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This article examines the severity of the 2008 arbitrage crash in the convertible bond market by estimating how expensive it would have been to liquidate portfolio securities immediately. We consider whether funds actually demanded immediate liquidity or were able to delay trades. Our results indicate that the cost of immediacy was high, but that convertible bond sellers could largely avoid selling at fire sale prices. These results can be explained by dealers recognizing when trades are liquidity-motivated rather than information-based and by a shift to riskless principal trading, allowing dealers to avoid taking bonds into inventory. [ABSTRACT FROM AUTHOR]

We analyze a model in which an anomaly is unknown to arbitrageurs until its discovery, and test the model implications on both asset prices and arbitrageurs' trading activities. Using data on 99 anomalies documented in the existing literature, we find that the discovery of an anomaly reduces the correlation between the returns of its decile-1 and decile-10 portfolios. This discovery effect is stronger if the aggregate wealth of hedge funds is more volatile. Finally, hedge funds increase (reverse) their positions in exploiting anomalies when their aggregate wealth increases (decreases), further suggesting that these discovery effects operate through arbitrage trading. [ABSTRACT FROM AUTHOR]

Pricing is integral to insurance design, directly influencing firm behavior and moral hazard, though its effects are insufficiently understood. I study a quasi-experiment in which deposit insurance premiums were changed for U.S. banks with unequal timing, generating differentials between banks in both levels and risk-based "steepness" of premiums. I find evidence that differentials in premiums resulted in distortions, including regulatory arbitrage, but also provided strong incentives to curb moral hazard. I find that firms that faced stronger pricing incentives to become (or remain) safer were more likely to subsequently do so than similar firms that faced weaker pricing incentives. [ABSTRACT FROM AUTHOR]

This paper studies how private equity creates value and its consequences for consumer welfare in the insurance industry, where PE investments grew tenfold following the financial crisis. PE firms add value through regulatory and tax arbitrage that increases profits relative to their non-PE counterparts. Crucially, the impact on consumer welfare is nuanced: in the short run, consumers benefit from more favorably priced products. But the arbitrage strategy also exposes them to more risk, as annual expected losses scaled by capital buffers rise by 50 percentage points. This creates the possibility of consumer harm in the event of a downturn. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

An increasing number of exchanges, mainly in the U.S., have adopted a commission structure called maker–taker fees in which traders placing limit orders (makers) are paid a rebate (negative trading commission) and traders placing market orders (takers) are charged a trading fee. The reason is that by paying rebates to makers, exchanges can expect to receive a large number of maker's orders and gain market share. Makers include arbitrageurs who make large transactions. Maker–taker fees constitute one of the most important commission structures for exchanges, because they are expected to attract arbitrageurs who are looking for rebate profits, on top of their trading profits. There have been many studies about arbitrage trading, but none we could find focused on the impact of arbitrage trading between markets with maker–taker fees where arbitrage traders place limit orders and markets without maker–taker fees where they place market orders. In this study, we investigated volatility and market liquidity by changing the amount of rebate under our proposed artificial markets, where there are or are not maker–taker fees. Then we checked the performance of arbitrage trading when the rebate increased. The results were that volatility in the market with maker–taker fees decreased and that in the market without maker–taker fees increased, and that market liquidity and arbitrage performance both increased in the market with maker–taker fees when rebates increased. The above results indicate that exchanges that operate markets adopting maker–taker fees can provide investors with more attractive markets than those that do not adopt them. However, if more arbitrageurs participate in the market with maker–taker fees to take advantage of these rebates, the cost burden on exchanges may increase unnecessarily. [ABSTRACT FROM AUTHOR]

This paper reads the sale of citizenship via citizenship‐by‐investment (CBI) programmes through an offshore lens. Scholarship on offshore industries has long positioned citizenship sales as part of offshore capitalism—without exploring the phenomenon in any depth. Research on CBI, in turn, has with some notable exceptions neglected the phenomenon's offshore nature. This paper argues that CBI is an outgrowth and increasingly integral part of offshore capitalism, offering a new form of what Susan Roberts calls "regulatory arbitrage", aiding elite wealth accumulation and power. The paper establishes this relationship by examining the countries and firms selling citizenship, their marketing strategies and customers and, crucially, the nature of the product which rests on the three "offshore pillars", as described by Ronen Palan—virtual residency, easy incorporation, and secrecy. Conceptualising CBI as an offshore provision can transform how the phenomenon is understood and opens new avenues of thinking about its socio‐structural role and impact in an unequal world. [ABSTRACT FROM AUTHOR]

