Arrival Of News Research Articles

Investor Attention, News and the Herd Behavior of Individual Stock Returns

Structured Abstract
 Purpose: This paper examines the herd behavior of individual returns around their industry average in stressful market condition, when industry-wide investor attention is high or low, and when good or bad industry-wide news reach the market.
 Design/Methodology: Decile portfolios are formed based on proxies of aggregate market condition, industry-wide level of investor attention and industry-wide news signal. Indicator variables identifying the months in the highest and lowest portfolios are then entered in regression tests to capture the degree to which returns herd around their industry. Both cross-sectional and absolute deviations from industry average are used to measure herding.
 Findings: The analysis reveals that for the majority of industries, herding exist in calm market conditions. Moreover, herding appears to be concentrated in months where the industries record abnormally low returns and in months in which the industries receive good news.
 Practical Implications: Understanding what drives the herd behavior of individual returns in financial markets portfolio may be of particular value to investment companies, such as hedge funds, who seek to time the market.
 Originality/Value: The paper offers new insights on the drivers of herding in financial markets. While prior studies focus on examining herding in market turmoil, this paper documents herding around extreme events and following news arrival.

The effect of conscientiousness on managerial learning from stock prices

ABSTRACT We examine whether and how CEOs’ conscientiousness affects managerial learning from stock prices. We use a deep learning-based natural language processing model to detect personality from textual records of an individual’s spoken language. Following personality psychologists’ description of the personality trait of conscientiousness, we hypothesize that conscientious CEOs are more willing and able to collect comprehensive decision-relevant information compared to others, and to the extent that they possess more relevant information prior to the arrival of news in the stock market, they have less to learn from it. From the sample of large U.S. public firms for which earnings call transcripts are available, we replicate the well-stylized investment-to-price relationship while reporting a weaker relationship for firms with conscientious CEOs. This finding supports the hypothesis that conscientious managers learn less from stock prices.

Dissecting the idiosyncratic volatility puzzle: A fundamental analysis approach

This paper argues fundamental information help resolve information uncertainty that leads to high idiosyncratic volatility premium. The IVOL-return relation is negative for stocks with poor fundamental strength but positive for stocks with strong fundamental strength. The arrival of fundamental news weakens the negative IVOL effect. Our findings are robust for alternative model specifications. Moreover, the negative IVOL effect dominates the positive IVOL effect due to arbitrage asymmetry that buying is easier than short selling stocks. Consistent with arbitrage asymmetry, the negative IVOL effect is stronger for stocks with low institutional ownership and following high investor sentiment. Overall, we provide a simple fundamental-based explanation for idiosyncratic volatility puzzle.

Understanding the Impact of COVID-19 on the Volatility Dynamism of Brics Stock Market

The current study is aimed at examining the impact of COVID-19 on the volatility of stock market returns in the BRICS economies known as trading partners. Due to the pandemic situation all over the world, it had influence on the working of the stock markets as the investors were vigilant of their investment position. Since the World Bank declared the first confirmed case of covid-19 on 31st December 2019, hence the daily data of the stock indices of the each of the BRICS economies has been fetch ranging from 1st January 2020 to 31st march2021. The impact of Covid-19 has been studied by taking the number of daily Covid-19 cases in the respective economies. The study employed GARCH family of models such as GARCH(1,1), EGARCH(1,1) and MGARCH(1,1) in order to understand the nature and dynamics of volatility of the stock markets of BRICS economies so that investors can make decisions regarding portfolio management while investing in such economies acting as global trading partners. The study found that except China and South Africa, the rising number of cases has significant impact on BRICS economies volatility. The level of persistence is found to be high, revealing that it takes time to volatility to wipe out from the market, indicating that the BRICS stock market remained volatile for a longer period of time due to the arrival of Covid news.

Sejarah Aceh: Jejak Peradaban Aceh Darussalam Hingga Kolonial Belanda 1530-1900

Abstract: Tracing the history of Aceh through traces of civilization that was built from the period of the Kingdom of Aceh Darussalam has been recorded in historical records as well as documentation in the form of pictures and map images. It is necessary to trace existing buildings and other objects from past human cultural works to explain the existence of civilization created from the two periodization of Aceh history. The method used is descriptive analysis. The results of the study describe that the objects produced during the Kingdom of Aceh Darussalam experienced development. During the Kingdom of Aceh Darussalam, crowds were more directed at the coast so that development in the interior was not so visible, while during the Dutch colonial period construction was carried out in almost all of Aceh, especially in the construction of land roads. It caused by the needs of the government related to the transportation of natural resources in the form of agricultural products produced by the community. In addition, road construction was also carried out to speed up the arrival of news from remote areas to cities and from the city center to the Dutch government in Singapore. The news delivery system is carried out through the post office and telegram. This study suggests the need for in-depth research related to archaeological traces by conducting excavations so that building structures can be found in order to reconstruct history through their remains.

