Impact Of Trading Research Articles

Ownership Structure, Incentives, and Asset Returns

We develop a dynamic equilibrium model to derive the endogenous relations among firms’ ownership structures, managerial incentives, and asset returns. Ownership concentration is positively related to managerial incentives (PPS) but is negatively related to its expected stock (dollar) return, and volatility. If initial block ownership is above (below) its steady-state level, block ownership and managerial PPS decrease (increase) over time to their steady-state levels, whereas the expected stock returns, return volatilities, and the price impacts of block trades increase (decrease). The convergence of these variables to their respective steady-state levels is faster for firms that are less productive, that are riskier, and whose earnings are more sensitive to managerial actions. The price impact of block trades is larger in firms that are more productive and smaller in firms whose earnings are more sensitive to managerial actions. Changes in the expected stock return over time are negatively affected by changes in block ownership but positively affected by changes in managerial incentives. Relative to owner-managed firms, more widely owned firms have lower expected stock returns and volatilities, faster dispersion of block ownership, and smaller but more variable price impacts of block trades. Compared with diffusely owned firms, firms with significant blockholdings have lower expected stock returns, higher return volatilities, and less powerful managerial incentives. This paper was accepted by Tomasz Piskorski, finance. Funding: H. W. (H.) Jung and Q. Zeng received an internal research grant (AUD 15,000) in 2015 from the Faculty of Business and Economics at the University of Melbourne. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4696 .

Impact of High-Frequency Trading with an Order Book Imbalance Strategy on Agent-Based Stock Markets

It is known that there is a positive correlation between order book imbalance and future returns. Although some previous studies using actual trading data have suggested that high-frequency trading (HFT) may take this characteristic into account, HFT firms have not disclosed their specific strategies. Furthermore, there has been a long-standing debate in the empirical research field as to whether HFT is the cause of flash crashes, but no final conclusion has been reached. In the present study, we analysed the impacts of HFT taking into account the correlation between order book imbalance and future returns on a stable market and on a market with a flash crash, using agent-based simulations, which are said to be capable of analysing events in their essence. We also analysed how HFT investment performance differs between those two market conditions. The results showed that HFT has the effect of further stabilizing the market when the market is stable but does not take place during flash crashes and so is unable to affect the market either for the good or the bad. The results also suggest that the proposed HFT’s performance is more sensitive to market price fluctuations than conventional HFT (i.e., HFT following a position market-making strategy) and tends to have high risk and high returns.

Algorithmic Trading and Post-Earnings-Announcement Drift: A Cross-Country Study

Synopsis The research problem This study investigates whether algorithmic trading matters to post-earnings-announcement drift (PEAD) across 41 countries. Motivation The increasing importance of algorithms has sparked interest in how computer-triggered trades affect the formation of securities prices. Thus, a large body of research has emerged to probe the instantaneous impact of algorithmic trading on price discovery; however, little work explores the role of algorithms in efficient pricing of low-frequency financial statements. In addition, the literature on PEAD always highlights firm-level drivers of this phenomenon, whereas its country-level institutional determinants remain silent. The test hypotheses H1: Earnings-announcement algorithmic trading does not impact PEAD. H2: Country-level investor protection does not impact the association between earnings-announcement algorithmic trading and PEAD. H3: Country-level information dissemination does not impact the association between earnings-announcement algorithmic trading and PEAD. H4: Country-level disclosure requirements do not impact the association between earnings-announcement algorithmic trading and PEAD. Target population Various stakeholders include market traders, firm managers, regulators, and scholars. Adopted methodology Ordinary Least Square (OLS) Regressions. Analyses We follow Saglam [( 2020 ) Financial Management, 49, 33–67] to measure algorithmic trading using the transaction-level data. Based on a global sample covering 41 markets, we estimate the regression of PEAD on four proxies for algorithmic trading after considering firm-specific controls and fixed effects of country and year. Findings We find a negative and significant association between earnings-announcement algorithmic activity and PEAD. The documented relation retains despite addressing the endogeneity problem. Further analyses indicate that algorithmic participation mitigates investor disagreement, alleviates trader distraction, and reduces market friction, thus facilitating efficient pricing of earnings information. Finally, the impact of algorithmic trading on PEAD is more prominent in countries with stronger investor protection, faster information dissemination, and stricter disclosure requirements.

The impact of carbon emission trading on green innovation of China's power industry

As the primary source sector of carbon emissions, the power industry's green innovation is an indispensable component of China's low-carbon transformation. This paper took China's Carbon Emission Trading Scheme as a quasi-natural experience and adopted the synthetic control method to evaluate the impact of Carbon Emission Trading Scheme pilot policy on power industry's green innovation based on the provincial industrial level data. Furthermore, this paper examined the impact mechanisms of Carbon Emission Trading Scheme on the power industry's green innovation through policy comparison, moderating and mediating effect analyses. The research findings show that: 1) there is heterogeneity in Carbon Emission Trading Scheme's effectiveness in inducing green innovation in China, the power industry only in Beijing and Guangdong has been significantly promoted, while other pilots have not; 2) the success of Beijing and Guangdong can be attributed to distinctive features of their carbon market and power industry, including the refined quota accounting mechanism, the application of paid quota allocation, the larger enterprise scale and more research and development investment; 3) enterprise scale and research and development investment have positive moderating effect and mediating effect in the promotional effect of the Carbon Emission Trading Scheme on green innovation respectively. Finally, some policy recommendations are put forward to promote the power industry's green innovation and carbon market construction.

