Limited Arbitrage Research Articles

Dissecting the lottery‐like anomaly: Evidence from China

AbstractThis paper dissects the lottery‐like anomaly in Chinese A‐share stocks by decomposing total stock returns into overnight and intraday returns. Our findings indicate that negative overnight returns are concentrated among lottery‐like stocks, and the lottery‐like anomaly is primarily driven by the overnight return component. Considering the unique institutional features of China, we further show that the lottery‐like anomaly induced by overnight returns is more pronounced in stocks with a high gambling preference among retail investors and high limits of arbitrage. Overall, our results suggest that investors' optimism and trading constraints have a substantial impact on market efficiency in China.

Salience, psychological anchors, and stock return predictability

This study examines whether salience theory predicts the effect of a 52-week high (WH) on stocks in the Taiwanese stock market. The evidence indicates enhanced profits for WH when stocks with extremely salient payoffs are excluded. Salience-induced overvaluation mitigates investors' underreaction to good news when prices are near the WH, thus leading to non-significant positive returns for stocks with salient upsides. In contrast, salient downsides exacerbate investors' underreactions to good news when prices are close to their WHs, leading to greater positive returns. These findings do not result from short-term return reversals or investor attention. This effect is more pronounced for the stocks with higher arbitrage limits. The overestimation of the salient upsides dominates the underreaction to the WH when investors experience capital losses, high-sentiment, and upward market states.

Passenger car market and regional price discriminations for new cars: Case study and empirical analysis for Poland 2004–2021

Abstract Poland disposes on a large stock of mostly aged private passenger cars. In contrast younger cars still play minor role in individual mobility. The contribution analyzes reasons of lagging modernization by focusing on new cars prices with respect to regional price discrimination. It finds in 2004-2021 substantially increasing new cars prices and regional price discrimination between Poland and Germany. Price differentials were positive, with lower prices in Poland, and increased during time. Producers discriminated by prices increasingly but also limited arbitrage transactions by controlling excess differentials. Optimal regional price discrimination could support car markets developments in Poland by stock age compositions shifts towards younger cars. However, applied discrimination proves in observed market environments not sufficient to achieve a great car market transition in near future.

Does uncertainty affect the limits of arbitrage? Evidence from the U.S. stock markets

This study analyzes how uncertainty affects the correction process of mispricing. We extract stock market data from the United States spanning from January 1976 to December 2016, and discover that uncertainty exerts a notable impact on traders’ decision-making processes. Various robustness tests have been conducted to validate the credibility of our findings. Notably, the extension of the sample duration until December 2022, encompassing the disruptive COVID-19 pandemic, serves to fortify the cohesion and reliability of our primary analysis, with the findings exhibiting consistency. Additionally, we examine how investor sentiment affects future returns under different uncertainty and overpricing ranks. An inverse relation between investor sentiment and uncertainty is also detected. We contribute to the existing literature by revealing potential features that affect the limits of arbitrage. Our results provide insights in designing arbitrage mechanisms and assist arbitrageurs in strategizing their operations with stocks under different magnitudes of uncertainty.

How to Make Long-Term Investments in a Stock Market? A Generic Strategy for Investors

Against the backdrop of increasingly fierce industrial competition nowadays, firms tend to have substantive business risk and/or information risk, increasing the estimation risk and limit of arbitrage for investors in their short-term investments in a stock market. It is thus important for investors to hold a long-term horizon for at least part of their investments in the stock market. This paper aims to introduce a long-term investment strategy that is practically feasible and potentially valuable to investors. To this end, we first develop a parsimonious model in which we identify the major determinants of a firm’s value. This model is used to select high-value firms from each industry for further fundamental analysis and valuation. We next expatiate on how to perform strategy analysis, accounting analysis, financial analysis, and prospective analysis, and therein apply the residual operating income valuation model, in the best possible manner to further value the selected firms and their long-term investment potential. Lastly, we expound the strategy of forming and adjusting a long-term investment portfolio in a way that potentially maximizes long-term portfolio return.

Limits of arbitrage and their impact on market efficiency: Evidence from China

This paper examines the impact of limits of arbitrage (LOA) on market efficiency by considering a large sample of Chinese stocks. Intraday market efficiency is measured using two widely used measures: the intraday return predictability measure and the variance ratio measure. We find that LOA plays a major role in driving market efficiency. We also use an exogenous shock, the introduction of Shenzhen Stock Connect, which increases trade volume and lowers LOA, to examine the impact of LOA on market efficiency. Our results indicate that this event improves market efficiency regarding both our measures. Therefore, we conclude that policy makers in China are acting in the right direction to elevate China's status in world markets by adapting policies that inherently make the country more attractive to global investors by lowering LOA and enhancing market efficiency.

