Improve Market Efficiency Research Articles

Corporate social responsibility and stock price informativeness: evidence from the global banking industry

ABSTRACT This study explores the impact of corporate social responsibility (CSR) performance on stock price informativeness (SPI) in the global banking industry. Drawing on a dataset of 217 banks from 45 countries between 2009 and 2020, we find that CSR performance significantly enhances SPI by ensuring that more bank-specific information is reflected in stock prices. These results align with signalling theory, which suggests that CSR performance reduces information asymmetry by signalling a firm’s commitment to long-term sustainability and ethical practices, thereby improving market efficiency. The effect is stronger in countries with mandatory CSR disclosure regulations, underscoring the role of regulatory frameworks in amplifying the benefits of CSR activities. Furthermore, we find that the relationship between CSR performance and SPI is more pronounced in investment banks than in commercial banks, reflecting the greater market sensitivity to CSR performance in institutions with higher risk profiles. Our findings provide evidence of a critical link between CSR performance and improved market transparency, highlighting the role of CSR initiatives in fostering more accurate stock price reflections.

Information Disclosure by Informed Intermediary in Double Auction

Abstract In this paper, I study the role of an informed intermediary in improving market efficiency through communication with a buyer and a seller. Based on a double auction setting, I introduce an agent who is partially informed about the private information of a buyer and a seller. The agent can disclose information to either both parties, one party, or none of them. I compare the two most common incentives of the agent, maximizing either trade probability or expected price, in information disclosure. The analysis shows that the former incentive improves efficiency more than the latter; the agent with the former always discloses all relevant information, resulting in not only a higher trade probability but also a higher expected price. The buyer and the seller mostly prefer the agent disclosing information to both parties. When the agent discloses information to only one party, the buyer and the seller desire to be the one who receives the information exclusively. They sometimes prefer the agent disclosing information exclusively to the other party over no information for all. The findings of this paper have important implications for the design of compensation schemes for intermediaries who play a significant role as advisors to market participants.

Intelligent Financial Forecasting with Granger Causality and Correlation Analysis Using Bayesian Optimization and Long Short-Term Memory

Financial forecasting plays a critical role in decision-making across various economic sectors, aiming to predict market dynamics and economic indicators through the analysis of historical data. This study addresses the challenges posed by traditional forecasting methods, which often struggle to capture the complexities of financial data, leading to suboptimal predictions. To overcome these limitations, this research proposes a hybrid forecasting model that integrates Bayesian optimization with Long Short-Term Memory (LSTM) networks. The primary objective is to enhance the accuracy of market trend and asset price predictions while improving the robustness of forecasts for economic indicators, which are essential for strategic positioning, risk management, and policy formulation. The methodology involves leveraging the strengths of both Bayesian optimization and LSTM networks, allowing for more effective pattern recognition and forecasting in volatile market conditions. Key contributions of this work include the development of a novel hybrid framework that demonstrates superior performance with significantly reduced forecasting errors compared to traditional methods. Experimental results highlight the model’s potential to support informed decision-making amidst market uncertainty, ultimately contributing to improved market efficiency and stability.

Ethereum futures and the efficiency of cryptocurrency spot markets

This paper examines the impact of the introduction of Ethereum futures contracts on the market efficiency of major cryptocurrency (Bitcoin, Ethereum, Ripple, Litecoin, and Dogecoin) spot prices. Using a multifractality-based approach and daily data from September 4, 2017 to February 16, 2024, the main results show a slight improvement in market efficiency. Specifically, the degree of multifractality persistence decreases, implying reduced market inefficiencies in major cryptocurrencies. The temporal linear correlation effect and thick tail effect are less pronounced post-launch. The asymmetry of the generalized Hurst exponent increases after the launch of Ethereum futures, with a higher persistence under the downward trend of cryptocurrencies noted. This downward trend emerges after the launch of Ethereum futures, coinciding with the final stage of the COVID-19 pandemic. Additional analysis shows that fat tails and temporal linear correlations are the main sources of multifractality. The results highlight the influence of introducing financial derivatives into the relatively new and volatile cryptocurrency area, which should concern traders, hedgers, investors, and regulators.

Can long-term institutional owners improve market efficiency in parsing complex legal disputes?

Long-horizon institutional investors can help mitigate information asymmetries around securities class action (SCA) lawsuits. We find that the machine readability of SCA complaint filings can predict the outcome and duration of class actions. Long-term institutional investor ownership leads to a more positive post-SCA announcement price reaction and increases the volatility ratio of prices as a measure of price informativeness. Furthermore, there is a significant interaction effect between long-term institutional ownership and SCA complexity on price informativeness consistent with a superior information processing ability about complex corporate events affecting portfolio firms.

