VFN-L May 1997

vfn-l@fam.tuwien.ac.at

4 participants
5 discussions

Gastvortrag
by Roland Dipplinger 28 May '97

28 May '97

Einladung zum Gastvortrag: Chancen und Risken aus Globalisierung und Euro f=FCr den regionalen= Wettbewerb=20 - am Beispiel des Wirtschaftsraumes Stuttgart Dr. Thomas R. FISCHER=20 Vorstandsvorsitzender Landesgirokasse Stuttgart Mo. 9. Juni 1997, 12:00 Uhr Hs 0001, UZA III Althanstra=DFe 39-45 A-1090 Wien ____________________________________________________________ Mag. Roland Dipplinger, Institut fuer Finanzierung und Finanzmaerkte Wirtschaftsuniversitaet Wien, Althanstrasse 39-45, A-1090 WIEN Tel.: ++431-31336-4173, Fax: -761, @: Roland.Dipplinger(a)wu-wien.ac.at "In practice, this works, but how about in theory?" Attributed to a French mathematician

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Seminar of Finance
by Gerti Scheiner 27 May '97

27 May '97

INSTITUT F=DCR H=D6HERE STUDIEN INSTITUTE FOR ADVANCED STUDIES =09 A-1060 Wien, Stumpergasse 56 Telefon: (0222) 59 9 91 Telefax: (0222) 597 06 35 SEMINAR IN FINANCE Christian Helmenstein, Gabriel Lee (Biweekly Mondays) Monday, 2. June 1997 E. J. Dockner, H. Elsinger, A. Gaunersdorfer (University of Vienna) "The Strategic Role of Dividends and Debt in Markets=20 with Imperfect Competition" Abstract While many existing models in the literature on financial structure do ignore product market strategies, Brander and Lewis (1986) argue that there are important linkages between the two. In particular they show that oligopolistic firms with limited liability follow a more aggressive output strategy as their leverage increases. In a follow up paper Glazer (1994) points out that this result crucially depends on the assumption that debt is short-term. If on the contrary debt is long-term and rival firms chose their equilibrium in period one and two, they do have an incentive to be more collusive in the first period than static oligopolists would be and hence output decreases. On the basis of this result Glazer concludes that the degree of price fluctuations on product market will increase with the level of firm=92s debt. In this paper we argue that the incentive to collude is driven by limited liability and the dividend policy of the firm. We find that increasing leverage causes firms in both periods to increase their output and hence be more aggressive. Moreover, we show by means of a numerical example that the symmetric game admits multiple equilibria some of which cause firms to choose asymmetric product market strategies. This leads us to conclude that firms with similar leverage and product market characteristics might very well choose quite different product market stragegies. Place: Institute for Advanced Studies, Stumpergasse 56, 1060 Vienna, SZ VI= =20 Time: 17:00h-18:30h Info: http://www.ihs.ac.at/fin/finsem.html =========================================================================

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(Fwd) JCIFinance - Final CFP - Special issue on "Improving Gen
by Andrea Gaunersdorfer 26 May '97

