Trading Volume Research Articles

Study on the Trade Effects of China’s FDI in ASEAN Countries under the RCEP Framework

As a developing country with the world’s highest foreign direct investment, China’s FDI to ASEAN will have an impact on trade between the two countries, helping to clarify the mechanism for China-ASEAN economic and trade cooperation and exploring ways to strengthen China-ASEAN economic and trade cooperation. Thus, using panel data from 2012 to 2020, this paper investigates the impact of FDI between China and ASEAN on trade volume. The findings indicate that China's direct investment in ASEAN has a significant negative impact on trade scale, specifically the trade substitution effect. Under the RCEP framework, the importance of ASEAN's position in China's foreign economic and trade cooperation network has become increasingly prominent. On this basis, it puts forward policy suggestions such as training corresponding talents, deepening structural adjustment, strengthening government guidance, and expanding correct cognition.

A political economy analysis of changes and continuities in Iran–Africa trade relations: a case of South–South dependency?

This paper provides a political economy analysis of the bilateral trade relations and patterns of exchange that existed between Iran and Sub-Saharan Africa (SSA) from 1962 to 2021. The paper contributes to the growing literature on Iran–Africa relations by empirically delineating not just the changes, but also the continuities between Iran and its African trading partners before and after the Iranian Revolution. It also adds nuance to the broader scholarship on South–South cooperation by showing the disparity between the rhetoric of South–South solidarity and reciprocity between Iran and SSA, on one side, and their low and fluctuating trade volumes, on the other. These volumes resulted from historical inertia or path dependency and structural factors, including trade complementarities and geopolitical conditions, more than demographic, cultural, or religious realities. They were also shaped by the agency and decision-making of Iranian and African leaders and officials regarding the extent to economically engage with each other.

Synchronous social media and the stock market

I examine stock discussions from real-time (synchronous) group chats on Discord and compare them with forum-style (asynchronous) postings on Reddit’s WallStreetBets. Findings suggest that popular stocks on Discord include fewer ”meme” stocks and are more diverse and profitable. A Discord-based long-short strategy outperforms several comparable strategies. Discord-based popularity predicts future trading volume, volatility, and returns, and this effect is stronger for smaller stocks. By contrast, popularity on WallStreetBets or on both platforms correlates with lower returns. Results suggest that academicians and practitioners should consider social interaction settings when evaluating the impact of social media on investment decisions.

STOCK MARKET RESPONSE TO METEOROLOGICAL DISASTER RISK: FROM LIQUIDITY PERSPECTIVE

This study investigates the impact of meteorological disaster risk perception on stock liquidity of Chinese listed companies by constructing meteorological disaster risk perception indicators through the Baidu index. Empirical evidence reveals that an increase in meteorological disaster risk perception decreases investors’ risk appetite and increases investors’ loss aversion, which in turn negatively affects stock liquidity. In addition, the increase in meteorological disaster risk perception also shrinks stock trading volume by reducing investor sentiment. The negative effect of meteorological disaster risk perception on stock liquidity is more pronounced in summer and winter and for firms located in water-rich areas. The findings of this study provide empirical evidence from investors’ perspective for understanding the impact of meteorological disasters on stock market liquidity.

Impacts of analyst coverage initiation on market quality

ABSTRACT We examine whether the initiation of financial analysts’ coverage leads to improved stock-price informativeness and increased market liquidity using the difference-in-differences methodology. The average value of daily total trading volume is significantly higher after than before initiation. The increase in trading volume is higher in the KOSDAQ market than in the KOSPI market, irrespective of investor type. Nonetheless, this increased rate is observed to decline more rapidly over time in the KOSDAQ market. When an analyst initiates coverage on a stock, both price informativeness and market liquidity improve, with different markets driving each effect. The enhancement of price informativeness is significant in the KOSPI, whereas the increase in market liquidity is primarily driven by firms listed on the KOSDAQ.

