Improve Market Liquidity Research Articles

Capital market opening, digital transformation and corporate risk-taking ——empirical evidence from Shanghai Shenzhen-Hong Kong stock connect

"Shanghai-Hong Kong stock connect" and "Shenzhen-Hong Kong stock connect", as important policies for the two-way opening of China's capital market, have positive effects on expanding investment channels for investors and improving market liquidity. Taking Shanghai Shenzhen-Hong Kong stock connect as a quasi natural experiment, this paper examines the impact of capital market opening on corporate risk-taking and its mechanism by using a multi period DID model. The empirical results show that the Shanghai Shenzhen-Hong Kong stock connect can reduce corporate risk-taking, and digital transformation plays a positive moderating role between the two; the mechanism analysis finds that the Shanghai Shenzhen-Hong Kong stock connect reduces corporate risk-taking by increasing analysts' attention, and this indirect impact process is also positively moderated by digital transformation. The heterogeneity test finds that the impact of Shanghai Shenzhen-Hong Kong stock connect on corporate risk-taking is mainly reflected in the companies with higher product market competition and faster regional marketization process. The above conclusions provide empirical evidence for the effectiveness of the capital market opening policy at micro level, and provide enlightenment for the role of digital transformation in promoting healthy development of the capital market.

Advanced AI Solutions for Securities Trading: Building Scalable and Optimized Systems for Global Financial Markets

Integrating Artificial Intelligence (AI) into securities trading software has revolutionized the financial markets by enhancing scalability, efficiency, and optimization. This paper explores the historical evolution and advancements in AI-driven trading systems, emphasizing their impact on global financial markets. The study investigates how machine learning, deep learning, and other AI technologies enable sophisticated trading strategies, improve market liquidity, and reduce transaction costs. It also addresses the challenges AI integration poses, including decision-making opacity, bias, increased market volatility, and data privacy concerns. The paper argues for the need for scalable architectures, optimization algorithms, and transparent governance to mitigate these issues. Additionally, human oversight remains crucial for evaluating AI outputs and maintaining accountability. This research aims to provide a comprehensive analysis of advanced AI systems in securities trading, highlighting their potential to enhance market efficiency and stability.

Unlocking liquidity through shortened settlement cycle: Empirical evidence from India

Many jurisdictions across the world are considering shortening the settlement cycle in equity markets from T + 2 to T + 1 with the primary objective of promoting investor protection, reducing risk and improving efficiency. In addition to achieving these objectives, shortened settlement cycles also lower the funding liquidity demand with lower margin requirements, which could unlock liquidity in less liquid stocks. Using the data from Indian markets, which incrementally migrated securities from T + 2 to T + 1 each month, we construct a series of quasi-natural experiments and find that shorter settlement cycles improve market liquidity, and the improvement in liquidity is greater for stocks with smaller market capitalization. Shortening of settlement cycles can be a path to improving overall liquidity and widening its availability to illiquid stocks.

Market Liquidity and Competition Among Designated Market Makers

Do competition and incentives offered to designated market makers (DMMs) improve market liquidity? We employ data from NYSE Euronext Paris to show that exogenous changes in contract design lead to significant decreases in quoted and effective spreads. In particular, market liquidity increases the most for stocks with the largest increase in competition among DMMs. Our analysis shows that competition among DMMs is an important aspect of contract design, along with elements such as rebates and requirements. This paper was accepted by Kay Giesecke, finance. Funding: M. G. Subrahmanyam thanks the Alexander von Humboldt Foundation for generous financial support via the Anneliese Maier Award. L. Pelizzon thanks the Leibniz Institute for Financial Research SAFE for financially sponsoring this research and Italian Ministry for University and Research [MIUR–2017RSMPZZ]. This work was also supported by the Deutsche Forschungsgemeinschaft [Grant 329107530]. Supplemental Material: The Internet Appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01801 .

Capacity limitation based local flexibility market for congestion management in distribution networks: Design and challenges

In this paper, five of the main design challenges of local flexibility markets (LFMs) for congestion management are identified and discussed based on desirable market properties from economics theory and literature review. The five main design challenges are low market liquidity, reliability, baselines, forecast errors at low aggregation levels, and the high cost of measurements. A comprehensive capacity-limitation based LFM design has been proposed to address the challenges and a simulation example is presented to illustrate the design. The proposed design facilitates market participants’ decision making by design elements that improve market liquidity, reliability and handling of forecast errors. Moreover, a new capacity limitation product is proposed that is not defined with respect to a baseline and does not require sub-meter measurements leading to lower costs and conflict-of-interest in delivery validation. Generic algorithms are also proposed for calculating utility and cost of the flexibility product. The proposed design and discussions can contribute to improving the inter-disciplinary area between engineering and economics while helping policy-makers and LFM stakeholders to further understand the problem from a multi-dimensional perspective.

