The economics of Information Technologies Standards &

This research investigates the problem of Information Technologies Standards or Recommendations from an economical point of view. In our competitive economy, most enterprises adopted standardization’s processes, following recommendations of specialized Organisations such as ISO (International Organisation for Standardization), W3C (World Wide Web Consortium) and ISOC (Internet Society) in order to reassure their customers. But with the development of new and open internet standards, different enterprises from the same sector fields, decided to develop their own IT standards for their activities. So we will hypothesis that the development of a professional IT standard required a network of enterprises but also a financial support, a particular organizational form and a precise activity to describe. In order to demonstrate this hypothesis and understand how professional organise themselves for developing and financing IT standards, we will take the Financial IT Standards as an example. So after a short and general presentation of IT Standards for the financial market, based on XML technologies, we will describe how professional IT standards could be created (nearly 10 professional norms or recommendations appear in the beginning of this century). We will see why these standards are developed outside the classical circles of standardisation organisations, and what could be the “key factors of success” for the best IT standards in Finance. We will use a descriptive and analytical method, in order to evaluate the financial support and to understand these actors’ strategies and the various economical models described behind. Then, we will understand why and how these standards have emerged and been developed. We will conclude this paper with a prospective view on future development of standards and recommendations.information technologies, financial standards, development of standards, evaluation of the economical costs of standards

Analytical study and computational modeling of statistical methods for data mining

Today, there is tremendous increase of the information available on electronic form. Day by day it is increasing massively. There are enough opportunities for research to retrieve knowledge from the data available in this information. Data mining and app

Knowledge extraction of financial derivatives options in the maturity with data science techniques

To improve the level of support in information systems and quality of services by questioning the daily routine of a team using a set of financial evidence has been an interesting and challenging problem for many researcher and decision maker professionals. As part of a well-known investment bank that deals financial instruments like European-style options derivatives, operational teams are well aware that the focus of their work are around the evolution on pricing until the expiry moment. The choice of knowing more about financial derivatives options, especially in the maturity period, was made after a long process of study on economics and financial concepts in a certain institution. A special attention was given in subjects where information technology teams have less knowledge, which are the mathematical operation of derivative financial options and their implications in financial terms. As well, the identification of areas of business could be studied with greater interest for a specific organisation

Using semantic values to facilitate interoperability among heterogeneous information systems

Includes bibliographical references (p. 30-32).Supported in part by the NSF. IRI-90-2189 Supported in part by the International Financial Services Research Center at MIT.Michael Siegal [sic], Edward Sciore, Arnon Rosenthal

Safe & Cheap

Summary Our project bundles investment research reports spanning the authors’ time in the Orange Value Fund (OVF), a student run hedge fund in the Whitman School of Management. Each report exemplifies the fund’s unique, bottoms-up investment style. In contrast to traditional valuation methods that focus exclusively on projecting company earnings, the OVF adopts a micro-level approach to analyzing investment opportunities. Analysts study company documents such as annual reports, credit agreements, and proxy statements to identify safe and cheap opportunities. Safe A safe company has access to capital markets, a super-strong financial position, honest and competent management, and an understandable business. We define strong finances as the absence of liabilities and presence of high quality assets on the balance sheet. OVF analysts look for marketable assets such as income producing real estate, cash, and natural resources such as oil and gas reserves. Honest and competent management protects and enhances long-term shareholder value. Managers should focus on long-term wealth creation instead of short-term stock price fluctuations. In addition, we do not invest in businesses that require continual access to capital markets. Businesses that need daily short-term financing to fund operations fare poorly when credit dries up. Finally, we avoid businesses we don’t understand. Cheap A business must be cheap to be considered for investment. Businesses are cheap when their stock price trades at a substantial discount to intrinsic value. We look for wide margins between 30% and 50% between market price and intrinsic value to shield against analyst mistakes. We prefer to be approximately right rather than exactly wrong. OVF analysts use several valuation methods to determine intrinsic value. Generally, we prefer to use valuation methods that minimize assumptions. For example, we value real estate companies using Net Asset Value (NAV), which is the intrinsic value of assets less liabilities. We use market capitalization rates and sales data to determine asset values. In other industries, such as oil exploration and production, we value natural resource reserves based on industry merger and acquisition activity. We may also apply a multiple to normalized adjusted earnings before interest, taxes, depreciation, and amortization (EBITDA) after subtracting capital expenditures (CAPEX). Closing Thoughts Due diligence in difficult economic times is important and the OVF’s investment style positions us to profit from incredible opportunities with minimal investment risk