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The Korean Emissions Trading Scheme (K-ETS) in its initial stages experiences low market liquidity driven by sellers' supply shortages. We empirically investigate for the first time the informational market efficiency of the K-ETS, specifically how the efficiency varies over time with policy measures taken to increase liquidity, such as individually constrained banking. Our findings indicate that temporary measures taken to improve liquidity in the K-ETS do not ensure informational market efficiency. We also find after the COVID-19 onset, inefficiency does not disappear even with a large surplus of allowances under pre-scheduled constrained banking. This study provides valuable implications for emerging carbon markets, which move forward toward more stringent caps. [ABSTRACT FROM AUTHOR]

In the financial sector, alternatives to traditional datasets, such as financial statements and Securities and Exchange Commission filings, can provide additional ways to describe the running status of businesses. Nontraditional data sources include individual behaviors, business processes, and various sensors. In recent years, alternative data have been leveraged by businesses and investors to adjust credit scores, mitigate financial fraud, and optimize investment portfolios because they can be used to conduct more in-depth, comprehensive, and timely evaluations of enterprises. Adopting alternative data in developing models for finance and business scenarios has become increasingly popular in academia. In this article, we first identify the advantages of alternative data compared with traditional data, such as having multiple sources, heterogeneity, flexibility, objectivity, and constant evolution. We then provide an overall investigation of emerging studies to outline the various types, emerging applications, and effects of alternative data in finance and business by reviewing over 100 papers published from 2015 to 2023. The investigation is implemented according to application scenarios, including business return prediction, business risk management, credit evaluation, investment risk prediction, and stock prediction. We discuss the roles of alternative data from the perspective of finance theory to argue that alternative data have the potential to serve as a bridge toward achieving high efficiency in financial markets. The challenges and future trends of alternative data in finance and business are also discussed. [ABSTRACT FROM AUTHOR]

In the article, we analyse the problem of the efficiency market hypothesis using entropy in moments of transition from a normal economic situation to crises or slowdowns in European, Asian and US stock markets and the economy in the years 2007–2023 (2008–2009, U.S. financial sector crises; 2020–2021, Pandemic period; and the 2022–2023 period of Russia's attack on Ukraine). The following hypothesis is put forward in the article: In periods of economic slowdown and economic crises, the entropy of prices and return rates decreases. According to the principles of physics, in an isolated system, entropy increases and decreases at the moment of external intervention, similar to finance, where during crises and economic slowdowns, there is interference from governments introducing new regulations and intervening in financial markets. The article uses the Shannon entropy method. This measure, as a statistical measure, does not require the assumption of stationarity of time series or a known probability distribution, unlike classical statistical methods. Our results confirm decreased entropy in stock markets during crisis. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to evaluate the presence of abnormal returns due to stock splits or reverse stock splits in the Brazilian capital market context. Design/methodology/approach: The event study technique was used on data from 518 events that occurred in a 30-year period (1987-2016), comprising 167 stock splits and 351 reverse stock splits. Findings: The results revealed the occurrence of abnormal returns around the time the shares began trading stock splits or reverse stock splits at a statistical significance level of 5%. The main conclusion is that stock split and reverse stock split operations represent opportunities for extraordinary gains and may serve as a reference for investment strategies in the Brazilian stock market. Originality/value: This study innovates by including reverse stock splits, as the existing literature focuses on stock splits, and by testing two distinct "zero" dates that of the ordinary general meeting that approved the share alteration and the "ex" date of the alteration, when the shares were effectively traded, reverse split or split. [ABSTRACT FROM AUTHOR]

GST implementation in India is a macroeconomic event that changed the operating conditions and profitability of businesses. This study attempts to empirically test the market reaction of GST implementation on seventeen sectors of the Indian economy through the event-study methodology. Additionally, we assess the semi-strong efficiency of the Indian stock market during the event. Findings show that the event has heterogeneously affected different sectors as the FMCG sector is positively affected while the Pharmaceutical and Power sectors are adversely affected. IT sector is moderately positively affected whereas, consumer durable, energy, media, metal, oil, and reality are moderately negatively impacted. We also infer that the market doesn't exhibit thorough efficiency of semi-strong form. The study is unique in its analysis of the sector-wise impact of GST implementation in India. This study is also useful for investors in making investment strategies and for policymakers to observe the policy impact on the stock market. [ABSTRACT FROM AUTHOR]

