Equity offerings, stock price crash risk, and the impact of securities regulation: international evidence

We examine whether earnings manipulation around seasoned equity offerings (SEOs) is associated with an increase in the likelihood of a stock price crash post-issue and test whether the enactment of securities regulations attenuate the relation between SEOs and crash risk. Empirical evidence documents that managerial tendency to conceal bad news increases the likelihood of a stock price crash (Jin and Myers, 2006; Hutton, Marcus, and Tehranian, 2009). We test this hypothesis using a sample of firms from 29 EU countries that enacted the Market Abuse Directive (MAD). Consistent with our hypothesis, we find that equity issuers that engage in earnings management experience a significant increase in crash risk post-SEO relative to control groups of non-issuers; this effect is stronger for equity issuers with poor information environments. In addition, our findings show a significant decline in crash risk post-issue after the enactment of MAD that is stronger for firms that actively manage earnings. This decline in post-issue crash risk is more effective in countries with high ex-ante institutional quality and enforcement. These results suggest that the implementation of MAD helps to mitigate managers’ ability to manipulate earnings around SEOs.

Earnings management and stock price crashes post-crossdelisting

We test whether cross-delisted firms from the major U.S. stock exchanges experience an increase in crash risk associated with earnings management. Consistent with our prediction, we find that earnings management have a greater positive impact on stock price crash risk post-cross-delisting when compared to a sample of still cross-listed firms. Moreover, our results suggest that this effect is more pronounced for crossdelisted firms from countries with weaker investor protection and poorer quality of their information environment. We further examine whether managers’ ability to manipulate earnings increases post-cross-delisting around seasoned equity offerings. Our evidence shows that cross-delisted firms that engage in earnings management to inflate reported earnings prior to a seasoned equity offering are more likely to observe a subsequent stock price crash.

Comovement of stock price risk in strategic alliance — Case: mobile internet

The focus of this study has been comovement of stock price risk level between two companies as they form strategic alliance. Thus the main reason has been to shed more light to possible increased risk level that the stockholder confronts when a company he owns forms a strategic alliance with another company. This study has centralized to interfirm cooperation between mobile and internet companies, which have furthered the development of mobile internet. The study has been divided into theoretical and empirical part. In theoretical part the main concepts riskiness of a stock (volatility), comovement and strategic alliance have been run through. In empirical part seven strategic alliances formed by mobile internet companies have been examined. Based on this, strategic alliance seems to increase comovement of stock price risk in some degree. This comovement seems to be stronger when core businesses or operating environments of cooperating companies differ more from each other.

Asymptotic analysis of stock price densities and implied volatilities in mixed stochastic models

In this paper, we obtain sharp asymptotic formulas with error estimates for the Mellin con- volution of functions de ned on (0;1), and use these formulas to characterize the asymptotic behavior of marginal distribution densities of stock price processes in mixed stochastic models. Special examples of mixed models are jump-di usion models and stochastic volatility models with jumps. We apply our general results to the Heston model with double exponential jumps, and make a detailed analysis of the asymptotic behavior of the stock price density, the call option pricing function, and the implied volatility in this model. We also obtain similar results for the Heston model with jumps distributed according to the NIG law.

Stock Price Reactions on M&A, Dividends and Game Releases. Evidence from Gaming Industry.

A rapidly growing gaming industry, which specializes on PC, console, online and other games, attracts attention of investors and analysts, who try to understand what drives changes of the gaming industry companies’ stock prices. This master thesis shows the evidence that, besides long-established types of events (M&A and dividend payments), the companies’ stock price changes depend on industry-specific events. I analyzed specific for gaming industry events - game releases with respect to its subdivisions: new games-sequels, games ratings and subdivision according to a developer of a game (self-developed by publisher or outsourced). The master thesis analyzes stock prices of 55 companies from gaming industry from all over the world. The research period covers 5 year, spreading from April 2008 to April 2013. Executed with an event study method, results of the research show that all the analyzed events types have significant influence on the stock prices of the gaming industry companies. The current master thesis suggests that acquisitions in the industry affect positively bidders’ and targets’ stock prices. Mergers events cause positive stock price reactions as well. But dividends payments and game releases events influence negatively on the stock prices. Game releases’ effect is up to -2.2% of cumulative average abnormal return (CAAR) drop during the first ten days after the game releases. Having researched different kinds of events and identified the direction of their impact, the current paper can be of high value for investors, seeking profits in the gaming industry, and other interested parties.