The impact of online trading from a personal and technical perspective on trade stocks in emerging markets

The impact of online trading from a personal and technical perspective on trade stocks in emerging markets

Development of an agent model to investigate the impact of wholesale power trading on the long-term power source selection

Development of an agent model to investigate the impact of wholesale power trading on the long-term power source selection

Agent simulation research of the impact of free market electricity trading on the power supply mix

Agent simulation research of the impact of free market electricity trading on the power supply mix

Boosting Exchange's Market Share: The Impact of No-Fee Trading on Market Quality

Boosting Exchange's Market Share: The Impact of No-Fee Trading on Market Quality

融资融券交易对节能环保股价波动影响的实证分析

融资融券交易对节能环保股价波动影响的实证分析

Power laws in market microstructure

We develop an equilibrium model for market impact of trades when investors with private signals execute via a trading desk. Fat tails in the signal distribution lead to a power law for price impact, while the impact is logarithmic for lighter tails. Moreover, the tail distribution of the equilibrium trade volume obeys a power law. The spread decreases with the degree of noise trading and increases with the number of insiders. In case of a monopolistic insider, the last slice traded against the limit order book is priced at the fundamental value of the asset reminiscent of [17]. However, competition among insiders leads to aggressive trading, hence vanishing profit in the limit. The model also predicts that the order book flattens as the amount of noise trading increases converging to a model with proportional transactions costs with non-vanishing spread.

Does Wash Trading Impact Information Dissemination in Security Markets?

Does Wash Trading Impact Information Dissemination in Security Markets?

A novel community‐based local electricity market for multiple communities with joint energy trading considering the risk of participation

AbstractGrowing penetration of distributed energy resources (DER) leads to emerging changes and issues in future power systems. A system oprator aims to maximise demnad side energy trading while minimising energy improts from upstream grid. This paper proposes a reliable local energy market (LEM) trading mechanism in which consumers and prosumers can trade self‐produced energy in a multiple community‐based market (MCBM) framework to optimally provide the balance. The proposed framework intends to maximise social welfare by fair allocation of resources, increasing the provided reserve for the main grid, and shaving the peak power resulting in enhancement of network flexibility. The overall model is formulated in the style of a bi‐level problem in which the lower‐level clears the market and the upper‐level is designated to redistribute the costs and benefits among market participants. Furthemore, the conditional value at risk (CVaR) approach is employed to evaluate how risk affects social welfare. Moreover, the impact of multiple communities and joint energy trading on the outcomes of the market is examined. The proposed model has been evaluated by analysing IEEE 33‐bus standard test system, and numerical studies verify the effectiveness of the proposed approach.

The hidden benefit: Emission trading scheme and business performance of downstream enterprises

There seems to be no empirical study on the impact of emission trading scheme (ETS) on downstream industries. However, carbon trading may affect the performance of downstream firms via cost effect or spillover effect through the industrial chain. Based on the data of listed companies, this paper innovatively constructs three-dimensional data of “upstream firm - downstream firm - time”, with a total of 40,437,001 valid observations, simulates the upstream and downstream relationship between enterprises, and estimates the impact of carbon trading on the downstream performance using the Pair Weighted Difference in Difference in Difference (PW-DDD) model. The results show that the effects of carbon trading on downstream performance is negative in the short run, but it has positive significance in the long run. Heterogeneity is also found: the downstream firms with significant industrial chain relationships are more positively affected, such as chemical, petrochemical, and cement. In addition, carbon trading may improve productivity in different sectors in the same region or the same industry in other areas, thus improving the performance of non-ETS-covered enterprises.

Reprint of: Do retail traders destabilize financial markets? An investigation surrounding the COVID-19 pandemic

Existing research suggests that retail trading is associated with volatility in financial markets. To extend the literature, we study the dynamic effects of retail trading on volatility during the COVID-19 pandemic. Using marketable retail trades identified from the Boehmer et al. (2021) algorithm and novel empirical methods discussed in Jordá (2005), we document a negative, persistent impact of retail trading on the stability of stock prices that is particularly stronger during the pandemic than during the pre-pandemic period. These results highlight how periods of crises – like the pandemic – affect the destabilizing influence of retail trading. To provide additional evidence, we replicate our empirical exercise during the 2008-09 financial crisis. Consistent with the COVID-19 period, we again find that retail trading leads to more volatility during the financial crisis vis-á-vis the pre-crisis period. These results again support the idea that periods of crises strengthen the link between retail trading and volatility.

Do algorithmic traders exploit volatility?