This paper tests the efficiency of a set of sports betting markets using detailed betting line movement from opening until closing for four different sportsbooks for each of 3,681 Major League Baseball games. The reliability of the markets' forecasts are assessed at several lead times. The forecasts are mostly reliable, but there are simple betting strategies that would have yielded significant profit. In addition, forecasts do not always improve monotonically as the games get closer, despite more information being available; forecasts at weekend day games' start times are significantly worse than forecasts 90 minutes earlier. Furthermore, analysis of the sequences of forecasts within individual games reveals that these betting markets do not incorporate information optimally. There is sufficient evidence to reject weak form market efficiency; specifically, betting lines tend to overreact, exhibiting significant negatively autocorrelated changes that could be exploited by sophisticated bettors. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.00456. [ABSTRACT FROM AUTHOR]

Executives have incentives to predict the price impact of disclosures to inform their insider trades, reporting discretion, and operating decision-making. Yet the extent to which they can do so accurately is unknown. We conduct a field study to provide direct evidence on executives' accuracy, recruiting over 650 U.S. public company executives to share their predictions of the stock price response to their companies' second quarter 2020 quarterly reports. Despite the market volatility and uncertainty at the onset of COVID-19, executives' predictions of the one-day price reaction are directionally correct in two-thirds of cases. Further, executives' short-window expectation errors predict returns. Following their companies' reports, executives trade against the market in line with their initial error, and stock prices largely converge to their expectations over the subsequent 100 trading days. Collectively, our results provide novel evidence of executives' superior ability to anticipate how the market prices information in quarterly financial reports, even in periods of extraordinary change. [ABSTRACT FROM AUTHOR]

A manager's prior experience may affect an organization's resource allocation decisions and its performance. I examine this issue in the context of National Football League (NFL) franchises. I find that teams with coaches from an offensive background spend 5.5% points more of their salary cap space on offensive personnel, while a defensive background does not significantly affect the share of resources allocated to defensive personnel. While additional salary resources committed to offensive and defensive personnel do improve team performance in those respective areas, marginal improvements in offensive and defensive performance associated with greater spending are generally unaffected by the coach's background, a finding suggesting that a coach's background does not confer an informational advantage on certain types of personnel. I further find no evidence of significant differences in the effects that spending on offence and defence respectively have on team performance, suggesting efficiency within this labour market. [ABSTRACT FROM AUTHOR]

Economists have analyzed noise trading from various viewpoints in the past, and they drew the most diverse conclusions. Noise trading has been interpreted as a facilitator of market liquidity, as a source of inefficiency, as a driver of easy money for more informed traders or for herds of uninformed ones. However, in an environment populated by High-Frequency traders, most financial theories need to be revisited, the theory about noise trading being one of them. By making use of a computer-based Agent-Based Model, this paper creates a scenario where a shock breaks the equilibrium and only some market participants receive, and act upon, such information. The results are that old-fashioned parameters, as informedness, arbitrage, market efficiency and herding behavior no longer carry the same meaning as they used to. The focus shifts from searching the mythical ‘true value’ of a security to executing orders with the shortest possible latency, to exploit trading opportunities and maximize profits. [ABSTRACT FROM AUTHOR]

A well-developed financial market system is crucial for supporting high-quality development in the new era. Initial Public Offerings (IPOs) are essential for enabling financial markets to facilitate new industrialization while accelerating the development of a manufacturing powerhouse. Therefore, research on IPO mechanism reform holds significant practical implications. This paper examines the impact of China's IPO mechanism reform by studying the equilibrium relationship between the primary market and the lending market. Using empirical analysis, we compare the average return on online subscriptions for newly listed shares using the risk-free rate before and after China's 2015 IPO mechanism reform, as well as analyze the underlying mechanisms driving excess returns during both periods. Our findings indicate a gradual alignment of returns in the primary and lending markets, with institutional arbitrage opportunities between them diminishing. The reforms have effectively enhanced market mechanisms, improved pricing capabilities, rationalized participant behavior, and optimized the securities market ecosystem. These improvements have contributed to the market's equilibrium and enhanced the market's ability to interpret information. However, further improvements to the IPO mechanism are necessary, including deeper market-oriented reforms, standardization of participant behavior, acceleration of a comprehensive registration system, and stronger information disclosure and quality management. [ABSTRACT FROM AUTHOR]

We investigate the impact of quantitative investment on market efficiency in China. We provide an illustrative model to show that quantitative investment enhances market efficiency. Empirically, we conduct both time‐series and cross‐sectional analysis. Regarding the time series dimension, we construct QuantDegree to measure the level of quantitative investment. We find that the performance of most anomalies decreases as QuantDegree increases. In the cross‐sectional dimension, we sort stocks into portfolios based on quant fund holdings and traditional anomalies. We find the anomaly return is lower within the groups with higher quant fund holdings, a result further confirmed by Fama–MacBeth regressions. [ABSTRACT FROM AUTHOR]

