2200 AAĞĞUUSSTTOOSS PPEERRŞŞEEMBBEE SSAAYYI II: : 662233 Bilgiye Erişim Merkezi ne Yeni Gelen Yayınlar Canoğlu, Mehmet Ali. Genel muhasebe. Ankara: Maliye Bakanlığı Gelirler Genel Müdürlüğü, 1972. Keseroğlu, Hasan S. Türkiye muhasebecilik bibliyografyası: kitaplar 1729 2007 / Hasan S. Keseroğlu, İlkim Mengülerek. İstanbul: İSMMMO, 2009. İİ SS MM MM MM OO BB i l gg i yy ee EE rr i şş im i MM ee rr kk ee zz i 1
Going for growth the economy of the EU. Belçika: European Communities, 2003. 15. yıl sempozyumu. Ankara: SPK, 1998. İİ SS MM MM MM OO BB i l gg i yy ee EE rr i şş im i MM ee rr kk ee zz i 2
Resmi Gazete YÜRÜTME VE İDARE BÖLÜMÜ BAKANLAR KURULU KARARLARI 2009/15292 Bazı Anlaşmaların Yürürlüğe Girdiği Tarihlerin Tespit Edilmesi Hakkında Karar 2009/15298 Kamu Kurum ve Kuruluşlarının Yurtdışı Teşkilatını Oluşturan Birimlerin Nitelik, Kurulduğu Şehir ve Ülke, Görev Alanı, Akredite Edildiği Ülkeler ve Bağlı Bulunduğu Misyonlar Hakkındaki 13/4/1999 Tarihli ve 12770 Sayılı Bakanlar Kurulu Kararında Değişiklik Yapılmasına İlişkin Karar 2009/15306 Bulgaristan Plakalı Taşıtlara Uygulanacak Standart Depo Muafiyetine İlişkin Esaslar Hakkında Kararın Yürürlükten Kaldırılmasına Dair Karar BAKANLIĞA VEKÂLET ETME İŞLEMİ Devlet Bakanı Mehmet Zafer ÇAĞLAYAN a, Kültür ve Turizm Bakanı Ertuğrul GÜNAY ın Vekâlet Etmesine Dair Tezkere YÖNETMELİKLER Sosyal Sigorta İşlemleri Yönetmeliğinde Değişiklik Yapılmasına Dair Yönetmelik İstanbul Üniversitesi Uzaktan Eğitim Uygulama ve Araştırma Merkezi Yönetmeliği Piri Reis Üniversitesi Ana Yönetmeliği Uşak Üniversitesi Uzaktan Eğitim Meslek Yüksekokulu Önlisans Eğitim-Öğretim ve Sınav Yönetmeliği TEBLİĞLER Yapı Malzemeleri Yönetmeliği (89/106/EEC) Kapsamında Alberk QA Uluslararası Teknik Kontrol ve Belgelendirme Ltd. Şti. nin Onaylanmış Kuruluş Olarak Görevlendirilmesine Dair Tebliğ (No: YİG/2009-07) Yapı Malzemeleri Yönetmeliği (89/106/EEC) Kapsamında Kalitest Belgelendirme ve Eğitim Hizmetleri Ltd. Şti. nin Onaylanmış Kuruluş Olarak Görevlendirilmesine Dair Tebliğ (No: YİG/2009-08) Yatırımlarda Devlet Yardımları Hakkında Kararın Uygulanmasına İlişkin 2009/1 Sayılı Tebliğ de Değişiklik Yapılmasına İlişkin Tebliğ (No: 2009/2) Yabancı Kitap Ve Süreli Yayınlar Journal of Financial Economics Volume 91, Issue 3, Pages 253 406 (March 2009) Has New York become less competitive than London in global markets? Evaluating foreign listing choices over time Pages 253-277 Craig Doidge, G. Andrew Karolyi, René M. Stulz We study the determinants and consequences of cross-listings on the New York and London stock exchanges from 1990 to 2005. This investigation enables us to evaluate the relative benefits of New York and London exchange listings and to assess whether these relative benefits have changed over time, perhaps as a result of the passage of the Sarbanes-Oxley Act in 2002. We find that cross-listings have been falling on US İİ SS MM MM MM OO BB i l gg i yy ee EE rr i şş im i MM ee rr kk ee zz i 3
exchanges as well as on the Main Market in London. This decline in cross-listings is explained by changes in firm characteristics instead of by changes in the benefits of cross-listing. We show that after controlling for firm characteristics there is no deficit in cross-listing counts on US exchanges related to SOX. Investigating the valuation differential between listed and non-listed firms (the cross-listing premium) from 1990 to 2005, we find that there is a significant premium for US exchange listings every year, that the premium has not fallen significantly in recent years, and that it persists when allowing for time-invariant unobservable firm characteristics. In contrast, no premium exists for listings on London's Main Market in any year. Firms increase their capitalraising activities at home and abroad following a cross-listing on a major US exchange but not following a cross-listing in London. Our evidence is consistent with the theory that an exchange listing in New York has unique governance benefits for foreign firms. 2. The evolution of cross-listings in New York and London over time 2.1. Regulatory and listing requirements for foreign listings in New York and London 2.2. Data sources on foreign listings in New York and London 2.3. The time-series of listings: levels and flows 3. The characteristics of foreign listings in New York and London over time 4. The determinants of listing choices 4.1. Competing risks models for New York and London listings 4.2. Changing firm characteristics and the propensity to list in New York and London 5. The valuation premium for New York and London listings over time 5.1. The cross-listing premium in New York and London over time 5.2. The cross-listing premium before and after 2001 6. Capital raising activity around US and UK cross-listings 7. Conclusions Strategic price complexity in retail financial markets Pages 278-287 Bruce I. Carlin İİ SS MM MM MM OO BB i l gg i yy ee EE rr i şş im i MM ee rr kk ee zz i 4
