Daily Price Limits Research Articles

Price limits, informed trading, and information consumption

We use a policy change in the daily price limit rules in the Chinese stock market to examine the impacts of price limits on stock market reactions to corporate earnings announcements. We find that compared with the control stocks’ prices, the prices of stocks that experience a relaxation in price limits become more sensitive to corporate earnings surprises. Further analysis shows that more effective responses are likely associated with the increased trading intensity of nonretail investors and short sellers. Overall, we suggest that the relaxation of the price limit contributes to the incorporation of firm-specific information into stock prices.

From Gambling to Gaming: The Crowding Out Effect

This paper investigates how noise trading behavior is influenced by limited attention. As the daily price limit rules of the Chinese stock market provide a scenario for the exhibition of salient payoffs, speculators elevate prices to attract noise traders into the market. Utilizing a series of distraction events stemming from mobile games as exogenous shocks, we obtain statistical evidence that the gambler-like behavior, termed as “Hitting game” may be crowded out. Consistent with our attention mechanism, indicators such as trading volume decline in response to these game shocks.

Price limit relaxation and stock price crash risk: Evidence from China

This paper investigates the impact of price limit relaxation on stock price crash risk, based on the change in the daily price limit in the ChiNext market in August 2020. We find that the widening of price limits significantly reduces stock price crash risk. This result is robust to parallel trend and placebo tests. Furthermore, we show that widening price limits can help lower information asymmetry between informed and uninformed traders, thereby decreasing bad news hoarding and reducing stock price crash risk.

Relaxing Daily Price Limits and Stock Market Cross-Correlation: Evidence from MF-X-DMA Analysis

The daily price limits in the ChiNext stock market were relaxed from [Formula: see text]% to [Formula: see text]% on 24 August 2020. Using the multifractal detrended moving average cross-correlation analysis (MF-X-DMA) method, we find that relaxing daily price limits leads to a greater degree of multifractality of the ChiNext stock market, suggesting that the relaxation of daily price limits harms stock market efficiency. In addition, the positive cross-correlation between ChiNext and Chinese main board stock markets becomes weaker, i.e., relaxing daily price limits also decreases the connection between ChiNext and other stock markets. Moreover, there is an increase in the degree of the cross-correlation multifractality between ChiNext and Chinese main board stock markets, suggesting that the linkage of ChiNext and other stock markets is more complicated and risky after the relaxation of daily price limits. Our findings fulfill related literature from the perspective of multifractality and have important implications for investors.

Price limit performance: New evidence from a quasi-natural experiment in China's ChiNext market

This study examines the impact of daily price limits on market performance and trading activity by using a quasi-natural experiment in China. It focuses on the case of the ChiNext market, where the daily price limits of stocks increased from 10% to 20% in 2020. The results show that, initially, the stock prices and occurrences of 10% price limit hits increase, but then decline after the new price limits have been implemented. The level of trading liquidity and volatility increases significantly, with a greater impact on the short term than the long term. These price limit performances are more pronounced for stocks with additional retail interest. The analysis of detailed trading data reveals that institutional investors initially purchase ChiNext stocks in large quantities, followed by retail investors who purchase smaller quantities. In the long run, institutional investors tend to increase their holdings, while retail investors tend to sell their holdings. Additionally, there is a temporary increase in investor attention, price synchronicity, and stock risks, followed by a decline. The findings suggest that wider price limits increase trading volumes and enhance long-term market efficiency, but encourage immediate price manipulation, causing short-term overreactions and long-term reversals. This study provides valuable insights for building an effective price limit system.

Effectiveness of price limits: Evidence from China's ChiNext market.

Starting from August 24, 2020, the daily stock price limits in China's ChiNext market have been adjusted from 10% to 20%. We use this reform to study the effectiveness of price limits in China's stock market. We test four hypotheses about price limits: delayed price discovery, volatility spillover, trading interference, and magnet effect. Using the event study method, we examine the differences in the behavior of stock price, trading volume, and volatility before and after the reform. We confirm the delayed price discovery, volatility spillover and trading interference hypothesis of price limits, and find that these negative effects of price limits are more serious when lower limits are hit. In addition, we examine the distribution of large price movements before and after the reform and find no evidence of the magnet effect of price limits. The present research has important implications for policymakers and investors in China's stock market.

Cranes among chickens: The general-attention‐grabbing effect of daily price limits in China's stock market

This paper examines the general-attention-grabbing effect of daily price limits in China's stock market. We show that stocks with large exposure to daily price limits attract more investor attention and have lower future returns. The exposure is measured empirically through the absolute beta with respect to the daily proportion of stocks that hit price limits. The general-attention-grabbing effect is not solely caused by stocks that recently hit price limits, is not subsumed by market volatility exposure, and does not reflect other stock market characteristics. Moreover, the effect is stronger among stocks that are heavily invested in by retail investors.

