Trading Volume Data Research Articles

The impact of social media discourse on financial performance of e-commerce companies listed on Borsa Istanbul

This research investigates how changes in public discourse on social media (particularly Twitter, now X) influence financial performance of companies listed on the e-commerce XTCRT index of Borsa Istanbul amid the COVID-19 pandemic. Utilising a unique 4-layer network structure, the study integrates daily closing prices, highest and lowest values, and trading volume data of these companies with Twitter discussions on six categories: culture, economy, health, politics, technology, world affairs. By using advanced computational methods such as multilayer network analysis, long short-term memory architecture, nonlinear autoregressive distributed lag analysis, we find a high responsiveness of the e-commerce index to social media content, particularly in discussions related to health, technology, world affairs. The study underscores that social media discussions significantly shape index composition, resulting in noticeable shifts in market dynamics, including fluctuations in stock prices, trading volumes, and market sentiment. Our study advocates for a more advanced approach to market analysis that incorporates the dynamics of online public discussions.

Green gold or carbon beast? Assessing the environmental implications of cryptocurrency trading on clean water management and carbon emission SDGs

This study addresses the ongoing debate concerning the environmental implications of cryptocurrencies. Specifically, it investigates the impact of Bitcoin trading volume on water and sanitation (Sustainable Development Goal (SDG) 6) and climate action (SDG 13). The research employs Ordinary Least Squares (OLS) panel data analysis to examine these relationships using a sample of 32 countries with available Bitcoin trading volume data from 2013 to 2020. The findings indicate that Bitcoin trading significantly and positively impacts progress towards SDG 6, suggesting potential benefits for water and sanitation initiatives. However, the study reveals a significant negative impact of higher Bitcoin trading volume on increased carbon emissions, underscoring the environmental costs associated with cryptocurrency activities. Similar impacts are observed for gold reserves, as their mining necessitates substantial energy consumption. These results highlight the need to regulate cryptocurrency trading and promote voluntary sustainable practices, particularly given the disparities between developed and emerging markets based on their governance frameworks. Additionally, the study considers the disparities between countries based on technology exports and economic policy uncertainty as influential determinants. The study's results emphasize the importance of proactive measures to ensure the responsible and sustainable use of cryptocurrencies. While cryptocurrencies offer significant economic returns, their early adoption stage necessitates further investigation into environmentally friendly approaches. Potential strategies include directing financial returns from cryptocurrencies towards alternative energy projects and supporting other environmental SDGs, thereby fostering a positive impact on the overall ecosystem. The study's implications extend to policymakers, regulators, and stakeholders, advocating for comprehensive and collaborative efforts to integrate sustainability into the rapidly evolving cryptocurrency market. This integration is crucial to ensure that the economic benefits of cryptocurrencies do not come at the cost of our environment.

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.

Analyzing the Influence of Environmental Awareness and Financial Performance in the Desire for Sustainabilitiny Investing in Indonesian Investors

With the development of policies and attention in maintaining environmental sustainability with the phenomenon of global warming and climate change which is an important issue now, this study aims to analyze the priorities of Indonesian investors in investing and its effects in trading activities, the first stage is a comparative study of trading volume variables between issuers that prioritize sustainability and issuers that prioritize the financial side, the first stage will be analyzed by linear regression method independent t-test aims to see if there is a significant difference based on historical trading volume data of both groups of issuers. In the second stage, environmental awareness and financial performance variables are used to see which variable is the biggest influence in investors' choice of investment. A qualitative approach will be used in this study and multiple linear regression will be used to conduct hypothesis testing, data collection will be conducted using an online questionnaire. It was found that investors' preference is now for sustainable investment over conventional, with the most influential factor being financial performance. The results of the qualitative research are supported by the results of quantitative research which found that trading volume activity for sustainable issuers is increasing in the period 2023 and trading volume activity for conventional issuers is decreasing in the period 2023 The findings of this study aim to help other companies to see the importance of environmental awareness factors in front of the eyes of young investors.

