Large Trading Volume Research Articles

Testing for Asset Price Bubbles using Options Data*

We present a new approach to identifying asset price bubbles based on options data. We estimate asset bubbles by exploiting the differential pricing between put and call options. We apply our methodology to two stock market indexes, the S&P 500 and the Nasdaq-100, and two technology stocks, Amazon and Facebook, over the 2014-2018 sample period. We find that, while indexes do not exhibit significant bubbles, Amazon and Facebook show frequent and significant bubbles. The estimated bubbles tend to be associated with large volatility and large trading volume. Since our approach can be implemented in real time, it is useful to both policy-makers and investors.

Carbon price prediction in China based on ensemble empirical mode decomposition and machine learning algorithms.

The carbon emission trading market is crucial for reducing emissions, conserving energy, and enhancing the climate and environment. Studying carbon price forecasting can encourage China's involvement in international carbon financial instruments trading and promote the development of China's pilot carbon markets. This study employs ensemble empirical mode decomposition (EEDM) to convert the initial carbon price, which is a non-stationary signal, into several intrinsic mode functions and residual terms. Subsequently, hybrid machine learning methods are used to estimate the carbon price. For empirical analysis, the three main carbon markets with large trading volumes, Guangdong, Hubei, and Shenzhen, are selected from the eight pilot carbon markets. To achieve short-term carbon price prediction, a hybrid model combining a genetic algorithm (GA) and back propagation (BP) neural network is used. This model effectively addresses the issue of neural networks falling into local optimization. The results indicate that the hybrid algorithm is significantly superior to other algorithms for short-term prediction. Additionally, another hybrid model, combining the least squares support vector machine (LSSVM) and particle swarm optimization (PSO) algorithms, is employed to reduce forecast error while minimizing search parameters, which is not possible with traditional neural network models.

Capital Market Reaction to the Strengthening of the Rupiah Exchange Rate against the US Dollar Exchange Rate

The purpose of this study is to analyze the response of Indonesian capital markets to differences in average abnormal returns and average trading volume activity before and after the strengthening of the IDR/USD exchange rate. This study is a quantitative study using the event study method. The sample used consists of the stocks included in the LQ45 index. the most active stocks with a large daily trading volume. Hypothesis testing and paired t-test. The results of the hypothesis test show that: there is no significant difference in the average abnormal returns before and after the strengthening of the rupiah against the US dollar because are sig. (2-tailed) value of 0.417 > 0.05; there is no significant difference in the average trading volume activity before and after the strengthening of the rupiah against the US dollar because sig. (2-tailed) value of 0.080 >0.05. Keyword: Market Reaction; Event Study; Abnormal Return; Trading Volume Activity; Exchange Rate

SYNTHESIS OF YORUBA TEXTILES AND MODERN TECHNOLOGY FOR A RESILIENT ECONOMY IN THIS TURBULENT ERA

A resilient economy is a strong base for large volume of trade as it provides a perfect solution to poverty. Synthesis of Yoruba textiles and modern technology for resilient economy in this turbulent era is the concern of this paper. It highlights how Yoruba indigenous textiles are face lifted by modern technology to eradicate economic downturn. The paper gives insight to improvement on the production of Yoruba textiles therefore giving opportunity of job creation and self-reliance through the fusion. The huge tragedy of the Nigerian experience hinges on the downturn of economy which is at its peak of crashing. Most Nigerian are not believing that indigenous textile contents can increase drastically the income of the nation if facelifted or rebranded. The high rate at which naira is depreciating is very alarming at present ($1USD = N 1440) being because of fluctuation now. The paper projects a way out of economic turbulence with the synthesis of Yoruba textiles and modern technology by building a resilient economy. The paper further discusses the new orientation and training involved; raw materials used, computer knowledge, recommendations, and conclusion.

Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response

This work studies synergies arising from combining industrial demand response and local renewable electricity supply. To this end, we optimize the design of a local electricity generation and storage system with an integrated demand response scheduling of a continuous power-intensive production process in a multi-stage problem. We optimize both total annualized cost and global warming impact and consider local photovoltaic and wind electricity generation, an electric battery, and electricity trading on day-ahead and intraday market. We find that installing a battery can reduce emissions and enable large trading volumes on the electricity markets, but significantly increases cost. Economically and ecologically-optimal operation of the process and battery are driven primarily by the electricity price and grid emission factor, respectively, rather than locally generated electricity. A parameter study reveals that cost savings from the local system and flexibilizing the process behave almost additively.