The Examination of the Relationship Between Bitcoin (BTC) Trading Volume in Türkiye and Google Trends Data on Bitcoin Searches in Google Search Engine

Bitcoin and cryptocurrencies have recently rekindled discussions in financial circles, both due to their technologies and price movements. The increasing inclination of investors who seek returns and embrace risk towards cryptocurrency markets is evident, driven by sudden price fluctuations. The potential of cryptocurrencies to serve as alternatives to traditional investment instruments continues to be debated within the financial framework. Researchers are persistently exploring financial instruments associated with the price fluctuations of Bitcoin and cryptocurrencies. This study investigates the interest in Bitcoin in Türkiye within the scope of Bitcoin trading volume and the "Bitcoin" search results on Google Trends. Bitcoin trade volume of BTCTurk and Paribu, two cryptocurrency exchanges operating in Türkiye, and Bitcoin search data on Google were included in the study. In this context, the long-term relationship between Bitcoin trading volume and Google Trends results is examined using the Engle-Granger cointegration test, and the existence of causality is explored through the Toda-Yamamoto causality test. According to the findings of the study, a cointegration relationship among the variables is identified. It is revealed that there is no bidirectional causality between Bitcoin trading volume and Google Trends search results. However, it is established that Google Trends is the cause of Bitcoin trading volume.

ANALYSIS OF INTERNATIONAL TRADE IN INDIA POST COVID-19

Amidst the unprecedented challenges posed by the COVID-19 pandemic, the global economy witnessed profound disruptions, particularly in the realm of international trade. As the world grappled with the economic fallout of the pandemic, India stood at the forefront of navigating the complexities of international trade in a post-COVID-19 landscape. This research endeavour seeks to shed light on the multifaceted impact of the pandemic on India's international trade dynamics and explore strategic pathways for resilience and recovery. Drawing upon a blend of quantitative analysis, policy evaluation, and sector-specific assessments, this study delves into four primary objectives. Firstly, it meticulously assesses the economic ramifications of the pandemic on India's international trade, unravelling shifts in trade volumes, patterns, and macroeconomic indicators. Secondly, it evaluates the efficacy of government policies and interventions aimed at bolstering trade resilience amidst unprecedented disruptions. Thirdly, it scrutinizes sectoral dynamics, discerning the differential impact of the pandemic across various industries and supply chains. Lastly, it endeavours to forecast future trends, offering insights into the trajectory of India's international trade and strategic imperatives for navigating the evolving global landscape. Furthermore, this research explores emerging themes such as supply chain resilience, digital transformation, and geopolitical dynamics, underscoring their significance in shaping India's trade trajectory in the post-pandemic era. By providing a nuanced understanding of the challenges and opportunities inherent in India's international trade landscape, this study aims to inform policymakers, businesses, and stakeholders alike, facilitating informed decision- making and fostering resilience in the face of uncertainty.

Analisis Reaksi Pasar Terhadap Keterlambatan Penyampaian Laporan Keuangan Interim

The late submission of interim financial reports causes companies listed on the Indonesia Stock Exchange (IDX) to pay a fairly large amount of fines. The Financial Services Authority (OJK) issued regulations for companies going public to report and publish audited annual financial reports, so that each company must submit accordingly and on time. Therefore, this research aims to determine the comparison of abnormal returns and stock trading volume before and when there is a delay in submitting financial reports. The data used in this research are the financial reports of companies registered on the IDX that are late in submitting financial reports. The results of this research show significant differences in abnormal returns and stock trading volume before and after the delay in submitting interim financial reports. The study uses the average difference test method, with the object of the financial statements of 35 listed companies that have not submitted financial reports for the period 2021 to 2022. After descriptive statistical analysis, there are company values that are recorded as poor in submitting their interim financial reporting. The results showed that there was a significant difference between the performance before and after the company's financial reporting on abnormal returns and stock trading volume on the submission of financial reports.

