The Business Review Journal

(The Journal of American Business Review, Cambridge)

Vol. 6* Number 1 * December 2017

The Library of Congress, Washington, DC   *   ISSN 1553 - 5827

The Library of Congress, Washington, DC   *   ISSN 2167-0803

Online Computer Library Center, OH   *   OCLC: 940146916

National Library of Australia   *   NLA: 49026139

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The primary goal of the journal will be to provide opportunities for business related academicians and professionals from various business related fields in a global realm to publish their paper in one source. The journal will bring together academicians and professionals from all areas related business fields and related fields to interact with members inside and outside their own particular disciplines. The journal will provide opportunities for publishing researcher's paper as well as providing opportunities to view other's work. All submissions are subject to a double blind peer review process.  The journal is a refereed academic journal which  publishes the  scientific research findings in its field with the ISSN 2167-0803 issued by the Library of Congress, Washington, DC.  The journal will meet the quality and integrity requirements of applicable accreditation agencies (AACSB, regional) and journal evaluation organizations to insure our publications provide our authors publication venues that are recognized by their institutions for academic advancement and academically qualified statue.  No Manuscript Will Be Accepted Without the Required Format.  All Manuscripts Should Be Professionally Proofread Before the Submission.  You can use for professional proofreading / editing etc...

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The Omniscience Model: Lagged Correlation

Dr. Jeffry Haber, Iona College, NY

Patrick Hardiman, Iona College, NY



The Omniscience Model is an attempt to harness the nearly limitless flow of real-time financial information with the geometric gains in computing power to predict future stock prices. A previous paper (1) reviewed various mathematical and statistical functions from a variety of fields to determine which would be applicable to stock price prediction. Correlation was one metric selected that had potential.  Correlation is the relationship between two streams of data and can be any value between (and including) -1.00 and +1.00. The extremes (-1.00 and +1.00) represent perfect correlation, whereas values close to 0.00 indicate non-correlation. The sign (positive or negative) indicates whether the relationship is direct or inverse (respectively). In a direct relationship as one stream increases the other stream would increase as well. In an inverse relationship as one stream increases the other decreases.  In an investing context correlation is important because it represents the inference that can be made by utilizing one stream of data to predict the value (or change in value) of a second stream. The problem with correlation is that it is not investable. Both streams of data are for the same temporal period, which means that by the time you have finished the calculation the ability to invest has passed based on the information provided by the correlation.  This paper seeks to develop the concept of lagged correlation – that is, where one stream of data is for period t and is used to calculate the correlation with a second stream of data at period t+x for possible use as a filter in The Omniscience Model.  Correlation is a ubiquitous metric in investing, usually used in the context of how a fund or security behaves in relation to an index, benchmark or sector. Many fund sponsors will tout that their fund is “uncorrelated” with stocks, which would position their fund as a risk diversifier for a portfolio. Since correlation is a widely-used statistic, it is not surprising that it has also been widely researched.  Most specifications of correlation are for extended periods, such as 10 or 15 years. Studies have shown (2) that correlation calculated over the long-term might not hold in shorter segments within this longer term. The typical calculation is based on returns (as opposed to prices).


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American Depository Receipts: An Analysis of the Underlying Stock Returns

Dr. Chih-Chieh (Jason) Chiu, Rider University, Lawrenceville, NJ



This paper examines option listing effects on American Depository Receipts and their local counterparts. I analyze a sample of 42 option listings from 15 different countries and obtain negative price effects on the day after option listing. One possible explanation for the findings is that option listing relaxes the short sale constraints for the underlying securities.  Do option listings have an impact on the underlying securities? According to Arrow-Debreu uncertainty theory, option listings move an incomplete stock market towards a state of equilibrium by increasing the number of assets to hedge against different states of nature. Black and Scholes (1973), however, see options as redundant assets and price them as such in their renowned options model. Consequently, numerous options models assume independence between stock and options market. Without a concrete theoretical direction, I examine the option listing effects on the underlying American Depository Receipts (1) (ADRs) and their local counterparts in the home country. I find a negative price effect on the first day post option listings. The negative abnormal returns are -0.66% and -0.64% for ADRs and their local counterparts respectively.  Empirical research has shown price effects of option introductions on the underlying assets. The milestone paper by Conrad (1989) suggests that option introductions have a permanent effect on stock prices. Using a sample of 96 option listings for the period of 1974 to 1980, she finds option introductions have a positive price effect starting three days before introductions. She attributes the increases to the heightened demand for stocks from traders.   The positive price effect from option listing, however, has been dampened by the introduction of options on the S&P 500 futures. Using a sample of 300 option listings from 1973 to 1986, Detemple and Jorion (1990) observe positive price effects and reduced volatility due to option listings.


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The Dead-Weight of Public Sector Companies in India: A Case Study

Dr. Anuja Gupta, Rutgers University, Camden, NJ



The Indian economy followed a socialistic model of economic development from 1947 to 1991, in which the government played a very large role in economic activity in the country. While liberalization started 25 years ago, there is still considerable dead-weight in the economy in the form of persistently loss-making public sector enterprises. Privatization, which was one of the planks of liberalization and a potential solution to this dead weight problem, has not made much progress in India. In this paper, we analyze the case of one such loss making public sector unit (PSU) - Mahanagar Telephone Nigam Limited (MTNL) with a view to gaining insight into the dire financial situation of many such PSUs. Further, we conduct a comparative analysis of MTNL with a private player in the same industry thus highlighting the glaring inefficiencies and poor performance of the PSU. We argue that there is no economic or strategic logic of carrying this dead weight in the economy. The assets tied up in these extremely inefficient companies could be put to much better use in India, a country with growth needs and ambitions.  The Indian economy was following a “socialistic” model of government and economic development after its independence in 1947. This led to an economy that was replete with government control, and with direct participation of the government in economic activity through the ever-expanding public sector. In 1991, India faced a balance of payments crisis which led to a bailout by the International Monetary Fund (IMF) (Makhija, 2006). This, of course, came with stipulations to open up the economy, and get rid of excessive regulations. Thus, the process of liberalization started in 1991 with the government working on deregulation, delicensing and privatization. While reasonable progress was made in deregulation and delicensing, very little headway has been made in the area of privatization. As of 2016, there are still 244 public sector units (PSUs) under central government control. Only 14 PSUs have been fully privatized. In this paper, we highlight the dire financial position that many PSUs are in, by examining one such loss making PSU in-depth. We study the public sector telecommunications services provider Mahanagar Telephone Nigam Limited (MTNL).


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Copyright: All rights reserved. No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, including photocopying and recording, or by any information storage and retrieval system, without the written permission of the journal.  You are hereby notified that any disclosure, copying, distribution or use of any information (text; pictures; tables. etc..) from this web site or any other linked web pages is strictly prohibited. Request permission/Purchase article (s):


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Index: The Library of Congress, Washington, DC:    ISSN: 1540 – 7780

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