Arrival Of News Research Articles

Intraday liquidity dynamics and news releases around price jumps: Evidence from the DJIA stocks

We study the dynamics of liquidity and news releases around jumps by identifying their intraday timing for the Dow Jones Industrial Average index constituents. Jumps are found to coincide with a significant increase in trading costs and demand for immediacy, amplified by the release of news. Liquidity supply remains nevertheless high and there is strong evidence of resilience. Liquidity shocks in the effective spread and the number of trades are the key drivers behind the occurrence of a jump. Order imbalance appears to be the most informative liquidity variable with respect to price discovery, especially after the arrival of news.

What Moves Stock Prices: Another Look

In 1989, Culter, Poterba, and Summers published an article examining the extent to which ex post movements in aggregate stock prices could be attributed to the arrival of news. They concluded that most of the 50 largest movements in the S&P 500 could not be matched with any convincing explanation for why future profits or discount rates might have changed. Since 1989, there has been an explosion in information technology, enhanced market regulation, and stock-trading innovation, as well as the introduction of new, equity-related financial products. The author explores whether Culter, Poterba, and Summers’s conclusion still holds, and reports that it is intact. If anything, the mystery has deepened, because the size of unexplained market movements has grown.

The determinants of quantile autocorrelations: Evidence from the UK

We empirically analyse the cross-sectional determinants of stock return autocorrelations in the UK in different quantiles of conditional return distributions. Autocorrelations in low quantiles are predominantly positive, whereas those in the remaining quantiles are negative. Autocorrelations in different quantiles depend on different sets of firm and trading characteristics: when returns are normal or high, prices react quickly to information, are driven by positive feedback traders, instantaneous news arrivals, and overshoot, trades are predominantly motivated by hedging/liquidity needs, and measured autocorrelations can be biassed by the bid–ask bounce effect and nonsynchronous trading. However, when returns are unusually low, prices are driven by information arriving sequentially and react sluggishly to it, and are influenced by trading on private information and/or negative feedback traders.

News, Volatility and Jumps: The Case of Natural Gas Futures

We investigate the impact of Thompson Reuters News Analytics (TRNA) news sentiment on the price dynamics of natural gas futures traded on the New York Mercantile Exchange (NYMEX). We propose a Local News Sentiment Level (LNSL) model, based on the Local Level model of Durbin and Koopman (2001), to construct a running series of news sentiment on the basis of the 5-minute time grid. Additionally, we construct several return and variation measures to proxy for the fine dynamics of the front month natural gas futures prices. We employ event studies and Granger causality tests to assess the effect of news on the returns, price jumps and the volatility. We find significant relationships between news sentiment and the dynamic characteristics of natural gas futures returns. For example, we find that the arrival of news in non-trading periods causes overnight returns, that news sentiment is Granger caused by volatility and that strength of news sentiment is more sensitive to negative than to positive jumps. In addition to that, we find strong evidence that news sentiment severely Granger causes jumps and conclude that market participants trade as some function of aggregated news. We apply several state-of-the-art volatility models augumented with news sentiment and conduct an out-of-sample volatility forecasting study. The first class of models is the generalized autoregressive conditional heteroskedasticity models (GARCH) of Engle (1982) and Bollerslev (1986) and the second class is the high-frequency-based volatility (HEAVY) models of Shephard and Sheppard (2010) and Noureldin et al. (2011). We adapt both models to account for asymmetric volatility, leverage and time to maturity effects. By augmenting all models with a news sentiment variable, we test the hypothesis whether including news sentiment in volatility models results in superior volatility forecasts. We find significant evidence that this hypothesis holds.

On the risk return relationship

Abstract While the risk return trade-off theory suggests a positive relationship between the expected return and the conditional volatility, the volatility feedback theory implies a channel that allows the conditional volatility to negatively affect the expected return. We examine the effects of the risk return trade-off and the volatility feedback in a model where both the return and its volatility are influenced by news arrivals. Our empirical analysis shows that the two effects have approximately the same size with opposite signs for the daily excess returns of seven major developed markets. For the same data set, we also find that a linear relationship between the expected return and the conditional standard deviation is preferable to polynomial-type nonlinear specifications. Our results have a potential to explain some of the mixed findings documented by previous studies.

