Stock Price Jumps Research Articles

Financial Reporting and Consumer Behavior

ABSTRACT We show that financial reporting influences consumer behavior by drawing consumer attention to announcing firms. Analyzing global positioning system (GPS) data, we document upticks in foot traffic to firms’ commercial locations immediately following their earnings announcements. This increase is more pronounced for announcements with substantial media attention, fewer concurrent announcements, heightened internet search volume, and extreme stock price jumps and earnings surprises—indicating that announcement coverage impacts consumer behavior by capturing attention. Furthermore, foot traffic increases with positive earnings for firms offering durable goods, suggesting consumers respond to news about firms’ financial prospects. Consumer attention patterns increase revenues and advertising effectiveness, ultimately suggesting that financial reporting serves a marketing function. Data Availability: All other data are available from the public sources cited in the text. JEL Classifications: G10; G11; G12; G14; G40; G41.

How to measure earnings surprises: Based on revised market reaction.

We investigate the robustness of earnings surprise measures in the context of a revised market reaction. While existing literature suggests that financial anomalies may distort cumulative abnormal returns (CAR) during annual announcements, our research proves that a revised market reaction offers a more accurate reflection of investor reactions to earnings correction. Specifically, we introduce an innovative adjustment to CAR using stock price jumps, and prove that the fraction of misses on the same side (FOM) provides a superior measure of earnings surprises. Furthermore, we find that investor trading patterns align with FOM, and the post-earnings announcement drift (PEAD) strategy based on FOM outperforms that based on analysts' forecast error.

Pattern Recognition in Microtrading Behaviors Preceding Stock Price Jumps: A Study Based on Mutual Information for Multivariate Time Series

Pattern Recognition in Microtrading Behaviors Preceding Stock Price Jumps: A Study Based on Mutual Information for Multivariate Time Series

Anticipating jumps: Decomposition of straddle price

We develop a novel method to decompose a straddle into two assets: a volatility risk asset and a jump risk asset. Using the price ratio of the jump risk asset to the straddle, we create a forward-looking measure (S-jump) that captures the stock price jump risk anticipated by the option market. We show that S-jump substantially increases before earnings announcements and strongly predicts the size and the probability of earnings-induced stock price jumps. We also find that S-jump amplifies the earnings response coefficient. Our jump risk asset captures the run-up and run-down return patterns observed for straddles around earnings announcements.

Jumps in stock prices: New insights from old data

We characterize jump dynamics in U.S. stock market returns using a novel series of intraday prices covering almost 90 years. Jump dynamics vary substantially over time. Trends in jump activity relate to secular shifts in the nature of news. Unscheduled news often involving major wars drives jump activity in early decades, whereas scheduled news and especially news pertaining to monetary policy drives jump activity in recent decades. Jump variation measures forecast excess stock market returns, consistent with theory. Results support models featuring a separate jump factor, such that risk premium dynamics are not fully captured by volatility state variables.

Why Stock Price Jumps? The Role of Disagreement and News

Why Stock Price Jumps? The Role of Disagreement and News

Private information in trades, R2, and large stock price movements

We investigate the relations between trading-conveyed private information and stock return distributions. Using high-frequency measures of private information, we find that private information in trades is associated with lower stock return synchronicity. We also find private information in trades is positively associated with stock price crashes and positive stock price jumps. Our results are robust to several specification checks, including the use of alternative private information proxies, various model specifications, and different time periods. Overall, we demonstrate that trading conveyed private information reduces stock return synchronicity and predicts the frequency of crashes and jumps. Our findings can be useful for market makers, regulators, and traders, who are interested in firm-specific return variation and extreme stock price movements at high frequencies.

What factors are associated with stock price jumps in high frequency?

We analyze the complete tick-level stock trading records at the Taiwan Stock Exchange and explore what factors are principally associated with stock price jumps in high frequency. Among the potential candidate variables suggested in the literature, liquidity proxies appear to be primarily associated with signed jumps in high frequency. The results from the least absolute shrinkage and selection operator (LASSO), the elastic net method, and principal component analysis further show that liquidity issues are more important than information or sentiment in understanding sudden and discontinuous price innovations to financial assets in high frequency.

Jumps and Diffusive Variance: A Granular Analysis of Individual Stock Returns

Jumps and diffusive changes in stock prices are different ways in which information is reflected in the prices. We use nonparametric methods to decompose returns on individual stocks into jumps and diffusive components. Contrary to the conventional assumption that jump intensity is positively related to diffusive variance, we find abundant evidence that realized jump intensity and diffusive variance are uncorrelated or negatively related for a majority of stocks. The jump-diffusion beta is found to positively contribute to the implied volatility smile of options on individual stocks. We also document a counter-cyclical pattern of realized jump sizes, which challenges the i.i.d. jump size assumption commonly seen in the literature. The findings provide useful guidance on modeling option prices.