There is mounting empirical evidence to suggest that the law of one price is violated in retail financial markets: there is significant price dispersion even when products are homogeneous. Also, despite the large number of firms in the market, prices remain above marginal cost and may even rise as more firms enter. In a non-cooperative oligopoly pricing model, I show that these anomalies arise when firms add complexity to their price structures. Complexity increases the market power of the firms because it prevents some consumers from becoming knowledgeable about prices in the market. In the model, as competition increases, firms tend to add more complexity to their prices as a best response, rather than make their disclosures more transparent. Because this may substantially decrease consumer surplus in these markets, such practices have important welfare implications. 2. The market for retail financial products 3. Strategic pricing and complexity 4. Competition, complexity, and financial literacy 5. Conclusion Appendix A. Appendix A.1. Proof of Proposition 1 Conditional volatility in affine term-structure models: Evidence from Treasury and swap markets Pages 288-318 Kris Jacobs, Lotfi Karoui We study the ability of three-factor affine term-structure models to extract conditional volatility using interest rate swap yields for 1991 2005 and Treasury yields for 1970 2003. For the Treasury sample, the correlation between model-implied and EGARCH volatility is between 60% and 75%. For the swap sample, this correlation is rather low or negative. We find that these differences in model performance are primarily due to İİ SS MM MM MM OO BB i l gg i yy ee EE rr i şş im i MM ee rr kk ee zz i 5
the timing of the swap sample, and not to institutional differences between swap and Treasury markets. We conclude that the ability of multifactor affine models to extract conditional volatility depends on the sample period, but that overall these models perform better than has been argued in the literature. 2. Conditional volatility in ATSMs 2.1. Affine term-structure models 2.2. The price of risk in ATSMs 2.3. Restrictions on conditional yield volatility in affine models 3. Data and estimation technique 3.1. Data 3.2. Estimation technique 4. Empirical results 4.1. Parameter estimates 4.2. Variance forecasts 4.3. Assessing the quality of the variance forecast 4.4. Further discussion on differences between swap and Treasury yields 4.5. Volatility and term-structure factors 4.6. The cross-section of yield volatility 4.7. Volatility and the market price of risk 5. Robustness analysis 5.1. An analysis of realized volatility 5.2. Yield levels versus yield differences 5.3. Interpolation method and sample period 5.4. The structure of the measurement errors 5.5. An analysis of instantaneous conditional volatility 6. Concluding comments Appendix A. Appendix Appendix B. Appendix Appendix C. Appendix İİ SS MM MM MM OO BB i l gg i yy ee EE rr i şş im i MM ee rr kk ee zz i 6
Dynamic order submission strategies with competition between a dealer market and a crossing network Pages 319-338 Hans Degryse, Mark Van Achter, Gunther Wuyts We analyze a dynamic microstructure model in which a dealer market (DM) and a crossing network (CN) interact for three informational settings. A key result is that coexistence of trading systems generates systematic patterns in order flow, which depend on the degree of transparency. Further, we study overall welfare, measured by the gains from trade of all agents, and compare it with the maximum overall welfare. The discrepancy between both measures is attributable to two inefficiencies. Due to these inefficiencies, introducing a CN next to a DM, as well as increasing the transparency level, not necessarily produces greater overall welfare. 2. Setup of the model 3. Markets in isolation 4. Coexistence of markets 4.1. Transparency 4.1.1. Equilibrium 4.1.2. Empirical predictions on order flow dynamics 4.2. Opaqueness 4.2.1. Complete opaqueness 4.2.2. Partial opaqueness 4.2.3. Empirical predictions on order flow dynamics 5. Welfare analysis 5.1. Overall welfare and inefficiencies: definitions 5.2. Overall welfare 5.2.1. Maximum overall welfare 5.2.2. Markets in isolation 5.2.3. Coexistence of markets İİ SS MM MM MM OO BB i l gg i yy ee EE rr i şş im i MM ee rr kk ee zz i 7