Purpose: This research aims to examine the time-varying behavior of the Weekend, Turn-of-the-Month, January, and Halloween effects in eight foreign exchange rates against the U.S. dollar from the Adaptive Market Hypothesis (AMH) perspective. It also explores whether these anomalies can generate excess returns compared to a buy-and-hold strategy. Design/methodology/approach: Using daily return data from January 2004 to December 2023 in a rolling-window framework, the study employs the Concordance Coefficient test and AR-GARCH models to assess the time-varying behavior of four calendar anomalies. It also assesses the statistical significance of the trading strategies implied by these anomalies using t-tests and applies F-tests for subperiod analysis. Findings: The results reveal a generalized time-varying presence of calendar anomalies in emerging currencies and, to a lesser extent, developed currencies. However, the trading strategies implied by these anomalies generally did not show statistical significance, except for the Turn-of-the-Month effect, which exhibited statistically significant unprofitability. Originality/value: The study pioneers an analysis of five calendar anomalies across various currencies from the standpoint of the AMH and proposes case-specific explanations for their occurrence. It also examines the potential for the anomalies' implied trading strategies to generate excess returns compared to a straightforward buy-and-hold strategy. Additionally, the study introduces the recently developed Concordance Coefficient test as a valuable alternative to other non-parametric methods. [ABSTRACT FROM AUTHOR]

The corporate bond market is larger, more illiquid, and presumably less efficient than the equity market. These features provide numerous profit opportunities for corporate bond mutual funds that are unique to the corporate bond market. However, whether corporate bond mutual funds have the valuation skills needed to take advantage of these opportunities is unclear. We introduce a novel measure to assess the valuation skills of investment-grade corporate bond mutual funds, which we refer to as the valuation accuracy score (VAS). VAS recognizes funds holding more underpriced and less overpriced corporate bonds as ex-ante having better valuation skills. It predicts future fund performance, is stable over time, and is unrelated to other sources of skill. Investors chase the performance of higher-VAS funds more aggressively and exhibit a convex flow–performance relation among these funds. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to address gaps and limitations in the literature on corporate governance and stock liquidity. It explores the potential benefits of increasing female representation in corporate leadership, which has been a subject of debate and policy intervention in recent years. Design/methodology/approach: Based on prior empirical studies and by integrating the insights of different theories, this study links gender diversity to stock liquidity and uses a multivariate panel regression approach. Findings: The results show that gender diversity, both on the board and in executive positions, positively and consistently affects stock liquidity across different business cycles. The findings reinforce the notion that diverse executive leadership is crucial and influential irrespective of the prevailing economic conditions. Practical implications: This study has practical implications for investors, managers and policymakers who are interested in the benefits of gender diversity in corporate leadership. It suggests that increasing the percentage of female executives and board members can improve stock market liquidity, which is a key indicator of market efficiency and firm value. Social implications: This study advocates for gender equality and diversity in corporate leadership, which can benefit society. It demonstrates that the presence of women directors can enhance financial stability and thus benefit the stakeholders and the community. Originality/value: This study contributes to the academic literature by examining the impact of gender diversity on board and executive levels on stock liquidity in the US market. Previous research on this topic has mainly relied on French or Australian data. Moreover, this study extends previous work through examining the case of executives' gender diversity. To the best of the authors' knowledge, this study is the first to analyze the relationship between gender diversity and stock liquidity across different business cycles, providing a nuanced understanding of how economic contexts affect this relationship. [ABSTRACT FROM AUTHOR]

This paper introduces a novel statistical testing technique known as segmented detrended multifractal fluctuation analysis (SMF‐DFA) to analyze the structured scaling properties of financial returns and predict the long‐term memory of financial markets. The proposed methodology is applied to assess the efficiency of major cryptocurrencies, expanding upon conventional approaches by incorporating different fluctuation regimes identified through a change‐point detection test. A single‐factor model is employed to characterize the endogenous factors influencing scaling behavior, leading to the development of a self‐explanatory machine learning approach for price forecasting. The proposed method is evaluated using daily data from three major cryptocurrencies spanning from April 2017 to December 2022. The analysis aims to determine whether the digital market has experienced significant changes in recent years and assess whether this has resulted in structured multifractal behavior. The study identifies common periods of local scaling among the three prices, with a noticeable decrease in multifractality observed after 2018. Furthermore, complementary tests on shuffled and surrogate data are conducted to explore the distribution, linear correlation, and nonlinear structure, shedding light on the explanation of structured multifractality to some extent. Additionally, prediction experiments based on neural networks fed with multi‐fractionally differentiated data demonstrate the utility of this new self‐explanatory algorithm for decision‐makers and investors seeking more accurate and interpretable forecasts. [ABSTRACT FROM AUTHOR]