26 May '97

------- Forwarded Message Follows ------- >From RBCALDWELL(a)DELPHI.COM Sun Mar 19 17:27:57 2000 >From: RBCALDWELL(a)DELPHI.COM To: "SNDE Mailing List" <SNDE(a)fas-econ.rutgers.edu> Subject: JCIFinance - Final CFP - Special issue on "Improving Generalization Date: Thu, 22 May 1997 20:36:11 -0400 (EDT) Status: RO X-Status: X-Keywords: X-UID: 265 ******************************************************************* F I N A L C A L L F O R P A P E R S ******************************************************************* Journal of Computational Intelligence in Finance Final Call for Papers Special Issue and Competition on "Improving Generalization for Nonlinear Financial Forecasting Models" The Journal of Computational Intelligence in Finance, a peer-reviewed technical journal, published by Finance & Technology Publishing, is seeking papers for review and publication in 1997 on "Improving Generalization for Nonlinear Financial Forecasting Models". For comparison of methods submitted, the target variable series and performance metrics are specified (though not required). The Journal of Computational Intelligence in Finance publishes applied research and practical applications of high quality that are based on sound theoretical, empirical or quantitative analysis. It provides the international forum for the convergence of the new multi-disciplined field of computational intelligence in finance. Papers published in the Journal are eligible for entry in an Annual Essay Award Contest. The Editorial Advisory Board of the Journal selects the best paper for which a cash award is presented each year. EDITORIAL ADVISORY BOARD Emilio Barucci, University of Florence - Italy Richard J. Bauer, Jr., St. Mary's University, Texas - USA Neil Burgess, London Business School - UK Oscar Castillo, UABC University - USA Jerry Connor, London Business School - UK Eric de Bodt, Universite Catholique de Louvain - France James F. Derry, Mgmt. Engineering Productivity Systems - USA Athanasios Episcopos, National Bank of Greece Andrew Flitman, Monash University - Australia Susan Garavaglia, Dun and Bradstreet - USA Ramo Gencay, University of Windor - Canada Sabyasachi Ghoshray, Florida International University - USA Lee Giles, NEC Research Institute - USA Christian Haefke, University of California at San Diego - USA Ypke Hiemstra, Vrije Universiteit - The Netherlands Yuval Lirov, Lehman Brothers - USA Ralph Neuneier, Siemens AG Corporate Research Center - Germany Zoran Obradovic, Washington State University - USA Marimuthu Palaniswami, University of Melbourne - Australia Carlos E. Pedreira, Catholic University, Rio - Brazil David B. Skalak, University of Massachusetts - USA Stephen Slade, Stern Business School, New York University - USA Leon Sterling, University of Melbourne - Australia Manoel F. Tenorio, University of Purdue - USA Halbert White, University of California at San Diego - USA Lei Xu, The Chinese University of Hong Kong SPECIAL TOPIC Improving Generalization for Nonlinear Financial Forecasting Models PUBLICATION DATE November 1997 PAPER SUBMISSION DEADLINE June 30, 1997 MOTIVATION The critical issue in applying neural networks and other data-driven forecasting systems is generalization, the performance on data not used for training. The key to generalization behavior is model complexity. Too simple a model cannot approximate the true relationship, and overly complex models adjust to the noise in the data. Nearly all financial applications of nonparametric models (such as neural networks and genetic algorithms) vary model complexity by adjusting the number of parameters. This special issue intends to highlight other methods to improve generalization, in particular regularization (e.g., neural network weight decay and smoothing) and techniques for combining models. Of particular interest are nonlinear methods including neural networks, genetic algorithms, nearest neighbor networks, polynomial networks, fuzzy logic, and hybrids. Nearly all studies apply cross-validation to select the best model. Alternatives to cross-validation include 'analytical' selection rules such as Akaike's Information Criterion, Schwartz's Information Criterion, and a number of others. Of particular interest are the statistical properties (i.e., bias and variance) of model selection methods in estimating out-of-sample performance. DATA, TARGET VARIABLES and PERFORMANCE METRICS Data: daily prices of a financial time series (see below) Target Variable: the relative difference in percent (RDP) between today's closing price and the price five (5) days ahead Performance Metrics: MSE (target). nRMSE and DS (to be used in the analysis). Participants are encouraged to use the forecast data, target variable and performance metrics specified for this special issue, which are available on the Web to those who submit a satisfactory abstract (including brief biography) as outlined below. Participants are not be restricted regarding the data used as inputs to their predictors. Especially interesting original methods using other forecast data, target variables and performance metrics will also be considered. The forecast series is derived from daily closing prices for a financial time series. The target variable is the relative difference in percent (RDP) between today's closing price and the closing price five (5) days ahead. The date, the underlying price series and the target variable series are all provided in the downloadable data file. The target metric is the MSE. Also, authors' analysis should include the normalized RMSE (RMSE normalized using the standard deviation of actual RDP values), and Directional Symmetry (percentage of correctly predicted directions with respect to the target variable). The forecast data provided