Dan Gabriel Anghel

Personal Details

First Name:	Dan Gabriel
Middle Name:
Last Name:	Anghel
Suffix:
RePEc Short-ID:	pan626
	[This author has chosen not to make the email address public]

Affiliation

(55%) Institutul de Prognoza Economica
Institutul National de Cercetari Economice (INCE)
Academia Romana

Bucureşti, Romania
http://www.ipe.ro/
RePEc:edi:ipacaro (more details at EDIRC)

(45%) Facultatea de Finante, Asigurari, Banci şi Burse de Valori
Academia de Studii Economice din Bucureşti

Bucureşti, Romania
http://www.fin.ase.ro/
RePEc:edi:ffasero (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

1. Anghel, Dan Gabriel, 2022. "No pain, no gain: You should always incorporate trading costs for a bias-free evaluation of trading rule overperformance," Economics Letters, Elsevier, vol. 216(C).

   Cited by:

   1. Deprez, Niek & Frömmel, Michael, 2024. "Are simple technical trading rules profitable in bitcoin markets?," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 858-874.

2. Cepoi, Cosmin-Octavian & Anghel, Dan-Gabriel & Pop, Ionuţ Daniel, 2021. "Asymmetries and flight-to-safety effects in the price discovery process of cross-listed stocks," Economic Modelling, Elsevier, vol. 98(C), pages 302-318.

   Cited by:

   1. Bastidon, Cécile & Jawadi, Fredj, 2024. "Trade fragmentation and volatility-of-volatility networks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
      • Cécile Bastidon & Fredj Jawadi, 2024. "Trade fragmentation and volatility-of-volatility networks," Post-Print hal-04478721, HAL.
   2. Papavassiliou, Vassilios G. & Kinateder, Harald, 2021. "Information shares and market quality before and during the European sovereign debt crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).

3. Anghel, Dan Gabriel, 2021. "Data Snooping Bias in Tests of the Relative Performance of Multiple Forecasting Models," Journal of Banking & Finance, Elsevier, vol. 126(C).

   Cited by:

   1. Anghel, Dan Gabriel, 2022. "No pain, no gain: You should always incorporate trading costs for a bias-free evaluation of trading rule overperformance," Economics Letters, Elsevier, vol. 216(C).

4. ANGHEL, Dan-Gabriel, 2021. "A reality check on trading rule performance in the cryptocurrency market: Machine learning vs. technical analysis," Finance Research Letters, Elsevier, vol. 39(C).

   Cited by:

   1. Sheng, Lin Wen & Uddin, Gazi Salah & Sen, Ding & Hao, Zhu Shi, 2024. "The asymmetric volatility spillover across Shanghai, Hong Kong and the U.S. stock markets: A regime weighted measure and its forecast inference," International Review of Financial Analysis, Elsevier, vol. 91(C).
   2. Stéphane Goutte & Viet Hoang Le & Fei Liu & Hans-Jörg Mettenheim, Von, 2023. "Deep Learning And Technical Analysis In Cryptocurrency Market," Working Papers halshs-03917333, HAL.
      • Goutte, Stéphane & Le, Hoang-Viet & Liu, Fei & von Mettenheim, Hans-Jörg, 2023. "Deep learning and technical analysis in cryptocurrency market," Finance Research Letters, Elsevier, vol. 54(C).
   3. Day, Min-Yuh & Ni, Yensen, 2023. "Be greedy when others are fearful: Evidence from a two-decade assessment of the NDX 100 and S&P 500 indexes," International Review of Financial Analysis, Elsevier, vol. 90(C).
   4. Liu, Qingfu & Tao, Zhenyi & Tse, Yiuman & Wang, Chuanjie, 2022. "Stock market prediction with deep learning: The case of China," Finance Research Letters, Elsevier, vol. 46(PA).
   5. Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
   6. Cynthia Weiyi Cai & Rui Xue & Bi Zhou, 2023. "Cryptocurrency puzzles: a comprehensive review and re-introduction," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(1), pages 26-50, June.
   7. Łęt Blanka & Sobański Konrad & Świder Wojciech & Włosik Katarzyna, 2022. "Is the cryptocurrency market efficient? Evidence from an analysis of fundamental factors for Bitcoin and Ethereum," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 58(4), pages 351-370, December.
   8. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.

5. Dan Gabriel ANGHEL, 2020. "Predicting Intraday Prices in the Frontier Stock Market of Romania Using Machine Learning Algorithms," International Journal of Economics and Financial Research, Academic Research Publishing Group, vol. 6(7), pages 170-179, 07-2020.

   Cited by:

   1. Mst. Shapna Akter & Hossain Shahriar & Reaz Chowdhury & M. R. C. Mahdy, 2022. "Forecasting the Risk Factor of Frontier Markets: A Novel Stacking Ensemble of Neural Network Approach," Future Internet, MDPI, vol. 14(9), pages 1-23, August.

6. Dan Gabriel ANGHEL & Elena Valentina ŢILICĂ & Victor DRAGOTĂ, 2020. "Intraday Patterns in Returns on the Romanian and Bulgarian Stock Markets," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 92-114, July.

   Cited by:

   1. Elena Valentina Ţilică & Victor Dragotă & Camelia Delcea & Răzvan Ioan Tătaru, 2024. "Portfolio management under capital market frictions: a grey clustering approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-36, December.
   2. Dragos HURU & Ioana MANAFI & Ionut PANDELICA & Marilena Carmen UZLAU, 2022. "Nonlinear Dependencies between Green Bonds and General Financial Market Indices," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 169-181, December.

7. Pop, Ionuț Daniel & Cepoi, Cosmin Octavian & Anghel, Dan Gabriel, 2018. "Liquidity-threshold effect in non-performing loans," Finance Research Letters, Elsevier, vol. 27(C), pages 124-128.