Analysis of Differences in Stock Trading Volume, Bid-Ask Spread, and Abnormal Returns Before and After a Stock Split: Empirical Evidence from Companies Listed on the Indonesian Stock Exchange

Analysis of Differences in Stock Trading Volume, Bid-Ask Spread, and Abnormal Returns Before and After a Stock Split: Empirical Evidence from Companies Listed on the Indonesian Stock Exchange

The Impact of RCEP Tariff Concessions on Intra-Regional Trade: An Analysis of Trade Creation and Diversion

This study delves into the economic ramifications of the Regional Comprehensive Economic Partnership (RCEP), particularly focusing on its tariff concessions and their influence on intra-regional trade dynamics within the Asia-Pacific region. By encompassing 15 East Asian and Pacific nations, RCEP stands as the world's largest trading bloc, accounting for approximately 30% of the global Gross Domestic Product (GDP). This research aims to dissect the core objectives of RCEP, emphasizing the significant tariff concessions that aim to eliminate up to 90% of the tariffs within the bloc over a stipulated timeline. Through a comprehensive analysis, this paper explores the dual phenomena of trade creation and trade diversion as intrinsic outcomes of RCEP's tariff concessions. Initial findings project an increase in intraregional exports by nearly 2%, translating to an augmentation of approximately US$42 billion. This surge in trade volume is attributed to the lowered costs of goods due to reduced tariffs, fostering economic growth and integration of regional markets. However, the study also highlights the nuanced impacts of trade diversion, where trade shifts from more efficient external sources to less efficient member states, induced by preferential treatment within the bloc. Furthermore, the paper navigates through the challenges and opportunities presented by RCEP, including the harmonization of trade policies and regulatory standards across diverse economic landscapes. The research underscores the strategic implications for RCEP member countries, advocating for a multifaceted approach to leverage the benefits of the agreement while mitigating potential downsides. This study contributes to the broader discourse on regional economic cooperation and integration, offering insights into the complex interplay between trade policies and regional dynamics in the Asia-Pacific.

Contribution to the harm assessment of darknet markets: topic modelling drug reviews on Dark0de Reborn

Amid the global opioid crisis, the volume of drug trade via darknet markets has risen to an all-time high. The steady increase can be explained by the reliable operation of darknet markets, affected by community-building trust factors reducing the risks during the process of the darknet drug trade. This study was designed to explore the risk reduction efforts of the community of a selected darknet market and therefore contribute to the harm assessment of darknet markets. We performed Latent Dirichlet Allocation topic modelling on customer reviews of drug products (n = 25,107) scraped from the darknet market Dark0de Reborn in 2021. We obtained a model resulting in 4 topics (coherence score = 0.57): (1) feedback on satisfaction with the transaction; (2) report on order not received; (3) information on the quality of the product; and (4) feedback on vendor reliability. These topics identified in the customer reviews suggest that the community of the selected darknet market implemented a safer form of drug supply, reducing risks at the payment and delivery stages and the potential harms of drug use. However, the pitfalls of this form of community-initiated safer supply support the need for universally available and professional harm reduction and drug checking services. These findings, and our methodological remarks on applying text mining, can enhance future research to further examine risk and harm reduction efforts across darknet markets.

Study of global illegal wildlife trade volume based on social network analysis and ARIMA model

Study of global illegal wildlife trade volume based on social network analysis and ARIMA model

Trading Volume Activity, Earning Quality and Stock Return Volatility of Listed Consumer Non-Cyclicals Companies in Indonesia: Does Corporate Reputation Play a Role?

This study aims to investigate the impact of trading volume activity and earning quality on stock return volatility, with corporate reputation as a moderating factor, within 175 consumer non-cyclicals companies listed on the Indonesia Stock Exchange (IDX) from 2017 to 2021. Findings indicate that trading volume activity positively influences stock return volatility, while earning quality negatively affects it. Moreover, corporate reputation amplifies the positive relationship between trading volume activity and stock return volatility, and it also enhances the negative relationship between earning quality and stock return volatility. The negative effect of earning quality on stock return volatility is reinforced by corporate reputation. The originality of this study is to add the corporate reputation variable as a moderating variable in moderating the effect of trading volume activity and earning quality on return volatility. This research is expected to be useful for investors when investing in the stock market. Besides that, it is also expected to be useful for companies to be more careful in carrying out the company's operational activities because it will be related to the provision of capital by investors.

The Impact of Trading Volume on Stock Price Volatility

In the capital markets, research on the impact of market trading volume on asset prices and returns has been ongoing since the 1960s, and remains a focus of attention for scholars and investors both domestically and internationally. Studies on the relationship between volume and price primarily concentrate on the volatility of stock prices in relation to trading volume, and the correlation between stock return rates and trading volume. In empirical research on the volume-price relationship, the main theories are inseparable from the study of market information. Trading volume encompasses the market's reaction speed to information and the extent of information's impact on the market, which are significant reasons why trading volume can affect stock market prices and returns.