Stock Crashes and Jumps Reactions to Information Demand and Supply: An Intraday Analysis

The objective of this paper is to examine the possible linkage between the intraday stock price crashes and jumps and public information by using data from the Chinese stock market and Baidu Index. We divided public information into two kinds of information: supply through online media and information demand across inquiries by individual investors. Using a large sample from Chinese listed firms from 2013 to 2019, our evidence clearly indicates that online information supply and demand both have a positive impact on the intraday crashes and jumps; this is, the firm with higher information supply and demand more likely to experience intraday crashes and jumps. The results are robust to an alternative measure of crash risk. Moreover, we further examine whether the market conditions have an impact on the relationship between information flow and intraday crashes and jumps, and find that the marginal effect of information supply on intraday price crashes and jumps is smaller in the bull market phase. Moreover, the bull market phase enhances the effect of information demand on intraday price crashes and jumps.

Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk

Extreme financial events usually lead to sharp jumps in stock prices and volatilities. In addition, jump clustering and stock price correlations contribute to the risk amplification acceleration mechanism during the crisis. In this paper, four Jump-GARCH models are used to forecast the jump diffusion volatility, which is used as the risk factor. The linear and asymmetric nonlinear effects are considered, and the value at risk of banks is estimated by support vector quantile regression. There are three main findings. First, in terms of the volatility process of bank stock price, the Jump Diffusion GARCH model is better than the Continuous Diffusion GARCH model, and the discrete jump volatility is significant. Secondly, due to the difference of the sensitivity of abnormal information shock, the jump behavior of bank stock price is heterogeneous. Moreover, CJ-GARCH models are suitable for most banks, while ARJI-R2-GARCH models are more suitable for small and medium sized banks. Thirdly, based on the jump diffusion volatility information, the performance of the support vector quantile regression is better than that of the parametric quantile regression and nonparametric quantile regression.

Predicting intraday jumps in stock prices using liquidity measures and technical indicators

AbstractPredicting intraday stock jumps is a significant but challenging problem in finance. Due to the instantaneity and imperceptibility characteristics of intraday stock jumps, relevant studies on their predictability remain limited. This paper proposes a data‐driven approach to predict intraday stock jumps using the information embedded in liquidity measures and technical indicators. Specifically, a trading day is divided into a series of 5‐min intervals, and at the end of each interval, the candidate attributes defined by liquidity measures and technical indicators are input into machine learning algorithms to predict the arrival of a stock jump as well as its direction in the following 5‐min interval. An empirical study is conducted using level‐2 high‐frequency data of 1271 stocks on the Shenzhen Stock Exchange of China to validate our approach. The results provide initial evidence of the predictability of jump arrivals and jump directions using level‐2 stock data as well as the effectiveness of using a combination of liquidity measures and technical indicators for such prediction. We also reveal the superiority of using random forest compared with other machine learning algorithms in building prediction models. Importantly, our study provides a portable data‐driven approach that exploits liquidity and technical information from level‐2 stock data to predict intraday price jumps of individual stocks.

Detecting Intra-Day Jumps in Stock Prices with High-Frequency Option Data

We develop a novel, option-based approach for detecting intraday jumps in stock prices. One of the components involved in intraday jump detection is instantaneous volatility, by which intraday returns are scaled. The existing intraday jump detection approaches assume that volatility does not change drastically over a short period, which, however, is in conflict with empirical evidence that volatility can exhibit large movements. We tackle this problem by introducing a method with a completely new proxy for instantaneous volatility, extracted from the option-implied volatility index. This allows us to detect jumps while there are large movements in volatility. We verify our approach with extensive ex-post Monte Carlo experiments. The results show that our approach is of high statistical power, is more robust to variation in volatility, and outperforms the baseline approaches from the literature in terms of both spurious and actual jumps. In the empirical part, we use eight years of high-frequency data on S&P 500 index options. We find that, in comparison with the conventional baseline approaches, our approach identifies fewer jumps, suggesting that true price variation coming from jumps is overstated. Moreover, our method identifies different locations for a large portion of jumps, which emphasizes the important role played by the volatility proxy.

An analysis of the clustering effect of a jump risk complex network in the Chinese stock market

With the development of Chinese financial market, the correlation between stock price jump risks cannot be ignored. This study uses the complex network method to analyze the clustering effect of stock price jumps. Taking a sample of stocks from the CSI 300 Index, the realized jumps are extracted from the 5-minute high frequency data using the MinRV method. The authors use the Minimum Spanning Tree algorithm to construct a complex network of stock price jumps. It is found that there is a clear correlation among stocks in the entire jump network. The jump in manufacturing industry stocks plays the most important role in the network. The Modular Q function and the Fast Unfolding algorithm are used to divide the entire complex network and study the differences in jump correlation between different communities. The result shows that the correlation among the financial industry stocks is stronger, and a large fluctuation in the price of one financial stock can cause the price of another financial stock to fluctuate significantly.