Gıda endüstrisi uluslararası alanda ve ülkelerin ekonomilerinde önde gelen imalat endüstrilerinden biridir. İnsanların yaşamlarını sürdürmeleri için temel ihtiyaçlarını kapsayan bir endüstri olması da bu önemliliği daha da artırmaktadır. Bu kapsamda, Türk imalat endüstrisinin gıda, içecek ve tütün sektöründe yer alıp borsada işlem gören 23 işletmenin etkinlikleri iki aşamalı ağ veri zarflama analizi (Network DEA) yöntemiyle incelenmiştir. Çalışma, işletmelerin finansal verileri referans alınarak 2017/12-2023/06 dönemlerini kapsamaktadır. İki aşamalı etkinlik analizinin birinci aşamasında bilançoya dayalı mali etkinlik ve ikinci aşamasında da piyasa etkinliği hesaplanmıştır. Bilançoya dayalı mali etkinlik sonuçları değerlendirildiğinde etkinlik ortalamaları en yüksek çıkan işletmeler sırasıyla ULUUN, BANVT ve DARDL’dir. İkinci aşamada incelenen piyasa etkinlik skorları genel olarak bilançoya dayalı mali etkinlik skorları ile karşılaştırıldığında oldukça düşük oluşmuştur. Piyasa etkinliği analizindeki etkinliği en yüksek işletmeler KENT, ERSU ve PINSU’dur. Çalışmanın ikinci analiz kısmında, elde edilen üretim ve piyasa etkinliklerinin Üretici Fiyat Endeksi (ÜFE) ile uzun dönemli ilişkisinin tespiti için, Johansen eş bütünleşme testi ve Granger nedensellik testi uygulanmıştır. Bunun sonucunda, etkinlik skorları ile ÜFE arasında uzun dönemli bir eş bütünleşme ilişkisinin olduğu tespit edilmiştir. Nedensellik testi, ÜFE ve piyasa etkinliği arasında, bilançoya dayalı mali etkinlik ile piyasa etkinliği arasında nedensellik olduğunu ortaya koymuştur.

This study examines the economic implications of fair value liability gains and losses arising from the adoption of Statement of Financial Accounting Standards No. 159 (hereafter, FAS 159). We find a positive correspondence between a firm's FAS 159 fair value liability gains and losses and current period stock returns, consistent with the notion that these gains and losses are priced by equity investors. However, further analysis indicates that fair value gains and losses from liabilities have a statistically significant negative association with future returns, suggesting that investors misprice this earnings component and subsequently correct the mispricing. We also find that the negative association for fair value gains is stronger for firms with lower levels of institutional ownership. [ABSTRACT FROM AUTHOR]

We investigate whether firms limit the volume of financial items in annual reports (including the financial statements and footnotes) to obfuscate poor future firm performance, and how investors react to this reduced volume. We estimate abnormal volume to capture managers' discretion over reporting in the 10-K and find that abnormally low volume predicts poor future earnings. This relation is more pronounced in firms where the market has difficulty in detecting managerial intervention in the disclosure process. We also find that abnormally low volume predicts negative future returns, suggesting that managers benefit from disclosing fewer financial items by delaying the incorporation of bad news into stock prices. Further corroborating our results, we find that the volume is abnormally low when there exist strong managerial incentives to withhold bad news and manipulate investor perceptions upward. Overall, our evidence is consistent with the notion that managers attempt to obfuscate poor future performance and inflate current stock prices by disclosing fewer financial items in the 10-K. [ABSTRACT FROM AUTHOR]

Purpose – This study aims to evaluate the presence of abnormal returns due to stock splits or reverse stock splits in the Brazilian capital market context. Design/methodology/approach – The event study technique was used on data from 518 events that occurred in a 30-year period (1987–2016), comprising 167 stock splits and 351 reverse stock splits. Findings – The results revealed the occurrence of abnormal returns around the time the shares began trading stock splits or reverse stock splits at a statistical significance level of 5%. The main conclusion is that stock split and reverse stock split operations represent opportunities for extraordinary gains and may serve as a reference for investment strategies in the Brazilian stock market. Originality/value – This study innovates by including reverse stock splits, as the existing literature focuses on stock splits, and by testing two distinct “zero” dates that of the ordinary general meeting that approved the share alteration and the “ex” date of the alteration, when the shares were effectively traded, reverse split or split.

