Order aggressiveness, pre-trade transparency, and long memory in an order-driven market

Recent empirical research has documented that the state of the limit order book influences stock investors' strategies. Investors place more aggressive orders when the same side of the order book is thicker, and less aggressive orders when it is thinner. We conjecture and demonstrate that this behavior is related to long memories of trading volume, volatility, and order signs in stock markets. We investigate our conjecture in two types of artificial stock markets: a transparent market, in which agents observe all limit orders on both sides of the book and order volumes at those prices before they trade; and a less transparent market, in which agents observe only the best five bid and ask quotes with the depth available at these limit prices. The first market structure resembles certain actual stock exchanges in the level of pre-trade transparency, such as the Australian Stock Exchange, NYSE OpenBook, and the London Stock Exchange, whereas the second market structure is consistent with stock exchanges such as Euronext Paris, the Toronto Stock Exchange, the Tokyo Stock Exchange, and Hong Kong Exchanges and Clearing. We demonstrate that our long memory results are robust with different levels of pre-trade transparency, implying that the strategy constructed by the state of the order book is key for explaining long memories in many actual stock exchanges.     

Financial regulations and bank credit to the real economy

We present a new agent-based model focusing on the linkage between the interbank market and the real economy with a stylised central bank acting as lender of last resort. Using this model we address the tradeoff between stability and economic performance for different structures of the interbank market. We also explore the efficacy of recent regulatory reforms using our richer model. Our results suggest that the effects of regulatory leverage ratios on the banking sector׳s performance can vary in a complex and non-monotonic way with the state of the economy, the degree of connectivity of the interbank market and the amount of information available to market participants on bank risks.     

The impact of reduced pre-trade transparency regimes on market quality

This paper studies the effects of pre-trade quote transparency on spread, price discovery and liquidity in an artificial limit order market with heterogeneous trading rules. Our agent-based numerical experiments suggest that full quote transparency incurs substantial transaction costs to traders and dampens trading activity in an order-driven market. Our finding reveals that exogenous restriction of displayed depth, up to several best quotes, does not benefit market performance. On the contrary, endogenous restriction of displayed quote depth, by means of iceberg orders, improves market quality in multiple dimensions: it reduces average transaction costs, maintains higher liquidity and moderate volatility, balances the limit order book, and enhances price discovery.     

Resiliency of the limit order book

This study contributes to our understanding of the liquidity replenishment process in limit order book markets. A measure of resiliency is proposed and quantified for different liquidity shocks through the impulse response functions generated from a high frequency vector autoregression. The model reveals a rich set of liquidity dynamics. Liquidity shocks were found to have immediate detrimental effects on other dimensions of liquidity but the replenishment process generally occurs quickly, indicating limit order books are resilient. Cross-sectionally, resiliency is found to be consistently high across all large stocks, consistent with competition for liquidity provision coming from computerized algorithms. For other stocks, greater variation in resiliency is observed, indicating more selective participation by these liquidity providers.     

Voluntary Disclosure by State-owned Enterprises Listed on the Stock Exchange of Hong Kong

This study examines the impact of international capital market pressures on the voluntary disclosure of three types of information (strategic, financial, and non-financial) in the annual reports of former wholly state-owned People's Republic of China (PRC) enterprises, listed on the Stock Exchange of Hong Kong (SEHK). Consistent with a cost­benefit framework, we find that PRC H-Share firms disclose significantly more strategic and financial information than other SEHK firms. Additional analysis of disclosures in their home listings on the PRC exchanges, however, suggests an alternative explanation. The fact that these firms have been selected for "showcasing" in international capital markets may also play a role in our findings. While H-Share firm disclosures in the PRC also appear sensitive to management's assessment of the associated costs, the magnitude of differences across listing locations suggests that disclosure practices on the SEHK may also reflect the effects of state-encouraged disclosure policies. Our findings contribute to the understanding of disclosure behavior among former wholly state-owned enterprises and to the emerging literature on the efficacy of the privatization process.     

U.S. Futures Exchanges Face"Mission Impossible"

For U.S. futures exchanges, controlling costs while maintaining market performance is an ongoing, difficult challenge. New market realities have made that challenge even more daunting in recent years as costs have escalated, competition has expanded, and the role of information technology has expanded. It is always difficult for regulatory statutes to keep pace with ever-changing markets. Futures markets are no exception. The basic statutory framework represented by the Commodity Exchange Act (CEA) was enacted in 1922, over seventy years ago. In order to maintain appropriate regulatory balance, periodic review and reform has been essential over the years. Our current federal regulatory systems were built for different markets with different competitive realities than we face today. Reforming the CEA to take into account those new market realities is vital to the survival of U.S. futures exchanges.     

Order book modeling and financial stability

Journal of Economic Interaction and Coordination - This paper presents an agent-based model of the financial order book, and investigates whether the volatility of market prices can be reduced by...     