This paper constructs an institutional investor network based on the heavy holdings of the same stock in China and conducts a social network analysis to investigate the influence of this network on stock price volatility from the perspectives of network structure (density) and location (centrality). The study demonstrates that institutional investor network density is negatively related to stock price volatility, while network centrality is positively related to it. Mechanism analyses further reveal that network density reduces stock price volatility by mitigating private information arbitrage behavior among institutional investors, whereas network centrality increases stock price volatility by creating private information arbitrage opportunities within the network. Additionally, the paper finds that information asymmetry enhances the positive effect of network centrality on stock price volatility. These findings are generally consistent across subsample analyses for different market states, reactions to good and bad news, and types of equity ownership, as well as in other robustness tests. The practical implications of these findings are significant for market stability regulation. [ABSTRACT FROM AUTHOR]

The article "Mastering the Digital Regulatory Maze: Strategies for Marketing Success in a Complex Landscape" by Daniel Sokol discusses the pervasive nature of digital regulation, covering areas such as privacy, antitrust, AI, and fintech. Marketers must navigate different levels of regulation, including traditional government regulation, public-private regulation, and purely private regulation through contracts. The article emphasizes the importance of understanding and responding to the complex digital regulatory environment to thrive in the digital age, highlighting the need for transparency, accountability, and proactive engagement with regulators to avoid more severe government regulation. [Extracted from the article]

Purpose: Organizations increasingly use knowledge arbitrage to stimulate innovation and achieve competitive advantage. However, in knowledge management its use in startups is yet unexplored. This study aims to examine the utilization of knowledge arbitrage by startups, specifically during COVID-19. Design/methodology/approach: This study employed an open-ended essay methodology to explore the drivers and barriers that startups face in utilizing knowledge arbitrage. We collected data from 40 participants to understand the role of knowledge arbitrage in startups' knowledge management practices. Findings: This study's findings highlight the significance of knowledge arbitrage for startups. The benefits identified include organizational benefits such as building networks, innovating new products and achieving competitive advantage and financial benefits such as cost reduction and sales growth. The study also identifies several technological and organizational drivers and barriers that startups confront during knowledge arbitrage. Originality/value: This study contributes to the existing literature on knowledge management by extending our understanding of knowledge arbitrage's role in startups. Additionally, it sheds light on the importance of knowledge arbitrage for startups and the challenges they face, particularly in a disrupted environment reared by COVID-19. The study provides insights for the scholars and practitioners interested in effective knowledge management in startups. [ABSTRACT FROM AUTHOR]

Introduction: This paper constructs a revenue model for an independent electrochemical energy storage (EES) power station with the aim of analyzing its full life-cycle economic benefits under the electricity spot market. Methods: The model integrates the marginal degradation cost (MDC), energy arbitrage, ancillary services, and annual operation and maintenance (O&M) costs to calculate the net profits of the EES power station. Using an iterative optimization approach, we determine the optimal MDC and analyze the economic end of life (EOL) for different types of EES power stations. Results: By examining real-world examples from the California energy market, we find that the full life-cycle benefits of an EES power station peak when its MDC is optimal, at $45/MWh-throughput. Under these conditions, the economic and physical EOL of commercial/industrial EES power station is 9 years, while the economic EOL of residential-grade EES power station is 8 years, which is shorter than their physical EOL of 9 years. Discussion: The study further indicates that the economic life of an EES power station is influenced by multiple factors, and operators need to determine the optimal economic EOL to maximize revenue based on battery degradation characteristics, market conditions and operational strategy. [ABSTRACT FROM AUTHOR]

One of the most interesting applications of blockchain is given by the automated market makers (AMMs). In the paper, we discuss how arbitrage activity between the AMMs and the other exchange nodes can affect the volumes of assets in liquidity pools of constant function AMMs. In particular, we argue that arbitrage superimposes to the constant function in determining the liquidity volumes within the same AMM and across different AMMs. Yet, despite representing an additional condition in the model, equilibrium arbitrage is typically not unique because it may depend on several elements, such as the amount of liquidity in the system and the number of exchange nodes. Hence, the paper discusses how the constant function and arbitrage jointly determine the relationship across the assets' liquidity volume in the pool but not a unique value for such volumes unless further constraints are introduced. Therefore, a platform interested in predicting the pool's liquidity volumes may face indeterminacy as to which equilibrium would prevail. Though arbitrage has been discussed in related literature, equilibrium indeterminacy does not seem to have been pointed out. [ABSTRACT FROM AUTHOR]

