Market Efficiency Research Articles

The Implementation of the KAM Reforms in Saudi Arabia: Stakeholder Perspectives on the Audit Expectations Gap

ABSTRACTThis exploratory study investigates the implementation of key audit matters (KAM) in audit reports under ISA701 to better understand how KAM disclosures may help narrow the audit expectations gap. The study draws on findings from semi‐structured interviews with 28 professional stakeholders from the Kingdom of Saudi Arabia (KSA). The study's aim was to explore the relationship between KAM disclosures and the audit expectations gap considering audit quality concerns. Our findings reveal that (i) financial statement users report that they better understand what the auditor does, which enhanced their confidence in the audit process; and (ii) at least some financial statement users report that KAM disclosures provide new information about companies' risks that management was not already providing. We further report that many interviewees (particularly Big Four external auditors) indicated that the KAM reforms did not enhance audit quality in KSA. Interestingly, a small number of non‐Big Four auditors believed that KAM disclosures have enhanced audit quality. The key message is that the KAM reforms did not necessarily improve audit quality but that they are perceived to have played a role in further narrowing the audit expectations gap and reducing the need for communication, thus creating market efficiencies.

Detecting anomalies in currency market of BRICS countries: insights into emerging economies

PurposeThe primary motive of the research paper is to investigate the efficiency of currency market of emerging economies (BRICS countries), by analyzing the presence and consistency of calendar anomalies – specifically the day-of-the-week (DOW) effect, the January effect and the Turn-of-the-month (TOM) effect. Daily data of around 23 years and 4 months were examined for the purpose of study. Segmented data of 8-year intervals were also analyzed to observe the changes in the pattern over a period. The findings are intended to offer insights into market behaviors and potential trading strategies for astute traders.Design/methodology/approachThe study employed a quantitative research design utilizing ordinary least squares regression with dummy variables and Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) (1,1) model. To study the calendar anomalies, the daily closing price of five currency pairs has been taken from Bloomberg’s software. The Kruskal–Wallis test was conducted to validate and check the robustness of analysis.FindingsThere are mixed results regarding calendar anomalies in the BRICS currency markets. Analysis of aggregate data revealed that DOW effect is present in all the currencies except USDZAR, with Monday showing positive return. The segmented analysis showed the presence of DOW effect in USDCNY and USDINR, while other currencies lacked consistent patterns. The January effect was evident for Indian Rupees and Chinese Yuan but not for other currencies. TOM effect was present only in Chinese Yuan. Overall, the Indian Rupee and Chinese Yuan displayed inefficiencies, indicating potential for trading strategies, while other currencies appeared efficient and lacked consistent patterns.Originality/valueThe literature on currency pricing anomalies is sparse, particularly in the context of developing and underdeveloped economies. While some research exists on anomalies within the currencies of developed nations, there is a significant gap in studies focusing on emerging economies. This paper seeks to address this deficiency by investigating the presence of calendar anomalies in the currencies of BRICS countries, providing new insights into the behaviors of these emerging economies.

Analyzing the Impact of COVID-19 on Indian Stock Market Efficiency: Sectoral Behavior

The study examines the impact of the COVID-19 pandemic on the Indian stock market, providing insights for informed investment decisions. It analyses efficiency and behavior across eight crucial sectors during two distinct periods: prepandemic (Period 1: February 15th, 2018 to February 14th, 2020) and pandemic (Period 2: February 17th, 2020 to February 15th, 2022). Addressing a literature gap on sector-wise pandemic effects, the study contributes valuable knowledge to investors and policymakers. Utilizing the variance-ratio methodology enhances understanding of market efficiency. The findings reveal distinct behavior in the Indian stock market between Period 1, characterized by stable, normally distributed returns, and Period 2, marked by increased volatility and extreme events, likely influenced by the pandemic.