Psychological factors such as emotional reactions, cognitive biases, and herd behavior influence investment decisions because they shape investor behavior, drive market dynamics, and affect rational decision-making. Similarly, financial literacy improves investment decisions by facilitating informed choices, minimizing biases, enhancing risk management, and promoting long-term financial planning. This study aims to examine the influence of psychological factors on investment decisions in the Nepalese share market, emphasizing the mediating role of financial literacy. Smart PLS 4.0 was used to analyze the structural relationships within the proposed theoretical model. Data were collected from the primary source using a structured questionnaire administered through a random sampling technique. The respondents included 410 active individual investors from the Nepal Stock Exchange (NEPSE). The study's findings reveal that psychological factors have a positive and significant effect on investment decisions among investors in the Nepalese stock market. Furthermore, the study revealed that financial literacy mediates the relationship between psychological factors and investment decisions by enhancing individuals' understanding and confidence, leading to more informed and rational investment choices. The results highlight the critical role of financial literacy in investment decisions in the share market. The findings indicate that investors with higher financial literacy levels are better equipped to mitigate the adverse effects of psychological biases, leading to more rational and informed investment decisions. By understanding the interplay between psychological factors and financial literacy, policymakers and financial institutions can develop targeted strategies to foster a more robust and resilient financial market in developing economies such as Nepal.

Purpose – The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia. Design/methodology/approach – The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019. Findings – Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs. Research limitations/implications – The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study. Practical implications – Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools. Originality/value – Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.

The announcement of a buyback informs the market about the company’s decision to repurchase its own shares. This announcement highlights the company’s price valuation and the inefficiencies that exist in the market. This study examines the share buyback announcement effect during the COVID-19 period. The study considered the stocks listed in the National Stock Exchange (NSE) that offered share buyback under tender offer mode during the pre-pandemic period between April 2016 and February 2020 and the post-pandemic period between March 2020 and March 2022. 75 firms in the pre-pandemic period and 43 in the post-pandemic period that announced share buyback under the tender offer method were analyzed. The event study methodology using a market model was employed to determine the presence of abnormal returns during the event period, which consisted of –21 days and +21 days. The findings of the study revealed the existence of abnormal returns in and around the announcement date. Besides, statistically significant cumulative abnormal average returns (CAAR) were also found on the event day, i.e., on Day 0. The study found that the impact of buyback announcements on stock returns significantly differed before and after COVID-19 for 10 and 21-day periods, with no significant differences for shorter periods. These insights can help traders and fund managers make informed portfolio adjustments during turbulent market periods surrounding buyback announcements. AcknowledgementThe authors express their sincere gratitude and special thanks to Dr. Krishna T.A., Assistant Professor, Department of Professional Studies, School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Bangalore, India, for encouraging, motivating and providing all the required support throughout this empirical investigation and to accomplish this research task.

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. [ABSTRACT FROM AUTHOR]

We examine whether investors display a lack of attention by not incorporating relevant public information into equity valuations. Beginning in 2019, ASC 842 requires the recognition of operating leases, which were previously only disclosed in the financial statement footnotes. This change in accounting standard results in firms with significant operating leases recognizing a considerable increase in debt. We find that firms with significant operating leases, on average, earned negative stock returns around the initial recognition of their operating leases. We find that the negative reaction to operating lease recognition does not vary with firm-level proxies for investor attention, which suggests a pervasive lack of attention to operating lease disclosures prior to ASC 842. Our results provide new evidence consistent with investors suffering from limited attention and equity prices reflecting a lack of fundamental analysis. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to measure the degree of volatility comovement between stock market liquidity and informational efficiency across Asia, Europe, North-South America, Africa, and the Pacific Ocean over three decades. In particular, the authors analyze the extent of the time-varying nexus between different aspects of stock market liquidity and multifractal scaling properties of the stock return series across various regions and diversified market conditions. This study further investigates several factors altering the degree of dynamic conditional correlations (DCCs) between the efficiency and liquidity of the domestic stock markets. Design/methodology/approach: The study measures five aspects of stock market liquidity – tightness, depth, breadth, immediacy, and adjusted immediacy. The authors evaluate the multifractal scaling properties of the stock return series to measure the level of stock market efficiency across the regions and diversified market conditions. The study uses the dynamic conditional correlation-multivariate generalized autoregressive conditional heteroscedasticity framework to quantify the degree of volatility comovement between liquidity and efficiency over the period. Findings: The study finds the presence of stronger volatility comovement between inefficiency and illiquidity due to the price impact characteristics of the stock markets irrespective of different regions and diversified market conditions. The extent of time-variation increased following the shock periods, indicating the significant role of the financial crisis in increasing the volatility comovement between inefficiency and illiquidity. The highest degree of time-varying correlation is observed in the developed stock markets of Northwestern and Northern Europe compared to the regional and emerging counterparts. On the other hand, weak DCCs are observed in the emerging stock markets of Europe. Originality/value: The output of the present study assists investors in identifying diversification opportunities across the regions. Additionally, the study has significant implications for market regulators, aiding in predicting future troughs and peaks. The prediction, in turn, helps formulate capital market development plans during dynamic economic situations. [ABSTRACT FROM AUTHOR]