This study develops a theoretical model to capture volatility-managed portfolios' risk-adjusted returns affected by investor sentiment, and uses Chinese A-share market data to analyze the volatility-managed effect based on the small-minus-big (SMB) factor and explain the abnormal returns of volatility-managed portfolios from the investor sentiment perspective. Our results show that the Sharpe ratio of the SMB factor significantly increases after volatility management, especially in low-sentiment periods. Moreover, the mechanism analysis shows that in low-sentiment periods, small-cap stock investors overreact to volatility compared to large-cap stock investors, making the SMB factor significantly positively correlated with lagged volatility. Additional analyses show that volatility-managed portfolios constructed through stocks with gambling characteristics can achieve higher abnormal returns, and the abnormal returns of volatility-managed portfolios cannot be corrected by arbitrage traders as arbitrage is ineffective, reflecting the immaturity of China's emerging capital market. [ABSTRACT FROM AUTHOR]

Current statistical query pricing models have not considered the problem that query results reveal sensitive attributes of datasets, making it difficult to incentivize sharing by compensating data providers accordingly. Therefore, this paper proposed a pricing model based on attribute privacy. Firstly, the model calculated query sensitivity based on the relaxed approximation Wasserstein mechanism (RAWM) proposed, improving efficiency by directly calculate the relaxed upper bound of output distribution pairs. Then, with bounding privacy loss, the model compensated data providers based on query sensitivity, noise variance and compensation parameters. Finally, by using cost-plus pricing on compensation, this paper designed several arbitrage-free pricing functions, which could be used in scenarios such as single compensation costs and multiple marginal costs. The experiment results show that the running time of calculating query sensitivity is reduced from linear complexity to constant complexity, with a utility cost of only 0.52% when data volume is 100 million. The pricing model provides fine-grained compensation to incentivize sharing. Pricing functions satisfy arbitrage freeness. [ABSTRACT FROM AUTHOR]

This study uses an artificial intelligence (AI) model to identify arbitrage opportunities in the retail marketplace. Specifically, we develop an AI model to predict the optimal purchasing point based on the price movement of products in the market. Our model is trained on a large dataset collected from an online marketplace in the United States. Our model is enhanced by incorporating user-generated content (UGC), which is empirically proven to be significantly informative. Overall, the AI model attains more than 90% precision rate, while the recall rate is higher than 80% in an out-of-sample test. In addition, we conduct a field experiment to verify the external validity of the AI model in a real-life setting. Our model identifies 293 arbitrage opportunities during a one-year field experiment and generates a profit of $7.06 per arbitrage opportunity. The result demonstrates that AI performs exceptionally well in identifying arbitrage opportunities in retail markets with tangible economic values. Our results also yield important implications regarding the role of AI in the society, both from the consumer and firm perspectives. [ABSTRACT FROM AUTHOR]

As more renewable energy sources are integrated into the United Kingdom's power grid, flexibility services are becoming integral to ensuring energy security. This has encouraged the proliferation of Lithium-ion battery storage systems, with 85 GW in development. However, battery degradation impacts both system lifespan and the economic viability of large-scale projects. With rising commodity costs and supply chain issues, maximising the value of energy storage is critical. Traditional methods of mitigating battery ageing rely on static limits based on inflexible warranties, which do not fully account for the complexity of battery degradation. This study examined an alternative, degradation-aware current derating strategy to improve system performance. Using an optimisation model simulating UK energy trading, combined with an electro-thermal and semi-empirical battery model, we assessed the impact of this approach. Interviews with industry leaders validated the modelled parameters and the relevance of the alternative strategy. Results show the degradation-aware strategy can extend battery lifetime by 5–8 years and improve net present value and internal rate of return over a 15-year period compared with traditional methods. These findings highlight the economic benefits of flexible, degradation-aware operational strategies and suggest that more adaptive warranties could accelerate renewable energy integration and lower costs for storage operators. [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]

To address information asymmetry in supply chains, the literature suggests that manufacturers should shift from market governance to hierarchical governance or a combination of both. However, as many manufacturers must continue to rely on market governance, this research introduces a governance strategy, manufacturer–retailer bridge governance (MRBG), for these manufacturers. The research examines the governance effects of MRBG on distributor behavior and the contingent effects of manufacturer status and MRBG strategic orientation. Hypotheses are developed based on in‐depth interviews with sales and purchasing managers and tested in two experiments with sales managers. The results show that MRBG has a double‐edged effect on distributor behavior, and this effect is moderated by manufacturer status. Moreover, the alignment between manufacturer status (high versus low) and MRBG strategic orientation (collaboration versus arbitrage) influences the effectiveness of MRBG. [ABSTRACT FROM AUTHOR]