Information dissemination in an experimentally based agent-based stock market

This paper builds an agent-based model to reproduce the results of an experimental stock market that studies how the market aggregates private information. The aim is to use experiments and agent-based modeling to analyze the trading behavior in experimental stock markets. Using the experimental environment and results, it is possible to formulate a hypothesis about the subjects’ behavior and thereby formalize (algorithmically) the trading behavior in an agent-based model. This may lead to a better understanding of how the market converges to an equilibrium and of the mechanism that allows dissemination of private information in the market.     

Learning,information processing and order submission in limit order markets

By introducing a genetic algorithm learning with a classifier system into a limit order market, this paper provides a unified framework of microstructure and agent-based models of limit order markets that allows traders to determine their order submission endogenously according to market conditions. It examines how traders process and learn from market information and how the learning affects limit order markets. It is found that, measured by the average usage of different group of market information, trading rules under the learning become stationary in the long run. Also informed traders pay more attention to the last transaction sign while uninformed traders pay more attention to technical rules. Learning of uninformed traders improves market information efficiency, but not necessarily when informed traders learn. Opposite to the learning of informed traders, learning makes uninformed traders submit less aggressive limit orders and more market orders. Furthermore private values can have significant impact in the short run, but not in the long run. One implication is that the probability of informed trading (PIN) is positively related to the volatility and the bid-ask spread.     

Determining the optimal market structure using near-zero intelligence traders

We evaluate an agent-based model featuring near-zero-intelligence traders operating in a call market with a wide range of trading rules governing the determination of prices, which orders are executed as well as a range of parameters regarding market intervention by specialists and the presence of informed traders. We optimize these trading rules using a population-based incremental learning algorithm seeking to maximize the trading volume. Our results suggest markets should choose a large tick size and ensure only a small fraction of traders are informed about the order book. The effect of trading rules regarding the determination of prices, priority rules, and specialist intervention, we find to have an ambiguous effect on the outcome.     

The Accuracy of Price-Earnings and Discounted Cash Flow Methods of IPO Equity Valuation

This paper compares estimates of value derived from conventional discounted cash flow and price earnings valuation methods to the market price. For a sample of 45 firms newly listed on the New Zealand Stock Exchange our results suggest that the best discounted cash flow method and the best price earnings comparable have similar accuracy. The median absolute pricing error is around 20% and the models explain around 70% of the cross-sectional variation in market price scaled by book value. The results serve to corroborate the findings of Kaplan and Ruback (1995).     

On the plausibility of sunspot equilibria

This study examines the plausibility of the emergence of sunspot equilibria in an agent-based artificial stock market. Using the agent-based model, we make the sunspots explicit so that we can test, e.g., by means of the Granger causality test, whether purely extrinsic uncertainty can influence price dynamics. In addition, through agent-based simulation, the coordination process, which is mainly driven by genetic programming, becomes observable, which enables us to analyze what agents perceive and whether they believe in sunspots. By manipulating different control variables, three series of experiments are conducted. Generally speaking, the chances of observing “sunspot equilibria” in this agent-based artificial stock market are small. However, the sunspot believers can never be driven out of the market. Nevertheless, they are always outnumbered by fundamental believers, which is evidence that the market as collective behavior is rational. We also find that lengthening the time horizon will make it difficult for sunspot believers to survive.     

Central bank disclosure as a macroprudential tool for financial stability

The Basel Capital Accord (pillar 3) states that disclosure of information (transparency) is essential to financial stability. This study analyzes, through inflation reports, the disclosure of information from the Central Bank of Brazil concerning the credit market. We consider credit risk and capital buffers as measures of financial stability in this analysis. Furthermore, in order to measure the perception of the monetary authority on the credit market, we built two indices based on the central bank’s communication on credit development. We performed a panel data analysis based on a sample of 125 banks for the period from June 1999 to September 2014 (7000 observations). The findings suggest that central bank communication regarding expectations concerning the credit market contributes to financial stability. Therefore, this kind of communication of central banks (about credit development) may constitute an important macroprudential tool to improve financial stability.     

A comparison of different trading protocols in an agent-based market

We compare price dynamics of different market protocols (batch auction, continuous double auction and dealership) in an agent-based artificial exchange. In order to distinguish the effects of market architectures alone, we use a controlled environment where allocative and informational issues are neglected and agents do not optimize or learn. Hence, we rule out the possibility that the behavior of traders drives the price dynamics. Aiming to compare price stability and execution quality in broad sense, we analyze standard deviation, excess kurtosis, tail exponent of returns, volume, perceived gain by traders and bid-ask spread. Overall, a dealership market appears to be the best candidate, generating low volume and volatility, virtually no excess kurtosis and high perceived gain.     