A Dynamic Limit Order Market with Fast and Slow Traders

We study the role of high-frequency trading in a dynamic limit order market. Being fast is valuable because it enables traders to revise outstanding limit orders upon news arrivals when interacting with slow market participants. On the one hand, the existence of fast traders can help to reduce the inefficiency that is rooted in the risk of being picked off after unfavourable price movements and therefore allows more gains from trade to be realized. On the other hand, slow traders face a relative loss in bargaining power which leads them to strategically submit limit orders with a lower execution probability, thereby reducing trade. Due to this negative externality, the equilibrium level of investment is always welfare-reducing. The model generates additional testable implications regarding the effects of high-frequency trading on order flow statistics. JEL Classification: G19, C72, D62

On the Risk Return Relationship

While the risk return trade-off theory suggests a positive relationship between the expected return and the conditional volatility, the volatility feedback theory implies a channel that allows the conditional volatility to negatively affect the expected return. We examine the effects of the risk return trade-off and the volatility feedback in a model where both the return and its volatility are influenced by news arrivals. Our empirical analysis shows that the two effects have approximately the same size with opposite signs for the daily excess returns of seven major developed markets. For the same data set, we also find that a linear relationship between the expected return and the conditional standard deviation is preferable to polynomial-type nonlinear specifications.

How (Un)Informed is Trading?

We propose and estimate a structural model of daily stock market activity to test competing theories of trading volume. The model features informed rational speculators and uninformed agents who trade either to hedge endowment shocks or to speculate on perceived information. To identify the model parameters, we exploit enormous empirical variation in trading volume, market liquidity, and return volatility associated with regular and extended-hours markets as well as news arrival. We find that the model matches market activity well when we allow for overconfidence. At plausible values of overconfidence and risk aversion, overconfidence--not hedging--explains nearly all uninformed trading, while rational informed speculation accounts for most overall trading. Without overconfident investors, over 99% of trading volume disappears even when informed rational traders disagree maximally. These findings illustrate that modest overconfidence can help explain stark patterns in stock market activity.

On the Risk Return Relationship

While the risk return trade-off theory suggests a positive relationship between the expected return and the conditional volatility, the volatility feedback theory implies a channel that allows the conditional volatility to negatively affect the expected return. We examine the effects of the risk return trade-off and the volatility feedback in a model where both the return and its volatility are influenced by news arrivals. Our empirical analysis shows that the two effects have approximately the same size with opposite signs for the daily excess returns of seven major developed markets. For the same data set, we also find that a linear relationship between the expected return and the conditional standard deviation is preferable to polynomial-type nonlinear specifications.

Public News Arrival and Cross-Asset Correlation Breakdown: Implications for Algorithmic Trading

This study models the role of public news arrivals on asset correlation in a trading environment populated by computerized algorithms. The model is empirically tested with the individual stock futures and its underlying spot markets, which are characterized by the mechanical cost-of-carry relation that is typically exploited by algorithmic trading. In normal circumstances, the return correlation between the stock futures and spot quotes is nearly perfect, because futures market makers peg their quotes to those of the underlying by using computerized algorithms. Our simple model predicts that this near-perfect correlation can occasionally break down with two conditions: one, the futures market is less liquid than the corresponding spot market; and two, the uncertainty surrounding the impact of the news on the underlying stocks is sufficiently large. This breakdown occurs because the futures market makers switch from automating the quote-matching process to manually monitor and update their quotes. By employing the comprehensive RavenPack database with firm-level news releases, we test and confirm our model predictions. In particular, the spot-futures return correlation falls as the news uncertainty rises, and this correlation breakdown is more prominent for small-cap stocks. Furthermore, for actively traded stocks, the impact of the news on the breakdown is more intense. If the overall stock market experiences extreme turbulence, however, this impact is weaker. We discuss the implications for the limits of algorithmic trading.

Public News Arrival and Cross-Asset Correlation Breakdown: Implications for Algorithmic Trading

Public News Arrival and Cross-Asset Correlation Breakdown: Implications for Algorithmic Trading

A Dynamic Limit Order Market with Fast and Slow Traders

We study the role of high-frequency trading in a dynamic limit order market. Fast traders' ability to revise their quotes quickly after news arrivals helps to reduce the inefficiency that is rooted in the risk of being "picked off", which increases trade. However, their presence induces slow traders to strategically submit limit orders with a lower execution probability, thereby reducing trade. Because speed is a source of market power, it enables fast traders to extract rents from others and triggers a costly arms race that reduces social welfare. The model generates a number of testable implications concerning the effects of high-frequency trading in limit order markets.

Investors’ Reaction to Sharp Price Changes in GCC Markets

We examine investors’ reactions to sharp price changes in seven equity markets of Gulf Cooperation Council (GCC) countries to uncover patters of price formation. We compare the price behavior and volatility of these markets within a 15-day window following the arrival of new information. We find that the arrival of unexpected news that results in sharp price changes significantly increases market volatility in all GCC markets and that the subsequent price adjustments mostly exhibit upward corrective patterns. Contrary to the findings from some markets in other developed and developing countries, these results are consistent with the prediction of Uncertain Information Hypothesis. In reaction to both good and bad news, investors initially set equity prices below their fundamental values and subsequent price trends register an upward adjustment. These findings suggest that investors in GCC equity markets react rationally to the arrival of unexpected news and that no contrarian investment strategy can be utilized to generate abnormal return.