Web search volume acceleration and cross-sectional returns

This study examines the effect of web search volume acceleration (WSVA) on predicting future stock returns. We find that WSVA can significantly and negatively predict future stock returns in the Chinese stock market. After controlling for firm characteristics and changing the WSVA measurement, the negative return predictability of WSVA is still significant, and WSVA provides more predictive information than is contained in the web search volume speed (WSVS). Our empirical results are unable to be explained by existing common risk factors. We find that the return predictability of WSVA becomes stronger for stocks that grab higher investor attention, during higher investor sentiment period, and for stocks that face higher limits of arbitrage, which supports for stock mispricing theory in behavioral finance.

Idiosyncratic volatility and firm-specific news: evidence from the Chinese stock market

This study investigates the effect of firm-specific news on the pricing of idiosyncratic volatility (IVOL) in China. Using a sample of non-financial A-share listed firms from January 2006 to June 2018, we find that the predictive ability of IVOL is much weaker around firm announcements compared to that without news, suggesting that the limited arbitrage cannot disentangle the IVOL puzzle completely in the emerging market. Additionally, we investigate the effect of news sentiment on the predictive ability of IVOL and find that it is much stronger following bad news compared to good news. Finally, when we include the macroeconomic variables known to predict returns to adjust the systematic risk, we obtain novel findings that the negative premium of IVOL becomes insignificant, suggesting that the negative premium is time-varying with macroeconomy.

Double Machine Learning: Explaining the Post-Earnings Announcement Drift

AbstractWe demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning that enable high-dimensional inference. Because the literature on post-earnings announcement drift (PEAD) is characterized by a “zoo” of explanations, limited academic consensus on model design, and reliance on massive data, it will serve as a leading example to demonstrate the challenges of high-dimensional analysis. We identify a small set of variables associated with momentum, liquidity, and limited arbitrage that explain PEAD directly and consistently, and the framework can be applied broadly in finance.

Limits of Arbitrage and Primary Risk-Taking in Derivative Securities

Abstract Classic option pricing theory values a derivative contract via dynamic delta hedging and treating the contract as redundant relative to the underlying security. Dynamic delta hedging proves highly effective in practice, but the remaining risk is still large because of the practical limits of arbitrage. Derivatives can play primary roles in risk allocation. This paper quantifies the percentage variance reduction of delta hedging on U.S. stock options, proposes a top-down return attribution framework to identify the remaining risk sources of the delta-hedged option investment, and constructs a statistical return factor model to explain the variations of the delta-hedged option returns. (JEL C13, C51, G12, G13)

Behavioural Finance, Overconfidence and Chinese Investors

Behavioural finance is the application of psychology to finance and investment. It yields insights into how investors think and behave and how financial markets behave. The main elements can be divided into two parts: limits of arbitrage and psychology. Behavioural finance is essentially the study of how people behave in the markets using models from psychology.

Firm-specific news and idiosyncratic volatility anomalies: Evidence from the Chinese stock market

In this paper, we examine the relationship between idiosyncratic volatility and future returns around the firm-specific news announcements in the Chinese stock market following. The results show that the pricing of non-news idiosyncratic volatility is more strongly negative compared to news idiosyncratic volatility. Such findings imply that limited arbitrage cannot fully explain the negative pricing of idiosyncratic volatility in the Chinese stock market. These results are robust after controlling for several well-known variables, such as market beta, firm size, book-to-market, momentum, liquidity, and maximum return. However, after adjusting by additional macroeconomic variables, the Chinese four-factor model and the salience trading volume factor, the average returns on zero-investment IVOL and non-news IVOL portfolios turn out to be insignificant, indicating that they may be one driver of the IVOL puzzle in the Chinese stock market.