Option Pricing Practices Based on the B-S-M Model in Carbon Markets

Carbon trading refers to the buying and selling of carbon emission allowances, stimulating businesses to adopt low-carbon development strategies through price signals. By allocating limited emission allowances to emitters, the carbon market encourages the reduction of carbon emissions to achieve emission reduction targets. Market participants can compensate for emissions exceeding their own quotas by trading carbon emission allowances, thus minimizing the cost of emission reductions. The Chinese carbon market has a large trading volume and numerous development opportunities, but currently, the carbon financial market is not fully open. In comparison to Western countries, China lacks carbon emission derivatives such as futures, options, and swap contracts. The risk and return of carbon emission allowance options are estimated and managed using the B-S-M option pricing model. This paper utilizes data from the Shenzhen Carbon Emission Allowance Exchange as a sample to conduct option pricing practices for carbon emission allowances using the B-S-M model. The aim is to promote research and development in the carbon emission allowance market and contribute to the achievement of emission reduction goals.

Unlearning investment biases

This study considers two causes—inexperience and overreaction to price movements—of a well-established phenomenon on the stock market called “excessive trading”: large trading volumes leading to decreased net portfolio returns via increased transaction costs. Creating a stylized hold or trade-scenario in a computer laboratory experiment, I confirm both inexperience and overreaction as robust drivers of trading activity. The 2 × 2 experimental design featuring overlapping treatments enables me to disentangle the two direct effects from their interaction. The results show no signs of dependencies between the two channels, suggesting that psychological distortions are immutable personal characteristics that cannot be unlearned.

Analiza uticaja pandemije Kovid-19 na robnu razmenu Republike Srbije sa zemljama Zapadnog Balkana

Doing business in the Western Balkan (WB) is characterized by close connections between member countries as well as a large volume of trade. Similarities in language, culture and friendly relations facilitate the exchange of goods. Due to the importance of WB as a grouping for the Republic of Serbia (hereinafter: RS), the work will be devoted to the analysis of the business operations of companies on WB, specifically business operations before, during and after the pandemic. The subject of this work is to determine and examine how the outbreak of the COVID-19 pandemic affected the business operations of companies in the WB. The goal of the work is to present the latest data on the trade of RS with the WB as a grouping, but also for individual WB countries, which will help companies and state authorities in creating a policy for the development of relations and incentives.

Is silence golden sometimes? Management guidance withdrawals during the COVID-19 pandemic

The many management guidance withdrawals during the COVID-19 pandemic have attracted considerable attention from the media, investors, and regulators. This study analyzes the determinants and consequences of these withdrawals. We find that guidance withdrawals are due to economic uncertainty, resulting from firms’ exposure to the COVID-19 pandemic rather than poor financial performance. Also, the effect of COVID-19 exposure on guidance withdrawals is stronger when firms face higher litigation risk. Further, guidance withdrawals result in abnormally large trading volumes and high analyst forecast dispersion but do not harm stock prices or the level of analyst earnings forecasts. Overall we believe the findings have implications for understanding corporate disclosure practices during periods with heightened economic uncertainty.

Emergence and structure of decentralised trade networks around dark web marketplaces

Dark web marketplaces (DWMs) are online platforms that facilitate illicit trade among millions of users generating billions of dollars in annual revenue. Recently, two interview-based studies have suggested that DWMs may also promote the emergence of direct user-to-user (U2U) trading relationships. Here, we carefully investigate and quantify the scale of U2U trading around DWMs by analysing 31 million Bitcoin transactions among users of 40 DWMs between June 2011 and Jan 2021. We find that half of the DWM users trade through U2U pairs generating a total trading volume greater than DWMs themselves. We then show that hundreds of thousands of DWM users form stable trading pairs that are persistent over time. Users in such stable pairs turn out to be the ones with the largest trading volume on DWMs. Then, we show that new U2U pairs often form while both users are active on the same DWM, suggesting the marketplace may serve as a catalyst for new direct trading relationships. Finally, we reveal that stable U2U pairs tend to survive DWM closures and that they were not affected by COVID-19, indicating that their trading activity is resilient to external shocks. Our work unveils sophisticated patterns of trade emerging in the dark web and highlights the importance of investigating user behaviour beyond the immediate buyer-seller network on a single marketplace.