PurposeMethodologyFindingsOriginalityResearch implicationsPractical implicationsThe general purpose of shipping market prediction is to help shipping analysts design a strategy to make shipping strategies. Previous studies have pointed out human emotions has an obvious impact on markets, however, few research focus on utilizing human emotions to improve the shipping market forecasting issue. This paper fills the gap in the literature of considering industrial individual emotions to shipping market prediction issueWe proposed a conceptual framework of an emotion-based shipping market prediction (ESMP) system focused on considering the multidimensional emotions of individual investors. To make it precisely, a two-fold Particle Swarm Optimization (TFPSO) algorithm is proposed conduct the shipping forecasting under the framework of ESMP, with advantages of automatically finding the architecture and hyperparameters of the Deep Long Short-Term Memory (DLSTM) simultaneously in the shipping market forecasting the values of the Baltic Dry, Dirty Tanker and Container indices of ocean transportation in the world ocean during the crisis period 2010–2022The prediction accuracy of the ESMP was higher than that of the models using the conventional factors in all learning periods of the study, and it was determined that the results complement the sentiment indicator employed to predict the shipping indices. Additionally, the accuracy of the proposed method is superior to conventional neural network models in all used error metrics. Additional Mann–Whitney U test on MSE difference between TFPSO-DLSTM and compared models demonstrates that the significant advantages boosted by TFPSO-DLSTMShipping sentiment has provided further proof that shipping decisions are significantly driven by emotions. our study shows that individual shipping investors’ sentiment are able to improve shipping rate forecasts significantly, although the magnitudes of the improvements are relatively small from an economic point of viewThis study undertook the initiative to procure BDI, BDTI, BCTI, COVID-19 data and shipping sentiment index data through in-depth interviews to provide first-hand perspectives into whether shipping sentiments impact the shipping market trend. It builds upon existing literature on the present stance of deep learning models, which largely relies non-sentiment factors. The study also extends prior literature on hyperparameter searching methods by highlighting the structure of searching method. Besides this, the contribution of this study also aligns with the prior discussions on evolving methodologies in temporal forecasting researchThis study implies that shipping sentiments enables to make better shipping marketing trends, and foster sustainable growth. Shipping fear index serves for measuring investors’ attitude and mood toward shipping markets in terms of the general and certain sectors or assets, possibly promoting the movement of price and providing long-term investors and active traders with trading or arbitrage opportunities. Due to the complexities and regulations of the shipping market, it is essential to collaborate exclusively with shipping sentiment indices with artificial intelligent model in shipping market forecasting research [ABSTRACT FROM AUTHOR]

Cross‐asset order flow provides an incremental and novel nonlinear price discovery channel. Structural vector autoregressions of synchronized intraday message data reveal distinct patterns in the comovement of order flow and its influence on returns and volatility. While cross‐market order flow usually reconciles prices through small‐stakes arbitrage in periods of low volatility and comovement during medium volatility associated with information arrival, it can exacerbate price dislocation from fundamental values during extraordinary volatility. While applying market‐wide circuit breakers (MWCB) mitigates the extreme negative spillovers by jointly halting markets, we identify room for further harmonization during the MWCB market reopening process. [ABSTRACT FROM AUTHOR]

Illiquid assets require a return premium; illiquidity is also a limit‐to‐arbitrage. We find that Amihud's illiquidity premium is significantly higher among underpriced stocks than among overpriced stocks. Excluding the most mispriced stocks leads to a higher and more reliably estimated illiquidity premium. Amihud's illiquidity measure is positively correlated with overpricing, consistent with arbitrage asymmetry, while inconsistent with Lou and Shu's contention that the return premium associated with the Amihud measure reflects mispricing rather than compensation for illiquidity. Our results demonstrate that it is important to account for their role as limits‐to‐arbitrage when evaluating the pricing of illiquidity measures. [ABSTRACT FROM AUTHOR]

We extend the framework introduced in 'Collective Arbitrage and the Value of Cooperation' by F. Biagini, A. Doldi, J.-P. Fouque, M. Frittelli, and T. Meyer-Brandis (arXiv: 2306.11599v2, 2024) in order to analyze collective dynamic risk measures. In segmented markets, we explore the implications of cooperation on dynamic risk measurement, focusing particularly on aggregation and time consistency. [ABSTRACT FROM AUTHOR]

This research proposes a novel arbitrage approach in multivariate pair trading, termed the Optimal Trading Technique (OTT). We present a method for selectively forming a "bucket" of fiat currencies anchored to cryptocurrency for monitoring and exploiting trading opportunities simultaneously. To address quantitative conflicts from multiple trading signals, a novel bi-objective convex optimization formulation is designed to balance investor preferences between profitability and risk tolerance. We understand that cryptocurrencies carry significant financial risks. Therefore this process includes tunable parameters such as volatility penalties and action thresholds. In experiments conducted in the cryptocurrency market from 2020 to 2022, which encompassed a vigorous bull run followed by a bear run, the OTT achieved an annualized profit of 15.49%. Additionally, supplementary experiments detailed in the appendix extend the applicability of OTT to other major cryptocurrencies in the post-COVID period, validating the model's robustness and effectiveness in various market conditions. The arbitrage operation offers a new perspective on trading, without requiring external shorting or holding the intermediate during the arbitrage period. As a note of caution, this study acknowledges the high-risk nature of cryptocurrency investments, which can be subject to significant volatility and potential loss. [ABSTRACT FROM AUTHOR]

