Trading Volume Research Articles

Cryptocurrencies as a vehicle for capital exodus: Evidence from the Russian–Ukrainian crisis

Cryptocurrencies provide an escape from the conventional financial system and its regulations and could therefore become increasingly popular in the midst of geopolitical uncertainties. We analyze the linkage of the Russia–Ukraine conflict and the trading volume of 16 major cryptocurrencies via event study methodologies, based on a geopolitical risk index. The results show that the trading volume of most cryptocurrencies is positively affected by the events of the conflict. This is especially true for payment tokens and most utility coins. Interestingly, stablecoins show only fewer trading volumes before the actual event. Among utility tokens, Ripple in particular is positively influenced.

Threshold integer-valued autoregressive model with serially dependent innovation

ABSTRACT Nonlinearity in count time series is commonly encountered in practice. To better explain the nonlinear phenomena in count time series, this article introduces a new threshold INAR(1) model using the idea of serially dependent innovation. The proposed model contains some existing INAR(1) models as special cases and establishes a connection between the threshold INAR(1) model and the threshold INARCH(1) model. The basic probabilistic and statistical properties of the new model are investigated. Model parameters, including the threshold variable, are estimated by the conditional least squares, modified quasi-likelihood and conditional maximum likelihood methods. Asymptotic properties and numerical results of the estimates are also studied. An application to the monthly trading volume of stock B in Shanghai Stock Exchange is conducted to show the practicability of the proposed model.

CEO-related announcements, trading activity, and calendar effects

ABSTRACT This study examines how abnormal trading volumes reflect new information, such as CEO-related announcements. Using hand-collected data on 1,236 CEO reappointments and turnovers from Polish firms listed on the Warsaw Stock Exchange, we show that investor reaction to CEO-related announcements is stronger in the winter-spring season than in the summer-fall season. However, in the summer-fall season, CEO reappointment triggers higher trading activity than CEO turnover announcements, while in the winter-spring season, the reaction is opposite. We also discover that abnormal trading volumes induced by CEO-related announcements are significantly lower at the beginning and end of the week than in the middle of the week. Furthermore, when it comes to day of the week anomalies, investors are much less interested in CEO reappointment than in turnover. The findings suggest that calendar effects influence investor reactions to CEO appointments, offering new insights into market anomalies and corporate governance mechanisms.

Determining the Relationships between Newly Established Logistics Enterprises in Turkey Based on Foreign Trade Volume and Legal Status

The purpose of this study is to determine how newly established logistics businesses (NELB) in Turkey affect foreign trade volume. Additionally, it aims to identify the relationships between NELB and businesses based on their legal status. The data used in this research, covering the years 2013-2021, were gathered from the open-access sites of the Turkish Statistical Institute (TUIK). IBM SPSS Statistics 20 software was used to analyze the data. The Pearson Correlation analysis method was chosen to evaluate the relationships between variables. According to the analysis results, NELB has a strong positive effect on Turkey's foreign trade volume (Sig. (2-tailed) = 0.03 and Pearson Correlation = 0.857**). Additionally, there is a strong positive relationship between NELB and businesses categorized as "real person" (Sig. (2-tailed) = 0.01 and Pearson Correlation = 0.887**) and "limited company" (Sig. (2-tailed) = 0.016 and Pearson Correlation = 0.767**), based on their legal status. However, no relationship was found between businesses with legal statuses such as ordinary partnership, joint-stock company, cooperative, and other types.

Determinants of China’s technological trade flows with Central Asian countries (CACs): a panel gravity approach

The purpose of this study is to examine the determinants of Chinese technological trade flows with Central Asian countries (CAC) with a revealed comparative advantage and the gravity model using two estimation techniques. The present study employs the Ordinary Least Square (OLS) and the Poisson pseudo-maximum likelihood (PPML) on the data set from 2003 to 2018 panel dataset. Results from the Gravity model demonstrates that low-tech, medium-tech, and high-tech trade flows boost economic development in China and Central Asian nations, adversely affecting all technology trade flows in geographical distance. The research indicated that exchange rates showed a negative indication in bilateral trade, medium-tech trade, and high-tech trade. In contrast, the low technology trade showed a positive flow between China and Central Asia. Furthermore, the results reveal that openness to trade and membership of the WTO show a positive indication. The trade openness policy on both sides thus has a major beneficial effect on trade volumes. It proposed increased liberalization of trade policy and greater involvement in global commerce. In addition, Central Asia and export industries should take additional initiatives to enhance and diversify high-tech exports. A competitive international market provides perfect equality in reciprocal trade interactions.