Assessment of immunization procedures for foot-and-mouth disease in large-scale pig farms in China based on actual data and dynamics

Foot-and-mouth disease (FMD) is an acute, highly infectious and pathogenic animal disease. In recent years, with the rapid development of the swine breeding industry in China, pig farms have shown a trend of larger-scale development. Large-scale pig farms employ standardized management, a high level of automation, and a strict system. However, these farms have a large trading volume, and increased transmission intensity of FMD is noted inside the farm. At present, the main control measure against FMD is pig vaccination. However, a standard for immunization procedures is not available, and currently adopted immunization procedures have not been effectively and systematically evaluated. Taking a typical large-scale pig farm in China as the research subject and considering the breeding pattern, piggery structure, age structure and immunization procedures, an individual-based state probability model is established to evaluate the effectiveness of the immune procedure. Based on numerical simulation, it is concluded that the optimal immunization program involves primary immunization at 40 days of age and secondary immunization at 80 days of age for commercial pigs. Breeding boars and breeding sows are immunized 4 times a year, and reserve pigs are immunized at 169 and 259 days of age. According to the theoretical analysis, the average control reproduction number of individuals under the optimal immunization procedure in the farm is 0.4927. In the absence of immunization, the average is 1.7498, indicating that the epidemic cannot be controlled without immunization procedures.

The Influence of Dividend Yield on Sales Volume of Sharia Constituent Shares Jakarta Islamic Index 70

Stock trading is something that is observed and occupied by stock investors, especially retail investors. The large trading volume indicates that the stock is in great demand by investors. Information about stock trading volume is a signal that investors will consider in making decisions. Shareholders can decide to invest by looking at the dividend yield. Investors trying to invest funds with stocks that have high dividend yields. The purpose of this study is to determine whether the dividend yield variable has a significant effect on the stock trading volume of the constituents of the Jakarta Islamic Index 70. This research method uses descriptive quantitative method with secondary data collection in documentation. The results of this study indicate that the dividend yield has no significant effect on the trading volume of the constituent shares of JII 70. These results indicate that there are other variables that should be suspected to be influenced by other variables, especially the company's fundamental variables. Some improvements in data collection criteria also seem to need to be done for further research in order to obtain optimal results.

Dynamic Realized Beta Models Using Robust Realized Integrated Beta Estimator

This paper introduces a unified parametric modeling approach for time-varying market betas that can accommodate continuous-time diffusion and discrete-time series models based on a continuous-time series regression model to better capture the dynamic evolution of market betas. We call this the dynamic realized beta (DR Beta). We first develop a non-parametric realized integrated beta estimator using high-frequency financial data contaminated by microstructure noises, which is robust to the stylized features, such as the time-varying beta and the dependence structure of microstructure noises, and construct the estimator's asymptotic properties. Then, with the robust realized integrated beta estimator, we propose a quasi-likelihood procedure for estimating the model parameters based on the combined high-frequency data and low frequency dynamic structure. We also establish asymptotic theorems for the proposed estimator and conduct a simulation study to check the performance of finite samples of the estimator. The empirical study with the S&P 500 index and the top 50 large trading volume stocks from the S&P 500 illustrates that the proposed DR Beta model effectively accounts for dynamics in the market beta of individual stocks and better predicts future market betas.

Liquidity and short-run predictability: Evidence from international stock markets

This study investigates the determinants of short-run predictability in international stock markets, where predictability is defined as the accuracy of the best-combined daily forecasts. Contrary to popular belief, illiquid markets, characterized by high transaction costs and large price impact, are not necessarily highly predictable. Instead, markets with larger trading volume are more predictable, especially after the global financial crisis and in emerging markets. Those with larger market capitalization, steeper upward trends, and positively skewed returns are less predictable. Company financial strength has limited influence. During the COVID-19 pandemic the markets have become more predictable, with stronger price trends. Emerging markets are less predictable when relatively over- or undervalued.

Background Noise? TV Advertising Affects Real-Time Investor Behavior

Using minute-by-minute TV advertising data covering some 300 firms, 327,000 ads, and $20 billion in ad spending, we study the real-time effects of TV advertising on investors’ searches for online financial information and subsequent trading activity. Our identification strategy exploits the fact that viewers in different U.S. time zones are exposed to the same programming and national advertising at different times, allowing us to control for contemporaneous confounding events. We find that an average TV ad leads to a 3% increase in EDGAR (Electronic Data Gathering, Analysis, and Retrieval) system queries and an 8% increase in Google searches for financial information within 15 minutes of the airing of that ad. These searches translate into larger trading volume on the advertiser’s stock, driven primarily by retail investors. The findings on retail investor ad-induced trading are corroborated with hourly data from Robinhood, a popular retail trading platform. We also show that ads induce searches and trading of companies other than the advertiser, including of close rivals. Altogether, our findings suggest that advertising originally intended for consumers has a nonnegligible effect on financial markets. This paper was accepted by Karl Diether, finance.