is separated into in-sample (10 years of daily data) and out-of-sample (2 years of daily data) sets. Participants are not restricted regarding the data used as input to their predictors. However, all data used should be disclosed in the paper presentaton, including the details of all techniques and formulas used to pre-process the data. Details on the predictor and the methods used for improving generalization should be presented in the paper. FORECAST HORIZON AND RE-TRAINING Participants should test performance of their predictors over the entire two-year out-of-sample dataset. Of interest are results of analyses and performance of predictors over the entire two-year prediction period: (1) without re-training and (2) with re-training (optional). The results from (1) and (2) can be useful for estimating the limits of the forecasting horizon for the prediction methods presented. For additional details on the forecast data, target variable and performance metrics, see: http://ourworld.compuserve.com/homepages/ftpub/call.htm Suggested references for the topic include: 1) Abu-Mostafa, J.S. [1990] "Learning from hints in neural networks", Journal of Complexity, 6, June, pp. 192. 2) Bishop C.M. [1995] Neural Networks for Pattern Recognition, Oxford University Press. 3) Caldwell, R.B. (editor) [1997] Nonlinear Financial Forecasting: Proceedings of the First INFFC, Finance & Technology Publishing. 4) Elder, John F. and Mark T. Finn [1991] "Creating `Optimally Complex` Models for Forecasting," Financial Analysts Journal, Jan/Feb, pp. 73-79. 5) Haykin, Simon [1994] Neural Networks: A Comprehensive Foundation, IEEE Press. 6) Ripley, Brian D. [1996] Pattern Recognition and Neural Networks, Cambridge University Press. 7) Swanson, N.R. and H. White [1995] "A Model Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks", Journal of Business and Economic Statistics 13. ABSTRACTS Submit 150 to 300 word abstract including full name(s) and affiliation(s) of the author(s), complete mailing address, email address and telephone numbers of all authors. Authors should provide a brief biographic sketch of themselves. Send to either the postal or email addresses below: Post: Editors JCIF P.O. Box 764 Haymarket, VA 20168 USA E-mail: 72672.261(a)compuserve.com PAPERS Submit three copies of each paper. Papers should be double- spaced, single-sided. Authors should provide a brief biographic sketch of themselves. Each copy submitted should include a page that contains the title of the paper, the full name(s) and affiliation(s) of the author(s), complete mailing address, email address and telephone numbers of all authors, and a 150 to 300 word abstract. The Journal reserves the right to edit all material to meet space requirements and to make grammatical and typographical corrections. The final text should be 4000 to 5000 words in length, containing no more than about 10 references, and be provided as follows: (1) Hardcopy: printed and double-spaced, with notations for the location of graphics, mathematical equations, given thereon, as necessary, (2) Softcopy: The preferred media format is IBM PC 3.5", 1.44MB. The preferred file format is Word 6/95/97 for Windows 3.1/95. Other acceptable software files (in the IBM PC format) are the following: Word/DOS 3.0 or later Word/Mac 4.0 or later Word/Win 2.0 through 7 WordPerfect 5.1 or later (for DOS or Windows 3.1/95). Any standard ASCII text file format using the preferred media format, including bracketed notations for the locations of symbols, equations or other non-ASCII characters. Tex and LaTex may be used for the development and generation of the hardcopy version of the paper, provided that a softcopy version is also submitted in any standard ASCII text file format using the preferred media format, including bracketed notations for citations and for the locations of symbols, equations or other non-ASCII characters. GRAPHICS The preferred graphics format is a Windows compatible format (.pcx, .bmp, .wmf). For other graphics formats, submit high-quality, camera-ready hardcopy. TEXT CITATIONS AND REFERENCES Papers should be limited to about 10 references. Encouraged are references to peer-reviewed journals as well as to books. Conference proceedings/compendiums are discouraged. Text citations must use the following format: last name(s) of author(s), publication date and suffix (as necessary) in brackets. Example: Watkins and McCoy [1993a] References must be listed alphabetically by the last name of the first author according to the following formats: Journal Article: authors' names, publication date and suffix (as necessary) in brackets, article title (in double quotations), periodical title (in italics), volume and number, pages cited. Book: authors' names, publication date and suffix (as necessary) in brackets, book title (in italics), publisher, publisher location, pages cited. Chapter in Book: authors' names, publication date and suffix (as necessary) in brackets, chapter title (in double quotations), editors' names, book title (in italics), publisher, location, pages cited. Send all manuscripts to the following postal address: Editors JCIF P.O. Box 764 Haymarket, VA 20168 USA *********************************************************************** F I N A L C A L L F O R P A P E R S *********************************************************************** ------------------------------------------------------------------ Dr. Andrea Gaunersdorfer Department of Business Administration University of Vienna Tel.: +43-1-29 1 28-466 Bruenner Strasse 72 FAX: +43-1-29 1 28-464 A - 1210 Wien e-mail: gauner(a)finance2.bwl.univie.ac.at http://www.bwl.univie.ac.at/bwl/fiwi1/members/gauner/gauner.htm =========================================================================