Does high-frequency trading cause stock prices to deviate from fundamental values?

We examine whether high-frequency trading (HFT) is associated with greater deviations of stock prices from firms’ fundamental, intrinsic values. Prior studies show that HFT can improve market liquidity and price discovery in the short-term, but countervailing effects can discourage new information acquisition, reduce the amount of fundamental news reflected in stock prices, and reduce institutional investors’ desire to invest and trade in stocks subject to high levels of HFT. We find that greater HFT leads to a greater deviation of stock prices from accounting-based valuation estimates. The results hold across univariate, multivariate, and cross-sectional tests, as well as a natural experiment that induces an exogenous shock to HFT for a small sample of firms. Our findings contribute to the understanding of the longer-term valuation implications of HFT, as well as the traditional valuation role of accounting variables.

Government Loan Guarantees, Market Liquidity, and Lending Standards

We study third-party loan guarantees in a model in which lenders can screen and sell loans before maturity when in need of liquidity. Loan guarantees improve market liquidity, reduce lending standards, and can have a positive overall welfare effect. Guarantees improve the average quality of nonguaranteed loans traded and thus, the market liquidity of these loans because of selection. This positive pecuniary externality provides a rationale for guarantee subsidies. Our results contribute to a debate about reforming government-sponsored mortgage guarantees by Fannie Mae and Freddie Mac, suggesting that the excessively high subsidies to these guarantees should be reduced but not completely eliminated. This paper was accepted by Kay Giesecke, finance.

Relative Efficiency of Bonds and Sukuk: A Case of Selected Islamic Countries

This study compares the efficiency of bonds and Sukuk through a comprehensive analysis using descriptive statistics and regression techniques. The dataset includes outstanding Sukuk and bonds issued between 1993 and 2023 in Malaysia, Indonesia, Pakistan, Turkey, and Saudi Arabia. Objectives focus on examining mean coupon rate and yield-to-maturity (YTM) differences, exploring yield variations, and assessing efficiency levels. The analysis employs t-tests and ordinary least squares (OLS) regression. Findings indicate that bonds and Sukuk are sold at a premium, with mean coupon rates exceeding YTMs. A separate analysis supports the hypothesis that Sukuk may have higher mean YTMs and coupon rates than bonds. Sukuk exhibits significantly higher YTMs, indicating greater returns compared to bonds across the selected countries. Sukuk also demonstrates higher efficiency, reflected by a positive and significant coefficient, implying a more efficient yield-to-tenure ratio. Policy implications highlight the need to enhance regulation quality and transparency in disclosing yield differentials and risks and promote Sukuk as an attractive investment option. Diversifying investment portfolios and improving market liquidity is also recommended. Limitations include limited data availability, preventing analysis of individual countries and segregation between corporate and sovereign issues within Sukuk. In conclusion, this study provides valuable insights into the efficiency of bonds and Sukuk, offering policy recommendations. Disparities in mean coupon rates, YTMs, and efficiency levels underscore the importance of informed decision-making. Policymakers and investors can utilize these findings to understand the potential benefits of Sukuk and implement strategies to foster a more efficient and robust market. Further research is necessary to address data limitations and explore specific country-level dynamics and Sukuk classifications.

High frequency market making during stressed periods

High frequency market makers (HFMMs) are often viewed as an unreliable source of liquidity provision. This paper studies the liquidity provision of HFMMs during high volatility periods when adverse selection is high. I find that, consistent with empirical evidence, HFMMs quote narrow spreads even when volatility is high, but they are not always present at the narrow spreads. Unlike traditional intermediaries, HFMMs manage adverse selection costs by decreasing the volume traded at the narrow spread. HFMMs effectively price discriminate between uninformed and informed investors by reducing the cross-subsidization from uninformed investors to informed investors. As a result, uninformed investors pay a lower effective spread than informed investors. HFMMs improve market liquidity and increase gains from trade especially when adverse selection is high. A policy to limit HFMMs’ speed is a double-edged sword.