We examine the predictability of stock returns using implied volatility spreads (VS) from individual (nonindex) options. VS can occur under simple no-arbitrage conditions for American options when volatility is time-varying, suggesting that the VS-return predictability could be an artifact of firms' sensitivities to aggregate volatility. Examining this empirically, we find that the predictability changes systematically with aggregate volatility and is positively related to the firms' sensitivities to volatility risk. The alpha generated by VS hedge portfolios can be explained by aggregate volatility risk factors. Our results cannot be explained by firm-specific informed trading, transaction costs, or liquidity. [ABSTRACT FROM AUTHOR]

We hypothesize that corporate takeover markets create significant constraints for short sellers. Both short sellers and corporate bidders often target firms with declining economic prospects. Yet, a target firm's stock price generally increases upon a takeover announcement, resulting in losses for short sellers. Therefore, short sellers should require higher rates of return when the takeover likelihood is higher. Consistent with this prediction, the return predictability of monthly short interest increases with industry-level takeover probability and decreases as takeover defenses are implemented. Our results suggest that efficient takeover markets create trading frictions for short sellers and can therefore inhibit overall market efficiency. [ABSTRACT FROM AUTHOR]

Multinational corporations (MNCs) are often seen as singular organizations, with a parent company controlling branches in other countries. But this is an abridged version of decentred corporate groups structured as clusters of separate legal entities in several jurisdictions held together by equity ties. The article argues that while the abridged version of the MNC matches those aspects of those organizations that are of interest to economists, it fails to capture the principal mechanism of interaction between business and the institutional and political environment. I argue that the abridged version is a barrier preventing political scientists from asking salient questions about the power of MNCs and their shareholders. Specifically, while political scientists and international political economists discuss the way these companies use their considerable financial resources to voice their views on unwanted regulatory changes, to threaten exit or if all else fails, to carry through with the threat, they ignore a third approach favoured by corporate groups, setting up subsidiaries in third countries to arbitrage rules. [ABSTRACT FROM AUTHOR]

Violations of no-arbitrage conditions measure the shadow cost of intermediary constraints. Intermediary asset pricing and intertemporal hedging together imply that the risk of these constraints tightening is priced. We describe a "forward CIP trading strategy" that bets on CIP violations shrinking and show that its returns help identify the price of this risk. This strategy yields the highest returns for currency pairs associated with the carry trade. The strategy's risk substantially contributes to the volatility of the stochastic discount factor, is correlated with both other near-arbitrages and intermediary wealth measures, and appears to be consistently priced across various asset classes. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We introduce a new approach for generating sequences of implied volatility (IV) surfaces across multiple assets that are faithful to historical prices. We do so using a combination of functional data analysis and neural stochastic differential equations (SDEs) combined with a probability integral transform penalty to reduce model misspecification. We demonstrate that learning the joint dynamics of IV surfaces and prices produces market scenarios that are consistent with historical features and lie within the sub-manifold of surfaces that are essentially free of static arbitrage. Finally, we demonstrate that delta hedging using the simulated surfaces generates profit and loss (P&L) distributions that are consistent with realized P&Ls. [ABSTRACT FROM AUTHOR]

This article presents examples of arbitrage deterrence from the pharmaceutical, chemical and auto industries. Based on these cases, it develops two models where a monopolist prices and spends to deter arbitrage. The models differ in whether the lower price is set by the firm or negotiated with a representative of consumers. In both models, imports into the high-price market are completely deterred, but the two markets are nonetheless linked by the threat of arbitrage. If this linkage is ignored and the absence of arbitrage is misattributed to exogenous factors, econometric estimates of firm bargaining power will be biased upwards. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Business Studies is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Low-beta stocks deliver high average returns and low risk relative to high-beta stocks, an opportunity for professional investors to "arbitrage" away. We argue that beta-arbitrage activity generates booms and busts in the strategy's abnormal trading profits. In times of low arbitrage activity, the beta-arbitrage strategy exhibits delayed correction, taking up to three years for abnormal returns to be realized. In contrast, when arbitrage activity is high, prices overshoot and then revert in the long run. We document a novel positive-feedback channel operating through firm leverage that facilitates these boom-and-bust cycles. This paper was accepted by Lukas Schmid, finance. Funding: This work is financially supported by National Natural Science Foundation of China [NSFC Grant Number 72303226] and the Key Program of National Natural Science Foundation of China [NSFC Grant Number 72233003]. Supplemental Material: The internet appendix and data are available at https://doi.org/10.1287/mnsc.2023.4929. [ABSTRACT FROM AUTHOR]

