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Im folgenden finden Sie eine Liste

der seit dem letzten Newsletter fer-

tiggestellten Schriftenreihen. Falls

Sie Interesse an einer der im folgen-

den angefhrten Ausgaben der Re-

port- oder Working Paper Series

haben, so wenden Sie sich bitte anunsere Homepage oder notfalls an

Frau Mag. Doris Prato (313 36/45

87, [email protected]):

CVa`ce?`"%

May 1998

Neal M. Stoughton, Josef Zechner

IPO-Mechanisms, Monitoring and Ownership Strucutre

This paper was accepted for publication in: Journal of Financial Economics

Abstract

This paper analyzes the effect of different IPO mechanisms on the structure of share

ownership and explores the role of underpricing and rationing in determining investors

shareholdings. We focus on the agency problem that results when large institutions are

the only investors capable of monitoring the firm whereas small shareholders free-ride

on these activities. The major conclusion of this paper is that some well-known aspects

of IPOs may be explained as rational responses by the issuer to the existence of regula-

tory constraints in public capital markets. We find that there is a two-stage offeringmechanism in which an investment banker acting in the interests of the issuer optimally

rations the allotment of shares to small investors in order to capture the benefits associ-

ated with better monitoring by institutions. Importantly, in our model, the existence of

underpricing (and oversubscription) is an indication that the issuer has received a higher

ex ante price than would have been obtained through a competitive Walrasian-type of-

fering process.

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May 1998

Christian Schittenkopf, Georg Dorffner, Engelbert J. Dockner

Volatility Prdiction with Mixture Density Networks

This paper was accepted for publication in: Proceedings of the International Conference

on Artificial Neural Networks, September 2-4 1998, Skvde, Schweden.

Abstract

Despite the lack of a precise definition of volatility in finance, the estimation of volatil-

ity and its prediction is an important problem. In this paper we compare the performance

of standard volatility models and the performance of a class of neural models, i.e. mix-

ture density networks (MDNs). First experimental results indicate the importance oflong-term memory of the models as well as the benefit of using non-gaussian probability

densities for practical applications.

CVa`ce?`"'

May 1998

Thomas Reutterer

Combined Market Structure and Segmentation Analysis Based on Brand Choice

Data: Overcoming the Limitations of Conventional Techniques with TopologicallyOrdered Feature Maps

This paper was accepted for publication in: Proceedings of the 1998 AMA Exchange

Colloquium July 23-25, Vienna.

Abstract

The objective and related issues of simultaneously performing competitive market

structure (CMS) and market segmentation analysis is well-documented in the marketing

literature. In this paper, an artificial neural network based approach of Kohonen (1982)

for the formation of topological ordered feature maps in introduced into the context of

brand choice data based combined CMS/segmentation analysis. Selected aspects of the

methodological basis are discussed and in a demonstration study using household-level

brand choice probabilities derived from diary household panel data the failure of con-

ventional methodology is opposed to the promising properties of the suggested neuro-

computing approach.

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June 1998

Friedrich Leisch, Andreas Weingessel, Evgenia Dimitriadou

Competitive Learning for Binary Valued Data

This paper was accepted for publication in: Proceedings of ICANN 98, International

Conference on Artificial Neural Networks, Skde, Sweden, September 2-4, 1998.

Springer Verlag.

Abstract

We propose a new approach for using online competitive learning on binary data. The

usual Euclidean distance is replaced by binary distance measures, which take possible

asymmetries of binary data into account and therefore provide a ``different point ofview'' for looking at the data. The method is demonstrated on two artificial examples

and applied on tourist marketing research data.

CVa`ce?`")

June 1998

Peter Tino

Spatial Representation of Symbolic Sequences through Iterative Function Systems

This paper is under review for publication in: IEEE Trans. on Systems, Man, and Cy-

bernetics (B)

Abstract

Jeffrey (1990) proposed a graphic representation of DNA sequences using Barnsley's

iterative function systems. In spite of further developments in this direction (Oliver et.

al, 1993; Roman-Roldan et. al, 1994, Li, 1997), the proposed graphic representation of

DNA sequences has been lacking a rigorous connection between its spatial scaling char-

acteristics and the statistical characteristics of the DNA sequences themselves. We 1)

generalize Jeffrey's graphic representation to accommodate (possibly infinite) sequences

over an arbitrary finite number of symbols, 2) establish a direct correspondence betweenthe statistical characterization of symbolic sequences, 3) show that for general symbolic

dynamical systems, the multifractal spectra in the sequence space coincide with the

spectra on spatial sequence representations.