Online portfolio selection of integrating expert strategies based on mean reversion and trading volume

In this paper, we propose an effective online portfolio selection strategy by integrating expert opinions, which are obtained based on mean reversion and trading volume. Existing studies have found that mean reversion and high volume premium exist in the stock market in the short term. Some online portfolio strategies have been proposed that rely upon mean reversion, but it is rare to consider both mean reversion and high volume premium, which is what we will do in this paper. First, a portfolio based on mean reversion and high volume premium is constructed using recent window data. Second, a pool of portfolios, also known as a pool of expert strategies or expert opinions, is established by changing the window size. Finally, the online gradient update algorithm is adopted to integrate a pool of expert strategies, and the MRvol strategy in this paper is proposed. Theoretically, we prove that the regret of MRvol is bounded. Empirically, we use the actual stock price and trading volume data from different markets to test the performance of MRvol. The results show that MRvol performs better than other online strategies in terms of final cumulative wealth and risk-adjusted return metrics in most cases, among which the annualized percentage yield on all datasets is 16%–37%, and the average values of Sharpe ratio, Calmar ratio and Information ratio are 1.113, 0.9216 and 0.0299, respectively. Additionally, MRvol has linear computational time complexity, and the running time of test results is all less than 1 s. Furthermore, it maintains strong robustness under different parameters and can bear reasonable transaction costs.

Investor gambling preferences and stock returns: evidence from the Shanghai A-share markets

PurposeThis study aims to examine the relationship between investor gambling preferences and stock returns, using data for all firms listed in Shanghai A-share market during 2016 and 2021.Design/methodology/approachThis study employs price and trading volume data to capture the behavioral characteristics and gambling preferences of investors. Using the Fama-French three-factor and five-factor models to estimate benchmark returns, this study investigates whether investing in gambling stocks can yield positive excess returns.FindingsThe study reveals that stocks identified as gambling stocks generate high returns in the month they are identified as such but subsequently experience a significant drop in excess returns compared to non-gambling stocks over the following one to six months. These results are found to be consistent across different methods used to classify gambling stocks and across various industry sectors.Research limitations/implicationsThis research provides insights into the risk-return tradeoff of different stock types and the factors that fuel irrational investment behavior. This research underscores the importance of considering the behavioral elements of investment, particularly in emerging markets where individual investors have a significant impact.Practical implicationsThis study advises investors to avoid adopting a gambler or speculative mindset and instead make well-informed and calculated investment decisions that are in line with investors financial objectives and risk appetite. This approach can help create a more stable and sustainable financial market.Originality/valueThis study provides new evidence on the relationship between gambling preferences and future stock returns in financial markets and sheds new light on the important role of irrational factors in investment decisions.

An analysis of the return–volume relationship in decentralised finance (DeFi)

The decentralised finance sector has recently experienced a surge in popularity, and has emerged from the shadows of the cryptocurrency space. Although the purposes of the currencies used in this new sector differ from traditional cryptocurrencies, they still possess monetary value and can be traded using fiat currencies on specialised decentralised exchanges. This paper investigates the dynamic volume–return relationship of the five largest decentralised finance tokens, to better understand this relationship given the similarities with cryptocurrencies and the possible benefits for traders and practitioners. We implement the quantile-on-quantile regression and an extreme value theory approach to examine the relationship between the daily returns of the prices and trading volumes of decentralised finance tokens at varying quantiles and at the extreme tails. Our results suggest that when trading volume is experiencing large increases, the returns of the prices of tokens appear to be significantly positive for some cases but negative for others. The extreme volume-return dependence is found to be asymmetric in the extreme negative and positive tails of the distributions, where the dependence below extreme negative thresholds is essentially non-existent but above extreme positive thresholds it is significant. This asymmetric extreme dependence between returns and volume may be beneficial for developing trading strategies that incorporate trading volume data, and may indicate an inefficient market.

Estimation of nonparametric regression function using shrinkage wavelet and different mother functions

Wavelet reduction is one of the most widely used methods for removing noise from the signal, primarily financial and banking data, and building a non-parametric regression model that enables us to study the phenomenon accurately. The appropriate choice of the wavelet mother, which results in concentrating the bulk of the signal strength on a few wavelet coefficients, is one of the most determining factors in noise removal and obtaining accurate regression function estimates. Given the importance of studying the price index in Iraq, many mother functions within the wavelet transformation have been studied. To determine which of them is more suitable for such a type of data, which gives accurate estimates of the relationship between trading volume as an independent variable and the Iraq market index as a dependent variable, the best or most appropriate functions were determined through the estimates that have less (MSE). It became clear that the best or relevant parts are (Coif1, Coif5, and rbio1.3). The study was applied to real data represented by the trading volume and price index data for the Iraqi market for the period from (2008) to April (2022). It became clear that the trading volume significantly affects the price index, but other variables must be studied.