Extrapolative Market Participation

This paper proposes a simple dynamic asset pricing model featuring extrapolative market participation by retail investors, that is, increased market participation following high returns in the stock market and high new participation growth (NPG). The model implies that extrapolative market participation induces asset bubbles and large trading volume and produces momentum and value effects simultaneously. More important, the model also implies that NPG positively predicts momentum strategy returns and negatively predicts value strategy returns. Using a composite measure for NPG, we find empirical support for these predictions. The momentum effect is 1.96% per month following high NPG and only 0.51% following low NPG, whereas the value effect is -0.10% per month following high NPG and 0.68% following low NPG. A similar, albeit weaker, pattern also holds for the time-series momentum and value effect.

Приграничная торговля российских регионов в 2013–2019 гг.

The article examines the cross-border trade of the Russian regions, taking into account the indicators of its volume and commodity structure, and the level of economic complexity. It is concluded that the cross-border trade in Russian regions has, in general, low intensity. This is due to several factors: most of the border areas are located on the economic periphery, both of Russia and of neighboring countries, there are physical and geographical barriers in many areas, and the development of the border area is low. In some cases, the low economic potential of a neighboring country or the existing geopolitical restrictions prevent the growth of trade. Against this background, two sections of the border area stand out: the Russian-Belarusian and the Russian-Chinese. In the first case, the development of trade is facilitated by the absence of customs barriers, historic ties and ethno-cultural proximity, a high degree of infrastructure development of border areas; in the second – by the huge scale of the economy of the neighboring country and a large potential volume of trade with it. In terms of the commodity structure of cross-border trade and its complexity, Russian border regions are usually suppliers of relatively simple goods: raw materials or products of the first processing stages, and import goods of higher complexity, which generally corresponds to the foreign trade specialization of Russia. In 2013–2019, the volume of cross-border trade in Russian regions significantly decreased, mainly due to a reduction in consumer and investment imports caused by a decrease in demand. The strongest decline occurred in cross-border trade with Ukraine. The volume of cross-border trade increased during this period in the Russian-Finnish and Russian-Estonian sections of the border (due to an increase in the volume of exports of nickel matte and mineral fertilizers). The changes in the commodity structure of cross-border trade that took place in 2013–2019 indicate the consolidation of the existing specialization of Russian border regions as suppliers of raw materials and simple products in trade with neighboring countries (except for the republics of the former USSR)

Is Silence Golden Sometimes? Management Guidance Withdrawals During the COVID-19 Pandemic

The large number of management guidance withdrawals during the COVID-19 pandemic has attracted considerable attention from the media, investors, and regulators. This study analyzes the determinants and consequences of such withdrawals. We find that guidance withdrawals are due to economic uncertainty resulting from firms’ exposure to the COVID-19 pandemic rather than poor financial performance. Also, the effect of COVID-19 exposure on guidance withdrawals is stronger when firms face higher litigation risk. Further, guidance withdrawals result in abnormally large trading volumes and high analyst forecast dispersion but do not negatively affect stock prices or the level of analyst earnings forecasts. Overall, we believe the findings have implications for understanding corporate disclosure practices during periods with heightened economic uncertainty.

The drift burst hypothesis

The drift burst hypothesis postulates the existence of short-lived locally explosive trends in the price paths of financial assets. The recent U.S. equity and treasury flash crashes can be viewed as two high-profile manifestations of such dynamics, but we argue that drift bursts of varying magnitude are an expected and regular occurrence in financial markets that can arise through established mechanisms of liquidity provision. We show how to build drift bursts into the continuous-time Itô semimartingale model, elaborate on the conditions required for the process to remain arbitrage-free, and propose a nonparametric test statistic that identifies drift bursts from noisy high-frequency data. We apply the test and demonstrate that drift bursts are a stylized fact of the price dynamics across equities, fixed income, currencies and commodities. Drift bursts occur once a week on average, and the majority of them are accompanied by subsequent price reversion and can thus be regarded as “flash crashes.” The reversal is found to be stronger for negative drift bursts with large trading volume, which is consistent with endogenous demand for immediacy during market crashes.

Finite Difference Method for the Hull–White Partial Differential Equations

This paper reviews the finite difference method (FDM) for pricing interest rate derivatives (IRDs) under the Hull–White Extended Vasicek model (HW model) and provides the MATLAB codes for it. Among the financial derivatives on various underlying assets, IRDs have the largest trading volume and the HW model is widely used for pricing them. We introduce general backgrounds of the HW model, its associated partial differential equations (PDEs), and FDM formulation for one- and two-asset problems. The two-asset problem is solved by the basic operator splitting method. For numerical tests, one- and two-asset bond options are considered. The computational results show close values to analytic solutions. We conclude with a brief comment on the research topics for the PDE approach to IRD pricing.