the social dynamics that promote social order in a darknet market, focusing on the impact of item descriptions on sales. In particular, the study examines how text contained in product listings can influence sales and contribute to social order. MethodTo conduct this analysis, we examined 4160 cocaine listings on AlphaBay, which was active from December 2014 to July 2017 and is one of the largest darknet markets in history. Using generalised additive models (GAMs), we assessed the impact of various listing description features, including content and semantic structure, on cocaine sales. ResultsThe results showed that sales increased by 61.6 % when listings included delivery information in their description, compared to offers that did not. In addition, the standardised sentiment score (ranging 0,1) of the product description increased positively, and estimated sales increased by 260.5 %. We also found that international shipping reduced sales by 28.3 %. Finally, we found that listings stating the product origin increased sales for all continents except Asia. ConclusionThe study sheds light on the characteristics of product advertising that facilitate social order within a darknet market. Listings that include delivery details in the description reduce uncertainty about a critical stage of the transaction process while using positive language increases trust. This study makes both an empirical and a theoretical contribution by demonstrating the influence of ad descriptions on sales and the intricate role of social influences in shaping market order.

Meta-Regulation--An Innovative and Dynamic Policy Instrument for Platform Economy

The emergence of a variety of digital companies breaks up the competitive landscape of the market, which simultaneously changes peoples lifestyles as well. As the main tool to convenient peoples lives and to motivate the economic development of society, the progress made by digital technology proposed high requirements for authorities to update and adopt new regulatory approaches to retain the market order. Currently, the policy regulation for online agencies, known as platforms, causes intense academic discussion due to the inapplicability of the traditional regulatory model and the conflicting views of technical innovation or governmental restriction. This paper will focus on an innovative regulatory doctrine, namely Meta-regulation which enables the government to set up the rules for platforms from a unique perspective, and meanwhile, exploring two dimensions including an explanation of the standardized concept of Meta-regulation and giving an analysis of its generalization within the context of the digital economy. A conceptually accepted definition of Meta-Regulation and a series of reasons produced to demonstrate the applicability of Meta-Regulation for platform regulating will be figured out at the end of this paper.

Effects of Institutional and Organizational Factors on the Changing Contours of Industrial Relations in Central and Eastern Europe

Since the early 1990s, the industrial relations systems in the transition economies of Central and Eastern Europe have been undergoing complex transformation processes. As the business environment has radically altered from a centrally planned system to an open competitive one, trade unions in the region have struggled to carve out a role in the new order. We draw upon organizational survey level evidence gathered from 1604 HR managers to examine trade union recognition, trade union coverage, as well as the perceived ongoing influence of the trade union movement in Croatia, Estonia, Hungary, Latvia, Lithuania, Romania, Serbia, Slovakia, and Slovenia. Our analysis points to the legitimacy challenges faced by the labour union movement in the dramatically altered political and economic situation that emerged in the region as a result of the attenuation of socialism and the advent of a new market order. It also lends further support to the growing body of evidence on the lack of preparedness of the union movement for the new dispensation that emerged following the collapse of communism in the region.

Research on Unfair Competition in the Digital Economy: Innovation, Regulation, and Balancing Strategies