Forecasting financial weather - can we foresee market sentiment? Spectrum of stock price behavior - NYSE case study

The desire to create a statistical or mathematical model, which would allow predicting the future changes in stock prices, was born many years ago. Economists and mathematicians are trying to solve this task by applying statistical analysis and physical laws, but there are still no satisfactory results. The main reason for this is that a stock exchange is a non-stationary, unstable and complex system, which is influenced by many factors. In this thesis the New York Stock Exchange was considered as the system to be explored. A topological analysis, basic statistical tools and singular value decomposition were conducted for understanding the behavior of the market. Two methods for normalization of initial daily closure prices by Dow Jones and S&P500 were introduced and applied for further analysis. As a result, some unexpected features were identified, such as a shape of distribution of correlation matrix, a bulk of which is shifted to the right hand side with respect to zero. Also non-ergodicity of NYSE was confirmed graphically. It was shown, that singular vectors differ from each other by a constant factor. There are for certain results no clear conclusions from this work, but it creates a good basis for the further analysis of market topology.

The Impact of a Corporate Name Change on Stock Price and Trading Volume of Canadian Companies

This thesis examines the impact of a corporate name change on stock price and trading volume of Canadian companies around the announcement date, the approval date, and the adoption date over the time period from 1997 to 2011. Name changes are classified into six categories: major and minor, structural and pure, diversified and focused, accompanied with a change in ticker symbol and without a change in ticker symbol, “Gold” name addition and deletion, and different reasons for name changes (e.g., merger and acquisition, change of structure, change of strategy, and better image). The thesis uses the standard event study methodology to perform abnormal return and trading volume analyses. In addition, regression analysis is employed to examine which type of a name change has the largest impact on cumulative abnormal returns. Sample stocks exhibit a significant positive abnormal return one-day prior to the approval day and one day after the adoption date. Around the approval date we observe significant abnormal returns for stocks with a structural name change. On the day after the adoption date we document abnormal returns for stocks with major, minor, structural, pure, focused, and ticker symbol name changes. If a merger or acquisition is the reason for a name change, companies tend to experience a significant positive abnormal return one-day before the approval date and on the adoption date. If a change of structure is the reason for a name change, companies exhibit a significant positive abnormal return on the approval date and a significant negative abnormal return on the adoption date. In case of a change of strategy as the reason for a name change, companies show a significant negative abnormal return around the approval date and a significant positive abnormal return around the adoption date.

Forecasting Intraday Stock Price Trends with Text Mining Techniques

In this paper, we describe NewsCATS (news categorization and trading system), a system implemented to predict stock price trends for the time immediately after the publication of press releases. NewsCATS consists mainly of three components. The first component retrieves relevant information from press releases through the application of text preprocessing techniques. The second component sorts the press releases into predefined categories. Finally, appropriate trading strategies are derived by the third component by means of the earlier categorization. The findings indicate that a categorization of press releases is able to provide additional information that can be used to forecast stock price trends, but that an adequate trading strategy is essential for the results of the categorization to be fully exploited.

The point and figure method of anticipating stock price movements; complete theory and practice,

Mode of access: Internet.

Stock price reaction following large one-day price changes: UK evidence

We examine the short-term price reaction of 424 UK stocks to large one-day price changes. Using the GJR-GARCH(1,1), we find no statistical difference amongst the cumulative abnormal returns (CARs) of the Single Index, the Fama–French and the Carhart–Fama–French models. Shocks bigger or equal to 5% are followed by a significant one-day CAR of 1% for all the models. Whilst shocks smaller or equal to -5% are followed by a significant one-day CAR of -0.43% for the Single Index, the CARs are around -0.34% for the other two models. Positive shocks of all sizes and negative shocks maller or equal to -5% are followed by return continuations, whilst the market is efficient following larger negative shocks. The price reaction to shocks is unaffected when we estimate the CARs using the conditional covariances of the pricing variables.

Stock price distributions and news:evidence from index options

We estimate the shape of the distribution of stock prices using data from options on the underlying asset, and test whether this distribution is distorted in a systematic manner each time a particular news event occurs. In particular we look at the response of the FTSE100 index to market wide announcements of key macroeconomic indicators and policy variables. We show that the whole distribution of stock prices can be distorted on an event day. The shift in distributional shape happens whether the event is characterized as an announcement occurrence or as a measured surprise. We find that larger surprises have proportionately greater impact, and that higher moments are more sensitive to events however characterised.