What Drives Daily Volatility in Lumber Futures Markets?

Abstract Most financial asset returns exhibit volatility persistence. This article investigates this phenomenon in the context of daily returns in lumber futures markets. The volatility of lumber futures is found to vary with different phases of the United States-Canada softwood lumber trade dispute. Daily price volatility was the highest in the post-Softwood Lumber Agreement period (2001–2005), followed by the Softwood Lumber Agreement period (1996–2000) and the period with trade disputes and temporary tariffs (1992–1995). An inverse relationship between inventory levels and volatility is found. Further, the inventory effect on volatility varies across the different periods of the trade dispute. An inverse relation between time to delivery and volatility is also found. Moreover, it is shown that although volatility persistence exists in the lumber futures market, the time gap between the arrival of news to the markets and the delivery time of futures contracts is the fundamental variable in explaining volatility persistence.

On the nonstationarity of the exchange rate process

We empirically investigate the nonstationarity property of the USD–JPY exchange rate by using a high frequency data set spanning 8years. We perform a statistical test of strict stationarity based on the two-sample Kolmogorov–Smirnov test for the absolute price changes, and Pearson's chi square test for the number of successive price changes in the same direction, and find statistically significant evidence of nonstationarity. Further, we study the recurrence intervals between the days in which nonstationarity occurs and find that the distribution of recurrence intervals is well approximated by an exponential distribution. In addition, we find that the mean conditional recurrence interval hTjT0i is independent of the previous recurrence interval T0. These findings indicate that the recurrence intervals are characterized by a Poisson process. We interpret this observation as a reflection of the Poisson property regarding the arrival of news.

Accounting Information Releases and CDS Spreads

We show that accounting information releases generate large and immediate price impacts, i.e. jumps, in credit default swap (CDS) spreads. Our approach is multivariate, which allows for identification of information events under the presence of confounding news, such as credit events and other simultaneous news arrivals. The economic impact of accounting news releases is twice as large as the impact of credit-related news. Good and bad news impact jumps in CDS spreads asymmetrically, and unscheduled announcements are more likely to cause jumps than scheduled ones. The arrival of accounting information is quickly absorbed in CDS spreads, suggesting efficient price discovery in the CDS market.

Public Information Arrivals, Limit Order Book, and Stock Price Changes

Using time-stamped company announcements released by the Australian Securities Exchange as a proxy for public information flows, we investigate how the information content of the limit order book varies with the arrival of news. We find that order book imbalance (the difference in slopes between order-driven demand and supply schedules) predicts subsequent short-term stock returns, and that the average slope negatively influences ex post innovation in short-term volatility. More importantly, these relationships actually strengthen after the arrival of public information. Our evidence is consistent with prior theoretical proposition that instead of immediately reducing information asymmetry, public information arrivals can temporarily increase the divergence of investors’ beliefs. As this amplifies the advantages of certain informed traders, those who are patient enough appear to rely on limit orders to capture gains from trades.

The intraday bid–ask spread behaviour of the JPY/USD exchange rate in the EBS electronic brokerage system

Using a sophisticated tool of the Periodic-Generalized Autoregressive Conditional Heteroscedastic (P-GARCH) model, this article investigates the market institutional effects of the determining role of trading activities and exchange rate volatility on the intraday behaviour of the 15-minute JPY/USD exchange rate bid–ask spreads in the Electronic Broking Service (EBS) of electronic brokerage market for the period from 1 January 2003 to 31 December 2005. We find that the exchange rate volatility both significantly and positively affects the bid–ask spreads, while the number of deals and quotation changes negatively affect the bid–ask spreads. These results are similar to those of past studies. We also find that a U-shaped pattern exists in the Tokyo trading hours and an inverted U-shaped pattern in the London trading hours, in addition to a round clock inverted U-shaped pattern of spread volatility. This inverted U-shaped pattern may be caused by unexpected news arrivals. The spread behaviour of the EBS global electronic broking market is not different from that of other electronic interdealer quotation markets and electronic broking markets.

The impact of macroeconomic news on quote adjustments, noise, and informational volatility

We study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances. We decompose bid and ask returns into a common (“efficient return”) factor and two market-side-specific components capturing market microstructure effects. The corresponding variance components reflect information-driven and noise-induced volatilities. We find that all volatility components reveal distinct dynamics and are positively influenced by news. The proportion of noise-induced variances is highest before announcements and significantly declines thereafter. Moreover, news-affected responses in all volatility components are influenced by order flow imbalances.

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