Online unfair competition is on the rise, disrupting market order and violating consumer rights in the context of the fast developing digital economy. Therefore, the purpose of this essay is to investigate the nature and forms of unfair competition in the internet economy and how it affects innovation. In addition, it examines the shortcomings of the existing regulatory frameworks and suggests balanced approaches and effective techniques to encourage the digital economy's healthy expansion. Defamation, misleading advertising, and web crawlers are common examples of unfair competition on the Internet. Another kind of unfair competition is data mining, which involves acquiring rivals' customer information or trade secrets. These behaviors not only undermine the principle of fair competition in the market but also mislead consumers and harm their interests. There is a certain relationship between unfair competition on the Internet and innovation. On the one hand, unfair competition on the Internet may stem from companies' excessive pursuit of innovation. To stand out in fierce market competition, companies may resort to unfair means to gain competitive advantages. On the other hand, unfair competition on the Internet may also inhibit innovation, as companies may reduce their innovation investment due to concerns about competitors taking unfair measures. The current regulatory mechanism has certain shortcomings in addressing unfair competition behavior on the Internet. On the other hand, the regulatory mechanism's definition of unfair competition on the Internet is not clear enough, which makes it difficult for regulatory agencies to accurately judge and handle it in the law enforcement process. At the same time, the regulatory mechanism's punishment for unfair competition on the Internet is not strong enough to effectively deter illegal behavior. To address unfair competition on the Internet, it is necessary to establish effective regulatory mechanisms and balance measures. Firstly, it is necessary to clarify the definition of unfair competition on the Internet and establish specific regulatory rules and standards. Secondly, it is necessary to strengthen the enforcement efforts of regulatory agencies, increase the severity of penalties, and ensure the effectiveness of regulatory mechanisms. At the same time, it is also necessary to strengthen corporate self-discipline, promote compliance with market rules, and jointly maintain market order. In short, unfair competition on the Internet has had a negative impact on the development of the digital economy, and it is necessary to establish effective regulatory mechanisms and balance measures to promote the healthy development of the digital economy.

High-frequency stock market order transitions during the US-China trade war 2018: A discrete-time Markov chain analysis.

Statistical analysis of high-frequency stock market order transaction data is conducted to understand order transition dynamics. We employ a first-order time-homogeneous discrete-time Markov chain model to the sequence of orders of stocks belonging to six different sectors during the US-China trade war of 2018. The Markov property of the order sequence is validated by the Chi-square test. We estimate the transition probability matrix of the sequence using maximum likelihood estimation. From the heatmap of these matrices, we found the presence of active participation by different types of traders during high volatility days. On such days, these traders place limit orders primarily with the intention of deleting the majority of them to influence the market. These findings are supported by high stationary distribution and low mean recurrence values of add and delete orders. Further, we found similar spectral gap and entropy rate values, which indicates that similar trading strategies are employed on both high and low volatility days during the trade war. Among all the sectors considered in this study, we observe that there is a recurring pattern of full execution orders in the Finance & Banking sector. This shows that the banking stocks are resilient during the trade war. Hence, this study may be useful in understanding stock market order dynamics and devise trading strategies accordingly on high and low volatility days during extreme macroeconomic events.

Research on the Governance form Mechanism of Platform Enterprise Monopoly Behavior Based on Evolutionary Game Theory

Abstract Platform enterprises, which aggregate numerous and scattered resources, connect multiple interdependent parties and facilitate interactions and transactions, also confront significant monopoly market challenges. These issues constrain the platform economy’s healthy development and severely disrupt the normal market order. This paper constructs a tripartite evolutionary game model comprising the government, platform enterprises, and users, to investigate the governance mechanisms of monopoly behavior in platform enterprises and employs Matlab for numerical simulation. The results indicate that government rewards, additional profits, monopoly penalties, and user collaboration positively impact the evolution of platform enterprise strategies toward non-monopolistic practices. Together, these four mechanisms constitute the governance mechanism for monopoly behavior in platform enterprises. Specifically, providing honorary rewards for non-monopolistic behavior in platform enterprises over a certain period is essential, as it represents an effective means for the government to encourage innovative development in internet platform enterprises. The profit mechanism is the main driving force behind platform enterprises adopting monopolistic strategies and facilitating their survival and development. To a certain extent, the penalty mechanism can suppress monopolistic behaviors in platform enterprises, but it cannot fundamentally eliminate them. Instead, this mechanism accelerates the evolution of platform enterprises towards non-monopolistic behaviors, marking it as a positive governance tool. The user collaboration mechanism, a form of collaborative governance, serves as a beneficial complement to the reward, penalty, and profit mechanisms. This is because users primarily engage with platforms as consumers, legally entitled to services, and their regulatory role constitutes only a part of their responsibilities, not the main economic activity on digital platforms.