Corporate carbon risk,voluntary disclosure,and cost of capital: South African evidence

The study examines the interplay among corporate carbon risk, voluntary disclosure, and cost of capital within the context of South Africa, a “rising power” in the climate policy debate. We develop a system of simultaneous equations models and analyze data drawn from firms traded on the Johannesburg Securities Exchange (JSE), for the period 2010 to 2015, using the three‐stage least squares procedure. We find that voluntary carbon disclosure is associated with lower overall (and equity) cost of capital, after controlling for corporate carbon risk. We also find that firms with higher carbon risk tend to provide better quality carbon disclosure and signal the possibility of high carbon risk to avoid negative market reactions resulting from concealing carbon information. Although the capital market does not appear to incorporate individual firm's carbon risk exposure into the required cost of capital, we find that it generally requires higher returns for companies operating in carbon‐intensive sectors. These findings suggest that firms could exploit the virtues of voluntary carbon disclosure to reduce their overall (and equity) cost of capital. Our findings also imply that regulators and policymakers could point to the cost of capital reducing role of voluntary disclosure to lure firms into voluntarily providing superior quality carbon disclosures.     

Forecasting temperature to price CME temperature derivatives

This paper seeks to forecast temperatures in US cities in order to price temperature derivatives on the Chicago Mercantile Exchange (CME). The CME defines the average daily temperature underlying its contracts as the average of the maximum and minimum daily temperatures, yet all published work on temperature forecasting for pricing purposes has ignored this peculiar definition of the average and sought to model the average temperature directly. This paper is the first to look at the average temperature forecasting problem as an analysis of extreme values. The theory of extreme values guides model selection for temperature maxima and minima, and a forecast distribution for the CME’s daily average temperature is found through convolution. While univariate time series AR-GARCH and regression models generally yield superior point forecasts of temperatures, our extreme-value-based model consistently outperforms these models in density forecasting, the most important risk management tool.     

A survey of network-based analysis and systemic risk measurement

The financial crisis led to a number of new systemic risk measures and a renewed concern over the risk of contagion. This paper surveys the systemic risk literature with a focus on the importance of contributions made by those emphasizing a network-based approach, and how that compares with more commonly used approaches. Research on systemic risk has generally found that the risk of contagion through domino effects is minimal, and thus emphasized focusing on the resiliency of the financial system to broad macroeconomic shocks. Theoretical, methodological, and empirical work is critically examined to provide insight on how and why regulators have emphasized deregulation, diversification, size-based regulations, and portfolio-based coherent systemic risk measures. Furthermore, in the context of network analysis, this paper reviews and critically assesses newly created systemic risk measures. Network analysis and agent-based modeling approaches to understanding network formation offer promise in helping understand contagion, and also detecting fragile systems before they collapse. Theory and evidence discussed here implies that regulators and researchers need to gain an improved understanding of how topology, capital requirements, and liquidity interact.     

数字化转型提升了上市公司信息披露质量吗？

信息披露是证券市场健康有序运行的重要环节,对于投资者而言,公司信息披露质量更是其实现价值投资的重要保证。利用2010—2020年深交所A股上市公司的面板数据实证考察了数字化转型对信息披露质量的影响。研究发现,数字化转型能够显著提升我国上市公司的信息披露质量,这一结论在处理内生性问题并进行多种稳健性检验后仍然成立。机制分析表明,数字化转型通过优化内部控制质量和提高财务稳定性提升了信息披露质量。进一步分析发现,多元化经营、融资约束会削弱数字化转型对信息披露质量的提升效应,而行业集中度提高则会强化这一积极影响,并且相较于非高新技术公司和中西部地区公司,数字化转型对高新技术公司和东部地区公司信息披露质量的提升作用更为明显。     

基于Agent的人工股票市场构建及其仿真研究

基于行为金融学和市场微观结构理论的部分研究成果,建立了一个基于Agent的人工股票市场,这在人工股票市场建模领域是全新的尝试。在文中构建的人工股票市场基础上,揭示了市场的信息传递原理,回答了在怎样的条件下市场可以达到弱势有效,同时,揭示了收益率分布尖峰厚尾的原因与其与市场效率的关系,指出了各类交易商在市场中占优的条件。在此基础上,本文将文中模型与中国的股票市场进行了类比,提出完善我国股市的三条建议。     

Does transparency of central banks communication affect credit market? Empirical evidence for advanced and emerging markets

This study examines the effect of the transparency of central banks communication on credit market. In particular, this study investigates how central banks’ effort to provide more detailed information about their objectives regarding the price stability (monetary policy transparency) and financial stability (financial stability transparency) policies are able to mitigate information asymmetry on credit market, through the net interest margin charged by the banks to engage in financial intermediation (credit spread). The findings denote central bank transparency is able to reduce the credit spread. Additionally, the evidence suggests the effect of central bank transparency on the credit spread is greater in emerging markets, where there is less information available on credit market. In brief, transparency in central banks communication is an important tool to mitigate the information asymmetry in the credit market.