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June 1998

Peter Tino, Georg Dorffner

Constructing finite-context sources from fractal representations of symbolic se-

quences

This paper under review publication in: Machine Learning

Abstract

We propose a novel approach to constructing predictive models on long complex sym-

bolic sequences. The models are constructed by first transforming the training sequence

n-block structure into a spatial structure of points in a unit hypercube. The transforma-

tion between the symbolic and Euclidean spaces embodies a natural smoothness as-sumption (n-blocks with long common suffices are likely to produce similar continua-

tions) in that the longer is the common suffix shared by any two n-blocks, the closer lie

their point representations. Finding a set of prediction contexts is then formulated as a

resource allocation problem solved by vector quantizing the spatial representation of the

training sequence n-block structure. Our predictive models are similar in spirit to vari-

able memory length Markov models (VLMMs). We compare the proposed models with

both the classical and variable memory length Markov models on two chaotic symbolic

sequences with different levels of subsequence distribution structure. Our models have

equal or better modeling performance, yet, their construction is more intuitive (unlike in

VLMMs, we have a clear idea about the size of the model under construction) and easier

to automize (construction of our models can be done in a completely self-organizedmanner, which is shown to be problematic in the case of VLMMs).

CVa`ce?`#!

June 1998

Peter Tino, Georg Dorffner, Igor Farkas

Building stochastic prediction machines from fractal representations of symbolic

sequences

This paper is under review for publication in: NIPS98

Abstract

We propose a novel approach to constructing predictive models on symbolic sequences.The models are constructed by first transforming the n-block structure of the trainingsequence into a spatial structure of points in a unit hypercube, such that the longer is thecommon suffix shared by any two n-blocks, the closer lie their point representations.Such a transformation embodies a natural smoothness assumption -- n-blocks with longcommon suffices are likely to produce similar continuations, whereas n-blocks withdifferent suffices may lead to a range of different future scenarios. Finding a set of pre-

diction contexts is then formulated as a resource allocation problem solved by vector

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quantizing the spatial n-block representation. Our predictive models are similar in spiritto variable memory length Markov models (VLMMs). We compare the proposed mod-els with both the classical and variable memory length Markov models on two chaotic

symbolic sequences with different levels of subsequence distribution structure. Ourmodels have a superior performance, yet, their construction is more intuitive (unlike inVLMMs, we have a clear idea about the size of the model under construction) and easierto automize (construction of our models can be done in a completely self-organizedmanner, which is shown to be problematic in the case of VLMMs).

CVa`ce?`#"

June 1998

Peter Tino, Georg DorffnerRecurrent neural networks with Iterated Function Systems dynamics

This paper was accepted for publication in: International ICSC/IFAC Symposium onNeural Computation (NC98), Technical University in Vienna, Austria, September 23-25, 1998.

Abstract

We suggest a recurrent neural network (RNN) model with a recurrent part correspond-ing to iterative function systems (IFS) introduced by Barnsley (1988) as a fractal imagecompression mechanism. The key idea is that 1) in our model we avoid learning the

RNN state part by having non-trainable connections between the context and recurrentlayers (this makes the training process less problematic and faster), 2) the RNN statepart codes the information processing states in the symbolic input stream in a well-organized and intuitively appealing way. We show that there is a direct correspondencebetween the entropy spectra characterizing the input stream and the spectra of general-ized dimensions of activations inside the RNN state space. We test both the new RNNmodel with IFS dynamics and its conventional counterpart with trainable recurrent parton two chaotic symbolic sequences. In our experiments, RNNs with IFS dynamics out-perform the conventional RNNs with respect to information theoretic measures com-puted on the training and model generated sequences.