Retraction Note: Fostering the efficiency of the natural resource market for a comprehensive, long-term energy transition

Retraction Note: Fostering the efficiency of the natural resource market for a comprehensive, long-term energy transition

Dynamic Spillovers from US (Un)Conventional Monetary Policy to African Equity Markets: A Time-Varying Parameter Frequency Connectedness and Wavelet Coherence Analysis

Since the implementation of unconventional monetary policies (UMPs) by the US in response to the global financial crisis (GFC) and the COVID-19 pandemic, there have been increasing concerns that these forward guidance and quantitative easing programmes have had spillover effects on global equity markets. We specifically question whether the implementation of these UMPs have had spillovers to African equities, which have been previously speculated to be decoupled from global markets and shocks. Time-varying-parameter (TVP) frequency connectedness and wavelet coherency methods were used to examine the dynamic time-frequency spillovers between daily time series of the US shadow short rate and African equities returns/volatility between 1 January 2007 and 31 March 2023. On one hand, the TVP frequency connectedness analysis reveals robust long-run spillovers from US monetary policy to African equity markets during specific periods: 2009, 2013, 2020, and 2021. These coincide with instances when the Federal Reserve announced their transition from conventional to unconventional monetary practices and vice versa. On the other hand, the wavelet analysis provides insights into the ‘sign’ of the spillovers, indicating mixed phase dynamics during UMPs responding to the GFC. In contrast, anti-phase or negative co-movements characterize UMPs implemented during the COVID-19 pandemic, implying that these policies increased both returns and volatilities to African equities. Altogether, we conclude that US UMP has increasing deteriorated market efficiency and amplified portfolio risk in African equities whilst during ‘normalization’ periods US monetary policy has little transmission effect.

Performance of Closed-Ended Mutual Funds: A Test of Market Efficiency

Background: Mutual funds, known for aggregating funds from a diverse investor base, play a crucial role in managing small funds smartly. This study evaluates the performance of selected closed-ended mutual funds and examines market efficiency in Nepal. Methods: Using a descriptive and casual-comparative research design, the study spans forty-eight months from January 15, 2018, to January 14, 2022. Monthly data from five mutual funds are analyzed, with metrics such as the Treynor ratio, Sharpe ratio, and Jensen alpha. The study variables include market returns, assets, expense ratios, fund age, liquidity, and mutual fund returns. Various statistical tests, including correlation analysis, Integrated Ranking Analysis, ANOVA test, t-test, and P-value tests, are conducted to determine the significance and statistical relevance of the study variables. Results: The findings reveal that mutual fund performance is influenced by factors such as return, age, liquidity, asset, and expense ratio. Expense ratio and age emerge as the most impactful factors, demonstrating a statistically significant relationship with market return (p < 0.05). All selected mutual funds outperform the market return (NEPSE), with Sanima Equity Fund securing the top rank in all three measures. Conclusion: The Sharpe ratio, Treynor ratio, and Jensen alpha measures for mutual funds surpass those of the market, indicating market inefficiency in its semi-strong form. Novelty: This study uniquely evaluates closed-ended mutual funds using comprehensive performance metrics, demonstrating their significant outperformance compared to market benchmarks. It underscores their effectiveness in actively managing passive funds and challenges the efficient market hypothesis.

Assessing the Performance of Initial Public Offerings (IPOs) in Indian Stock Markets: A Longitudinal Study from 2014-2023

This research investigates the performance of Initial Public Offerings (IPOs) in the Indian stock market over a decade (2014-2023). The study aims to assess both short-term and long-term IPO returns and the factors influencing their performance. Using a quantitative, longitudinal approach, data was collected from the National Stock Exchange (NSE) of India, covering 500 IPOs. The analysis examines short-term listing day returns and long-term performance up to five years post-IPO, focusing on variables like firm size, market sentiment, and sector-specific dynamics. The findings reveal substantial variations in IPO performance, with average listing day returns ranging from 18.98% to 26.35%, particularly peaking during market volatility in 2020. However, long-term returns exhibit a decline, with many IPOs underperforming five years after listing. Regression analysis shows that firm size and market sentiment significantly affect IPO performance, with larger firms and favorable market conditions driving better outcomes. The technology and pharmaceutical sectors outperformed, while real estate IPOs lagged. The study highlights the importance of due diligence for investors and the need for continued regulatory reforms to improve market transparency and efficiency. This research fills a gap in the literature by providing a comprehensive, decade-long perspective on IPO performance in India.

