Huong N. Higgins

Corporate system security: towards an integrated management approach

This paper discusses an integrated security approach that engages multiple functional levels in an organization from the Board and management to IT staff and individual users. The discussion presents security issues at the policy setting level and important control implementations at the gateway interface, internal network, and corporate files. As this approach involves multiple layers, the security environment can be strengthened. This discussion can be used as a guideline for corporate security management, as the components for a security audit, and as an internal communication to enhance corporate security awareness. The comprehensive view presented in this discussion is beneficial to managers, auditors, controllers, and consultants who work on security issues.

Earnings Forecasts of Firms Experiencing Sales Decline: Why So Inaccurate?

Many studies show that earnings forecasts of poor-performance firms contain large errors (Kothari et al. [2005], Brown [2001], Hwang et al. [1996], and Elgers and Lo [1994]). Why are these forecasts so inaccurate? This article demonstrates that inflexible costs, as captured through operating and financial leverage above the industrys normal range, are associated with large forecast errors during sales decline. The results are useful in helping investment managers better evaluate analyst forecast ability, and help analysts improve their forecasts by better anticipating the impact of inflexible costs on earnings

MaVis: Machine Learning Aided Multi-Model Framework for Time Series Visual Analytics.

The ultimate goal of any visual analytic task is to make sense of the data and gain insights. Unfortunately, the continuously growing scale of the data nowadays challenges the traditional data analytics in the ”big-data” era. Particularly, the human cognitive capabilities are constant whereas the data scale is not. Furthermore, most existing work focus on how to extract interesting information and present that to the user while not emphasizing on how to provide options to the analysts if the extracted information is not interesting. In this paper, we propose a visual analytic tool called MaVis that integrates multiple machine learning models with a plug-andplay style to describe the input data. It allows the analysts to choose the way they prefer to summarize the data. The MaVis framework provides multiple linked analytic spaces for interpretation at different levels. The low level data space handles data binning strategy while the high level model space handles model summarizations (i.e. clusters or trends). MaVis also supports model analytics that visualize the summarized patterns and compare and contrast them. This framework is shown to provide several novel methods of investigating co-movement patterns of timeseries dataset which is a common interest of medical sciences, finance, business and engineering alike. Lastly we demonstrate the usefulness of our framework via case study and user study using a stock price dataset.

LoVis: Local Pattern Visualization for Model Refinement

Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre‐selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present an integrated framework with visual representations that allows the user to incrementally build and verify models in three model spaces that support local pattern discovery and summarization: model complementarity, model diversity, and model representivity. Visual representations are designed and implemented for each of the model spaces. Our visualizations enable the discovery of complementary variables, i.e., those that perform well in modeling different subsets of data points. They also support the isolation of local models based on a diversity measure. Furthermore, the system integrates a hierarchical representation to identify the outlier local trends and the local trends that share similar directions in the model space. A case study on financial risk analysis is discussed, followed by a user study.

Auditing disclosure risks of on‐line broker‐dealers

This paper discusses disclosures required of on‐line broker‐dealers, and recommends various internal measures that on‐line broker‐dealers should take to comply with securities trading regulations. On‐line trading is transforming the relationship between investors and broker‐dealers. While the services offered by on‐line broker‐dealers may be different from those offered by full‐service brokers, the differences are diminishing, and both activities are subject to the same rules and regulations. A GAO report of May 2000, revealed that many on‐line broker‐dealers did not comply with disclosure requirements, resulting in complaints by customers who lost money or financial opportunities. As the SEC is strengthening its examinations, this article is helpful to firms that offer trading on‐line to comply with disclosure requirements for investor protection. This article is especially helpful for internal auditors of these firms in implementing internal policy and procedures to ensure adequate disclosures and to mitigate risks of investors’ litigation.

Progressively consolidating historical visual explorations for new discoveries

A significant task within data mining is to identify data models of interest. While facilitating the exploration tasks, most visualization systems do not make use of all the data models that are generated during the exploration. In this paper, we introduce a system that allows the user to gain insights from the data space progressively by forming data models and consolidating the generated models on the fly. Each model can be a a computationally extracted or user-defined subset that contains a certain degree of interest and might lead to some discoveries. When the user generates more and more data models, the degree of interest of some portion of some models will either grow (indicating higher occurrence) or will fluctuate or decrease (corresponding to lower occurrence). Our system maintains a collection of such models and accumulates the interestingness of each model into a consolidated model. In order to consolidate the models, the system summarizes the associations between the models in the collection and identifies support (models reinforce each other), complementary (models complement each other), and overlap of the models. The accumulated interestingness keeps track of historical exploration and helps the user summarize their findings which can lead to new discoveries. This mechanism for integrating results from multiple models can be applied to a wide range of decision support systems. We demonstrate our system in a case study involving the financial status of US companies.

Using Quarterly Earnings to Predict Stock Price

The main purpose of this paper is to develop a stock price prediction model based on quarterly earnings forecasts. The prediction model is based on the residual income model by Ohlson (1995), and adjustment for autocorrelation by Higgins (2009). Prior research has not used quarterly data out of concern for seasonality. However, seasonality can be removed by including four consecutive quarterly terms of abnormal earnings in each price equation. The prediction results suggest that quarterly earnings forecasts can be useful inputs to models of price forecasts.