주가변동성에 대한 가격제한폭의 확대 효과 : 포트폴리오 수익률과 EGARCH 모형을 이용하여

This study examines how the relaxation of daily price limits affected volatility in the Korean stock market. Previous studies failed to conclusively determine the effect of relaxed price limits on volatility, with some concluding that volatility actually increased, while others empirically demonstrated the opposite. Such inconsistent results were due to the lack of a control group in those studies. In order to avoid the same pitfalls, we compared two markets: the KOSPI and KOSDAQ indices. Over the years, both markets experienced gradual relaxation of their respective daily price limits. This study focuses on four separate events where the price limit of one market was relaxed compared to before, while its counterpart maintained the status quo. Moreover, we employed a single criteria when selecting stocks for portfolios in each market, with regards to company size and book-to-market ratio. It is expected that such precautions will improve homogeneity between the portfolios compared, thus excluding factors other than the relaxation policy that might affect stock price volatility. Through such efforts, we reached the conclusion that the net effect of recent relaxations in price limits was to decrease the volatility of stock prices.

Price limit change and magnet effect: The role of investor attention

We explore the effect of the price limit change from 10% to 20% on the magnet effect in the ChiNext board in the Chinese stock market. The results show that the greater daily price limit range increase the magnet effect, while investor attention has a significant positive impact on the magnet effect after the price limit change.

The effect of daily price limits on stock liquidity: Evidence from the Chinese stock market

AbstractThis study uses a group of specially treated stocks to examine the effect of daily price limits in the Chinese stock market. We show that lower price limits significantly reduce liquidity and widen spreads. These results remain consistent when using difference‐in‐differences regressions with propensity score matching and after conducting regression discontinuity analyses. The findings are more prominent for firms experiencing lower‐limit hits, with higher disclosure quality and lower probability of informed trading before the limit changes. These findings suggest that restrictive price limits cause unexpected behaviour of informed traders, enhance information asymmetry, reduce stock liquidity, and hinder price discovery process.

Determinants of the successful single stock futures market in Thailand

Thailand’s Single Stock Futures market has grown recently over the last ten years, evidenced by its 8th place in top 10 exchanges in the world by number of single stock futures traded in 2021. Since the main goal of any futures exchange is to list a successful contact, it is important to demonstrate the determinants of the success of Single Stock Futures. This study uses the sample consisting of 89 companies, on which stocks are underlying for Single Stock Futures in the period between January 2017 and December 2021, and finds that the best fitting method in modelling determinants of the success of Single Stock Futures is the fixed effects model. As expected, the results confirm the existence of a positive relationship between characteristics of underlying stock, including size, volatility, and liquidity, and the successful futures contract. Furthermore, the findings show the negative effects of the first year of contract trading and the tightened daily price limit of Single Stock Futures in response to the COVID-19 pandemic situation on contract success. AcknowledgmentThe author is grateful to the Department of Economics, Faculty of Economics, Kasetsart University for financial support to conduct this research.

HGNN: Hierarchical graph neural network for predicting the classification of price-limit-hitting stocks

In some stock markets, stock prices are not allowed to rise above a daily limit to restrain the surge of price (called price limit). When the price limit occurs, investors tend to chase the continuing upward momentum for profit-making. However, For the stocks that hit daily price limit, we observe whether they close at daily price limit will lead to the opposite price trends of the next trading day. Therefore, this work aims to predict whether a stock that hits its daily price limit will also close at the same price level (i.e., Type I or Type II). The occurrence of price limit is driven by different levels of market state. For example, it can result from macro-economic changes of the whole market, or it can be traced to some industry-specific factors. A challenging task is to learn a better stock representation with less uncertainty by comprehensively considering the hierarchical property of market state. Accordingly, we design a novel hierarchical architecture, called Hierarchical Graph Neural Network (HGNN), to investigate the market state at hierarchical view for stock type prediction. In HGNN, we construct the stock relation graph and merge stock information hierarchically extracted from multiple views of market state, including node view, relation view and graph view, which takes both historical sequence pattern and stock relation into consideration. Our key innovation is the introduction of hierarchical structure makes the predictive model able to more comprehensively infer the hierarchical property of market state. Further, it also provides the deeper insight for the actual investment practice. To validate the effectiveness of our method, we conduct back-testing on the two-year historical data of more than 2500 main-board stocks in two China stock markets, SSE and SZSE. To support further study of the stock type prediction task, we have published two long-range stock datasets (Datasets are available at https://drive.google.com/file/d/1TXiAyqt3rHveuzdGT6YtswU1e-tBSFUe/view?usp=sharing). Extensive experiments show that our method outperforms the state-of-the-art solutions including ALSTM, GCN and GAT with the improvements of at least 3.54% on average in accuracy. In addition, the average return ratio of SSE and SZSE has improved by 18.57% and 8.75%, respectively.