The Impact of Investor Psychology on Investment Decisions

This article aims to explore the significant impact of investor psychology on investment decisions, taking overconfidence and risk tolerance theories as examples to analyze their influence on decision-making. The examination of the investment psychology during the Chinas 2018 A-share crash highlights that investors are susceptible to psychological biases and emotional reactions in the face of market changes. These biases include representativeness bias, where investors may rely too heavily on stereotypes, leading to misjudgments; panic, which can trigger impulsive and irrational selling; overconfidence, causing investors to overestimate their abilities and knowledge; and catastrophic thinking, where investors focus excessively on potential worst-case outcomes. These psychological factors contribute to increased market volatility and can lead to irrational judgments and behaviors among investors. Such behavior is detrimental to the market's stable development, as it can result in mispricing of assets and increased risk of misinvestment. The article emphasizes the critical role of investor psychology in rational decision-making and market efficiency. By recognizing the personal and environmental drivers of these biases, investors can adopt strategies to counteract their influence. This includes improving self-awareness, seeking a variety of perspectives, and employing tools to regulate emotional reactions to market shifts. Policymakers can further contribute by fostering market transparency and educating investors, which can diminish the impact of psychological biases on investment choices. In conclusion, a concerted effort to comprehend and tackle investor psychology is essential for cultivating a more stable and efficient investment landscape.

Elite university bachelor’s degree salary premium for master’s graduates: empirical evidence from China

ABSTRACT The labor market’s response to the signaling value of multiple qualifications (bachelor’s and master’s degrees) held by master’s graduates influences labor market efficiency and individual decisions to invest in education. Using data from the ‘Sample Survey of Chinese Higher Education Institutions’ conducted in 2017 and 2019, this study analyzes whether master’s graduates with bachelor’s degrees from elite universities earn salary premiums. The findings indicate that bachelor’s degrees from elite universities provide approximately a 12% salary premium for master’s graduates, attributable to both human capital and labor market signaling mechanisms. When accounting for individual human capital differences, the salary premium is approximately 9.1%. Furthermore, the empirical results show that these premiums are statistically significant only for master’s graduates from elite universities. The distribution of salary premiums exhibits a U-shaped trend, increasing from low to high salary quantiles. The signaling/screening mechanism’s salary premiums rise with starting salaries, while negative signaling effects are observed at lower quantiles.

Unlocking the Reversal Anomaly in the A-share Market: Insights from Marginal Funds of Institutional Investors

Fama's Efficient Market Hypothesis (1970) posits that all valuable information is swiftly reflected in stock prices in mature markets. However, anomalies like momentum and reversal effects persist in China's A-share market. This study investigated the short-term reversal effect using turnover rates and institutional investor marginal funds. Significant reversals were found in high-turnover stocks due to speculative trading and deviations from intrinsic value. Institutional investors played a crucial role in correcting mispricing. The Institutional Holding Change Ratio (IHCR) demonstrated that institutional investors' marginal funds strongly explain the reversal anomaly and act as a style factor with positive returns. This research highlights the impact of institutional and individual investor behaviors on market efficiency and investment strategies.

The Market Efficiency Debate: Developments of Financial Market Theories

A united hypothesis to explain the price movement of financial assets has been a significant research goal over the past few decades. This paper reviews the existing literature on both Efficient Markets and Behavioral Finance, concluding with more contemporary literature that attempts to combine the two theories. It summarizes the EMH and Behavioral Finance concepts, the debate between them, and the evidence supporting both. By recognizing the vast swaths of evidence in support of both hypotehses, one can conclude that the questions surrounding the efficiency or inefficiency of markets remain unanswered. Instead, the evidence supporting both hypotheses suggests the necessity of a compromise to rationalize these results instead of relying on one hypothesis or another. The compromise between both hypotheses, in turn, makes the debate between efficiency and inefficiency almost moot. Researchers must account for human misbehavior and rational expectations on a spectrum when formulating new market hypotheses, remaining open to changes in market theory as new variables continue to be discovered.

Decoding Market Efficiency: A Multifractal Approach to Financial Market Dynamics

Financial markets were traditionally analyzed using the Efficient Market Hypothesis (EMH), which argued that asset prices fully reflected all available information. This led to the belief that price movements were random, making it impossible to consistently outperform the market. However, emerging research had highlighted that markets could exhibit multifractality, where inefficiencies existed across different time scales, challenging the traditional EMH framework. Our study built on these insights by investigating the market efficiency of several asset classes, including stock market indices, cryptocurrencies (Bitcoin, Ethereum, USDT, and Binance), commodities (gold and crude oil), fixed income (U.S. 10-Year & 30-Year Treasury Bonds), and foreign exchange (EUR/USD & GBP/USD). Utilizing the Multifractal Detrended Fluctuation Analysis (MFDFA) method, we analyzed the multifractal properties and efficiency of each asset class. The results revealed that traditional assets exhibited monofractality, adhering more closely to the EMH. In contrast, cryptocurrencies displayed significant inefficiencies, likely due to their volatility, market structure, and susceptibility to external shocks. The findings provided valuable insights into portfolio diversification and risk management, suggesting that inefficiencies in certain asset classes might present both opportunities and risks for investors.