Risk Assessment of Trade Barriers: The Implications of Indonesia's Halal Certification Law on International Commerce

Introduction: The importance of halal standards in international commerce has increased as the volume of cross-border trade has increased. This article investigates the ramifications of Indonesia's Halal Product Guarantee Law on global trade, specifically focusing on its arrangement with World Trade Organization rules, explicitly the Specialized Technical Barriers to Trade Agreement. Even though the Halal Product Guarantee Law requires halal certification, it could be seen as a trade barrier because it could impose too many regulations and encourage discrimination against imported goods. Objective: This study discusses the implications for international trade of the Halal Product Guarantee Law's potential violations of the Technical Barriers to Trade Agreement and emphasizes the necessity of globally consistent and uniform halal certification standards. Method: This study analyzes the Halal Product Guarantee Law about important World Trade Organization principles like National Treatment and Most Favored Nation using a descriptive qualitative research methodology and drawing from previous literature reviews. Result: According to the findings, the Halal Product Guarantee Law may violate World Trade Organization regulations, which would have a significant impact on international trade and raise questions regarding the uniformity, adaptability, and consistency of halal certification. Implication: The article emphasizes the significance of evaluating the Halal Product Guarantee Law's compliance with World Trade Organization agreements to support the expansion of the halal market, respect religious beliefs, and ensure fair and balanced international commerce.

Google search and cross-section of cryptocurrency returns and trading activities

This paper examines the effect of investor attention on the cross-section of cryptocurrency returns and trading activities. We find that cryptocurrencies associated with higher abnormal Google search volume subsequently exhibit higher returns, higher volatility, and higher trading volume. The results are robust to alternative sample periods and alternative search keywords, providing concrete support to the attention-induced price pressure hypothesis and consistent with prior studies on the equity market. The effect is more pronounced among larger cryptocurrencies. Only a partial reversal after the initial return increase is observed, implying that investor attention permanently impacts cryptocurrency prices.

Mapping trade opportunities in Central and South Asia: an exploration through trade indices and metrics

Purpose This study aims to explore the possibilities for regional (intra and inter) trade between Central Asia and South Asia (CASA) by examining several frameworks that impact the economic integration of these regions. Design/methodology/approach The study uses five indices, specifically the export diversification index, export survival rates, export sophistication, revealed comparative advantage and intra-industry trade index in South and Central Asia, from 2005 to 2021. Findings The findings show a twofold increase in the potential trade value between the two regions compared to the actual level. The intra-regional trade in Central Asia accounts for less than 5% of trade with all countries, whereas the intra-regional trade in South Asia accounts for 1.5%, and the inter-regional trade accounts for between 0.2 and 4% of total trade to all destinations. The intensity of trade measurements shows that inter-regional trade flows are modest because they make up a relatively small percentage of the entire trade volume, which includes all destinations, ranging from 0.2 to 4 %. Research limitations/implications These findings have a significant impact on the successful implementation of trading-related measures, initiatives and institutional mechanisms for encouraging improved trade between and within both regions. Practical implications Export diversification initiatives aim to provide favourable outcomes for all parties involved, focusing on expanding the range of goods and services exchanged. CASA countries with higher diversification have shown more favourable outcomes than those relying on a restricted range of products. The authors expect policies promoting a broader array of exports to boost market shares, while maintaining a narrow focus may hinder new export prospects. In addition, regional value chains and policy initiatives promoting trade and investment could lead to increased value additions to exports, technological transfers and job creation. Social implications The study emphasises the significance of trade-related policies, programmes and institutional mechanisms in promoting trade between CASA. It emphasises the need for policies that support investment and trade, adopt new requirements of the Agreement on Trade Facilitation and promote diversity and latency. Originality/value Based on economic analysis, the study offers practical insights and strategies for businesses in the CASA regions. It also provides analytical tools for academics and decision makers, as well as policy and programme recommendations for government agencies, development partners, researchers and individuals interested in trade dynamics in these regions.

GC-MS and ICP-OES Analysis of Secondary Metabolites from Selected Wheat Varieties in India

Triticum aestivum L. serves as a major global commodity with annual trade volumes reaching around 150 million tons. Wheat varieties including Sharbati, Black Wheat, Chawal Keti, and Sonamoti are extensively cultivated across many regions in India. Besides its nutritional value, wheat has medicinal importance especially for diabetic, cancer, and cardiac patients. These medicinal values are because of the presence of phytochemicals, which are natural compounds that offer health benefits, including antioxidant properties. This study focuses on the qualitative and quantitative assessment of phytochemicals from selected wheat varieties using standard protocols and analyzed with their ICP-OES and GC-MS reports.