The Liquidity Effect of the U.S. QE on Sovereign Yield Spreads of Commodity-Exporting Countries

This paper investigates the liquidity effect of the U.S. QE on the sovereign yield spreads of commodity-exporting countries by employing the two-stage least squares approach. The key contributions of the paper are in terms of our empirical findings. First, our results show that the U.S. QE has an economically and statistically significant liquidity effect in terms of both the HPW illiquidity measure and the TIPS liquidity premium. This is of policy importance because adjusting for the liquidity premium is a key stage in modeling inflationary expectations. Second, our results show that the U.S. QE reduced the liquidity premium with improved market liquidity and hence reduce sovereign yield spreads of most commodity-exporting countries. This finding is of macroeconomic importance as reduced sovereign yield spreads have been shown to lead to higher real activity and higher credit activity.

Does high-frequency trading actually improve market liquidity? A comparative study for selected models and measures

The increasing volume of messages sent to the exchange by algorithmic traders stimulates a fierce debate among academics and practitioners on the impacts of high-frequency trading (HFT) on capital markets. By comparing a variety of regression models that associate various measures of market liquidity with measures of high-frequency activity on the same dataset, we find that for some models the increase in high-frequency activity improves market liquidity, but for others, we get the opposite effect. We indicate that this ambiguity does not depend only on the stock market or the data period, but also on the used HFT measure: the increase of high-frequency orders leads to lower market liquidity whereas the increase in high-frequency trades improves liquidity. We hypothesize that the observed decrease in market liquidity associated with an increasing level of high-frequency orders is caused by a rise in quote volatility.

Paths and policy adjustments for improving carbon-market liquidity in China

Weak liquidity and low spot prices for carbon are critical problems facing China's carbon markets. This study aims to quantify the liquidity of China's carbon markets under different carbon-pricing strategies by analyzing a model which integrates linkages between the government, markets, and firms. It uses the decoupling theory to set the penalty scale, aiming to reduce the effects of carbon emissions on economic growth. The model permits the study of policy adjustments for certified emission reduction, traders' coverage, and free quotas — the benchmarks of quota allocation. The theoretical analysis indicates that the effect of policy on market liquidity is mainly reflected in the penalty scale and quota-allocation rules. Numerical simulations indicate that the issuance of new quotas accounts for 3.3% of total allocated quotas and raises carbon prices by about 45%. Further, the carbon price rises with an increase in free quotas and then rapidly drops after a peak price of 57 yuan/ton. Our findings indicate that adding free quotas in the short term and expanding trading volume in the middle and long term will improve market liquidity and raise carbon prices.

Adopting IFRS as a Moderating Variable on the Relationship Between Accounting Information and Market Responses

The purpose of this paper is to examine the impact of adopting International Financial Reporting Standards (IFRS) on the relationship between accounting information and market responses. We investigate the impact of the IFRS adoption on financial statements quality and whether this impact, if any, has a positive implication of the capital market (especially, market liquidity and equity value). Data were collected from 147 non- financial firms listed on the Saudi exchange for the period 2015-2019. The findings show that disclosure quality has increased because of IFRS adoption. Moreover, the results find that the adoption of IFRS improves market liquidity and increases the share price of firms listed on the Saudi exchange. Our findings show that adopting IFRS have positive economic implications on the Saudi business environment.

Price limit and stock market quality: Evidence from a quasi-natural experiment in the Chinese stock market

The daily price limit changed from 10% to 20% in the ChiNext market in 2020. This event can be considered a quasi-natural experiment of change in market trading mechanisms. We employ the difference-in-difference (DID) approach to test the effect of this price limit change on the firm-level market quality indicator. We find that the implementation of the new price limit range significantly improves market liquidity and increases market volatility and the probability of informed trading. Our findings remain robust to a parallel trends test, a placebo test, alternative matching, a regression-discontinuity design, and alternative measures.

Credit Policy and Housing Market Liquidity: An Empirical Study in Beijing Based on the TVP-VAR Model

Although there is a consensus that the housing market is deeply affected by credit policies, little research is available on the impact of credit policies on housing market liquidity. Moreover, housing market liquidity is not scientifically quantified and monitored in China. To improve the government's intelligence in monitoring the fluctuation of the housing market and make more efficient policies in time, the dynamic relationship between credit policy and housing liquidity needs to be understood fully. On the basis of second-hand housing transaction data in Beijing from 2013 to 2018, this paper uses a time-varying parameter vector autoregressive model and reveals several important results. First, loosening credit policies improves the housing market liquidity, whereas credit tightening reduces the housing market liquidity. Second, both the direction and the duration of the impacts are time-varying and sensitive to the market conditions; when the housing market is downward, the effect of a loose credit policy to improve market liquidity is weak, and when the housing market is upward, market liquidity is more sensitive to monetary policy. Finally, the housing market confidence serves as an intermediary between credit policy and housing market liquidity. These results are of great significance to improve the intelligence and efficiency of the government in monitoring and regulating the housing market. Several policy recommendations are discussed to regulate the housing market and to stabilize market expectations.