What drives most jumps in global crude oil prices? Fundamental shortage conditions, cartel, geopolitics or the behaviour of financial market participants

AbstractSeveral studies have emphasised the potential role of oil price volatility as a leading macroeconomic indicator, since it provides prominent information to energy traders, market participants and policymakers. In an effort to shed fresh insights into the underlying factors of wide oil price changes, the objective of this paper is twofold: First to capture large oil price changes caused by the arrival of surprising news (i.e., jumps); second to distinguish between short‐, medium‐ and long‐term determinants of jumps in oil prices due to changes in oil supply and demand fundamentals, factors associated with the market power of oil producers, speculation, geopolitical risks and OPEC's spare capacity. Using an empirical mode decomposition (EMD), we find that oil supply and demand forces are the most prevalent in determining oil price changes in the long run, which is quite predictable. OPEC gains increasing importance in the medium‐ and long‐terms. Our method also allows us to show that OPEC's use of spare capacity moderately reduces oil price volatility in the short‐term, thus suggesting a limited stabilising influence on the oil market. We consider broader policy implications for our results.

Impact of network investor sentiment and news arrival on jumps

This paper studied the influence of news announcements and network investor sentiment on Chinese stock index and index futures market jumps. A machine learning text analysis algorithm was employed to measure investor forum sentiment. It was found that news arrivals were an important reason for jump occurrences, jumps were significantly associated with network investor sentiment, and while occasionally the news and network investor sentiment resulted in simultaneous market jumps, they appeared to be relatively independent. The network investor sentiment time-lag and asymmetric effects were also tested, from which it was found that network investor sentiment had a significant asymmetric effect on the jumps, but time-lag effects had little influence. News announcements and the top 25% of the extreme network sentiments were found to explain more than 50% of the jumps, with extreme sentiments tending to increase the volatility of the news-related jumps and persistently influencing returns after the news-related jumps.

Twitter Sentiment Analysis and Influence on Stock Performance Using Transfer Entropy and EGARCH Methods.

Financial economic research has extensively documented the fact that the impact of the arrival of negative news on stock prices is more intense than that of the arrival of positive news. The authors of the present study followed an innovative approach based on the utilization of two artificial intelligence algorithms to test that asymmetric response effect. Methods: The first algorithm was used to web-scrape the social network Twitter to download the top tweets of the 24 largest market-capitalized publicly traded companies in the world during the last decade. A second algorithm was then used to analyze the contents of the tweets, converting that information into social sentiment indexes and building a time series for each considered company. After comparing the social sentiment indexes’ movements with the daily closing stock price of individual companies using transfer entropy, our estimations confirmed that the intensity of the impact of negative and positive news on the daily stock prices is statistically different, as well as that the intensity with which negative news affects stock prices is greater than that of positive news. The results support the idea of the asymmetric effect that negative sentiment has a greater effect than positive sentiment, and these results were confirmed with the EGARCH model.

News Arrival, Time-Varying Jump Intensity, and Realized Volatility: Conditional Testing Approach

Abstract This paper introduces new econometric tests to identify stochastic intensity jumps in high-frequency data. Our approach exploits the behavior of a time-varying stochastic intensity and allows us to assess how intensely stock market reacts to news. We describe the asymptotic properties of our test statistics, derive the associated central limit theorem and show in simulations that the tests have good size and reasonable power in finite-sample cases. Implementing our testing procedures on the S&P 500 exchange-traded fund data, we find strong evidence for the presence of intensity jumps surrounding the scheduled Federal Open Market Committee (FOMC) policy announcements. Intensity jumps occur very frequently, trigger sharp increases in realized volatility and arrive when differences in opinion among market participants are large at times of FOMC press releases. Unlike intensity jumps, volatility jumps fail to explain the variation in news-induced realized volatility.

Forecast Revisions as Instruments for News Shocks

Upon arrival of macroeconomic news, economic agents update their beliefs about the long-run fundamentals of the economy. I show that signals about the agents’ long-run expectations, proxied by the economic outlook revisions of professional forecasters, convey sufficient information to identify the effects of expected future technological changes, or news shocks. A major advantage of this approach from the existing news shock literature is that it does not depend on an empirical measure for technology, or on assumptions about common trends and timing of the technological change. I show that technological news shocks cause a strong anticipation effect in investment and an increase in hours, while there is less evidence of consumption smoothing over time---in line with news-driven business cycle models featuring a key role of financial frictions.

Is there news in inventories?

We identify total factor productivity (TFP) news shocks using standard VAR methodology and document a new stylized fact: in response to news about future increases in TFP, inventories rise and comove positively with other major macroeconomic aggregates. We show that the standard theoretical model used to capture the effects of news shocks cannot replicate this fact when extended to include inventories. We derive the conditions required to generate a procyclical inventory response by using a wedges approach. To explain the empirical inventory behavior, we consider two mechanisms: sticky wages and the presence of knowledge capital accumulated through learning-by-doing. Only the latter moves the wedges to qualitatively match the empirical behavior. The desire to take advantage of higher future TFP through knowledge capital drives output and hours choices on the arrival of news and leads to inventory accumulation alongside the other macroeconomic variables. The broad-based comovement a model with knowledge capital can generate, supports the view that news shocks are an important driver of aggregate fluctuations.

The Market Reaction to Corporate News in Emerging Markets: Evidence from India

In the context of market efficiency, the stock market reaction to arrival of corporate news has been widely researched in the context of developed markets. However, developed markets are distinct from emerging markets. In this paper, we employ a unique dataset of firm-specific news and examine the market reaction associated with the arrival of different firm-specific corporate news in the public domain in the context of India, an emerging market. We study the price and trading volume reaction associated with firm-specific news for a sample of stocks listed on the National Stock Exchange of India using the event study methodology. We observe that the Indian equity market incorporate new information through firm-specific corporate news releases promptly. Besides, our results underscore the role of earnings news and analyst calls in reducing information asymmetry among investors in an emerging market context. Our study reveals that the observations in the context of developed markets cannot be generalized in the context of the India.

Macroeconomic news announcements and market efficiency: Evidence from the U.S. Treasury market

• This paper investigates the impact of scheduled macroeconomic news announcements on the U.S. Treasury market’s efficiency. • This paper employs a robust method to construct market inefficiency measures using intraday data and controlling for microstructure noise. • The U.S. Treasury market is less efficient in the five-minute interval before news arrival. • Investor heterogeneity provides a possible explanation for the decreased market efficiency before scheduled news announcements. We investigate the impact of scheduled macroeconomic news announcements on the U.S. Treasury market’s efficiency. Using intraday data and controlling for microstructure noise, we employ a robust method to construct market inefficiency measures. We find that the U.S. Treasury market is less efficient in the five-minute interval before news arrival. Our findings are robust for different sample periods, macroeconomic news announcements, and market inefficiency measures. We find that investor heterogeneity provides a possible explanation for the decreased market efficiency before scheduled news announcements.

Preparing Silver Linings for a Cloudy Day: The Consequences of Preemptive Benefit Finding.

Waiting for important news is stressful. In four studies, we assess the utility of preemptive benefit finding, a coping strategy in which people seek silver linings in bad news before receiving news, for emotional well-being across several waiting periods (waiting for bar exam results, the outcome of political elections, and results of a fictitious health risk assessment). Our findings support the effectiveness of preemptive benefit finding while waiting, such that identifying benefits in bad news while waiting predicts more positive emotions during the wait (Studies 3 and 4) and buffers people against the emotional consequences of bad news by boosting post-news positive emotions (Studies 2-4). Importantly, engaging in preemptive benefit finding does not backfire if a person ultimately receives good news (Studies 1, 3, and 4). We discuss results from a mini meta-analysis and consider implications of our findings for interventions to improve well-being while waiting and after news arrives.

The Real Side of the High-Volume Return Premium

Prior literature demonstrates that increased trading activity of a firm’s stock is associated with abnormal future stock returns (the high-volume return premium) and interprets this phenomenon as evidence that increased visibility generates reductions in cost of capital. Motivated by this interpretation, we investigate whether increased trading activity entails changes in real corporate actions. We document a positive relation between abnormal trading volume, future investment expenditures, and financing cash flows. This positive relation is not subsumed by the arrival of investment-related news or other corporate disclosures or by subsequent earnings information and is concentrated among firms with high financial constraints and firms with lower levels of investor recognition. This paper was accepted by David Simchi-Levi, finance.

