With the surge of Internet-based corporate communication, organization, andinformation management, financial markets have undergone radical transformation. Inthe interconnected economy of today, market participants are forced to acceptcyberattacks, data breaches, system failures, or security flaws as any other (varying)cost of doing business. While cyberspace encompasses practically any firm indeveloped economies and a large portion in developing ones, combatting such risks isdeemed a question of firm-specific responsibility: the situation resembles an ‘every manfor himself’ scenario. Consulting standard financial theory, rational utility-maximizinginvestors assume firm-specific (idiosyncratic) risk under expectations of additionalcompensation for shouldering such risk – they are economically incentivized. The omnipresence of cyberattacks challenges fundamental assumptions of the CapitalAsset Pricing Model, Optimal Portfolio Theory, and the concept of diversifiability. Thethesis problematizes underlying rationality notions by investigating the effect of acyberattack on stock volatility. Explicitly, the use of stock volatility as a proxy for riskallows for linking increased volatility to higher risk premiums and increased cost ofcapital. In essence, we investigate the following research question: What is the effect ofa disclosed cyberattack on stock volatility for firms listed in the USA?. Using event study methodology, we compile a cyberattack database for events between2010 and 2015 involving 115 firms listed on US stock exchanges. The specified timeperiod cover prevailing research gaps; due to literature paucity the focus on volatilityfits well. For a finalized sample of 189 events, stock return data is matched to S&P500index return data within a pre-event estimation window and a post-event window tocalculate abnormal returns using the market model. The outputs are used to estimateabnormal return volatility before and after each event; testing pre and post volatilityagainst each other in significance tests then approximates the event-induced volatility.Identical procedures are performed for all subsamples based on time horizon, industrybelonging, attack type, firm size, and perpetrator motivation. The principal hypothesis, that stock volatility is significantly higher after a cyberattack,is found to hold within both event windows. Evidence on firm-specific characteristics ismore inconclusive. In the long run, inaccessibility and attacks on smaller firms seem torender significantly larger increases in volatility compared to intrusion and attacks onlarger firms; supporting preexisting literature. Contrastingly, perpetrator motive appearsirrelevant. Generally, stocks are more volatile immediately after an attack, attributableto information asymmetry. For most subsamples volatility seem to diminish with time,following the Efficient Market Hypothesis. Summing up, disparate results raisequestions of the relative importance of contingency factors, and also about futuredevelopments within and outside academic research.

本文主要在探討在何種情況下(意即金融危機發生前後)會發生比較顯著的跨國從眾現象。本文採用2003年10月1日到2009年2月28日期間的台灣加權指數、台灣50、美國S&P 500和道瓊工業指數報酬率資料，並利用Hwang and Salmon (2004)測量從眾行為指標的方法，檢驗在此段期間下，台灣投資人行為是否有明顯跟隨美國投資人行為的現象。實證研究發現整體而言在此段期間下，台灣存在顯著的跨國從眾行為。細部以月為單位探討從眾行為顯著結果的變化，可以歸納出以下三個結論：一、從眾行為主要發生在經濟情況相對穩定的情況下，意即在金融危機日趨嚴重以前，測量從眾行為的指標反而比較顯著。例如在2007年和2008年時的經濟情況比在2003年、2004年和2005年時還要衰退，但測量到存在顯著跨國從眾行為的月數反而較少。二、持續存在顯著跨國從眾行為的最長期間為2005年3月到2006年1月，歷經11個月。三、最常被觀測到有顯著從眾行為現象的月份為1月、11月和12月。 / This paper mainly examines under what conditions herding behavior is likely to become more significant and obvious, in which I modify Hwang and Salmon (2004)’s methodology and use the returns data of Taiwan Weighted Index, Taiwan 50 stock Index, S&P 500 stock and Dow Jones Industry Index of the sample period of October 1, 2003 to February 28, 2009 to test if there is any multinational herding behavior. I find that Taiwan investors in this sample period follow (even imitate) the investment actions of American investors. In more details about the herding patterns, we have found three main phenomena. First, herding behavior mainly occurs significantly during relatively quiet period, say, before the financial crisis, rather than when the market is under stress. The economic situations in 2007 and 2008 are much worse than in 2003, 2004, and 2005, but numbers of months exhibiting significant herding in these bad situations are less. Second, the longest lasting time of herding is March 2005 to January 2006, which totally lasts for eleven months, and this period is before 2007 and 2008 in which the financial systems are destroyed badly. Third, herding always happens in January, November and December given the sample period.