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April 1998

Sara Dolnicar, Friedrich Leisch, Andreas Weingessel, Christian Buchta,

Evgenia Dimitriadou

A Comparison of Several Cluster Algorithms on Artificial Binary Data Scenarios

from Travel Market Segmentation

Introduction

Social scientists confronted with the problem of segmenting individuals into plausiblesubgroups usually encounter two main problems: First: there is very little indicationabout the correct choice of the number of clusters to search for. Second: different clusteralgorithms and even multiple replications of the same algorithm result in different solu-

tions due to random initializations and stochastic learning methods. In the worst casenumerous solutions are found which all seem plausible as far as interpretation is con-cerned. The consequence is, that in the end clusters are postulated that are in fact cho-sen by the researcher, as he or she makes decisions on the number of clusters and thesolution chosen as the final one. In this paper we concentrate on the power and stabil-ity of several popular clustering algorithms under the condition that the correct numberof clusters is known. Artificial data sets modeled to mimic typical situations from tour-ism marketing are constructed. The structure of these data sets is described in severalscenarios, and artificial binary data are generated accordingly. These data, ranging fromvery simple to more complex, real-data-like structures, enable us to systematically ana-

lyze the behavior of the cluster methods. Section 3 gives an overview of all clustermethods under investigation. Section 4 describe our experimental results, comparingfirst all scenarios and then all cluster methods. To accomplish this task, several evalua-tion criteria for cluster methods are proposed. Finally: Sections 5 and 6 draw someconclusions and give an outlook on future research.

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May 1998

Robert Heinkel, Alan Kraus, Josef Zechner

The Effect of Green Investment of Corporate Behavior

Abstract

This paper examines the effect of exclusionary ethical investing on corporate behav-ior. We assume a simple world with a finite number of firms that possess one of twoproduction technologies, clean or polluting. We also assume two types of riskaverse investors, neutral and green. Neutral investors are willing to consider in-vestments in clean or polluting firms relative to clean firms. When this price differential

just covers the cost of a firm switching from the polluting technology to the clean tech-nology, share-price-maximizing firms will change their technologies, e.e., corporate

behavior is affected by ethical investing. However, our numerical examples, constructed

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with empirically reasonable parameter values, indicate that a large proportion of greeninvestors is required to overcome reasonable switching costs. In addition, the averagecost of a capital across all firms in the economy is shown to be nonmonotonically con-

cave in the proportion of green investors. That is, the economys cost of capital is lowestif there are either very few green investors, of very many. Our casual empirical observa-tions lead us to conclude there are very few green investors.

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May 1998

Neal M. Stoughton, Kit Pong Wong, Josef Zechner

IPOs and Product Quality

Abstract

In this paper we study a product market motive for going public. We model a firm thatproduces a good with unknown quality. When a firm makes an IPO, consumers use in-formation incorporated into the stock market price to make an inference about the pro-duct quality. Firms that produce higher quality products are more likely to proceed withan IPO. The model is extended to allow for product market competition with a rivalfirm. We study the announcement effects on the rivals stock price when it is publiclytraded and the likelihood of a competitor going public when the rival is privately held.Our results predict the presence of hot issue markets depending on the distribution of

the market size uncertainty.

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May 1998

Vojislav Maksimovic, Alex Stomper, Josef Zechner

Capital Structure, Information Acquisition and Investment Decisions in Industry

Equilibrium

Abstract

We analyze how financial structure affects the incentives of firms to acquire costly in-formation about demand or cost conditions in the industry. In industry equilibrium, thevalue of information obtained by building prototypes or postponing investment deci-sions is determined by the rival firms use of such information. Although the number offirms which acquire costly information adjusts until they have the same value as unin-formed firms, their cash flows generally differ in their riskiness. This implies that afirms financial structure is a major determinant of its information acquisition strategy.As a consequence there is a relationship between the distribution of financial structuresin an industry and the equilibrium in the market for information. We characterize thedistribution of financial structures that supports the industry equilibrium. We find that if

the new investment must be financed internally, then leverage is negatively related to

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information acquisition expenditure whereas if the new investment is financed exter-nally, the relationship is the converse. We show how the existence of a tax advantage ofdebt affects the firms incentives to acquire information and the number of informed

firms.

