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What It Takes to Be an Security Analyst

What It Takes to Be an Information Security Analyst – Business Journal NEPA

Original article printed by DAVE GARDNER BUSINESS JOURNAL NEPA / PUBLISHED: MARCH 2, 2015

Information security analysts play a big part in the battle against the bad guys. The U.S. Bureau of Labor Statistics (BLS) forecasts that demand for these highly-skilled workers will grow at a rate exceeding 36 percent between 2012 and 2022. Considering that cybersecurity specialists safeguard the computer networks which house corporate secrets and financial data, pay rates for these jobs will undoubtedly be healthy.

Recent news headlines detail failures in recent corporate IT security. According to the Wall Street Journal, the country’s second-biggest health insurance company, Anthem Blue Cross, became the victim of hackers who stole records for millions of customers and employees. Hackers also infiltrated Hollywood in an attempt to derail the Sony film, “The Interview.”

Commercial systems must use real-time monitoring and scalable threat-detection, explains Daniel Sputa, director of information security with UM Tech. Companies must establish processes that protect the security and integrity of data, emails, files and human resource documents. Security demands that specific company data goes only to designated recipients. Moreover, systems must be in place to stop anyone who tries to disable a computer system or shut it down.

Sputa is a native of the Czech Republic and attended Marywood University. While still in the Czech Republic, Sputa developed an interest in computer technology. He says he built an entire computer in his teens and later studied electronics and cybernetics before earning multiple degrees, including a master’s in financial information systems.

“Success in my job involves a mix of specialized knowledge, including technical and math subjects, as well as business and financial information,” says Sputa.

Despite his technical knowledge, Sputa explains that the instrumental key to secure data depends upon people, not computers. He says because security breaches usually involve human failings, system users must be properly trained.

“A cyber-infection can be spread because of too-simple passwords or user carelessness. These are the biggest problems we face,” says Sputa. “One wrong click can let a cyber infection loose.”

Identity theft, according to Sputa, is one of the most familiar types of cyber-crime. There’s a big market for stolen financial data, like credit-card numbers. Identity theft helps promote the spread of malware, viruses, and spyware — all of which can haunt a business. According to Sputa, access to company email boxes may now be the biggest threat security specialists face.

Hackers regularly steal contact lists for business, create fake emails, spread infections and sell company data to competitors. Unfortunately, it’s unrealistic to expect that every computer user in business is trained to recognize and avoid every threat.

“Corporate espionage, which involves a formal cyber-attack against an entire company, has become a big problem, says Sputa. “These attacks may be designed to disable an entire system and shut that company down.”

The personal qualities needed to become a security specialist, according to Sputa, do not necessarily include suspicion. Instead, good security requires a deep understanding of human behavior, as well as the ability to foresee scenarios hackers may attempt.

Security analysts must understand the many ways IT connects us, possess strong IT systems knowledge and learn some programming as it pertains to security vulnerabilities. Strong knowledge of basic business processes is also needed.

“At the end of the day, however, security technology leads back to people,” says Sputa. “The weakest part of a system is the users. We can’t expect them to be technicians. Training can never be complete.”

The complexity of cybersecurity, according to Sputa, requires the analyst to think regarding prevention. If a breach does occur, rapid detection and damage control are essential, but additional layers of security should then be constructed to avoid similar attacks in the future.

These multiple layers of IT security, according to Sputa, resemble watertight doors on a large ship that can be quickly closed if the hull is breached.

To maintain security, he reviews ongoing reports that indicate which types of attacks are attempted. These attempts easily total in the hundreds to thousands, as hackers scan computer systems, poking for holes.

“It’s interesting to see these various pokes. As more cloud systems come into use, security must grow alongside it,” says Sputa. “This is all part of an open season on computer systems, where even one security mistake can be very costly.”

Salaries for information security analysts can range from $60,000 to $100,000 annually.

Every work day for the analyst is different as they review security audits; devise methods to improve systems and reduce costs; study prevention, issues, and trends; identify new products and services, and conduct employee training.

One example of a new threat, Sputa says, is that cybercriminals can use a computer virus to hack encrypted data and then hold the information they seize hostage for ransom. In these situations, the business must act quickly to save its data and customers.

Information security analysts also study cyber forensics to determine why a system failure occurred. Once again, however, effective information security leads back to people.

“Consider the example of where a company’s cleaning service was using the computers at night,” says Sputa. “This was possible because the passwords were stuck on the computers with sticky notes and no one thought to investigate the cleaning company.”

As he looks into his crystal ball, Sputa expects the number of cyber attackers will only expand. The crime will become more sophisticated, but technology on the horizon will be very useful in prevention.

“IT systems will eventually be using multiple security technologies, like biometrics, that can identify the fingers of designated users,” says Sputa. “When biometrics is combined with conventional passwords, it creates the multiple-layer security systems now preferred.”

A Data Mining Model for an Effective Trading System

Authors: Ahmed Gomaa, Marywood University, Daniel Sputa, Marywood University, Rex Dumdum, Marywood University

Abstract

This research explored how financial statement ratios can help predict future stock direction on a quarterly basis. The study focused on a contrarian investment strategy and tested whether financial data could be collected, organized, and used to train predictive models effectively.