We present novel, robust methods for inference on market (non-)efficiency, volatility clustering, and nonlinear dependence in financial return series. In contrast to existing methodology, our proposed methods are robust against nonlinear dynamics and tail-heaviness of returns. Specifically, our methods only rely on return processes being stationary and weakly dependent (mixing) with finite moments of a suitable order. This includes robustness against power-law distributions associated with nonlinear dynamic models such as GARCH and stochastic volatility. The methods are easy to implement and perform well in realistic settings. We revisit a recent study by Baltussen, van Bekkum, and Da (2019 , J. Financ. Econ. , 132, 26–48) on autocorrelation in major stock indexes. Using our robust methods, we document that the evidence of the presence of negative autocorrelation is weaker, compared with the conclusions of the original study. [ABSTRACT FROM AUTHOR]

The growing prevalence of stock market chat rooms and social media suggests that communication between traders may affect market outcomes. Using data from a series of laboratory experiments, we study the causal effect of trader communication on market efficiency. We show that communication allows markets to convey private information more effectively. This effect is robust to a wide range of information settings. The presence of insiders limits the impact, whereas posted reputation scores in the communication platform magnify it. These findings illustrate the need to consider social interactions when designing market institutions to leverage the social motives that foster information aggregation. This paper was accepted by Axel Ockenfels, behavioral economics & decision analysis. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4967. [ABSTRACT FROM AUTHOR]

Conventional wisdom suggests that sectors/industries provide systematic performance and that business cycle rotation strategies generate excess market performance. However, we find no evidence of systematic sector performance where popular belief anticipates it will occur. At best, conventional sector rotation generates modest outperformance, which quickly diminishes after allowing for transaction costs and incorrectly timing the business cycle. The results are robust to alternative sector and business cycle definitions. We find that relaxing sector rotation assumptions and letting any industry excess return predict future returns of other industries results in predictability not significantly different than what would be expected by random chance. [ABSTRACT FROM AUTHOR]

Previous literature has established the need to separate insider from expert trading in both financial and betting markets. This type of separation proves difficult to achieve because experts and insiders co‐exist in most market settings. Utilizing novel betting markets in which expert skill bettors are unable to use their expert knowledge, this study uncouples insider from expert betting, and allows more direct measurement of insider trading than in previous empirical tests. Evidence is presented in favor of some predictions from the highly cited Shin model of insider trading. Price setters charge an extra premium in markets with high proportions of insiders when compared to equivalent markets. The insiders accurately predict event outcomes, and their presence exacerbates a well known bias in betting market prices whereby returns on betting favorites is higher than for bets on longshots. [ABSTRACT FROM AUTHOR]

Some US-listed country exchange-traded funds (ETFs) suffer from chronic and meaningful mispricing in the form of premiums or discounts relative to their fundamental value despite the presence of the creation/redemption mechanism. This mispricing is mainly attributed to the staggered information flow due to nonoverlapping time zones between the market where the ETF is listed and its underlying home market. This study provides out-of-sample evidence on the price formation of Gulf Cooperation Council (GCC) country ETFs and gauges the impact of mispricing on their underlying home markets. The GCC context is particularly insightful because these markets have nonoverlapping time zones with the US and follow distinct trading schedules. Our sample comprises daily data from three countries' iShares that exclusively track the Qatari, Saudi, and Emirati stock markets from 17 September 2015 to 14 March 2023. The results show that GCC ETFs are driven mainly by their net asset values (NAVs), albeit imperfectly, while the S&P500 exerts a relatively mild influence on these ETFs compared to other country ETFs, as reported by prior studies. Moreover, we find that crude oil prices positively and significantly impact GCC ETFs' pricing. When we control for unsynchronized trading days between the US and the GCC home markets, we find a structural difference between overlapping and nonoverlapping trading days. This structural difference manifests in a sluggish adjustment to correct mispricing in the ETF market on the day the home market is closed; however, other variables, including the S&P500, show no discernible difference, which refutes the overreaction explanation. This recurrent pattern is reflected in a clear day-of-the-week pattern in the price discovery these ETFs offer to their underlying home markets. [ABSTRACT FROM AUTHOR]

The paper discusses market trends and analyzes the regularities that appear on the Serbian national housing market and regional submarkets. It is assumed that, apart from the common market driving forces, the market for newly constructed houses and the market for the existing housing stock behave like two separate segments of the housing market with the imperfect adjustment of prices. The prime focus of the analysis is on the divergence between the prices in those two segments, with a special interest in the process of mutual adjustments. Granger causality tests are employed in order to reveal whether there is a causal relationship between the price indices in those two segments and it has been found that there is a causality relation between the existing housing market and the newly constructed house market prevailing among the regional submarkets. The same methodology is applied to test if there is any such causality between the regional markets. The results have confirmed a likely influence of the Belgrade new construction market on the other regional markets. The findings will help understand the process of price adjustments between the two market segments and will lead to policy recommendations. [ABSTRACT FROM AUTHOR]