This study examines the impact of trading by Algorithmic Traders (ATs) and Non-Algorithmic Traders (NATs) on volatility, and conversely, the impact of volatility shocks on ATs and Non-ATs. ATs are classified as High-Frequency Traders (HFTs) and Buy-side Algorithmic Traders (BATs). Using jump robust volatility estimates, we find that excessive directional and non-directional trading by BATs and HFTs increases volatility, whereas that by NATs marginally decreases volatility. Conversely, all traders increase their non-directional trading one hour following a volatility shock. BATs carry out more directional trades during a volatility shock, whereas HFTs withdraw from such activities.

Insider Trading, Stock Volatility, and Market Liquidity in the Korean Capital Market

Abstract While the positive and negative effects of insider trading have been discussed in the each firm levels, there is little evidence for the effects of insider trading on financial markets. This paper aims to provide empirical evidence for the effects of insider trading in the capital market. This study examines the association between insider trading and information asymmetry in firms with certain information environments. If capital market regulation for insider trading is effective and companies are well-governed, the positive rather than negative effects of insider trading in the capital market will be enhanced. The empirical results indicate that insider selling (buying) in firms with high ratios of foreign share ownership is associated with a decrease in stock volatility (market liquidity). The results in this paper enables policy makers and practitioners to understand the impact of corporate insider trading on outsiders in the capital market. Our findings may also help to reform regulation of insider trading as it is applied in real business environments, especially in firms with high levels of information asymmetry.

The impact of fractional trading on risk aversion for non-professional investors

We study the impact of fractional trading (FT) on non-professional investors' decision-making under uncertainty. Using the expected utility framework, we show that with the recent easiness to trade in stock markets and with the option to buy or sell a fraction of a share of a stock or ETF (exchange-traded fund), the risk appetite of non-professional investors might have gone up, increasing market participation and demand for stocks. Furthermore, we show that this change in the non-professional investor's risk aversion behavior varies by household income levels. Based on constructed probabilities, we estimate that approximately 83 billion dollars have been newly invested in stock market post-FT. Our results suggest that easy access to trade stocks and FT allows households with lower discretionary income a new tool to diversify their portfolio and participate in the stock markets by investing in different stocks and ETFs while at the same time having a significant impact on the stocks' price levels and price dynamics observed in the markets.

Insider trading, gender diversity within the board room, CEO pay gap, and stock price crash risk

AbstractThis paper investigates the impact of insider trading and managerial attributes on future stock price crashes. We conduct a series of regressions addressing the managerial attributes determinants of future stock price crashes including gender diversity, CEO age, and CEO power (measured by CEO pay disparity, CEO tenure and CEO duality). Our empirical results reveal a positive association between insider purchases and price crash risk. This implies that other than compensation and career concerns, insiders hoard bad news to fulfil their trading incentives. Our positive coefficients of insider sales also suggest that insider sellers can assess inside information promptly and anticipate shortly before the crashes. We further document that the presence of female directors on boards can mitigate stock price crash risk. However, firms with powerful or younger Chief Executive Officers are more likely to experience crashes. Overall, we highlight the importance of corporate managerial attributes in dealing with information asymmetry problems.

Algorithmic trading and block ownership initiation: An information perspective

This paper examines the impact of algorithmic trading (AT) on investors' incentives to initiate block ownership in U.S. public companies. We find that a one standard deviation change in AT activity reduces the block ownership initiation likelihood by 3.5%. Using the SEC's randomised tick size pilot experiment in 2016 as a negative shock to AT, we show that the effect of AT on block ownership initiation is causal. Further evidence supports the information-hindering explanation that AT discourages sophisticated investors from acquiring information, which results in a decrease in block ownership initiation. We find that the effect of AT is more pronounced among information-sensitive investors and that institutional investors reduce their information-gathering activities in AT-targeted stocks. Additional tests exploring information-based trading behaviour in the presence of AT provide strong evidence to support the explanation of information-hindering, and our results hold across a battery of robustness tests.

Bitcoin's Position in Indonesian Currency Law

The influence of technology and developing information is indeed extraordinary on human life. Economic development is also inseparable from the growing technology, for example in transaction procedures in a matter. Bitcoin came up with a new innovation in the form of a cryptocurrency that uses a payment network from user to user. Crypto has become a phenomenon nowadays. Based on the results of the Global Web Index Survey, 10% of internet users in Indonesia already own digital currencies, it can be said that Indonesia is ranked 5th most crypto users in the world. Crypto or can be called crytocurrency has a fairly high risk. Then kirpto has a change in value that is only enthusiasm at any time. Krip toalso has a lack of regulation and still leaves legality issues. This research was conducted to determine the existence of bitcoin trading to the national economy. The use of bitcoin over time is increasing in Indonesia. For this reason, before the use of bitcoin is increasing, we must understand the influence and impact of bitcoin trading on the national economy. Only nine countries have legalized bitcoin, it is questionable why many countries have not legalized bitcoin. This study aims to find out how bitcoin trading conditions in Indonesia. The results of this journal will discuss the effect of bitcoin trading on the national economy even though in Indonesia it is prohibited and recognized as legal tender. This can be viewed from several factors such as from the nominal side of bitcoin which is considered excessive, especially in the picture of the economic crisis.