Hard to arbitrage, hard for analysts to forecast

This paper examines whether limits to arbitrage (LA) affect analysts' earnings forecast accuracy. Using the LA index, which is constructed from unique trading constraints in the Chinese stock market and other commonly used measures, we find that forecast accuracy is much lower for stocks with high LA. Moreover, our results are more suited to explanations of cognitive bias that turn to investor sentiment or limited attention and cannot be fully explained by more objective factors, including analyst ability, broker size, broker experience, and commission pressure. We also find that LA amplifies analyst forecast dispersion. Such results indicate that LA distorts analysts’ earnings expectations and provides new insight into how LA affects anomaly returns.

The Ex Ante Likelihood of Bubbles

The limits of arbitrage explain how a speculative bubble is sustained; they do not explain how likely one is to occur. To do that, you need a theory about the thing that sporadically causes arbitrageur constraints to bind. I propose a first such theory, which is based on social interactions between speculators. The theory says that bubbles should be more likely in assets where increases in past returns make excited-speculators relatively more persuasive to their peers. I empirically verify this ex ante prediction about bubble likelihoods and show that it is robust to some ex post disagreement about bubble definitions. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4351 .

Cross-sectional seasonalities and seasonal reversals: Evidence from China

We are the first to comprehensively investigate cross-sectional seasonalities and seasonal reversals in the Chinese A-share market. Empirically, utilizing monthly data from January 1995 to December 2019, we provide new supporting evidence that there are seasonalities and seasonal reversals in the cross-section. Interestingly, the occurrence of seasonal reversals takes a longer time than that of the U.S. market. Next, we explore explanations for cross-sectional seasonalities. The tests based upon macroeconomic risk, mood beta, and limits of arbitrage suggest that seasonalities are likely driven by temporary mispricing. Finally, we construct seasonality and seasonal reversal factors to study the investment value of seasonalities. We find that these factors can significantly increase the monthly Sharpe ratio by up to 0.34 and capture incremental information in predicting future returns relative to other well-known factors.

The cash conversion cycle spread: International evidence

The cash conversion cycle (CCC) is important for fundamental analysis as an indicator of management effectiveness in cash and financing. However, there is a lack of empirical evidence for its implications on asset pricing except for the very recent findings that high CCCs negatively predict stock returns in the U.S. By investigating 47 developed and emerging markets from 1993 to 2018, we find a mild CCC effect across the globe. The Low-minus-High equal-weighted hedge portfolios sorted by components of CCC yield significant Fama-French five-factor alphas ranging from 0.277 to 0.730% per month. Our results are consistent with a mispricing explanation by analyzing earnings prediction, announcement returns around future earnings, and limits of arbitrage although there is also some evidence for a risk-based explanation. Moreover, the CCC effect is stronger in emerging markets than developed markets and for markets with more political risk and less integrated with the global market.

What drives the distress risk–return puzzle? A perspective on limits of arbitrage

AbstractEmpirical research has documented a negative relationship between distress risk and stock returns. This negative risk–return trade‐off, known as the distress puzzle, poses a challenge to asset pricing models. In this study, we provide a new explanation of the distress puzzle by considering the effect of arbitrage asymmetry. We find that the negative distress risk–return relation is stronger in stocks that have higher limits of arbitrage. The investors are virtually unable to short sell mispriced high distress risk stocks due to the low supply of lendable stocks from institutions and that arbitrage is costly. In addition, we show that the limits of arbitrage effect is distinct from liquidity effect in explaining the distress puzzle.

Behavioral Biases and the Asset Growth Anomaly

We find evidence that the asset growth anomaly is due, in part, to investors’ behavioral biases. Two-way sorts based on asset growth and proxies for known behavioral biases (anchoring, recency, nominal price illusion, and lottery-seeking) indicate that the asset growth anomaly is stronger in stocks that investors affected by behavioral biases tend to buy and non-existent or negative in stocks they tend to sell. These results are not explained by limits of arbitrage or investor sentiment and hold in both portfolio analyses and regressions. The evidence suggests that behavioral investors’ attraction to certain stocks drives the asset growth anomaly.

The Unpriced Information in Fail-to-Deliver Short Sales: Implications for Regulation and Identifying Limited Arbitrage

The Unpriced Information in Fail-to-Deliver Short Sales: Implications for Regulation and Identifying Limited Arbitrage

Anomalies and the Expected Market Return

ABSTRACTWe provide the first systematic evidence on the link between long‐short anomaly portfolio returns—a cornerstone of the cross‐sectional literature—and the time‐series predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high‐dimensional setting. We find that long‐short anomaly portfolio returns evince statistically and economically significant out‐of‐sample predictive ability for the market excess return. The predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence.