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Full-Text Articles in Business
Analyzing Cyber Threats Affecting The Financial Industry, Anna Skelton
Analyzing Cyber Threats Affecting The Financial Industry, Anna Skelton
Student Works
As critical infrastructure, financial institutions must execute the highest level of cybersecurity as the threat of a crippling cyberattack continues to develop. Malicious actors, including disenfranchised employees, state sponsored actors, and traditional hackers, all have motivations to target the financial industry, and do so frequently. However, the threat changes slightly between resource rich large institutions and their smaller, community bank counterparts. The complex and multifaceted threat must be fully understood in order to properly address and analyze solution options to preserve the security of these institutions and the economy that they contribute to.
A Regression Model To Predict Stock Market Mega Movements And/Or Volatility Using Both Macroeconomic Indicators & Fed Bank Variables, Timothy A. Smith, Alcuin Rajan
A Regression Model To Predict Stock Market Mega Movements And/Or Volatility Using Both Macroeconomic Indicators & Fed Bank Variables, Timothy A. Smith, Alcuin Rajan
Publications
In finance, regression models or time series moving averages can be used to determine the value of an asset based on its underlying traits. In prior work we built a regression model to predict the value of the S&P 500 based on macroeconomic indicators such as gross domestic product, money supply, produce price and consumer price indices. In this present work this model is updated both with more data and an adjustment in the input variables to improve the coefficient of determination. A scheme is also laid out to alternately define volatility rather than using common tools such as the …
Detecting Deception In Asynchronous Text, Fletcher Glancy
Detecting Deception In Asynchronous Text, Fletcher Glancy
Annual ADFSL Conference on Digital Forensics, Security and Law
Glancy and Yadav (2010) developed a computational fraud detection model (CFDM) that successfully detected financial reporting fraud in the text of the management’s discussion and analysis (MDA) portion of annual filings with the United States Securities and Exchange Commission (SEC). This work extends the use of the CFDM to additional genres, demonstrates the generalizability of the CFDM and the use of text mining for quantitatively detecting deception in asynchronous text. It also demonstrates that writers committing fraud use words differently from truth tellers.