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None
by Elke PENDL 13 May '97

13 May '97

VSX WORKSHOPS Einladungen zu den Vortraegen "Bewertung von Zinsoptionen: Eine empirische Studie fuer den deutschen Optionsmarkt'' Professor Wolfgang Buehler (Universitaet Mannheim) Freitag, 23. Mai 1997 von 15:30 - 17:00 im Hoersaal 8 des Betriebswirtschaftlichen Zentrums der Universitaet Wien, Bruenner Strasse 72, 1210 Wien "Components of the Bid-Ask Spread: a General Approach'' Professor Hans Stoll (Vanderbilt University) Freitag, 6. Juni 1997 von 15:30 - 17:00 im Hoersaal 8 des Betriebswirtschaftlichen Zentrums der Universitaet Wien, Bruenner Strasse 72, 1210 Wien "Debt and Equity as Information Revelation Mechanisms'' Professor Michel Habib (London Business School) Freitag, 13. Juni 1997 von 15:30 - 17:00 im Hoersaal 8 des Betriebswirtschaftlichen Zentrums der Universitaet Wien, Bruenner Strasse 72, 1210 Wien =========================================================================

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(Fwd) Computational Finance Graduate Programs
by Andrea Gaunersdorfer 09 May '97

09 May '97

------- Forwarded Message Follows ------- >From compfin(a)CSE.OGI.EDU Sun Mar 19 17:27:57 2000 >From: Computational Finance <compfin(a)CSE.OGI.EDU> To: "SNDE Mailing List" <SNDE(a)fas-econ.rutgers.edu> Subject: Computational Finance Graduate Programs Date: Wed, 7 May 1997 11:53:59 -0700 (PDT) Reply-to: Computational Finance <compfin(a)CSE.OGI.EDU> Status: RO X-Status: X-Keywords: X-UID: 262 ======================================================================= COMPUTATIONAL FINANCE at the Oregon Graduate Institute of Science & Technology (OGI) Master of Science Concentrations in Computer Science & Engineering (CSE) Electrical Engineering (EE) Upcomming MS Application Deadline for Fall 1997: May 15 & June 15! New! Certificate Program Designed for Part-Time Students. For more information, contact OGI Admissions at (503)690-1027 or admissions(a)admin.ogi.edu, or visit our Web site at: http://www.cse.ogi.edu/CompFin/ ======================================================================= Computational Finance Overview: Advances in computing technology now enable the widespread use of sophisticated, computationally intensive analysis techniques applied to finance and financial markets. The real-time analysis of tick-by-tick financial market data, and the real-time management of portfolios of thousands of securities is now sweeping the financial industry. This has opened up new job opportunities for scientists, engineers, and computer science professionals in the field of Computational Finance. The strong demand within the financial industry for technically sophisticated graduates is addressed at OGI by the Master of Science and Certificate Programs in Computational Finance. Unlike a standard two year MBA, the programs are directed at training scientists, engineers, and technically oriented financial professionals in the area of quantitative finance. The master's programs lead to a Master of Science in Computer Science and Engineering (CSE track) or in Electrical Engineering (EE track). The MS programs can be completed within 12 months on a full-time basis. In addition, OGI has introduced a Certificate program designed to provide professionals in engineering and finance a means of upgrading their skills or acquiring new skills in quantitative finance on a part-time basis. The Computational Finance MS concentrations feature a unique combination of courses that provides a solid foundation in finance at a non-trivial, quantitative level, plus the essential core knowledge and skill sets of computer science or the information technology areas of electrical engineering. These skills are important for advanced analysis of markets and for the development of state-of-the-art investment analysis, portfolio management, trading, derivatives pricing, and risk management systems. The MS in CSE is ideal preparation for students interested in securing positions in information systems in the financial industry, while the MS in EE provides rigorous training for students interested in pursuing careers as quantitative analysts at leading-edge financial firms. The curriculum is strongly project-oriented, using state-of-the-art computing facilities and live/historical data from the world's major financial markets provided by Dow Jones Telerate. Students are trained in the use of high-level numerical and analytical software packages for analyzing financial data. OGI has established itself as a leading institution in research and education in Computational Finance. Moreover, OGI has strong research programs in a number of areas that are highly relevant for work in quantitative analysis and information systems in the financial industry. ----------------------------------------------------------------------- Admissions ----------------------------------------------------------------------- Applications for entrance into the Computational Finance MS programs for Fall Quarter 1997 are currently being considered. The deadlines for receipt of applications are: May 15, 1997 Notification by June 15, 1997 June 15, 1997 Notification by July 15, 1997 In keeping with OGI policy, we will consider applications received after June 15, 1997 on a space available basis. A candidate must hold a bachelor's degree in computer science, engineering, mathematics, statistics, one of the biological or physical sciences, finance, econometrics, or one of the quantitative social sciences. Candidates who hold advanced degrees in these fields or who have experience in the financial industry are also encouraged to apply. Applications for the Certificate Program are considered on an ongoing basis for entrance in any quarter. ---------------------------------------------------------------------- Contact Information ---------------------------------------------------------------------- For general information and admissions materials: Visit our web site at: http://www.cse.ogi.edu/CompFin/ or contact: Office of Admissions Oregon Graduate Institute P.O.Box 91000 Portland, OR 97291-1000 E-mail: admissions(a)admin.ogi.edu Phone: (503)690-1027 For special inquiries: E-mail: compfin(a)cse.ogi.edu ====================================================================== ------------------------------------------------------------------ Dr. Andrea Gaunersdorfer Department of Business Administration University of Vienna Tel.: +43-1-29 1 28-466 Bruenner Strasse 72 FAX: +43-1-29 1 28-464 A - 1210 Wien e-mail: gauner(a)finance2.bwl.univie.ac.at http://www.bwl.univie.ac.at/bwl/fiwi1/members/gauner/gauner.htm =========================================================================

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VFN-L May 1997