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May 1998

Christian Schittenkopf, Georg Dorffner, Engelbert J. Dockner

Identifying Stochastic Processes with Mixture Density Networks

Abstract

In this paper we investigate the use of mixture density networks (MDNs) for identifyingcomplex stochastic processes. Regular multilayer perceptrons (MLPs), widely used intime series processing, assume a gaussian conditional noise distribution with constantvariance, which is unrealistic in many applications, such as financial time series (whichare known to be heteroskedastic). MDNs extend this concept to the modeling of time-varying probability density functions (pdfs) describing the noise as a mixture of gaus-sians, the parameters of which depend on the input. We apply this method to identifyingthe process underlying daily ATX (Austrian stock exchange index) data. The resultsindicate that MDNs modeling a non-gaussian conditional pdf tend to be significantlybetter than traditional linear methods of estimating variance (ARCH) and also betterthan merely assuming a conditional gaussian distribution.

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March 1998

Thomas Reutterer

Panel-Data Based Competitive Market Structure and Segmentation Analysis Using

Self-Organizing Feature Maps.

Abstract

In this paper the Self-Organizing (Feature) Map (SOM) methodology as originallyproposed by Kohonen (1982) is employed in the context of Competitive Market Struc-ture (CMS) and segmentation analysis using household-specific brands preferences de-rived from diary panel data as input patterns for SOM training. The adaptive SOM algo-rithm results in a representation of competitive structures among rival brands at thesegment-level, i.e. for submarkets with (internally) more homogeneous brand choicefeatures. This property of SOM-based CMS/segmentation analysis allows the two inter-dependent tasks to be performed simultaneously, as it is frequently claimed in the mar-keting literature.

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June 1998

Friedrich Leisch, Andreas Weingessel, Kurt Hornik

On the Generation of Correlated Artificial Binary Data

Abstract

The generation of random variates from multivariate binary distributions has not gainedas much interest in the literature as, e.g., multivariate normal or Poisson distributions.Binary variables are important in many types of applications. Our main interest is in thesegmentation of marketing data, where data come from customer questionnaires withyes/no questions. Artificial data provide a valuable tool for the analysis of segmenta-tion tools, because data with known structure can be constructed to mimic situationsfrom the real world (Dolnicar et al. 1998). Questionnaire data can be highly correlated,when several questions covering the same field are likely to be answered similarly by asubject. In this paper we present a computationally fast method to simulate multivariatebinary distributions with a given correlation structure. The implementation of the algo-rithm in R, an implementation of the S statistical language, is described in the appendix.

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Folgende Aufstellung bietet einen berblick ber Aktivitten des SFB in den Vormo-naten:

April01 16.00 Ini 2 Statistical

ModellingH.H. Bock(Uni Aachen)

Wahrscheinlichkeitsmodelle inder Cluster Analyse

02 16.15 Ini 2 StatisticalModelling

Monika Drfler Strukturtheorie fr rekurrenteneuronale Netze

23 09.00-

12.00

13.00-15.00

Ini 3 Market

Segmentationand ProductPositioning

C. Buchta

S. DolnicarK. GrablerA. WeingesselF. LeischM. Natter

Adaptive Modelling Workshop:

Zwischenbericht ber Quanti-sierungsergebnisse fr autologi-stisch generierte BinrdatenBericht zum Ausbaustand desSimulationsmodells Biermarkt


Seminar Strasser u. Schacher-mayer

24 13.00-OpenEnd

Ini 3 MarketSegmentationand Product

Positioning

H. Mitlhner Verteiltes Rechnen mit PVM-Parallel Virtual Machine; Pro-grammierung adaptiver Agenten

an Finanzmarktbeispielen

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29 14.00 J. van Schuppen(CWI Amsterdam)

Systemtheory and systemidenti-fication of compartmentalsystems

May07 16.15 Ini 2 Statistical

ModellingH.-G. Feichtinger(UNI Wien)

konometrisches Seminar:Gabor Analysis: Methoden derlokalen Fourier Transformation


Seminar Strasser u. Schacher-mayer

08 14.00-15.30

Adaptive Friday Duffie Darell Research Initiative 6


Jrg Rahnenfhrer(Uni Dsseldorf)

Simple competitive learning:der Gewissenalgorythmus

June17 16.00 Ini 2 Statistical

ModellingUsula Gather/UniDortmund

Robuste Dimensionsreduktionin Regressionsmodellen

19 13.30 Adaptive Friday Research Initiative 6

19 18.00 Mitarbeiterver-sammlung


Iris Pigeot Grafische Modelle

26 16.00 Ini 3 MarketSegmentationand Product Po-sitioningIni 2 StatisticalModelling

Adaptive Modelling Workshop

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Als neue SFB-Mitarbeiter begren wir

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Mag. Thomas Dangl.