The Brussels effect in Ankara: the case of climate policy

Abstract Turkey’s Europeanization process provides a particularly interesting case study of the extra-jurisdictional impact of European Union (EU) law, both through policy convergence and through the so-called Brussels effect. Formally, Turkey must adopt certain EU rules due to its status as an EU candidate country, but its candidacy process has been lengthy and uncertain, resulting in partial and uneven adoption of EU rules. Nevertheless, EU-style policymaking has persisted in various policy areas, including environmental and climate policy. This paper aims to analyze the convergence of climate change policies between the EU and Turkey by employing multidimensional scaling, a method that enables the visualization and examination of the connectivity and intensity of cooperation between states. For the period from 2007 to 2023, our comparative analysis demonstrates that policy divergence occurs when the EU’s share of Turkey’s total trade decreases and when political challenges are experienced. On the other hand, periods of policy convergence coincide with periods of increased trade volume and expanded trade opportunities. The results suggest that through its market size and regulatory capacity, the EU exerts soft power which forces Turkey to align its climate policies with the EU to protect and maintain its competitiveness in the European marketplace.

Liquidity risk analysis via drawdown-based measures

Trading volumes are key variables in determining the degree of an asset's liquidity. We examine the volume drawdown process and crash recovery measures in rolling-time windows to assess exposure to liquidity risk. The time-varying windows protect our financial indicators from the massive amount of volume transactions that characterize the opening and closing of the stock market. The empirical study is carried out for three Nasdaq-listed assets from April to September 2022. Firstly, we shape all of the volume time series using a weighted-indexed semi-Markov (WISMC) model, as well as the EGARCH and GJR models for comparisons. Next, we calculate drawdown-based risk measures on real and synthetic data, simulated from all the considered econometric models. Finally, we employ the Kullback-Leibler divergence to compare real and simulated risk indicators. Results reveal that the WISMC model reproduces all the drawdown-based risk measures better than the EGARCH and GJR models do for all the considered stocks.

The Spatial–Temporal Evolution and Driving Factors of the Coastal Tourism Economy in China

Tourism has emerged as a pivotal element of China’s economic development, particularly within its coastal cities. This paper presents a comprehensive analysis of China’s coastal city tourism economic development, focusing on 53 coastal cities. Through a meticulous combination of literature analysis and data crawling, a robust database is constructed, encompassing tourism resources and revenues. This study delineates the spatial–temporal evolution pattern of China’s coastal city tourism development and employs geo-detector methods to quantitatively analyze the impact factors driving this evolution. Key findings reveal distinct trends in the coastal tourism economy of China from 2009 to 2019, characterized by spatial stability, similar trends in adjacent spatial units, and localized spatial structures. Notably, factors such as actual foreign investment, the presence of star-rated guesthouses, tourism industry employment, airport activity, and import–export trade volume exert significant influence on the domestic tourism economy. Similarly, tourism employment, airport activity, availability of star-rated hotels, import–export trade, and utilization of foreign capital emerge as influential factors shaping inbound tourism. Policy recommendations emphasize the need for government intervention to optimize tourism development strategies for coastal cities. This entails balancing resource exploitation with environmental protection and enhancing the quality of tourism services, fostering sustainable growth and long-term prosperity.

Static Resilience Evolution of the Global Wood Forest Products Trade Network: A Complex Directed Weighted Network Analysis

This paper analyzes the static resilience of global wood forest products trade networks across upstream, midstream, downstream, and recycling sectors using a complex directed weighted network approach. By examining topological features and resilience from 2002 to 2021, this study reveals significant structural evolution and scale expansion in these networks. It finds improvements in network efficiency and resilience, alongside an increase in weighted hierarchy highlighting the prominent roles of core countries like China, the US, and Germany. While these countries bolster network resilience, they also introduce certain vulnerabilities. This study finds notable disassortative mixing without trade volume weights and diversified trends with weights, offering new insights into network dynamics. Core nodes must address disruption risks, enhance diversity, and establish emergency response mechanisms. In the recycling sector, this paper highlights weak trade connections and low resilience, with the US maintaining dominance, China’s influence waning, and India’s rapid ascent. This paper concludes by emphasizing the need for refined indicator systems and deeper explorations into resilience enhancement strategies for operational and targeted suggestions.

Spatial correlation of local government implicit debt tail risks in China and its spillover effects on the banking system

This study empirically examines the spatial correlation of implicit debt tail risks among local governments across provinces in China and its impact on systemic risks within the banking sector. By applying the TENET (Härdle et al., 2016) and the impulse response separation method, our analysis reveals a complex interconnection among these implicit debt tail risks. Moreover, we observe that a higher degree of spatial correlation amplifies the effect on bank systemic risk, resulting in a pronounced spillover effect in the banking system. Finally, our findings indicate that increased spatial correlation is correlated with a decrease in the capital adequacy ratio, a heightened trading volume of urban construction investment bonds, and an increased nonperforming loan ratio.