We find that short sellers manage risks by strategically borrowing shares in stocks with significant ownership by passive investors. This practice increases securities lending demand for stocks with substantial passive ownership, resulting in improved price efficiency, higher lending fees, and increased short interest in these stocks. Consistent with the risk mitigation motive, these stocks show reduced risks of unexpected fee hikes and loan recall, longer loan durations, and attract more informed short sellers. These effects are particularly pronounced in hard-to-borrow stocks where short-sale constraints are binding. Our study suggests that passive investing helps alleviate short-sale constraints by reducing the risks associated with stock borrowing. [ABSTRACT FROM AUTHOR]

This paper studies the dynamic relationship between the parallel and official exchange rates of Chinese Yuan (CNY) to U.S. Dollar (USD). Motivated by recent evidence about Bitcoin as a major channel for capital flight from China, we construct a parallel exchange rate implied by Bitcoin prices in the Chinese and U.S. crypto exchanges. While the dual exchange rates are cointegrated, adjustment to long-run equilibrium is non-linear and depends on policy changes and state of the markets. Results from our Markov Switching Vector Error Correction model suggest the parallel exchange rate responds one-to-one in the long-run with respect to an official exchange rate innovation. However, an unexpected change in the parallel rate is countered by an opposite and state-dependent response in the official exchange rate, suggesting active exchange policy interventions by the Chinese monetary authority. [ABSTRACT FROM AUTHOR]

The paper deals with behavioural finance as a stream of finance challenging the principles of classical finance by adding psychology to the reasons that could explain observed anomalies within financial markets and which, at the same time, are caused by real human behaviour. The aim of this paper is to explore investors' behavioural, biases and their disturbances of rationality, focusing mostly on the factors of loss aversion, cultural influences, and overconfidence, and how these elements influence investment decisions. It is based on empirical data and behavioural experiments, which help to show how such biases deviate from efficient market theories; this work also tests some methods that can mitigate these negative effects, therefore providing insights on how to integrate the teaching of behavioural finance more effectively into the decision-making process of investors and experts within finance. Precisely, this work is aimed at attempting to provide a model of psychological behaviour for Moroccan investors, based on an investigation carried out in the field among 93 regular investors in the Casablanca Stock Exchange. The main findings of the study underline how behavioural biases deviate from predictions by efficient market theories. This paper has only focused on the specific context of Moroccan investors, yet giving insight into how psychological factors impact investment decisions through new insights into life where these financial behaviours take place. There is, hence, uniqueness to the research through the theoretical and practical approaches integrated therein, with the use of empirical data and behavioural experiments that sustain the discussion on behavioural finance. Presenting the case of Moroccan investors adds a significant contribution to proposing a context-specific model, which may bring important implications for financial decision-making within the region. [ABSTRACT FROM AUTHOR]

For the last 50 years or so, the efficient market hypothesis (EMH) has been the central pillar of economic thought and the building block of portfolio theory. However, the validity of the EMH still remains controversial, and the methods of testing for market efficiency have been criticised. This study utilises entropy analysis combined with generalised autoregressive conditional heteroscedasticity (GARCH) regressions to provide extensive empirical evidence on informational efficiency. This study develops an innovative method for studying market efficiency by investigating the complexity of information saturated in the market. To this end, we have used daily West Texas Intermediate (WTI) crude oil futures contracts from June 1983 to February 2016, giving a total data set of 282,427 observations. The results of the Jensen–Shannon informational criteria indicate that the informational efficiency measure has never reached the maximum value of 1 which indicates weak-form efficiency. Our study has further found that the informational efficiency measure tends to decrease consistently during recessionary periods and to increase in subsequent periods. We have also found that the entropy measure tends to increase over time, supporting the adaptive market hypothesis over the EMH. [ABSTRACT FROM AUTHOR]

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Previous research has debated about whether institutionalization could improve market efficiency. We develop a theoretical model under anticipated utility combined with probability weighting to study the impacts of institutionalization on market participation and market efficiency. The prudence attitude towards probability uncertainty leads to limited participation by investors. We find that institutionalization influences the market in two different ways. An increase in the total sector size of institutional investors facilitates price discovery but discourages market participation from naïve investors due to information asymmetry. However, when the top institution has high market power, this large institution trades strategically to induce more participation by naïve traders for risk-sharing at the cost of lowering information efficiency. [ABSTRACT FROM AUTHOR]