5.3. The role of inefficiencies 5.3.1. Markets in isolation 5.3.2. Coexistence of markets 5.4. Further welfare considerations and extensions 6. Conclusion Appendix A. Proofs Appendix B. Formal definitions of inefficiencies Collateral pricing Pages 339-360 Efraim Benmelech, Nittai K. Bergman We examine how collateral affects the cost of debt capital. Using a novel data set of secured debt issued by U.S. airlines, we construct industry-specific measures of collateral redeployability. We show that debt tranches that are secured by more redeployable collateral exhibit lower credit spreads, higher credit ratings, and higher loan-to-value ratios an effect which our estimates show to be economically sizeable. Our results suggest that the ability to pledge collateral, and in particular redeployable collateral, lowers the cost of external financing and increases debt capacity. 2. Collateral and debt financing implications for the airline industry 3. The airline ETC market 3.1. Historical development 3.2. ETC and EETCs 4. Data and summary statistics 4.1. Sample construction 4.2. Tranche and airline characteristics 4.3. Redeployability measures 5. Empirical analysis 5.1. Airline risk and the endogeneity of the redeployability measures İİ SS MM MM MM OO BB i l gg i yy ee EE rr i şş im i MM ee rr kk ee zz i 8
5.2. Redeployability and credit spreads 5.3. Credit spreads and collateral redeployability in industry downturns 5.4. Redeployability and credit ratings 5.5. Redeployability, loan-to-value, and tranche maturity 5.6. Redeployability and credit spreads controlling for LTV 6. Conclusion Appendix A. Ordered probit construction Corruption in bank lending to firms: Cross-country micro evidence on the beneficial role of competition and information sharing Pages 361-388 James R. Barth, Chen Lin, Ping Lin, Frank M. Song Building on the important study by Beck, Demirguc-Kunt, and Levine [2006. Bank supervision and corruption in lending. Journal of Monetary Economics 53, 2131-2163], we examine the effects of both borrower and lender competition as well as information sharing via credit bureaus/registries on corruption in bank lending. Using the unique World Bank data set (WBES) covering more than 4,000 firms across 56 countries with information on credit bureaus/registries, assembled by Djankov, McLiesh, and Shleifer [2007. Private credit in 129 countries. Journal of Financial Economics 84, 299 329], and bank regulation data collected by Barth, Caprio, and Levine [2006. Rethinking Bank Regulation: Till Angels Govern. Cambridge University Press, New York] to measure bank competition and information sharing, we find strong evidence that both banking competition and information sharing reduce lending corruption, and that information sharing also helps enhance the positive effect of competition in curtailing lending corruption. We also find that the ownership structure of firms and banks, legal environment, and firm competition all exert significant impacts on lending corruption. 2. A simple bargaining model 3. Data and variables İİ SS MM MM MM OO BB i l gg i yy ee EE rr i şş im i MM ee rr kk ee zz i 9
3.1. The sample 3.2. Bank corruption 3.3. Competition 3.4. Information-sharing 3.5. Bank ownership 3.6. Firm characteristics and controls 3.7. Additional banking sector and country controls 4. Empirical results 4.1. Banking competition and corruption in lending 4.2. Information sharing, competition and corruption in lending 4.3. Competition and corruption in countries with and without private bureaus 5. Robustness tests 5.1. Probit analysis and instrument variable analysis 5.2. More macro controls 6. Conclusions Accruals, cash flows, and aggregate stock returns Pages 389-406 David Hirshleifer, Kewei Hou, Siew Hong Teoh This paper examines whether the firm-level accrual and cash flow effects extend to the aggregate stock market. In sharp contrast to previous firm-level findings, aggregate accruals is a strong positive time series predictor of aggregate stock returns, and cash flows is a negative predictor. In addition, innovations in accruals are negatively contemporaneously correlated with aggregate returns, and innovations in cash flows are positively correlated with returns. These findings suggest that innovations in accruals and cash flows contain information about changes in discount rates, or that firms manage earnings in response to marketwide undervaluation. 2. Data and empirical methods İİ SS MM MM MM OO BB i l gg i yy ee EE rr i şş im i MM ee rr kk ee zz i 10
2.1. Data 2.2. Test methods 2.3. Descriptive statistics 3. Aggregate accruals and cash flows as predictors of future aggregate earnings and returns 3.1. Persistence of aggregate earnings components 3.2. Forecasting aggregate returns: univariate tests 3.3. Forecasting aggregate returns: multivariate tests 4. Contemporaneous relations between innovations in aggregate accruals, innovations in aggregate cash flows, and aggregate stock returns 5. Sector- and industry-level evidence 5.1. Forecasting sector-level earnings and returns 5.2. Forecasting industry-level earnings and returns 6. Conclusion İİ SS MM MM MM OO BB i l gg i yy ee EE rr i şş im i MM ee rr kk ee zz i 11