Religious practices, fasting, and individuals’ trading behavior: a natural experiment

Purpose This study aims to investigate the effect of seasonality caused by fasting as a religious practice on trading activity. Design/methodology/approach The authors use an unbiased sample of daily trading by individuals and institutions on the Boursa Kuwait. The authors use panel data on trading activities and Tobit regression models to examine the effect of Muslims’ religious practice of fasting during the holy month of Ramadan on trading behavior. Findings The authors find that during the holy month of Ramadan, Muslims’ religious practice of fasting leads to a decline in the frequency of both overall stock market trading and the ratio of individual trading volume to total trading volume. The authors find a significant decrease in individual buy-side trading as a proportion of total trading volume and simultaneously a significant increase in institutional buy-side trading. Practical implications This study’s findings have important implications for the main players in stock markets of countries with a Muslim majority. Market-makers should be aware of the significant increase in the proportion of institutional buy-side trading volume to total trading volume to minimize the cost of trading with better-informed traders (adverse selection). Originality/value To the best of the authors’ knowledge, this is the first study that investigates individuals’ trading activity during Ramadan.

Mastering Market Movements: Technical Indicators in IDX30 Stock Selection

This study aims to analyze the role of trading volume, foreign money flows, and technical chart patterns in shaping investment decisions for IDX30 companies. Utilizing descriptive quantitative research, data was collected from Stockbit and IPOT, focusing on stock price charts, trading volume, and broker summaries from January 1, 2020, to December 31, 2022. The findings reveal that purchase decisions were made when stock prices broke out of a chart pattern, accompanied by increased trading volume and foreign accumulation. Conversely, sell decisions occurred when stock prices broke down from a pattern, followed by increased volume and foreign distribution. These results underscore the importance of technical analysis in guiding informed investment decisions, helping investors avoid irrational practices and potential losses. The study highlights the practical need for investors to integrate technical indicators, such as volume and foreign flows, into their decision-making processes to enhance investment outcomes.

The Evolution of the International Monetary System: Impacts on Multinational Firms and Global Trade

The International Monetary System is a global system that regulates money transfers between different nations. It includes currency exchange rates, regulation of international payments, legislations on capital movements, and worldwide financial organizations, including the IMF. The paper analyses how the change over time of the International Monetary System (IMS) affects the international trade and MNCs. Thus, from the Bretton Woods new exchange regime established in 1944–1971 to the current floating exchange rate system, it has been analyzed in regard to the IMS's historical development. Because of the increasing volatility in the currency rate, this has consequently had an impact on the stability of international transactions and trade volumes. The IMS study investigates exchange rate systems, capital flow regulations, and the role of international financial organizations, including the IMF. For its methodology, the study resorted to a mixed-method approach, combining numbers analysis-that includes regression models and time- series data on exchange rates, amounts of international trade, and MNC performance-with analysis of the historical record and inspection of IMF policy documents. The study shows that trade goes down during big financial troubles, like the Asian Financial Crisis in 1997 and the Global Financial Crisis in 2008. It also shows that changes in exchange rates make global trade smaller, especially during these crises. Also, while IMF help, such as structural adjustment programs, often causes short-term problems for multinational companies (MNCs), like less foreign direct investment (FDI), these policies usually help stabilize economies and lead to recovery in the long run. The outcomes indicate how the IMS, international trade, and multinational corporations are intertwined. Acquainting oneself with the fluctuating money system is very crucial because it enables a person to make informed decisions about the world economy. Keywords: International Monetary System (IMS), Exchange Rate Volatility, Multinational Corporations (MNCs), International Trade, International Monetary Fund (IMF)

A Novel Stock Price Prediction and Trading Methodology Based on Active Learning Surrogated with CycleGAN and Deep Learning and System Engineering Integration: A Case Study on TSMC Stock Data

Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC’s stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes.

Media tone and trading activity on the Sydney Stock Exchange 1901–1950

We examine how media reports influenced trading volumes and order imbalances on the Sydney Stock Exchange (SSX) from 1901 to 1950, focusing on wool market reports as a substitute for broader financial advice in the absence of a specialised investment press. Given wool's status as Australia's primary export and its integration with various sectors, we construct a weekly media sentiment index based on news about wool sales and auctions from the Sydney Morning Herald. Our findings reveal that positive news about the wool market correlates with increased trading volumes and reduced order imbalances on the SSX. This relationship persisted during significant events such as the UK government's wool purchase plans, the 1929 Wall Street Crash, World War II-related trading restrictions, and the short selling ban.

Thermal coal futures trading volume predictions through the neural network

Purpose Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020. Design/methodology/approach The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases. Findings A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume. Originality/value The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.