A study of the impact of financial news media coverage patterns on stock price fluctuations

Abstract This paper examines the impact of media communication on the stock market in the context of the digital age, particularly its role in stock price volatility. With the significant increase in the speed, breadth and depth of media communication, the efficiency of stock market information transmission has been dramatically improved, significantly impacting stock price volatility. The article first explores the specific impact of media coverage characteristics and methods on stock price volatility by quantitatively analyzing media coverage’s breadth, manner and content. Then, the article utilizes multiple linear regression models to deeply explore the impact of various news texts on stock price volatility in different reporting modes (e.g., live reporting, television, and text reporting) in the Shanghai and Shenzhen stock markets. The findings show a significant association between the breadth of media coverage and stock prices at the 1% significance level. In addition, the regression coefficients of content and manner of coverage on stock price changes are 0.096 and 0.229, respectively, which are both significant at the 5% significance level. The research results of this paper not only reveal the close relationship between media communication and stock market volatility and provide theoretical support for predicting dramatic stock market volatility, which helps to promote the construction of a healthy and orderly capital market order.

Thermodynamic Analysis of Financial Markets: Measuring Order Book Dynamics with Temperature and Entropy.

This study bridges finance and physics by applying thermodynamic concepts to model the limit order book (LOB) with high-frequency trading data on the Bitcoin spot. We derive the measures of Market Temperature and Market Entropy from the kinetic and potential energies in the LOB to provide a deeper understanding of order activities and market participant behavior. Market Temperature emerges as a robust indicator of market liquidity, correlating with liquidity measures such as Active Quote Volume, bid-ask spread and match volume. Market Entropy, on the other hand, quantifies the degree of disorder or randomness in the LOB, providing insights into the instantaneous volatility of price in the high-frequency trading market. Our empirical findings not only broaden the theoretical framework of econophysics but also enhance comprehensive understanding of the market microstructure and order book dynamics.

A general spammer indicator of rating systems uncovering rating preferences and bias

With the rapid development of e-commerce recently, massive spammers purposely distort the ranking of goods, which affects the market order and the fair competition of businesses seriously. Therefore, identifying such spammers is significant to rational decision making of customers. However, it is difficult to discriminate between normal users and malicious spammers on extremely large rating networks. In this paper, we propose a common indicator based on the historic rating records from spammers and normal users, which is widely applied to many existing methods. It is inspired by the idea that normal users have their preferences and rating bias shown in rating ladder, while spammers do not have such rating ladders in practice. Such an indicator is complement with other existing methods including Deviation-based Ranking (DR), Iterative Group-based Ranking (IGR) and Iterative Balance Ranking (IBR). Experimental study on three real rating networks shows that this indicator can significantly improve the accuracy of DR, IGR and IBR. To deal with malicious spammers, DR, IGR and IBR are improved by at least 9.38%, 2.90% and 2.53%, respectively. To deal with random spammers, DR, IGR and IBR are improved by at least by 5.52%, 17.12% and 32.24%, respectively.

Retail ETF investing

AbstractUsing marketable order flow data, we analyze key characteristics of aggregate retail exchange‐traded fund (ETF) investing from 2010 to 2021, including allocations, holding period and investment performance. Retail traders allocate 12% more dollar volume to leveraged and inverse ETFs versus nonretail traders. Retail ETF trades distinctly increase with prior ETF returns, in contrast to contrarian stock trading. Estimated ETF holding periods are longer for retail investors versus nonretail. Finally, retail and nonretail ETF trades perform similarly over hypothetical holding periods up to one quarter. Overall, we provide policy‐relevant insights into retail investing behaviours, which have been the subject of recent concern.