Er hat seine Ttigkeit am 01. 04 1998 aufgenommen und stellt seitdem seine Dienste

dem SFB ganztags als wissenschaftlicher Mitarbeiter der Initiative 6 zur Verfgung.Ebenso freuen wir uns

Univ.-Doz. Gerhard Sorger

seit 08. 05. 1998 als stellvertretenden Projektleiter fr Herrn Prof. Otrubas Initiative 6Artificial Financial Markets beim SFB willkommen heien zu drfen!

Aus dem SFB ausgeschieden sind

Mag. Thomas Hudetz (per 01.04. 1998, Initiative 2)

Mag. Reinhard Zimmermann (mit 31.05. 1998, Initiative 6)

D73Rc\eW`cdTYVc"!"#@\e`SVc"**)

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Diese Kooperationsveranstaltung unseres SFB-AM mit dem Berliner SFB (im groenSitzungssaal der WU Wien) dient dem Informationsaustausch ber Forschungsergebnis-se und -vorhaben, der Suche nach gemeinsamen Forschungsinteressen und mglichen

gemeinsamen Projekten. Mitarbeiter ALLER INITIATIVEN des SFB sind herzlich ein-geladen, daran teilzunehmen. Um ausreichende Mengen an Kaffee und hnlich banalerRessourcen zu garantieren, bitten wir allerdings um Anmeldung (e-mail).

Programmbersicht

(Die Vortragstitel sind derzeit noch nicht vollstndig bekannt)

Samstag, 10. 10.

13.00 13.30 Begrung13.30 15.00 Vortrge des SFB Berlin

15.00 15.30 Kaffeepause15.30 17.00 Vortrge des SFB Berlin

Sonntag, 11. 10.

9.00 10.30 Harald Hruschka:Dynamische Markenwahlmodelle, Modellierung der Mar-kenwahl mittels Multilayer-Perzeptronen

10.30 11.00 Kaffeepause

11.00 12.30 Harald Hruschka:Nichtlineare Verallgemeinerungen des MNL-Modells

12.30 13.30 Mittagspause

13.30 15.00 SFB-AM Initiative 3:Kombinierte Positionierungs / Segmentierungs-Analysen

15.00 15.30 Kaffeepause

15.30 17.00 SFB-AM Initiative 3:Vektorquantisierung mit knstlichen Binrdaten

Montag, 12. 10.

9.00 10.30 Plenarsitzung:Gemeinsamkeiten Unterschiede Kooperationsmglich-keiten

10.30 11.00 Kaffeepause11.00 12.30 Diskussion12.30 Ende des Workshops

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Von hier aus gehts zu allen SFB-Informationen:

:^acVddf^

Herausgeber und Medieninhaber:

Spezialforschungsbereich Selbstlernende Modelle in den Sozial-und WirtschaftswissenschaftenWirtschaftsuniversitt WienAugasse 2-6, A-1090 WienDer Spezialforschungsbereich Selbstlernende Modelle in den Sozial-und Wirtschaftswisschenschaften wird aus Mitteln des Fonds zurFrderung der wissenschaftlichen Forschung finanziert.

Verlags- und Herstellungsort: Wien

Fr den Inhalt verantwortlich: Josef Mazanec (Sprecher des SFB)

Redaktion & Produktion: Sara Dolnicar

Grundlegende Richtung:

SFB-internes Informationsmedium

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2URaeZgV?Vhd]VeeVcJahrgang 2 Nr. 1 - Juni 1998 http://www.wu-wien.ac.at/am/news.html

Inhalt

Neuerscheinungen in der Schriftenreihe des SFB .............. 1

SFB Aktivitten 1998 (5. Quartal) im berblick................... 9

SFB-Mitarbeiterupdate ...................................................... 10

SFB-Klausur: 3 Tage Zeit fr Interdisziplinaritt................ 11

Einladung zum SFB-Workshop der Marktforscher ........... 11

Impressum......................................................................... 13

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