How do banking analysts behave around unanticipated news? Evidence from operational risk event announcements

We study earnings per share (EPS) forecast revision and accuracy of banking analysts around operational risk event announcements in U.S. banks. We find that first announcements of operational risk events are more informative than their settlement announcements. Optimistic banking analysts revise their forecasts downward more aggressively around operational risk disclosures, thereby improving forecast accuracy. Career concerns of banking analysts cause an upward bias in forecast revision and deterioration in forecast accuracy only if the potential employer is a systemically important bank (SIB). We find consistent evidence linking competition among banking analysts with optimistic and inaccurate forecasts, which is consistent with analysts seeking to use inflated forecasts to curry favour and attract businesses to their brokerage house around the time of operational risk disclosures. Global settlement has no favourable impact on analyst forecast accuracy around operational risk event announcements. We find evidence supporting a materiality threshold of $10 million for the informativeness of operational risk event announcements in SIBs. Overall, our results shed light on optimism bias in banking analyst behaviour upon the arrival of unanticipated news.

High-frequency volatility modeling: A Markov-Switching Autoregressive Conditional Intensity model

We develop a Markov-Switching Autoregressive Conditional Intensity (MS-ACI) model with time-varying transitional probability, and show that it can be reliably estimated via the Stochastic Approximation Expectation–Maximization algorithm. Applying our model to high-frequency transaction data, we detect two distinct regimes in the intraday volatility process: a dominant volatility regime that is observable throughout the trading day representing the risk-transferring trading activity of investors, and a minor volatility regime that concentrates around market liquidity shocks which mainly capture impacts of firm-specific news arrivals. We propose a novel daily volatility decomposition based on the two detected volatility regimes.

Random Evolving Lotteries and Intrinsic Preference for Information

We introduce random evolving lotteries to study preference for non‐instrumental information. Each period, the agent enjoys a flow payoff from holding a lottery that will resolve at the terminal date. We provide a representation theorem for non‐separable risk consumption preferences and use it to characterize agents' attitude to non‐instrumental information. To address applications, we characterize peak‐trough utilities that aggregate trajectories of flow utilities linearly but, in addition, put weight on the best (peak) and worst (trough) lotteries along each path. We show that the model is consistent with recent experimental evidence on attitudes to information, including a preference for gradual arrival of good news and the ostrich effect, that is, decision makers' tendency to prefer information after good news to information after bad news.

Mobility Before Government Restrictions in the Wake of COVID-19

During the first wave of COVID-19 in Europe, people’s retail and recreation mobility, a high-frequency proxy for consumption, correlated negatively with the increase in the number of COVID-19 daily cases. We propose a new identification strategy to assess how much of the drop in retail mobility was driven by the arrival of bad epidemiological news, represented by the reported daily new cases. In particular, we exploit the time span between the first reported COVID-19 infection in a country and the implementation of a nationwide stay-at-home order. As during this period people were free to move, we argue that this approach allows us to disentangle the role of virus fear from that of restriction in the observed mobility reduction. We find that a 1% increase in daily cases reduces retail mobility by approximately 0.02%, a sizable effect in the context of the first wave of the pandemic. This suggests that the epidemic affects household spending and economic activity, even in the absence of mandatory mobility restrictions.

AN EMPIRICAL APPROACH TO THE STUDY OF INFORMATION EFFICIENCY AND HISTORICAL INERTIA OF STOCK MARKETS

There is no consensus among experts (both theorists and practitioners) about the possibilities of forecasting in organized markets, and therefore two points of view have been formed. Proponents of the first believe that the capital market is information-efficient and does not provide grounds for reliable prediction of market prices, since they can only change if news arrives, the appearance of which does not follow any regularity, therefore, there are no regularities in pricing. Proponents of the second theory admit historical inertia in pricing, at least in organized stock markets, justifying this by the inertia of market actors ' thinking, which extends to decision-making processes, hence the grounds for relatively reliable forecasting of market prices. Both points of view can be supported statistically, but there is no mathematical evidence in favor of either of them. However, if we accept as exhaustive the results of the empirical test, which is based on the tools of probability theory and mathematical statistics, we will have to accept the presence of historical inertia in pricing in organized stock markets, which may increase or decrease, but is always present