Management of enterprise carbon emissions data falsification considering government regulation and media monitoring

The accuracy of carbon emission data is essential for various disciplines to maintain reasonable expectations and certainty regarding the carbon emission rights trading market. However, the management of carbon emission data quality faces many challenges, especially due to the harmful behavior of enterprises that falsify such data, which seriously disrupts the order and credibility of the carbon market. Currently, few studies focus on the behavior and mechanism of corporate carbon emission data fraud, which requires an in-depth stakeholder analysis to obtain theoretical and empirical support for the formulation of effective regulatory policies. To investigate the influence of government regulation and media monitoring on addressing enterprise carbon emission data falsification, as well as to analyze the game behaviors and equilibrium outcomes among the government, media, and enterprises under different policy combinations and market environments, this study develops an evolutionary game model incorporating the government, media, and enterprises as three key stakeholders. Furthermore, numerical simulations are conducted for empirical validation. The key findings of this research highlight the significant impact of government regulation and media monitoring on deterring enterprise carbon emission data falsification, thus effectively reducing falsification motives and behaviors and enhancing the quality of carbon emission data. Additionally, the game between the government, media, and enterprises reveals the existence of multiple evolutionary stable strategies. Of these, the optimal strategy is the comprehensive implementation of all three elements: government regulation, media monitoring, and corporate integrity disclosure. This paper comprehensively examines the influence of government regulation and media monitoring on enterprise carbon emission data falsification and addresses the gaps in existing research. Moreover, it provides theoretical guidance and policy recommendations for establishing a high-quality carbon market.

Sustainable Technology Strategies for Transportation and Logistics Challenges: An Implementation Feasibility Study

Transportation and logistics are the basic building blocks in the socio-economic development of a country. The pandemic altered the landscape of the transportation and logistics sector where organizations had to look for new technology-based solutions. Block chain and digital trucking are emerging concepts, which were further accelerated by COVID-19, to manage the challenges in the transportation and supply chain industry. This study, therefore, investigates the challenges faced by the transportation industry during and post COVID-19 and, consequently, identifies relevant sustainable strategies to combat these challenges in a developing-economies context. Data were collected through interviews from 20 key personnel working in managerial positions in the transportation industry of Pakistan, a developing economy. The findings of this study indicate that the challenges faced by the transportation industry are reduced import–export, local market orders and revenues, supply limited to a few edible items, increase in e-commerce, new entrants in the market and operational issues, such as lack of standard operating procedures (SOPs), performance management and training of drivers. These challenges were more pronounced in the COVID-19 era; however, they are still impacting the industry. Thus, in the post-COVID-19 era, transportation companies need to opt for efficient strategies, such as contactless deliveries, expansion in e-commerce, tech-based performance management of drivers and digital trucking for sustainability, in a developing economy’s transportation and logistics sector.

The Role of China’s Social Credit Management in the Socialist Market Economy System

Social credit management is a global issue, has become an important aspect of the economic development of various countries. In the process of social transformation in modern China, the social credit management system has undergone three stages of evolution: from scratch to existence, from existence to reality, and from reality to refinement. Based on the review of the transformation of market order and the construction of social credit system in China, this paper makes a systematic analysis of its main content and internal logic. From the perspective of Huntington’s “political decline” and other Chinese and foreign theories, this paper discusses the relationship between the three main bodies of social credit construction — market, society, government and market order respectively, in an attempt to provide inspiration and ideas for the construction of China’s future social credit system.

Fraud in Listed Companies in China: Causes, Impacts and Countermeasures

In recent years, the issue of fraud in Chinese listed companies has been a topic of great concern. This paper aims to explore the causes of fraud in listed companies in China, its impact on the economy and investors, and propose some countermeasures. Through a comprehensive analysis of relevant literature and cases, we found that the root causes of fraud include insufficient supervision, moral hazard, financial pressure, and information asymmetry. These problems have had a negative impact on the development of China's capital market, harmed the interests of investors, and weakened the credibility of the market. However, by strengthening supervision, improving corporate governance mechanisms, improving information transparency, and strengthening investor education, we can effectively deal with fraud problems and maintain market order and investors' rights and interests.