Deskundigheid achter de groene tafel: wat mag een arbiter of bindend adviseur in een procedure met eigen wetenschap? De mogelijkheid om arbiters of bindend adviseurs met bepaalde deskundigheid te benoemen, wordt gezien als een belangrijk voordeel van arbitrage en bindend advies ten opzichte van overheidsrechtspraak. In deze bijdrage staat de vraag centraal onder welke voorwaarden arbiters en bindend adviseurs die vanwege bepaalde kennis en deskundigheid zijn benoemd gebruik mogen maken van eigen wetenschap en zelfstandig onderzoek kunnen verrichten naar de feiten, zodat zij zelfstandig tot een oordeel kunnen komen zonder dat dit consequenties heeft voor de afdwingbaarheid en geldigheid van een arbitraal vonnis of een bindend advies. [ABSTRACT FROM AUTHOR]

Copyright of Research Papers of the Wroclaw University of Economics / Prace Naukowe Uniwersytetu Ekonomicznego we Wroclawiu is the property of Uniwersytet Ekonomiczny we Wroclawiu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A blockchain replaces central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows cross-exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, exposes arbitrageurs to costly default risk. We show with Bitcoin network and order book data that cross-exchange price differences coincide with periods of high settlement latency, asset flows chase arbitrage opportunities, and price differences across exchanges with low default risk are smaller. Blockchain-based trading thus faces a dilemma: Reliable consensus protocols require time-consuming settlement latency, leading to arbitrage limits. Circumventing such arbitrage costs is possible only by reinstalling trusted intermediation, which mitigates default risk. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data. [ABSTRACT FROM AUTHOR]

Using establishment‐level data of U.S. public firms, we construct a novel measure of geographic linkage between firms. We show that the returns of geography‐linked firms have strong predictive power for focal firm returns and fundamentals. This effect is distinct from other cross‐firm return predictability and is not easily attributable to risk‐based explanations. It is more pronounced for focal firms that receive lower investor attention, are more costly to arbitrage, and during high sentiment periods. The cross‐firm information spillovers and return predictability are also stronger for geographic peers with economic linkages and with positive information. Our results are broadly consistent with sluggish price adjustment to nuanced geographic information. [ABSTRACT FROM AUTHOR]

The paper examines market co‐movement between pairs of financial assets in the time‐frequency domain. Recent finance literature confirms the integration of cryptocurrencies and financial assets, which may bring more investments with the possibility of surplus liquidity in the cryptocurrency segment, leading to financial instability. The novelty of this paper is examining the integration of cryptocurrencies and the indices of equity, sustainability, renewable energy, and crude oil for the daily observations from 2015 to 2021 by using the wavelet coherency method. The empirical results signify no integration in the short‐term scales and grow stronger in the medium‐term scales, especially during the COVID‐19 period, and further exhibit weaker heterogeneous associations in the long‐term scales. However, the sustainability, clean energy indices follow similar dynamics of the equity market and crypto pairs. In contrast, the global crude oil index showcases the minor integration with cryptocurrencies compared with other traditional asset classes. Hence, the cryptocurrency market fails to confirm the safe haven features, especially during the COVID‐19 periods (Medium‐term), which facilitate the domestic and international investors expecting to hedge their price risk in equity markets using cryptocurrencies may have to look for short‐term. The lead–lag heterogeneous effects of the asset‐pairs may pave arbitrage opportunities for investors. [ABSTRACT FROM AUTHOR]

The study examines the effects of market conditions, volatility and liquidity shocks on the arbitrage profits during pre‐COVID and COVID periods. The study uses a conditional quantile regression and finds no significant difference in the impact of market conditions on the arbitrage profits during pre‐COVID and COVID crisis periods. The increase in volatility combined with low liquidity during the COVID period makes arbitrage non‐viable. However, the decline in volatility during the COVID period encourages investors to initiate arbitrage. The results are useful to fund managers and market analysts to develop suitable trading strategies and stock market regulators to take necessary steps to improve price discovery mechanisms and market efficiency. [ABSTRACT FROM AUTHOR]