This article offers a historical analysis of American interwar agricultural economists and their interest in information. Believing that the main problem facing farmers was a lack of information, agricultural economists designed an information policy aiming to produce, format, and disseminate information. Using administrative archives, the article analyzes the motivations of these economists and the implementation of this policy. As the article shows, the policy was a prerequisite for theoretical discussions about information, and it established institutional tools that are still used today, such as the USDA market news service. [ABSTRACT FROM AUTHOR]

Purpose: This paper analyzes alternative performance evaluation models applied to equity mutual funds under conditional and unconditional approaches in the Brazilian market. Design/methodology/approach: The analysis is conducted using CAPM's single factor, Fama–French three and five factors, under their conditional and unconditional versions in a sample of 896 equity mutual funds from 2008 to 2019. Findings: The results suggest that the use of three- or five-factor models is especially relevant to reduce the effect of market anomalies in performance assessment. Additionally, results show that conditional approaches, adding time-varying alphas and betas with macroeconomic variables, provide higher explanatory power than their unconditional peers. Originality/value: The results are relevant in the unique economic environment characterized by historically high interest rate and high market volatility. [ABSTRACT FROM AUTHOR]

Purpose: We aim to examine the impact of COVID-19 on the efficiency of Gold and Bitcoin returns. In particular, our efficiency tests are based on the popular calendar anomaly, the turn-of-the-month (TOM) effect in these markets. Design/methodology/approach: We define the TOM days as the final trading day of a month and initial three trading days of the immediate next month. To understand the TOM effect, we estimate the typical Ordinary Least Squares (OLS) regression model using the Heteroskedasticity and Autocorrelation Consistent (HAC) standard errors and covariances. Findings: Though in the full sample, a positive and significant TOM effect is observed only for Bitcoin, during COVID period, the TOM effect appears in Gold returns and becomes stronger for Bitcoin, implying that the considered securities become inefficient during COVID period. Practical implications: Based on these results, we create a trading strategy which is found to surpass the buy-and-hold strategy for both the full sample as well as the COVID period for Bitcoin while only during the COVID period for Gold. Our results provide useful implications for investors and policymakers as the Gold and Bitcoin markets can be timed by taking positions especially based on the behavior of the TOM effect. Originality/value: We examine the TOM effect in the two important securities – Gold and Bitcoin. Though, a few studies have examined this anomaly in currency, equity and cryptocurrency markets, however, they have not considered the Gold market. Additionally, no study has examined the impact of COVID-19 on the TOM effect in these markets, and hence, market efficiency. We believe that our study is the first to examine the TOM effect in these markets simultaneously. [ABSTRACT FROM AUTHOR]

Following the implementation of capital market reforms, Saudi Arabia has opened up its stock market to international investors. This liberalisation has led to the inclusion of the stock exchange into leading global indices, which is expected to lead to an influx of foreign investment. Thus, it is important and timely to examine the efficiency of the Saudi stock market. To that end, this paper conducts a comprehensive investigation of the predictive ability and profitability of a popular trading rule, the moving average rule, using firm-level data for the Saudi stock market over the period 1st January 2008 to 31st December 2017. The results show that the moving average rule has predictive ability; the active rules for a majority of the sample companies outperformed the corresponding passive strategy. Furthermore, the results were economically significant as the rules were profitable even after the consideration of transaction costs. The results also showed that the most profitable moving average rules were those with short-moving average lengths, and that the introduction of a bandwidth, which serves to eliminate weak trading signals, had a positive impact on rule profitability. Importantly, the analysis showed that the most profitable component of the rule related to short selling as these trades resulted in higher profits than the long positions. A disaggregated analysis of sectors showed that the trading rules outperformed the buy-and-hold strategy in all seven industries considered, while the analysis of covariance revealed the importance of careful selection of filter size. Overall, the analysis documents significant inefficiencies in the Saudi stock market and suggests that the employment of a simple trading rule, based on past price data, can yield substantial profits across companies and sectors even in a costly trading environment. These findings suggest that the recent reforms that were implemented to improve the efficiency of the Saudi stock market have been suboptimal, and that further regulatory reform is required. [ABSTRACT FROM AUTHOR]

This paper proposes the use of social media as a proxy for financial information. Using an extended sample of 53,580,759 tweets and employing text analysis tools (Latent Dirichlet Allocation and Term Frequency–Inverse Document Frequency), we determine the information being exchanged on any given day. We train machine‐learning classifiers and forecast crypto price movements for more than 8000 cryptocurrencies and gauge market efficiency through successful forecasts based on public information. We propose various metrics of market efficiency for cryptocurrency assets and demonstrate that market efficiency is higher during the first 6 months after the Initial Coin Offering. We also examine the efficiency behavior of individual currencies during crisis periods. [ABSTRACT FROM AUTHOR]