Several data mining models were applied to identify the variables with the strongest predictive value. The findings suggested that the Association Rule model performed best, reaching 71.43% accuracy in predicting stock direction while using only a limited number of variables.

The analysis was based on published financial statements from companies listed in the S&P 500 over a five-year period.

Introduction

Investors use different methods to predict stock price direction, including fundamental analysis, technical analysis, and behavioral analysis. During this process, they choose which variables to focus on, often based on experience, investment style, or strategy.

At Marywood University in Scranton, Pennsylvania, the Pacer Investment Fund gave graduate students the opportunity to manage real investments under faculty supervision. The team focused primarily on value investing and contrarian strategies, aiming for long-term profitability with controlled risk.

Their investment decisions typically began with fundamental analysis, reviewing financial statements and calculating key ratios to evaluate a company’s financial health. This was followed by technical analysis to study stock price trends and behavioral analysis to consider broader economic, political, and market influences.

Because each stage of the decision-making process depends on valid assumptions and reliable data, understanding which financial variables matter most is critical.

Research Problem

One of the main challenges in fundamental analysis is determining which financial variables most strongly influence stock price direction. It is also difficult to identify the threshold values those variables should reach in order to suggest whether a stock price is likely to rise or fall.

The goal of this study was to identify the most important financial variables affecting stock price direction and determine the thresholds associated with them, particularly in the context of a contrarian investment strategy.

Related Work

Previous research on contrarian investing often focused on constructing portfolios using selected variables and then measuring performance over time. However, many studies did not clearly explain why specific variables were chosen.

For example, prior research used measures such as beta, market risk premium, earnings-to-price ratio, book-to-market value, cash flow-to-price, and growth rates. While these studies showed results, they often lacked clear justification for variable selection.

This study aimed to address that gap by using data mining techniques to identify which variables actually matter most when analyzing financial statements.

Methodology

The analysis used published financial statements from S&P 500 companies over the previous five years. Quarterly financial data was collected, and a variety of ratios were calculated, including measures related to liquidity, capital structure, and inventory management.

The initial dataset included thousands of rows of company-quarter observations. Data was reviewed internally for validity and completeness. Because the study focused on contrarian stocks, only companies with a beta of 1 or lower were included.

The data was then matched to stock price movement by quarter, indicating whether the stock price increased or decreased compared with the previous quarter.

Different data mining models were then trained using part of the dataset and tested on the remaining portion. The models received only financial ratios as input and attempted to predict whether the stock price would go up or down. Their predictions were then compared with actual market outcomes.

Results

Association Rules (AR)

The Association Rule model identified meaningful relationships among variables linked to stock price increases and decreases.

For stock price increases, the strongest indicators included:

  • High P/E ratio
  • High current ratio
  • High quick ratio
  • Inventory turnover within a specific range

The most relevant thresholds included:

  • Current ratio ≥ 2.2
  • Inventory turnover between 1.8 and 4.2
  • Cash ratio ≥ 0.64
  • P/E ratio > 14.04

For stock price decreases, the model found significance in:

  • High receivables turnover
  • Low current ratio
  • Low cash ratio
  • Low debt-to-equity ratio
  • Low P/E ratio

Decision Trees (DT)

The Decision Tree model confirmed the importance of the P/E ratio. When the P/E ratio was below 11.46, more than half of the stocks decreased in price. When the P/E ratio was 14 or higher, stocks were much more likely to increase in price.

This suggested a general pattern: higher P/E ratios were associated with stronger stock price growth.

Naïve Bayes (NB)

The Naïve Bayes model produced similar results, also indicating that higher P/E ratios were associated with an increased likelihood of stock price growth.

Neural Network (NN)

The Neural Network model calculated probabilities for each variable and its significance. For example, when the P/E ratio was below 14, the model predicted stock price decline in 57% of cases.

Which Model Performed Best?

Each model approached prediction differently, and their accuracy varied. Among the models tested, the Association Rule model proved to be the most accurate.

It achieved a prediction accuracy of 71.43%, making it the strongest performer in this study.

Conclusion

This research demonstrated that financial statement ratios can be used to predict stock price direction with a meaningful level of accuracy. More importantly, it showed that data mining methods can help identify not only which variables matter, but also the threshold values that may signal upward or downward movement.

The study also validated the overall learning method: collecting financial data, loading it into a system, training models, and testing predictions on separate data. In that sense, the project successfully proved the concept and established a foundation for further work.

Among all tested models, Association Rules delivered the strongest results and highlighted a small group of financial indicators with the greatest predictive relevance.

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Daniel Sputa, highlighted in Business Journal NEPA’s “20 Under 40,” serves as the decision support manager at UM Technologies Exchange, managing business intelligence for a leading energy management platform. His tech journey started at 16, leading to advanced studies in the US, including a master’s degree and Lean Six Sigma Green Belt certification. From early beginnings in tech support and cleaning cafeteria tables, Sputa rose through diverse roles, valuing simplicity, honesty, and continuous learning. He believes success in leadership stems from a broad understanding across life, business, and technology, always staying updated on new developments.