Tech-Enabled Financial Data Access, Retail Investors, and Gambling-Like Behavior in the Stock Market

Advancements in technology have reduced information acquisition costs, creating an improved information environment for retail investors. Specifically, new technologies, such as application programming interface (API), deliver high-volume, institutional-like raw data directly to Main Street investors. Although greater availability of information can be beneficial, it may also exacerbate retail investors’ existing trading deficiencies. Exploiting the sudden shutdown of Yahoo! Finance API, the largest free API for retail investors, this study examines how access to tech-enabled raw financial data affects retail investment. We find that retail trading volumes in stocks favored by active retail investors dropped by 8.6%–10.5% within one month of the API shutdown. The remaining retail trades collectively became more predictive of future returns, suggesting less gambling-like behavior after the API shutdown. Moreover, our randomized controlled experiment affirms the underlying mechanism: tech-enabled access to high-volume historical price data increases individuals’ overconfidence, which further leads them to engage in excessive trading. The study reveals an unintended consequence of technology-led, wider data access for retail investors. This paper was accepted by D. J. Wu, information systems. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.01379 .

Secondary flexibility market mechanism design and response behavior analysis among multi-microgrids with high proportional BTM-RERs

As the proportion of behind-the-meter (BTM) renewable energy resources (RERs) in the distribution network continues to rise, the precise role of RERs in the flexibility market, whether as “Recipients” or “Providers”, is becoming less distinct. This observation has led us to explore the integration of flexible load resources and RERs into flexibility markets. Such integration facilitates resource sharing among multiple microgrids (MGs). This paper focuses on the potential emergence of a secondary flexibility (SecFlex) market in the power sector among high proportional BTM-RERs microgrids. Within the concept of SecFlex market, this paper outlines a comprehensive process supporting secondary flexibility trading, which includes “SecFlex market organization–trading volume collation–response schedule optimization”. At first, the market mechanism of the SecFlex market is proposed, where players submit an empirical load curve to the market organizer. During the clearing stage, a social welfare equilibrium problem is solved. Subsequently, the equilibrium trading volume is adjusted in the collation stage to account for the distribution network (DN) voltage magnitude balance. Finally, a response schedule strategy is designed to analyze the behaviors of the players. The cvxpy framework is used to solve social welfare and competitive equilibrium in this paper. The results show significant cost reductions for two players, by 77.8% and 24.0% respectively. However, costs for Player 3 have increased substantially, up to 4.6 times the baseline when flexibility is limited. These findings provide theoretical guidance for the development of the SecFlex market.

Trading Activity in the Corporate Bond Market: A SAD Tale of Macro-Announcements and Behavioral Seasonality?

This study investigates the determinants of trading activity in the U.S. corporate bond market, focusing on the effects of Seasonal Affective Disorder (SAD) and macroeconomic announcements. Employing the General-to-Specific (Gets) Autometrics methodology, we identify distinct behavioral responses between retail and institutional investors to SAD, noting a significant impact on retail trading volumes but not on institutional trading or bond returns. This discovery extends the understanding of behavioral finance within the context of bond markets, diverging from established findings in equity and Treasury markets. Additionally, our analysis delineates the influence of macroeconomic announcements on trading activities, offering new insights into the market’s reaction to economic news. This study’s findings contribute to the broader literature on market microstructure and behavioral finance, providing empirical evidence on the interplay between psychological factors and macroeconomic information flow within corporate bond markets. By addressing these specific aspects with rigorous econometric techniques, our research enhances the comprehension of trading dynamics in less transparent markets, offering valuable perspectives for academics, investors, risk managers, and policymakers.