The effectiveness of government stock purchase during market crash: Evidence from China

During the Chinese stock market crash in 2015, the Chinese government formed a “national team” to directly purchase stocks of more than 1000 firms. We find that the national team's interventions lower the stock price crash risk for these firms; however, they also increase stock price synchronicity, transaction cost and decrease idiosyncratic information. The stabilizing role of the “national team” disappears in the long run after the crisis period. Firms with more noise traders and a lower level of investor confidence benefit more from the national team ownership, consistent with the conjecture that the national team improves market liquidity and investor confidence.

Does quantitative easing affect market liquidity?

We argue that central bank large-scale asset purchases—commonly known as quantitative easing (QE)—can reduce priced frictions to trading through a liquidity channel that operates by temporarily increasing the bargaining power of sellers in the market for the targeted securities. For evidence we analyze how the Federal Reserve’s second QE program that included purchases of Treasury inflation-protected securities (TIPS) affected a measure of liquidity premiums in TIPS yields and inflation swap rates. We find that, for the duration of the program, the liquidity premium measure averaged about 10 basis points lower than expected. This suggests that QE can improve market liquidity.

Aggregate liquidity premium and cross-sectional returns: Evidence from China

The Chinese stock market incurs huge illiquidity costs. Liquidity has different aspects but literature rarely measures it from an aggregate perspective. To capture liquidity along various dimensions and more consistently, we propose an aggregate liquidity premium with a partial least squares approach by aggregating information on 12 liquidity-related firm characteristics in the Chinese stock market. The aggregate liquidity predictor generates significantly higher expected stock returns than individual characteristics, remains robust to different information aggregation methods, and reflects the multidimensional feature of liquidity. Behavioral mispricing theory helps explain the aggregate liquidity premium because illiquidity limits arbitrage to correct mispricing. As liquidity is important and traditional factor models limitedly explain it, we develop a new-factor model to capture the aggregate liquidity premium. The aggregate liquidity premium in China indicates that policymakers should improve market liquidity.

Correlation ambiguity, listing choice, and market microstructure

This paper investigates the implication of correlation ambiguity to investor behavior, asset pricing and issuers' listing choices from a market microstructure perspective. We introduce two markets to a multi-asset model: Market A is transparent and Market B is opaque, or Market A with low and Market B with high ambiguity perceived by investors. Each firm can choose only a unique market to list its stock. Due to ambiguity aversion, a naïve investor's demand function demonstrates piecewise linear. Such trading behavior may lead to different equilibria and hence different market-clearing prices for each listing outcome. Rational entrepreneurs make optimal listing decisions depending on asset prices in alternative markets. We further demonstrate that a crafted design of specific features of the microstructure, such as listing standards and disclosure requirements, can effectively reduce the perceived ambiguity and induce more naïve investors' participation to improve market liquidity, maintain greater volume, and lower the cost of capital, so as to attract more new listings.

The Creation of the Integrated Natural Gas Market in the Baltic Region and its Legal Implications

Abstract A common natural gas market in the Baltic region, which is in operation since 1 January 2020, means a single entry–exit tariff system for the natural gas transmission among Finland, Estonia, Latvia, and a common Latvian–Estonian balancing zone. Finland joined the market with a separate balancing zone, certain rules, contracts, invoices and billing, with a decision for full integration to be taken not earlier than in 2022. Lithuania is not currently the common market participant, because it is not ready to join it with such revenue splitting conditions as Finland, Estonia and Latvia. But still common entry–exit tariff zone countries are actively working to find a viable solution for market expansion. Lithuania and other neighbouring Member States of the European Union (hereinafter – the EU), first and foremost, Poland, are welcome to join. The creation of an integrated regional natural gas market in the Baltics in the long term will stimulate the interest of traders in the region, strengthen security of supply and improve market liquidity. Increased market competition, predictable prices in the long term, transparent tariffs, digital communication and customer-oriented business strategies are just a small part of benefits that will inevitably develop with time.