This paper studies an equity market of stochastic dimension, where the number of assets fluctuates over time. In such a market, we develop the fundamental theorem of asset pricing, which provides the equivalence of the following statements: (i) there exists a supermartingale numéraire portfolio; (ii) each dissected market, which is of a fixed dimension between dimensional jumps, has locally finite growth; (iii) there is no arbitrage of the first kind; (iv) there exists a local martingale deflator; (v) the market is viable. We also present the optional decomposition theorem, which characterizes a given nonnegative process as the wealth process of some investment‐consumption strategy. Furthermore, similar results still hold in an open market embedded in the entire market of stochastic dimension, where investors can only invest in a fixed number of large capitalization stocks. These results are developed in an equity market model where the price process is given by a piecewise continuous semimartingale of stochastic dimension. Without the continuity assumption on the price process, we present similar results but without explicit characterization of the numéraire portfolio. [ABSTRACT FROM AUTHOR]

Most insurance contracts are inherently linked to financial markets, be it via interest rates, or—as hybrid products like equity‐linked life insurance and variable annuities—directly to stocks or indices. However, insurance contracts are not for trade except sometimes as surrender to the selling office. This excludes the situation of arbitrage by buying and selling insurance contracts at different prices. Furthermore, the insurer uses private information on top of the publicly available one about financial markets. This paper provides a study of the consistency of insurance contracts in connection with trades in the financial market with explicit mention of the information involved. By defining strategies on an insurance portfolio and combining them with financial trading strategies, we arrive at the notion of insurance–finance arbitrage (IFA). In analogy to the classical fundamental theorem of asset pricing, we give a fundamental theorem on the absence of IFA, leading to the existence of an insurance–finance‐consistent probability. In addition, we study when this probability gives the expected discounted cash‐flows required by the EIOPA best estimate. The generality of our approach allows to incorporate many important aspects, like mortality risk or general levels of dependence between mortality and stock markets. Utilizing the theory of enlargements of filtrations, we construct a tractable framework for insurance–finance consistent valuation. [ABSTRACT FROM AUTHOR]

Concepts are introduced and applied for analyzing and selecting arbitrage portfolios in the face of uncertainty about initial positions and risk preferences. A stochastic arbitrage opportunity is defined as a zero-cost investment portfolio that enhances every feasible host portfolio for all admissible utility functions. The alternative to the existence of such investment opportunities is the existence of a solution to a dual system of asset pricing restrictions based on a class of stochastic discount factors. Feasible approaches to numerical optimization and statistical inference are discussed. Empirical results suggest that equity factor investing is appealing for all risk-averse stock investors with a wide range of initial position and sufficiently low transactions costs by mixing multiple factor portfolios with high after-cost appraisal ratios, low mutual correlation, and negative exposures to the relevant host portfolios. These findings weaken the case for risk-based explanations for the profitability of factor investing. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4892. [ABSTRACT FROM AUTHOR]

Arbitrage trading is a common quantitative trading strategy that leverages the long-term cointegration relationships between multiple related assets to conduct spread trading for profit. Specifically, when the cointegration relationship between two or more related series holds, it utilizes the stability and mean-reverting characteristics of their cointegration relationship for spread trading. However, in real quantitative trading, determining the cointegration relationship based on the Engle-Granger two-step method imposes stringent conditions for the cointegration to hold, which can easily be disrupted by price fluctuations or trend characteristics presented by the linear combination, leading to the failure of the arbitrage strategy and significant losses. To address this issue, this article proposes an optimized strategy based on long-short-term memory (LSTM), termed Dynamic-LSTM Arb (DLA), which can classify the trend movements of linear combinations between multiple assets. It assists the Engle-Granger two-step method in determining cointegration relationships when clear upward or downward non-stationary trend characteristics emerge, avoiding frequent strategy switches that lead to losses and the invalidation of arbitrage strategies due to obvious trend characteristics. Additionally, in mean-reversion arbitrage trading, to determine the optimal trading boundary, we have designed an optimized algorithm that dynamically updates the trading boundaries. Training results indicate that our proposed optimization model can successfully filter out unprofitable trades. Through trading tests on a backtesting platform, a theoretical return of 23% was achieved over a 10-day futures trading period at a 1-min level, significantly outperforming the benchmark strategy and the returns of the CSI 300 Index during the same period. [ABSTRACT FROM AUTHOR]