Investigating the Determinants of Herd Behavior: an Application of the Hwang–Salmon Method to the Turkish Banking Sector

Efficient financial markets are important for pricing assets at fair value. In an efficient market, investors are rational in the face of fast and accurate information flow, evaluate the information correctly, and reflect it in their pricing decisions. However, particularly in times of crisis and uncertainty, it is observed that some market participants hesitate in their decision-making processes, imitate the behavior of other individuals whom they consider reputable because they cannot rely on their own knowledge and experience, and try to follow the trend. This tendency, which is called herd behavior, destroys market efficiency and prevents correct price formation. Therefore, it is important to identify its determinants. The purpose of the study is analyzing the precense and determinants of herding behavior in the Turkish banking sector during the period 17.10.2017–10.11.2023. Herd behavior is identified using the Hwang–Salmon method, and logistic regression analysis and the Kruskal–Wallis test are applied to identify its determinants. The findings reveal that herding behavior is associated with the rise in risks and returns as well as the fall in interest rates and exchange rates.

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.

Corporate ESG performance and credit misallocation: Evidence from China

Using data from Chinese-listed companies from 2010 to 2020, this paper investigates the impact of corporate environmental, social, and governance (ESG) performance on credit misallocation. We find that ESG performance significantly reduces credit misallocation, which remains robust to various sensitivity tests. Mechanism analysis reveals that ESG reduces credit misallocation by alleviating information asymmetry. This “information supplementary effect” is primarily evident at the firm rather than the macro level. Further discussion reveals a more pronounced impact of ESG in firms with lower profitability and higher market competition, as well as those located in less financially developed regions. However, this impact becomes less salient during periods of credit expansion. Our research highlights the importance of corporate ESG performance in deterring information asymmetry, and provides insights into improving the resource allocation efficiency in the credit market.

Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency Data

Understanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While high accuracy is critical for researchers and investors, market nonlinearity and hidden dependencies pose challenges. In this study, the filtered historical simulation is used to generate pathways for the next hour on the one-minute step for Bitcoin and Ethereum quotes. The innovations in the simulation are standardized historical returns resampled with the method of block bootstrapping, which helps to capture any hidden dependencies in the residuals of a conditional parameterization in the mean and variance. Ordinary bootstrapping requires the feed innovations to be free of any dependencies. To deal with complex data structures and dependencies found in ultra-high-frequency data, this study employs block bootstrap to resample contiguous segments, thereby preserving the sequential dependencies and sectoral clustering within the market. These techniques enhance decision-making and risk measures in investment strategies despite the complexities inherent in financial data. This offers a new dimension in measuring the market risk of cryptocurrency prices and can help market participants price these assets, as well as improve the timing of their entry and exit trades.

Do News Reports and Google Searches Impact Bitcoin Prices? An Analysis of Granger Causality

Objective: to analyze the Granger causality relationship between the number of Bitcoin news articles, the number of Google searches related to the term "Bitcoin," and the price of the cryptocurrency. The study aims to understand how these variables influence each other using an econometric time series model. Theoretical framework: the paper is based on studies on financial market efficiency, cryptocurrencies, and Granger causality theory. It reviews research that explores the impact of media information and online searches on the price of financial assets, such as Bitcoin, while also discussing Bitcoin’s role as both a speculative asset and a transactional currency. Method: the construction of a time series with weekly data, from 2015 to 2018, covering Bitcoin price, the number of news articles, and Google searches. The Vector Autoregressive (VAR) model was used to conduct the Granger causality test and verify if there are causal relationships between these variables. Results and Discussion: the findings indicate a triple causality relationship among the studied variables. Bitcoin's price influences both the number of news articles and Google searches, and in turn, the articles and searches also influence Bitcoin's price. This suggests that increased interest and media coverage of Bitcoin drive its demand and price. Research implications: the research suggests that Bitcoin's price behavior is influenced not only by traditional economic factors but also by media information and the volume of online searches. This highlights the importance of informational factors in the cryptocurrency market and the influence of digital media on asset pricing. Originality/value: the study proposes integrating data from news articles and Google searches with Bitcoin price behavior, applying Granger causality analysis. Its contribution lies in demonstrating the importance of media in the dissemination and pricing of cryptocurrencies, providing a new perspective for the literature on digital assets and speculative behavior.