Multifractal Analysis of the Impact of Fuel Cell Introduction in the Korean Electricity Market

This study employs multifractal detrended fluctuation analysis to investigate the impact of fuel cell introduction in the Korean electricity market via the lens of multifractal scaling behavior. Using multifractal analysis, the research delineates discrepancies between peak and off-peak hours, accounting for the daily cyclicity of the electricity market, and proposes a crossover point detection method based on the Chow test. Furthermore, the impacts of fuel cell introduction are evidenced through various methods that encompass multifractal spectra and market efficiency. The findings initially indicate a higher degree of multifractality during off-peak hours relative to peak hours. In particular, the crossover points emerged solely during off-peak hours, unveiling short- and long-term dynamics predicated on a near-annual cycle. Additionally, the average Hurst exponent for the short-term was 0.542, while the average for the long-term was 0.098, representing a notable discrepancy. The introduction of fuel cells attenuated the heterogeneity in the scaling behavior, which is potentially attributable to the decreased volatility in both the supply and demand spectra. Remarkably, after the introduction of fuel cells, there was a discernible decrease in the influence of long-range correlation within multifractality, and the market exhibited an increased propensity toward random-walk behavior. This phenomenon was also detected in the market deficiency measure, from an average of 0.536, prior to the introduction, to an average of 0.267, following the introduction, signifying an improvement in market efficiency. This implies that the introduction of fuel cells into the market engendered increased supply stability and a consistent increase in demand, mitigating volatility on both the supply and demand sides, thus increasing market efficiency.

Does RMB exchange rate regime reform decrease corporate risk-taking? Evidence from China

This paper investigates the impact of the RMB exchange rate regime reform on corporate risk-taking, utilizing Chinese non-financial firm-level data from 2000 to 2022. The results show that the RMB exchange rate regime reform helps decrease corporate risk-taking by decreasing the RMB exchange rate expectations uncertainty, enhancing capital allocation efficiency, and stabilizing the investor structure. These results remain robust to changing variable measurements and estimation methods, removing policy shocks and endogeneity concerns. Our findings highlight the significance of currency liberalization in stabilizing corporate risk attitudes and promoting the improvement of market efficiency.

Predictive crypto-asset automated market maker architecture for decentralized finance using deep reinforcement learning

This study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities, to improve the liquidity provision of real-world AMMs. The proposed architecture augments Uniswap V3, a cryptocurrency AMM protocol, by using a novel market equilibrium pricing to reduce divergence and slippage losses. Furthermore, the proposed architecture involves a predictive AMM capability, for which a deep hybrid long short-term memory (LSTM) and Q-learning reinforcement learning framework is used. It seeks to improve market efficiency through obtaining more accurate forecasts of liquidity concentration ranges, where liquidity starts moving to expected concentration ranges prior to asset price movement; thus, liquidity utilization is improved. The augmented protocol framework is expected to have practical real-world implications through (1) reducing divergence loss for liquidity providers; (2) reducing slippage for crypto-asset traders; and (3) improving capital efficiency for liquidity provision for the AMM protocol. The proposed architecture is empirically benchmarked against the well-established Uniswap V3 AMM architecture. The preliminary findings indicate that the novel AMM framework offers enhanced capital efficiency, reduced divergence loss, and diminished slippage, which could potentially address several of the challenges inherent to AMMs.

The Beneficial Role of Silicon Valley's Technological Innovations and Venture Capital in Strengthening Global Financial Markets

This research explores the substantial influence of Silicon Valley's technology environment on worldwide financial markets. It looks at how Silicon Valley's technology developments and venture capital improve market efficiency, boost economic growth, and help to stabilize world financial institutions. The results highlight the importance of Silicon Valley's venture capital firms in not just providing vital financing but also in strategically selecting and growing entrepreneurs to include into a larger economic network. The increase in productivity in different industries is thanks to the groundbreaking advancements in artificial intelligence and microprocessor technology in Silicon Valley. These progressions have not just improved operational effectiveness greatly but also encouraged the emergence of fresh markets. Indoor air quality challenges emphasize the need for innovation in building design. (Ashayeri, M., Piri, S., & Abbasabadi, N, 2024). Dimensionality reduction in multimodal deep learning enhances accuracy and reduces computational costs, critical for efficient AI applications. (M Bodaghi, M Hosseini, R Gottumukkala, 2024) Silicon Valley, a prime example of innovation, still showcases its significant impact on shaping present financial environments as it continues to prosper. Despite the fact that the total quantity of money invested in various countries throughout the globe is always growing, this is the case. The accomplishments of this area may serve as a model for other regions that are interested in promoting economic growth via the deployment of cutting-edge technology and creative investment methods. These places can learn from this region's successes.