Price limit and stock market quality: Evidence from a quasi-natural experiment in the Chinese stock market

The daily price limit changed from 10% to 20% in the ChiNext market in 2020. This event can be considered a quasi-natural experiment of change in market trading mechanisms. We employ the difference-in-difference (DID) approach to test the effect of this price limit change on the firm-level market quality indicator. We find that the implementation of the new price limit range significantly improves market liquidity and increases market volatility and the probability of informed trading. Our findings remain robust to a parallel trends test, a placebo test, alternative matching, a regression-discontinuity design, and alternative measures.

The effect of relaxing daily price limit: Evidence from the ChiNext market of China

On August 24, 2020, the daily price limit for stocks listed on the ChiNext market is relaxed from 10% to 20%. We conduct an event study and find that stock returns tend to be positive upon the widening of daily price limit. Moreover, the relaxation of daily price limit has no significant effect on price efficiency but significantly improves liquidity and induces higher return volatility.

Cranes Among Chickens: The General-Attention-Grabbing Effect of Daily Price Limits in China’s Stock Market

Cranes Among Chickens: The General-Attention-Grabbing Effect of Daily Price Limits in China’s Stock Market

The Effect of Relaxing Daily Price Limit: Evidence from the Chinext Market of China

The Effect of Relaxing Daily Price Limit: Evidence from the Chinext Market of China

MAX is not the max under the interference of daily price limits: Evidence from China

Proposing a proxy (MAX) for extremely positive returns (EPR) that trigger lottery demand, Bali et al. (2011) observe a negative relation between MAX and future stock returns in US. However, for China’s stocks with daily price limits, we observe that MAX understates EPR and their negative relation with future returns. By presenting a revised MAX (RMAX) explicitly considering price limits, we show that RMAX, free of such underestimations, exhibits persistent differences from MAX in explaining future returns. More importantly, attracted by stocks (consecutively) closing at the price limits, retail attention and subsequent retail trades contribute to the success of RMAX.

Option pricing under finite moment log stable process in a regulated market: A generalized fractional path integral formulation and Monte Carlo based simulation

In contrast to a non-regulated market, a regulated market can be defined as a market affected by external factors, which cause abnormal behaviors in market prices. Nevertheless, these behaviors are not enough to ignore the fundamental principles of finance, while many econophysicists do so. In this paper, it is considered that returns are driven by a finite moment log stable process in the most general form. Then a potential function is used to model the rest of the regulations. Consequently, the pricing problem is formulated as an integral whose kernel can be found solving an inhomogeneous space-fractional diffusion equation. Given the inhomogeneous equation with the Riesz-Feller fractional derivative and the potential function, a new path integral seems to be necessary to formulate the solution of the kernel equation. Thus, a Generalized Fractional Path Integral will be derived, and an Asymmetric Fractional Path Integral Monte Carlo algorithm will be developed to find the results. Finally, a daily European call option is priced in a real market with a daily price limit rule, and the maximum and minimum price for a typical contract is calculated as some example applications of the proposed approach.

Optimal portfolio allocation with imposed price limit constraint

Daily price limits are adopted by many securities exchanges in countries such as the USA, Canada, Japan and various other countries in Europe and Asia, in order to increase the stability of the financial market. These limits confine the price of the financial asset during all trading stages of any trading day to a range, usually determined based on the previous day’s closing price. In this paper we study the portfolio optimization problem with impose the price limit constraint. The dynamic programming technique is applied to derive the Hamilton–Jacobi–Bellman equation and the method of Lagrange multiplier is used to tackle the constraint. Optimization problem solution results, using numerical method show that the equilibrium path of wealth and investment in risky assets has a different way than in the absence of price limits.

The Impact of Price Limits on Stock Volatility and Price Delay: Evidence from China

This paper uses a difference-in-difference methodology to tackle the identification issue in estimating price limits’ impacts on market efficiency. Examining the Special Treatment policy in China, I show that 5-basis-point tightening in daily price limits (from ± 10% to ± 5%) significantly reduces annualized volatility by 6.5 basis points (t =5.00) yet increases price delay by 63% from the previous year (t =7.40). Trading activity and liquidity significantly decrease under new limits but return increases by equal-weighted average of 27% (t = 3.22) in 12 months. Evidence suggests that in the long-run price limits are effective in reducing volatility and improving firm value yet causing delayed price discovery and lower liquidity.