Is the Chinese stock market efficient? Evidence from a combined liquidity trading strategy

PurposeThis study examines the stock market efficiency in China to offer trading strategy guidance to investors and efficiency evaluation insight to policymakers.Design/methodology/approachThis study examines the stock market efficiency in China with a new combined liquidity trading strategy by blending technical analysis into a liquidity buy-and-hold strategy.FindingsOur results show that the combined strategy generates significant excess returns in the whole sample period, suggesting that the Chinese stock market is not consistent with the weak form efficient hypothesis. In addition, the combined strategy yields more significant risk-adjusted excess returns after the 2004 split-share reform, indicating the stock market efficiency in China does not exhibit a distinct upgrade after the reform. Our further test results reinforce the main conclusions after taking transaction costs, market states, short-selling reform and other issues into consideration.Originality/valueOur study contributes to the literature in two ways: First, we shed light on the mixed documented results about the market efficiency form in China. Second, we contribute to the mixed relation between the 2004 split-share reform and market efficiency in China.

Development of an integrated model for SME marketing and CRM optimization

The development of an integrated model for optimizing marketing and customer relationship management (CRM) in small and medium-sized enterprises (SMEs) presents a crucial advancement in enhancing business competitiveness and customer engagement. This paper proposes a unified model that merges scalable software solutions for social media analytics with AI-driven CRM to provide a comprehensive framework for SMEs to enhance their marketing strategies and customer interactions. Social media platforms have become vital channels for customer outreach, engagement, and feedback, yet SMEs often face challenges in leveraging these platforms due to limited resources and expertise. Integrating social media analytics into CRM systems offers businesses the ability to analyze customer behaviors, preferences, and sentiment on a large scale. The proposed model utilizes artificial intelligence (AI) to streamline customer interactions, enabling personalized communication and improving customer retention. By incorporating AI into CRM, SMEs can automate routine tasks, such as responding to customer inquiries and segmenting audiences based on their social media activity. This allows for more efficient marketing strategies and enables businesses to proactively address customer needs, ultimately driving customer loyalty and long-term growth. Furthermore, the unified model focuses on scalability, ensuring that SMEs can adopt and adapt these technologies according to their evolving needs and resources. This paper also explores the benefits of integrating these technologies, including the potential for improved data-driven decision-making, enhanced customer experience, and increased operational efficiency. In conclusion, this integrated model presents a strategic pathway for SMEs to optimize their marketing and CRM efforts through the adoption of scalable, AI-driven tools that enhance customer engagement and boost business performance. Keywords: SME Marketing, CRM Optimization, Social Media Analytics, AI-Driven CRM, Customer Engagement, Scalable Software Solutions, Business Performance, Personalized Communication, Customer Retention, Data-Driven Decision-Making.

Analisis Return on Investment (ROI) Digital pada UMKM: Sebuah Pendekatan Metodologis

As productive economic enterprises owned by individuals or individual business entities, MSMEs are faced with the reality where digital technology is developing increasingly rapidly, which forces them to immediately carry out digital transformation to achieve business growth and sustainability. Investments in digital technology, from digital marketing to the use of e-commerce platforms, have become important to maintain the competitiveness of MSMEs in an increasingly competitive market. In carrying out digital transformation, MSMEs need to measure and assess the performance of digital investments by comparing the profits obtained from the investment against the costs incurred in the process of digitizing their business. This is the main challenge faced by MSMEs in measuring the effectiveness of their digital investments. This is where the concept of Return on Investment (ROI) becomes very relevant. In this research, it was found that calculating digital ROI requires careful planning and continuous evaluation so that MSMEs can make the right decisions regarding the technology they invest in, both short-term and long-term calculations. Increasing ROI requires a strategic approach that involves reducing costs, increasing revenue, monitoring performance, and utilizing technology and automation that is appropriate to the business being run, through a combination of digitalization strategies, product innovation, good customer service, operational efficiency, and creative marketing. By optimizing every element in investment, it is ensured that every rupiah invested provides maximum returns. By implementing these strategies, MSMEs can increase their competitiveness in the market, strengthen relationships with customers, to increase the competitiveness and sustainability of their business.