Index futures mispricing: a multi-regime approach to the NIFTY 50 Index futures

PurposeThis study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023.Design/methodology/approachThe study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing.FindingsThe study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes.Practical implicationsThese findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market.Originality/valueThe Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants.Research highlights (1)This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes.(2)We highlight how factors like volatility, futures volume, and open interest vary in their impact.(3)The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing.(4)We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities.

Divergent jump characteristics in brown and green cryptocurrencies: The role of energy-related uncertainty

We examine jump, positive-jump, negative-jump, and co-jump behaviors in brown (energy-intensive) and green (energy-efficient) cryptocurrency markets. We further investigate how the newly proposed energy-related uncertainty index (EUI) correlates with jumps and co-jumps of these two types of cryptocurrencies. Our results show that jumps have decreased in recent years, signaling an improvement in market efficiency. Brown cryptocurrencies are more likely to jump than green cryptocurrencies, especially in negative jumps. When more than two cryptocurrencies experienced jumps concurrently, most co-jumps were in a negative direction (80%), with a large proportion being contributed by brown cryptocurrencies (61.7%). Finally, the higher the level of EUI, the higher (lower) the likelihood of a positive (negative) jump in brown (green) cryptocurrencies; however, co-jumps' likelihood is positively correlated with EUI for only brown cryptocurrencies. These results provide crucial insights into revealing jump dynamics, guiding tail risk, and linking energy-related uncertainty to jump behavior in brown and green cryptocurrency markets.

The impact of a preopening session on subsequent trading: An experimental analysis

AbstractIn this study, we examine the influence of a preopening call market (CM) session on the subsequent continuous double auction (CDA) trading phase within a combined market structure (CMDA). We propose that the introduction of a CM phase before the CDA could potentially mitigate price volatility during the CDA by facilitating the disclosure of agents' private information through the preopening pricing phase. Our findings reveal a positive relationship between the preopening price and the prices traded during the subsequent phase, alongside a decreased deviation of prices from their fundamental value when high‐quality information is available. These results suggest that a well‐defined preopening phase has the potential to improve market efficiency.

The impact of refinancing transaction policy adjustment on stock market volatility

This paper investigates the impact of refinancing transaction policy adjustment on the volatility of convertible bond market. Through GARCH model analysis, it is found that the refinancing policy adjustment significantly affects the spillover effect between CSI 300 index and CSI convertible bond index, indicating that the policy adjustment leads to differences in the speed of market reaction to common information and the degree of change in yield, which reflects the market's sensitivity to the policy change as well as the adaptability of market participants. The empirical results show that the convertible bond market has a significant dynamic impact on the stock market during the policy adjustment period, accompanied by the improvement of market efficiency, which supports the hypothesis that the refinancing policy adjustment has a significant impact on the spillover effect between the two markets.

Extending dual-class stock: A proposal

Abstract The increasing use of dual-class voting structures in public companies—and the frequency with which such structures contain sunset provisions—raises the issue of when and how such sunset provisions should be modified, extending the company’s use of the dual-class structure. Recent decisions have applied the entire fairness legal standard to dual-class extensions, but, in the recent Trade Desk case, the Delaware Chancery court concluded that the extension complied with the MFW standard and should therefore receive the protection of the business judgment rule. We question the practicality of applying either entire fairness or MFW to dual-class extensions. Instead, we argue for a contractual approach in which the initial charter specifies the conditions under which a dual-class structure can be extended. The contractual approach increases the information available to shareholders at the IPO stage, thereby improving market efficiency. We argue that extensions that comply with such charter provisions should be insulated from entire fairness review.

Optimal trading with regime switching: Numerical and analytic techniques applied to valuing storage in an electricity balancing market

Accurately valuing storage in the electricity market recognizes its role in enhancing grid flexibility, integrating renewable energy, managing peak loads, providing ancillary services and improving market efficiency. In this paper we outline an optimal trading problem for an Energy Storage Device trading on the electricity balancing (or regulating) market. To capture the features of the balancing (or regulating) market price we combine stochastic differential equations with Markov regime switching to create a novel model, and outline how this can be calibrated to real market data available from NordPool. By modelling a battery that can be filled or emptied instantaneously, this simplifying assumption allows us to generate numerical and quasi analytic solutions.We implement a case study to investigate the behaviour of the optimal strategy, how it is affected by price and underlying model parameters. Using numerical (finite-difference) techniques to solve the dynamic programming problem, we can estimate the value of operating an Energy Storage Device in the market given fixed costs to charge or discharge. Finally we use properties of the numerical solution to propose a simple quasi-analytic approximation to the problem. We find that analytic techniques can be used to give a benchmark value for the storage price when price variations during the day are relatively small.