Maximum likelihood estimation of stock volatility using jump-diffusion models

We investigate whether there are systematic jumps in stock prices using the Brownian motion approach and Poisson processes to test diffusion and jump risk, respectively, on Johannesburg Stock Excha...

The Power Law Characteristics of Stock Price Jump Intervals: An Empirical and Computational Experimental Study.

For the first time, the power law characteristics of stock price jump intervals have been empirically found generally in stock markets. The classical jump-diffusion model is described as the jump-diffusion model with power law (JDMPL). An artificial stock market (ASM) is designed in which an agent’s investment strategies, risk appetite, learning ability, adaptability, and dynamic changes are considered to create a dynamically changing environment. An analysis of these data packets from the ASM simulation indicates that, with the learning mechanism, the ASM reflects the kurtosis, fat-tailed distribution characteristics commonly observed in real markets. Data packets obtained from simulating the ASM for 5010 periods are incorporated into a regression analysis. Analysis results indicate that the JDMPL effectively characterizes the stock price jumps in the market. The results also support the hypothesis that the time interval of stock price jumps is consistent with the power law and indicate that the diversity and dynamic changes of agents’ investment strategies are the reasons for the discontinuity in the changes of stock prices.

Stock Price Jumps and News Sentiment: A Case of Investor Overreaction

The research paper develops an understanding on how news based sentiment capture investor behaviour reflected in price jumps in stock markets. It compares the impact on two models of stock price jumps; the non-parametric model proposed by BNS and the wavelet based method. The study is also a perspective on the semi strong form of market efficiencyUsing the high frequency data from the stock and options market along with the actual high frequency news data from Bloomberg, the two alternative methodologies of jumps have been tested. In addition, options trades have been simulated to see whether profits can be earned from the news sentiment captured by jumps.Methodologically, jumps based on wavelets were found to be better related with the news sentiment compared to the BNS method. Also, the news sentiment based jumps were found to present opportunities in the simulated trades that could be exploited for earning profits suggesting that investors overreact.The paper uses an innovative method for computation of the news based sentiment. To the best of our knowledge, the paper is the first to evaluate jumps and news sentiment using the actual news data. A perspective on the semi strong form of market efficiency is presented, that too by departing from the event study based models.

Jumps in Stock Prices: New Insights from Old Data

We characterize jump dynamics in stock market returns using a novel series of intraday prices covering almost 90 years. Jump dynamics vary substantially over time. Trends in jump activity relate to secular shifts in the nature of news. Unscheduled news often involving major wars drives jump activity in early decades, whereas scheduled news and especially news pertaining to monetary policy drives jump activity in recent decades. Jump variation measures forecast excess stock market returns, consistent with theory. Results support models featuring a separate jump factor such that risk premium dynamics are not fully captured by volatility state variables.

Press freedom and jumps in stock prices

Press freedom varies substantially across countries. In a free environment, any news immediately becomes public knowledge through mediums including various electronic media and published materials. However, in an unfree environment, (economic) agents would have more discretionary powers to disclose good news immediately, while hiding bad news or releasing bad news slowly. We argue that this discretion affects stock prices and that stock markets in countries with a free press should be better processors of economic information. Using an equilibrium asset-pricing model in an economy under jump diffusion, we decompose the moments of the returns of international stock markets into a diffusive risk and a jump risk part. Using stock market data for a balanced panel of 50 countries, our results suggest that in countries with a free press, the better processing of bad news leads to more frequent negative jumps in stock prices. As a result, stock markets in those countries are characterized by higher volatility, driven by higher jump risk and more negative return asymmetry. The results are robust to the inclusion of various controls for governance and other country- or market-specific characteristics. We interpret these as good stock market characteristics because a free press improves welfare and increases economic growth.

The Arrival of News and Jumps in Stock Prices

This paper introduces a non-parametric framework to statistically examine how news events, such as company or macroeconomic announcements, contribute to the pre- and post-event jump dynamics of stock prices under the intraday seasonality of the news and jumps. We demonstrate our framework, which has several advantages over the existing methods, by using data for i) the S&P 500 index ETF, SPY, with macroeconomic announcements and ii) Nasdaq Nordic Large-Cap stocks with scheduled and non-scheduled company announcements. We provide strong evidence that non-scheduled and some macroeconomic announcements contribute jumps that follow the releases and also some evidence for pre-jumps that precede the scheduled arrivals of public information, which may indicate non-gradual information leakage. Additionally, our results show that releases of unexpected information are not reacted to uniformly across Nasdaq Nordic markets, even if they are jointly operated and are based on the same exchange rules.