Mycoheterotrophy, whereby plants acquire both carbon and nutrients from a fungal partner, is an evolutionarily puzzling phenomenon. According to biological market models, mycoheterotrophs have nothing to offer and thus should be shunned as trading partners by discriminating fungi. Nevertheless, mycoheterotrophy is common, particularly among orchids, and an estimated 10% of all plant species are facultatively mycoheterotrophic at early stages in their life cycle. Reconciling mycoheterotrophy with biological market models, I describe how mycoheterotrophs could use arbitrage trading to net a profit of carbon and nutrients, without acquiring either from the abiotic environment. The model requires that mycoheterotrophs simultaneously buy and sell both carbon and nutrients, exploiting variability in the trading ratios offered by mycorrhizal fungi. The model relies on several conditions, including the ability of the mycoheterotroph to form indirect hyphal associations with two or more neighbouring autotrophic mycorrhizal associations, the existence of variable carbon:nutrient exchange ratios among these associations and the ability of mycoheterotrophs to invert the net‐direction of resource trade. Evidence that these conditions occur in a state of nature varies from incontrovertible to plausible given available models. The arbitrage model provides evolutionary rationale for mycoheterotrophy from both the plant and fungal perspective. Accordingly, mycoheterotrophs match trading ratios offered by autotrophic plants and, thus, need not be antagonists. The model makes novel predictions that distinguish it from source‐sink models, most notably in the existence of resource exchange inversions at the plant‐mycorrhizal interface. Finally, the model emphasizes market inefficiencies as the foundation on which mycoheterotrophs construct an arbitrage niche. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Many asset pricing models assume that expected returns are driven by common factors. We formulate a model where returns are driven by a string, and no-arbitrage restricts each expected return to capture the asset's granular exposure to all other asset returns: a correlation premium. The model predicts fresh properties for big stocks, which display higher connectivity in bad times, but also work as correlation hedges: they contribute to a negative fraction of the correlation premium, and portfolios that are more exposed to them command a lower premium. The string model performs at least as well as many existing linear factor models. [ABSTRACT FROM AUTHOR]

Liberal theories of property assume a linear and serialized temporal orientation facilitated by title registries, recording systems, and the exaltation of land archives. These assumptions, what I call 'property-time', can be productively put into relation with dominant geographic theories of space in two ways. First, I argue that distinguishing liberal notions of property-time from geographic space–time allows us to observe a key dialectic involving the attachment of value (and capital) to space, a process that tracks alongside contradictions of capitalist desire: the desire to continually reshape space (becoming) and to extract enduring and permanent, individuated value from it (being). Second, liberal temporalities of property struggle to apply in illiberal global contexts. Here, states enact urban accumulation regimes that deviate from linear, individuated and sequential notions of property by manipulating records and surgically erasing past ownership. These acts are often characterized as forms of state variegation and provisionality. They are also endemic to how both capital and value are created and/or arbitraged by states through the land regime. These formations remain difficult to connect to traditional theories about the production of value under capital because assumed time logics of property limit our theoretical capacity to describe illiberal urban accumulation regimes. [ABSTRACT FROM AUTHOR]

This paper studies the social value of closing price differentials in financial markets. We show that arbitrage gaps exactly correspond to the marginal social value of executing an arbitrage trade. Moreover, arbitrage gaps and price impact measures are sufficient to compute the total social value from closing an arbitrage gap, which may emerge for different reasons, including nonpecuniary benefits of holding particular assets. Theoretically, we show that, for a given arbitrage gap, the total social value of arbitrage is higher in more liquid markets. We compute the welfare gains from closing arbitrage gaps for covered interest parity violations. [ABSTRACT FROM AUTHOR]

Based on previous-day trading of US-domiciled international ETFs, we examine European ETFs' underlying constituent stocks at the opening of their home markets for arbitrage opportunities. We find that the value of these ETFs, measured as the change between the ETF's iNAV at the end of the US trading session and next-day price of the home ETF's underlying constituent stock at the opening, are positively related. This impact lasts for approximately five minutes at the opening of European markets. Fast traders can explore arbitrage opportunities. The US market, therefore, transmits and drives pricing information to foreign markets, while European markets are relatively efficient in incorporating this information early the next day. Finally, using recently sampled data, we document that the S&P 500 Index and UK stock index are negatively correlated, while all other EU markets show a positive association with the US market. Therefore, the UK stock market provides good diversification opportunities for US investors. [ABSTRACT FROM AUTHOR]

A recurring theme in the literature and discussion of stock market analysis is the concept of the January effect which essentially implies buy in December and sell in January. The January effect is based on the perceived seasonality trend where short term investors hope to take advantage of the arbitrage opportunity between December and January. However, this perceived anomaly continues is seen as a myth and has been strongly rejected by many market participants in the current dispensation. The purpose of this paper was to investigate the January effect in the Nikkei 225, JSE, CAC 40, DAX, and the NASDAQ Index from February 2019 to February 2024. A F-test statistics was used to explore the phenomenon. The findings indicated that there is no significant difference between the realised returns in January and those of the other month for the most recent five years. This led to the conclusion that, while the January effect may have been observed, it is no longer evident in financial markets, hence market participants should avoid using this type of arbitrage approach because it may result in massive losses. [ABSTRACT FROM AUTHOR]