Perbandingan Buyback Stock Perusahaan Swasta dan Perusahaan Badan Usaha Milik Negara (BUMN) saat Pandemi Covid-19

This study aims to analyze the differences between the Abnormal Stock Returns Trading Volume Activity of private and state-owned companies before and after the announcement of stock buybacks in the midst of the Covid-19 pandemic. The samples used were private companies and BUMN that carried out Buyback Stock during the Covid-19 pandemic. This research uses secondary data in the form of daily company stock price data, daily company trading volume data, as well as outstanding share data from private companies and BUMN listed on the Indonesia Stock Exchange. The calculation of expected return uses the Market Adjusted Model method. Testing the hypothesis using the paired sample t-test difference test. The results of this study are sig. (tailed) on the abnormal return variable, the probability value is greater than 0.05 (0.080 and 0.893 > 0.05) or the average cumulative abnormal return of private companies is greater than the average. Abnor¬¬mal cumulative stock returns of BUMN companies. On the trading volume activity variable, the results show the TVA significance value is smaller than 0.05, namely 0.043 and 0.43. so that it is stated that the cumulative average trading volume activity of BUMN companies is greater than the average cumulative trading volume activity of private companies

Forecasting Agricultural Commodity Prices Using Dual Input Attention LSTM

Fluctuations in agricultural commodity prices affect the supply and demand of agricultural commodities and have a significant impact on consumers. Accurate prediction of agricultural commodity prices would facilitate the reduction of risk caused by price fluctuations. This paper proposes a model called the dual input attention long short-term memory (DIA-LSTM) for the efficient prediction of agricultural commodity prices. DIA-LSTM is trained using various variables that affect the price of agricultural commodities, such as meteorological data, and trading volume data, and can identify the feature correlation and temporal relationships of multivariate time series input data. Further, whereas conventional models predominantly focus on the static main production area (which is selected for each agricultural commodity beforehand based on statistical data), DIA-LSTM utilizes the dynamic main production area (which is selected based on the production of agricultural commodities in each region). To evaluate DIA-LSTM, it was applied to the monthly price prediction of cabbage and radish in the South Korean market. Using meteorological information for the dynamic main production area, it achieved 2.8% to 5.5% lower mean absolute percentage error (MAPE) than that of the conventional model that uses meteorological information for the static main production area. Furthermore, it achieved 1.41% to 4.26% lower MAPE than that of benchmark models. Thus, it provides a new idea for agricultural commodity price forecasting and has the potential to stabilize the supply and demand of agricultural products.

Guide to Downloading Brokerage Trading Volume Data from Bloomberg

Guide to Downloading Brokerage Trading Volume Data from Bloomberg

The Impact of the Covid-19 Pandemic on Stock Investment in the Indonesian Capital Market

The COVID-19 pandemic has hit various sectors, including the stock market where many people are hesitant to invest in stocks. Many industries have been affected by Covid-19, where since March 2020 the Indonesia Stock Exchange Composite Stock Price Index (IHSG) has decreased because many investors sold their shares, but since the third week of May 2020 to early June 2020 has shown an increase indicating stock trading has begun to show improvement. This study aims to analyze which sector stocks are still able to survive during the COVID-19 pandemic, by using stock trading volume data, Composite Stock Price Index (IHSG), weekly and monthly market capitalization values with a sample of 20 stocks - the highest stocks. based on sales volume and transaction value on the Indonesian stock exchange for the period March 2020 to June 2020 obtained from the Financial Services Authority (OJK) weekly report and the Indonesia Stock Exchange (IDX) Monthly Report. The results show that during the COVID-19 pandemic, investors can still get benefits in investing in stocks if every decision made by these investors is supported by careful calculations.

Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market

Research on stock market prediction has been actively conducted over time. Pertaining to investment, stock prices and trading volume are important indicators. While extensive research on stocks has focused on predicting stock prices, not much focus has been applied to predicting trading volume. The extensive trading volume by large institutions, such as pension funds, has a great impact on the market liquidity. To reduce the impact on the stock market, it is essential for large institutions to correctly predict the intraday trading volume using the volume weighted average price (VWAP) method. In this study, we predict the intraday trading volume using various methods to properly conduct VWAP trading. With the trading volume data of the Korean stock price index 200 (KOSPI 200) futures index from December 2006 to September 2020, we predicted the trading volume using dynamic time warping (DTW) and a genetic algorithm (GA). The empirical results show that the model using the simple average of the trading volume during the optimal period constructed by GA achieved the best performance. As a result of this study, we expect that large institutions will perform more appropriate VWAP trading in a sustainable manner, leading the stock market to be revitalized by enhanced liquidity. In this sense, the model proposed in this paper would contribute to creating efficient stock markets and help to achieve sustainable economic growth.

Market Response to Disclosure of Corporate Social Responsibility (CSR) during the COVID-19 Pandemic

This article aims to disclose the CSR on stock price and trading and examine the profitability as the reinforcement variable between CSR disclosure on stock prices and the trading volume during the COVID-19 Pandemic. The object of the study was the LQ45 companies in the Indonesia stock exchange between 2019-2020. However, the study only involved 38 companies. We used the 2019 and 2020 data accessed from the official site of the Indonesia Stock Exchange (IDX) www.idx.com. The closing stock price and trading volume data were obtained from www.yahoofinance.com. Data were tested and analyzed using Partial Least Square (PLS) technique. The study found that the disclosure of CSR was positively but not significantly influential on the stock price. The disclosure of CSR is negatively but not significantly influential on the trading volume. The profitability does not strengthen the influence of CSR disclosure on stock price, and the profitability does not strengthen the negative influence of CSR disclosure on trading volume. The research findings suggest the company management continues to run CSR programs despite the unstable economic conditions due to the COVID-19 Pandemic

How does informed trader trade the “reverse risk”? --OTM Option volume as risk factor for stock prediction

This article researches the implied risks in the trading volume data of the SSE (Shanghai Security Exchange) 50 ETF options, and explores the power of these risks to predict the return of the underlying stocks. By analyzing the trading behavior of informed option traders, we believe that the trading volume of the OTM (out-of-money) options can capture these traders’ expectations of the “reverse risk”. We divide “reverse risk” into two types of risk information: the first one is the option selection process: the trader decides whether to choose a OTM option or a non-OTM option to hedge their private information, which represents whether they expect the future stock to reverse; the second risk information is measured by the change amount of the current OTM option trading volume, which depicts the new information shock brought by these informed traders’ behavior. The empirical result shows that the option selection process can significantly predict the future returns of the SSE 50 index, while the information shock caused by the change in OTM options trading volume has solid pricing power among individual stocks. We then use Granger causality test to explore the mechanism of risk transmission for the “reverse risk” trading process, and conclude as: the risk information held by informed traders(those who believe the future market will “reverse”) first affected their decision-making process, and their decisions in the options market will transmit the risk to the SSE 50 index trading market, and then cause the current information shocks, and these shocks will eventually affect the constituent stocks of the SSE 50.

Optimal Threshold Determination for Securities Exchange Volumes Using Improved Maximum Product of Spacing Methodology

To Statisticians, the structure of the extreme levels which exist in the tails of the ordinary distributions is very important in analyzing, predicting and forecasting the likelihood of an occurrence of extreme event. Extreme events are defined as values of the event below or above a certain value called threshold. A well chosen threshold helps to identify the extreme levels. Several methods have been used to determine threshold so as to analyze and model extreme events. One of the most successful methods is the maximum product of spacing (MPS). However, there is a problem encountered while modeling data through this method in that the method breaks down when there is a tie in the exceedances. This study offers a solution to model data even when it contains ties. In the study, a method that improved MPS method for determining an optimal threshold for extreme values in a data set containing ties was derived. The Generalized Pareto Distribution (GPD) parameters for the optimal threshold were derived and compared to GPD parameters determined through the standard MPS model. The study improved the standard MPS methodology by introducing the concept of frequency and used Generalized Pareto Distribution (GPD) and Peak over threshold (POT) methods as the basis of identifying extreme values. The improved MPS models and the standard models were applied to Nairobi Securities Exchange (NSE) trading volume data to determine the GPD parameters for different sectors registered in NSE market and their performance compared. It was realized that the improved MPS model performed better than the standard models. This study will help the Statisticians in different sectors of our economy to model extreme events involving ties.