The Impact of Digital Economy on Entrepreneurial Activity in Chinese Cities: Theoretical Mechanisms and Policy Recommendations

This study delves into the mechanism of the digital economy's impact on entrepreneurial activity in Chinese cities, and analyzes the role of the digital economy in enhancing entrepreneurial activity with empirical data. It is found that the digital economy significantly promotes the enhancement of urban entrepreneurial activity by reducing entrepreneurial costs, improving market efficiency, and optimizing resource allocation. Meanwhile, this study also puts forward targeted policy recommendations with a view to further utilizing the role of digital economy in promoting urban entrepreneurship development.

Comparative Analysis of the Impact of Advanced Information Technologies on the International Real Estate Market

This study examines the impact of advanced information technologies, such as big data, the Internet of Things (IoT), and artificial intelligence (AI), on the global real estate market. Through comparative analysis of real estate markets in North America, Europe, and Asia, and specific case studies from the US, UK, and China, we quantified the effectiveness of these technologies from 2018 to 2023. Our findings show that big data analytics improved market forecasting accuracy by 15.2%, IoT in smart buildings reduced energy consumption by 10.3% and enhanced security by 17.5%, and AI in property management increased operational efficiency by 17.6% and reduced maintenance costs by 13.2%. North America showed the highest technology acceptance, with a 20%-30% improvement in market efficiency, followed by Europe at 15%-25%, and Asia at 10%-20%. The successful application of these technologies depends on policy environment, market acceptance, and enterprise management. This research offers valuable insights and quantitative evidence for policymakers, industry practitioners, and researchers, highlighting the need for future studies on the integrated effects and long-term impacts of technology convergence.

Mobile phones, income diversification, and poverty reduction in rural Bangladesh

AbstractThe widespread adoption of mobile phones (MPs) presents the possibility of creating employment and self‐employment opportunities. Although several studies have documented the impact of MPs on income, the link between MP ownership and poverty reduction channeled by income diversification has not been fully explored. This paper aims to examine this relationship using nationally representative panel data and fixed effect models to account for confounding factors and unobserved heterogeneity. Results indicate that MP ownership is associated with increased income diversification, particularly through on‐farm and off‐farm self‐employment, as well as non‐earned income. This relationship is more pronounced in households with lower levels of education and deprived areas. In addition, owning a MP is also found to decrease poverty via income diversification. Therefore, policies aimed at enhancing access to mobile technologies could create a resilient income portfolio by decreasing transaction costs and improving market efficiency, ultimately mitigating poverty in rural regions.

Herding during different types of crises: The COVID-19 health crisis and Russia–Ukraine political crisis

We analysed herding behaviour in the recent pandemic and conflict. We employed the cross-sectional dispersion of daily stock returns to estimate herding's intensity in the Saudi stock market. We conducted all analyses for the entire sample and four sub-samples. Additionally, we investigate the existence of the asymmetry in the investors' responses; whether there are differences between up and down markets and between high-volatility and low-volatility days. We found that herding did not occur in the pre-COVID-19, occurred in the during-COVID-19, disappeared in the post-COVID-19 and did not occur during the Russia–Ukraine conflict. Robustness checks confirm our finding that herding manifested in the during-COVID-19 period. Additionally, no difference exists between bearish and bullish or high-and low-volatility days, pushing aside the asymmetry in the investors' responses. This study may raise investors' awareness of their cognitive bias's influence on their decisions, improving market efficiency by increasing the rationality of investors' decisions.

Shades of grand corruption among allocative efficiency and institutional settings. The case of Italy

This paper examines the underexplored impact of the procedures in public contracts on market allocation efficiency that are at risk of corruption. We integrated the GMM-SYS estimator with analyses of a balanced panel database of all the provinces in Italy between the period 2013–2021. The use of a high level of spatial granularity in an under-researched context revealed two distinct scenarios. In competitive markets, a more entrepreneurial and less widespread form of corruption emerged which may enhance market efficiency (bottom-up pattern). Conversely, in a context with limited market competition and low institutional quality, corruption indicators may be less useful due to potential pre-bribery behavior (top-down pattern). Our study underscores the capacity of corrupt suppliers to exploit market opportunities and secure selection. We found that corruption risk practices within public contracts have the potential to improve market efficiency, thus rendering them appealing to corrupt suppliers due to their capacity to deliver economic advantages. This also applies to provinces with high-quality governance. Additionally, the paper highlights the limitations of indicators in alternative contexts. The paper has significant implications for policymakers in defining assignation criteria in high-quality contexts, but also offers insights into the effectiveness of grand corruption indicators for academics.