Characterizing Data Breach Severity: A Data Analytics Approach

Data breaches have been causing havoc for many years and continue to rise as organizations find new ways to do business using technology. Companies spend time finding ways to protect themselves from data breaches. Cybersecurity communities share and network with one another to stand against the one that thrives to take organizations down. Avoiding data breaches remains to be a top priority and companies need to resolve a data breach dispute as soon as it happens. Resolving data breaches as soon as they happen is an important task and requires a quantitative prediction of breach likelihood to mitigate risk and prepare for response (Jeyaraj et al. 2021). Data breaches can be due to unintended disclosure (DISC), Hacking or malware (HACK), payment card fraud (CARD), insider accessing sensitive information (INSD), loss or stolen assets or records (PHYS), loss of portable devices (PORT), and loss of stationary digital equipment such a server (STAT) (Ayyagari 2012). In this study, a Cyber Security Risk Quantification and Mitigation Framework is discussed. First, a breach level index model is introduced to quantify and classify the severity of a data breach incident based on the type of data asset, account details, or financial details, which was exposed. Then, a likelihood-Impact analysis is discussed to assess the risk involved in each type of data breach. The proposed framework is applied to data breaches gathered from S&P 500 organizations to prescribe strategies that can help firms reduce the likelihood and impact of data breaches. Our results suggest that hacking and malware need to be reduced as they are the highest impact and highest probability when it comes to a data breach. The results of this study help organizations identify the likelihood and impact of a data breach and determine a plan of action on how to mitigate the risks. An interactive Tableau dashboard is built which can serve as a valuable tool to estimate the risk and impact of various types of data breaches. Implications for research and practice are discussed

Text Analytics for Sports Fan Engagement in Social Media

Digital communications have greatly increased the engagements between customers and businesses of all varieties, to include university athletic programs and their fans. Interaction and engagement through social media content plays a critical role in developing the relationship between fans and their favorite teams [1] and boosting brand popularity [2]. In this study we reviewed the existing literature pertaining to the use of sentiment analysis and content categorization for university sports fan engagements. Dozens of sources were examined and their methodologies explored. This study seeks to demonstrate that the use of text mining and sentiment analysis can provide significant time savings to the athletics departments for the betterment of their data understanding. In turn this process will yield improved fan engagement of a growing fan base without increased personnel hours being expended. Using the textual data gathered from Basketball Season Ticket Holder Survey Results at a major Midwestern university in the United States, multiple analytical models were created, using several different text mining packages, each one seeking to classify the polarity of the comments being examined. The study explored the possibility of classifying comments as positive or negative at the sentence level or by combining several statements originative from a single comment. Statements were further categorized according to the subject matter of the comment. Inconsistencies were found between what these models determined and what a basic understanding of English suggests is actually true. Through tweaking of models and usage of more effective text mining algorithms performance improved. Ultimately, it was determined that text mining and sentiment analysis models would be capable of performing the necessary analysis. Implications for research and practice are discussed

The Information Content of 10-K Narratives: Comparing MD&A and Footnotes Disclosures

This paper examines the characteristics and variations within firms’ 10-K filings over a 20 year time period. We find that investors’ reaction to textual characteristics of the MD&A in 10-Ks is much stronger and more timely than their reaction to textual characteristics of the notes to the financial statements. Characteristics of the MD&A and footnotes are also predictive of future returns, volatility, and firm profitability. Our evidence suggests that investor pay limited attention to the footnotes compared to the MD&A and that firms exploit biases in investors’ information processing through their disclosure choices within 10-K filings

The study of relationship between Asian stock exchanges and New York stock exchange

U ovom se radu istražuju veze između tržišta kapitala 5 azijskih zemalja, uključujući Maleziju, Indoneziju, Filipine, Japan i Tursku, i onih u USA uz primjenu korelacijske i Vektorske Auto Regresivne analize (VAR). Koristili smo mjesečne podatke za razdoblje 1995. ÷ 2010. Američke su se burze uspoređivale sa svim azijskim burzama dok je najslabija bila korelacija japanskih i azijskih tržišta. Rezultati VARa pokazuju interakcije multilateralne dobiti među tržištima. Ukupno, rezultati pokazuju da prošla dobit (povijesna), bilo vlastita ili drugih burzi, pomaže u objašnjavanju postojeće dobiti na tržištu. Ovo je u suprotnosti s učinkovitosti slabog oblika. Osim toga, ustanovili smo značajan učinak preljevanja američkog tržišta kapitala na svih 5 azijskih tržišta. "Block exogenity" testom smo ustanovili da najviše vanjskog utjecaja dolazi iz američkih burza. No njihov utjecaj na japanska tržišta kapitala je relativno slab.This paper investigates the linkages between equity markets of 5 Asian countries, including Malaysia, Indonesia, the Philippines, Japan and Turkey and those in USA employing correlation analysis and Vector Auto Regressive (VAR). We used monthly data for the period 1995 ÷ 2010. The US stock markets were correlated with all Asian stock markets and Japan was correlated least strongly with the other Asian markets. The VAR results show significant multilateral returns interactions among the markets. Overall, the results show that historical returns, either own or from other stock markets, help explain market current returns. This is in contrast to weak form efficiency. Additionally, we found a significant spillover effect from the US equity market to all 5 of the Asian markets. In block exogenity test we found that USA is the most exogenous. But the influence of the US on the stock markets of Japan is relatively weak

Data Analytics for Digital Entrepreneurship: A Case Study on Airbnb

This project focuses on Airbnb, a peer-to-peer businesss, and analyzes how a micro-entrepreneur can use the features and affordances of these online platforms to improve their image and potential profit

Bidder Earnings Forecasts in Mergers and Acquisitions

We provide evidence on the benefits and costs of pro-forma earnings forecasts by bidding firms during acquisitions. We find that these forecasts are associated with a higher likelihood of deal completion, expedited deal closing, and with a lower acquisition premium − but only in stock-financed acquisitions. Our results are most consistent with forecast disclosure positively affecting the value perceptions of target shareholders persuading them to agree to acquisitions with stock. Our findings reveal that the effects of these public disclosures are stronger when private communication with target shareholders is more constrained. However, benefits of forecast disclosure only accrue to bidders that have built a credible forecasting reputation prior to the acquisition. Explaining why not all bidders forecast, we provide evidence on high forecasting costs, namely higher likelihood of post-merger litigation and CEO turnover, particularly for bidders with a weak forecasting reputation and for those that underperform post-merger