Point forecasting of intraday volume using Bayesian autoregressive conditional volume models

AbstractIn this paper, we apply Bayesian inference to model and forecast intraday trading volume, using autoregressive conditional volume (ACV) models, and we evaluate the quality of volume point forecasts. In the empirical application, we focus on the analysis of both in‐ and out‐of‐sample performance of Bayesian ACV models estimated for 2‐minute trading volume data for stocks quoted on the Warsaw Stock Exchange in Poland. We calculate two types of point forecasts, using either expected values or medians of predictive distributions. We conclude that, in general, all considered models generate significantly biased forecasts. We also observe that the considered models significantly outperform such benchmarks as the naïve or rolling means forecasts. Moreover, in terms of root mean squared forecast errors, point predictions obtained within the ACV model with exponential distribution emerge superior compared to those calculated in structures with more general innovation distributions, although in many cases this characteristic turns out to be statistically insignificant. On the other hand, when comparing mean absolute forecast errors, the median forecasts obtained within the ACV models with Burr and generalized gamma distribution are found to be statistically better than other forecasts.

Do Institutional Investors Enhance Accounting Earnings Attributes in the Korean Market?

ABSTRACTThis study examines whether the effectiveness of institutional monitoring depends on the economic conditions of emerging capital markets. We use trading volume data by investor type to compute a proxy for total institutional ownership. We then analyze the impact of the proxy variable on accounting earnings attributes and examine whether the association between the two depends on an expectation of market growth. We find that the effect of institutional monitoring decreases when market growth is expected to be low, implying that market growth may be a critical determinant of institutional investors’ long-term monitoring effectiveness in emerging capital markets.

When to adopt a service innovation: Nash equilibria in a competitive diffusion framework

We study the optimal timing of adoption of a service innovation that a new entrant firm brings to a market populated by two incumbent firms. Our analysis is based on a model of competitive diffusion dynamics that extends the monopolistic Bass model to include customer churn processes, as well as a potential market expansion driven by the innovation. We obtain expressions for the time trajectories of the customer bases, i.e., the numbers of customers that use old and new service processes for the competing firms, as well as sharper, closed-form characterizations for the setting with a stable market and homogeneous imitation process.In modeling competitive dynamics we consider settings where incumbents anticipate a potential failure of the innovation. We use the trajectories for the customer bases to model an optimal adoption response problem faced by one of the incumbent firms when the adoption time for the other incumbent can be anticipated or is pre-announced, and analyze this problem in the absence of market expansion or intra-generational customer churn. Using the optimal response results, we provide the Nash equilibrium analysis of the adoption decisions by competing incumbent firms and derive sufficient conditions for the “now-now”, “now-never” and “never-never” adoption equilibria. We use the trading volume data from the foreign exchange markets to estimate the parameters of the competitive diffusion dynamics for our model and to conduct a numerical investigation of the impact of the uncertainty associated with the success of the innovation on the incumbents’ Nash equilibrium adoption times.

Financial Applications of Stable Distributions: Implications on Turkish Stock Market

Purpose- The aim of this study is modelled by examining the trading volumes of the tourism companies located in the high-risk tourism sector and traded in BIST. This modelling will gain point of view for the tourism firms as well as make an important contribution to the decision making of investors who want to invest in this sector. Methodology- The study is conducted for a sample of 2803 daily trading volumes over the period 01.01.2007-28.09.2017. Then, it is used daily returns rather than daily trading volume data because it provides the ability to measure investment performance independently of the scale used. Daily return data is modelled with stable distributions used with increasing interest in many application areas and that are well-suited to financial asset returns. Parameter estimates is made by using quantiles method which is one of the most known estimation methods. Findings- By means of the Chi-square test and graphs, it is seen that normal distribution was not suitable for trading volume data. Stable distribution parameters for the log-returns data are estimated according to the quantiles method and obtained the stable parameters 𝛼, 𝛽, 𝛾 and 𝛿 . Stable density function is obtained using the MATLAB STBL command according to estimated parameters. Conclusion- Estimated parameter values indicate that stable distributions can be used as a suitable model for modelling the transaction volume data of analysed index. It has been concluded that it is more appropriate to use the scale parameter of the stable distribution instead of the standard deviation as the risk measure.