Design Study of Characteristic Scuba Diving Tourism in Sanya, Hainan Province

In recent years, with the development of diving tourism, the diving tourism industry in Sanya, Hainan Province, China has rapidly emerged. The tourism management department has been promoting and advertising this unique tourism feature, making diving tourism one of the major attractions that bring in more and more domestic and foreign tourists. We have noticed that there is great potential and room for the development of diving tourism in Sanya, Hainan Province, but there are also problems with tourist safety, marketing strategies, and poor management. To promote the development of diving tourism in Sanya, Hainan Province, we suggest the following: First, strengthen the measures to ensure the safety of tourists, including improving the quality of diving tourism services, strengthening the construction of management systems, and enhancing tourist safety measures to improve their sense of security. Secondly, optimize the marketing strategies to increase the awareness and competitiveness of diving tourism products. Thirdly, strengthen management and regulatory measures, perfect management systems, and standardize market order. Fourthly, establish a comprehensive diving tourism plan, design diving routes, and enhance the quality of diving facilities and services. Based on these suggestions, we can enhance the development of diving tourism in Sanya, Hainan Province, increase its economic and social benefits, and raise its level of development significantly.

Exploring the Status Quo of All-for-one Tourism Development in Meishan City, China: Insights from Local Travel Influencers

All-for-one tourism is a newly proposed concept that aims to promote the transformation of national tourism development in China. Few research studies have focused on the current industry practice of all-for-one tourism in China. A qualitative case study, grounded in an interpretivist paradigm, was conducted to investigate the status quo of all-for-one tourism development in Meishan City, China. In-depth interviews were conducted with 15 local travel influencers based on opinion leadership theory. Thematic content analysis was utilized to analyze study data. Study findings revealed that implementing all-for-one tourism was limited by the lack of a clear tourism strategy, uncoordinated development, and low-quality public services. But efforts had been made to support all-for-one tourism in the public service system, tourism supply chain, tourism market order, destination brand strategy, and marketing effectiveness.

Adulteration Detection of Pork in Mutton Using Smart Phone with the CBAM-Invert-ResNet and Multiple Parts Feature Fusion.

To achieve accurate detection the content of multiple parts pork adulterated in mutton under the effect of mutton flavor essence and colorant by RGB images, the improved CBAM-Invert-ResNet50 network based on the attention mechanism and the inversion residual was used to detect the content of pork from the back, front leg, and hind leg in adulterated mutton. The deep features of different parts extracted by the CBAM-Invert-ResNet50 were fused by feature, stitched, and combined with transfer learning, and the content of pork from mixed parts in adulterated mutton was detected. The results showed that the R2 of the CBAM-Invert-ResNet50 for the back, front leg, and hind leg datasets were 0.9373, 0.8876, and 0.9055, respectively, and the RMSE values were 0.0268 g·g-1, 0.0378 g·g-1, and 0.0316 g·g-1, respectively. The R2 and RMSE of the mixed dataset were 0.9264 and 0.0290 g·g-1, respectively. When the features of different parts were fused, the R2 and RMSE of the CBAM-Invert-ResNet50 for the mixed dataset were 0.9589 and 0.0220 g·g-1, respectively. Compared with the model built before feature fusion, the R2 of the mixed dataset increased by 0.0325, and the RMSE decreased by 0.0070 g·g-1. The above results indicated that the CBAM-Invert-ResNet50 model could effectively detect the content of pork from different parts in adulterated mutton as additives. Feature fusion combined with transfer learning can effectively improve the detection accuracy for the content of mixed parts of pork in adulterated mutton. The results of this study can provide technical support and a basis for maintaining the mutton market order and protecting mutton food safety supervision.

Seventy‐five years West German currency reform: Crisis as catalyst for the erosion of the market order

AbstractThe paper analyzes the role of the 1948 currency and economic reform in West Germany for growth and social cohesion in Germany and Europe. It describes the theoretical foundation by Walter Eucken and the implementation and defense of the new economic order by Ludwig Erhard. The paper stresses the positive impact of the market economy on growth and welfare in Germany and Europe. Then, it is shown that Eucken's constituting principles of the market economy were gradually eroded after euro introduction in course of crises, with the negative repercussions on growth and equality being explained. The policy conclusion is that only the reconstitution of price stability in the euro area can prevent a further decline of welfare in Europe. The 1948 economic reforms provide an important blueprint for the necessary reform process.