A Study on Combating Illegal Trade in Green Sea Turtles based on a Multi-Model Strategy

This study addresses the long-standing and increasingly complex issue of global illegal wildlife trade, proposing a system solution aimed at monitoring and combating this trade, with a special emphasis on the protection of green sea turtles. The ARIMA Predict Model was utilized to demonstrate that increased enforcement efforts and higher operational expenses are significantly associated with reduced volumes of illegal wildlife trade. An illegal trade risk model was developed, employing SLSQP to enhance accuracy, constrained by budget allocation, maximum RFID reader capacity, and device failure rates. Furthermore, the Sea Turtle Movement Model was crafted by examining seasonal behaviors, agent-based movement patterns, temperature preferences, stochastic elements, and simulation time steps, enabling the delineation of migratory paths of sea turtles across different seasons. Ant Colony Optimization (ACO) was applied to optimize patrol routes. The findings underscored the necessity for additional governmental support, encompassing legal authorization, environmental access, international collaboration, and financial backing. The project’s effectiveness was assessed using the Risk Reduction Coefficient Model and Monte Carlo simulations, which significantly reduced illegal trade by 34.43%, with projections indicating continued declines over the subsequent five years.

Multi-market P2P trading of cooling–heating-power-hydrogen integrated energy systems: An equilibrium-heuristic online prediction optimization approach

In this paper, an equilibrium-heuristic online prediction optimization approach is proposed for multi-market peer-to-peer (P2P) electricity–hydrogen trading of integrated energy systems (IESs) with uncertainties. First, the IES, consisting of a hydrogen energy storage subsystem and a combined cooling, heating, and power subsystem, is constructed in the distribution network to improve energy utilization and market efficiency. Then, a bi-level optimization model for IESs, participating in the P2P electricity–hydrogen energy trading, the real-time electricity, and the ancillary service markets, is proposed, in which the top-level model can be formulated as a P2P electricity–hydrogen trading pricing model through the social welfare maximization problem, and the lower-level model is used to maximize the IES operating profit. To effectively solve the bi-level model, the game equilibrium-based ADMM distributed algorithm is used to obtain the P2P trading volume and prices of the top-level model, and a new hybrid heuristic algorithm, called hybrid sand cat swarm optimization and improved honey badger algorithm (SCIHB), is proposed to solve the lower level model. SCIHB utilizes a hybrid mechanism and an adaptive learning rate parameter to balance local exploitation and global exploration. Furthermore, a new online learning probabilistic prediction method based on the hidden Markov model and wavelet transform is introduced to describe the uncertainties of wind power, electricity prices, and frequency regulation prices. Finally, case studies are conducted on an IEEE 33-bus test system, and the numerical results verify the effectiveness of the proposed P2P trading model and solution approach.

Research on the application of deep learning techniques in stock market prediction and investment decision-making in financial management

Introduction: This paper introduces a deep learning approach based on Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and attention mechanism for stock market prediction and investment decision making in financial management. These methods leverage the advantages of deep learning to capture complex patterns and dependencies in financial time series data. Stock market prediction and investment decision-making have always been important issues in financial management.Methods: Traditional statistical models often struggle to handle nonlinear relationships and complex temporal dependencies, thus necessitating the use of deep learning methods to improve prediction accuracy and decision effectiveness. This paper adopts a hybrid deep learning model incorporating CNN, BiLSTM, and attention mechanism. CNN can extract meaningful features from historical price or trading volume data, while BiLSTM can capture dependencies between past and future sequences. The attention mechanism allows the model to focus on the most relevant parts of the data. These methods are integrated to create a comprehensive stock market prediction model. We validate the effectiveness of the proposed methods through experiments on real stock market data. Compared to traditional models, the deep learning model utilizing CNN, BiLSTM, and attention mechanism demonstrates superior performance in stock market prediction and investment decision-making.Results and Discussion: Through ablation experiments on the dataset, our deep learning model achieves the best performance across all metrics. For example, the Mean Absolute Error (MAE) is 15.20, the Mean Absolute Percentage Error (MAPE) is 4.12%, the Root Mean Square Error (RMSE) is 2.13, and the Mean Squared Error (MSE) is 4.56. This indicates that these methods can predict stock market trends and price fluctuations more accurately, providing financial managers with more reliable decision guidance. This research holds significant implications for the field of financial management. It offers investors and financial institutions an innovative approach to better understand and predict stock market behavior, enabling them to make wiser investment decisions.