   Cited by:

   1. Hamdi, Helmi & Hakimi, Abdelaziz, 2019. "Does Liquidity Matter on Bank Profitability? Evidence from a Nonlinear Framework for a Large Sample," Business and Economics Research Journal, Uludag University, Faculty of Economics and Administrative Sciences, vol. 10(1), pages 13-26, January.
      • Abdelaziz Hakimi & Helmi Hamdi, 2020. "Does Liquidity Matter on Bank Profitability Evidence from a Nonlinear Framework for a Large Sample," Post-Print hal-03511285, HAL.
   2. Kashif Abbass & Abdul Aziz Khan Niazi & Abdul Basit & Tehmina Fiaz Qazi & Huaming Song & Halima Begum, 2021. "Uncovering Effects of Hot Potatoes in Banking System: Arresting Die-Hard Issues," SAGE Open, , vol. 11(4), pages 21582440211, December.
   3. Shabir, Mohsin & Jiang, Ping & Hashmi, Shujahat Haider & Bakhsh, Satar, 2022. "Non-linear nexus between economic policy uncertainty and bank lending," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 657-679.
   4. Nizam, Esma & Ng, Adam & Dewandaru, Ginanjar & Nagayev, Ruslan & Nkoba, Malik Abdulrahman, 2019. "The impact of social and environmental sustainability on financial performance: A global analysis of the banking sector," Journal of Multinational Financial Management, Elsevier, vol. 49(C), pages 35-53.
   5. Phung, Quang Thanh & Van Vu, Huong & Tran, Huy Phuoc, 2022. "Do non-performing loans impact bank efficiency?," Finance Research Letters, Elsevier, vol. 46(PB).
   6. Adina Ionela Străchinaru & Bogdan Andrei Dumitrescu, 2019. "Assessing the Sustainability of Inflation Targeting: Evidence from EU Countries with Non-EURO Currencies," Sustainability, MDPI, vol. 11(20), pages 1-13, October.
   7. Andreea Maura Bobiceanu & Ioana Georgiana Fä‚Rcaè˜, 2022. "Covid Crisis Effects On Non-Performing Loans In The Romanian Banking Market," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 30, pages 25-37, December.
   8. Meral KAGITCI & Leonardo BADEA & Vasile Cosmin NICULA, 2021. "The Catch-up Effect of Economic Growth. Evidence from the European Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 76-86, December.
   9. Jihen Bouslimi & Abdelaziz Hakimi & Taha Zaghdoudi & Kais Tissaoui, 2024. "The complex relationship between credit and liquidity risks: a linear and non-linear analysis for the banking sector," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
   10. Rahbar , Farhad & Behzadi Soufiani , Mohsen, 2021. "The Impact of Macroeconomic and Banking Variables on Non-Performing Loans in Oil Cycles: Evidence from Iran," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 16(2), pages 135-164, June.
   11. Teodor Hada & Nicoleta Bărbuță-Mișu & Iulia Cristina Iuga & Dorin Wainberg, 2020. "Macroeconomic Determinants of Nonperforming Loans of Romanian Banks," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
   12. Rim Boussaada & Abdelaziz Hakimi & Majdi Karmani, 2022. "Is there a threshold effect in the liquidity risk–non‐performing loans relationship? A PSTR approach for MENA banks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1886-1898, April.
   13. Jiajia, Liu & Kun, Guo & Fangcheng, Tang & Yahan, Wang & Shouyang, Wang, 2023. "The effect of the disposal of non-performing loans on interbank liquidity risk in China: A cash flow network-based analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 105-119.

8. Dan Gabriel ANGHEL, 2017. "Intraday Market Efficiency for a Typical Central and Eastern European Stock Market: The Case of Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 88-109, September.

   Cited by:

   1. Dan Gabriel ANGHEL & Elena Valentina ŢILICĂ & Victor DRAGOTĂ, 2020. "Intraday Patterns in Returns on the Romanian and Bulgarian Stock Markets," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 92-114, July.
   2. Tihana Škrinjarić & Zrinka Orlović, 2020. "Economic Policy Uncertainty and Stock Market Spillovers: Case of Selected CEE Markets," Mathematics, MDPI, vol. 8(7), pages 1-33, July.
   3. Claudiu Tiberiu Albulescu & Aviral Kumar Tiwari & Phouphet Kyophilavong, 2021. "Nonlinearities and Chaos: A New Analysis of CEE Stock Markets," Mathematics, MDPI, vol. 9(7), pages 1-13, March.
   4. Dan Gabriel Anghel, 2020. "What Can Machine Learning Tell Us About Intraday Price Patterns in a Frontier Stock Market?," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(5), pages 205-220, October.

9. Dan Gabriel Anghel, 2015. "Market Efficiency and Technical Analysis in Romania," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(2), pages 164-177, April.

   Cited by:

   1. Shi, Huai-Long & Zhou, Wei-Xing, 2017. "Wax and wane of the cross-sectional momentum and contrarian effects: Evidence from the Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 397-407.
      • H. -L. Shi & W. -X. Zhou, 2017. "Wax and wane of the cross-sectional momentum and contrarian effects: Evidence from the Chinese stock markets," Papers 1707.05552, arXiv.org.
   2. Bley, Jorg & Saad, Mohsen, 2020. "An analysis of technical trading rules: The case of MENA markets," Finance Research Letters, Elsevier, vol. 33(C).
   3. Ali Fayyaz Munir & Mohd Edil Abd. Sukor & Shahrin Saaid Shaharuddin, 2022. "Adaptive Market Hypothesis and Time-varying Contrarian Effect: Evidence From Emerging Stock Markets of South Asia," SAGE Open, , vol. 12(1), pages 21582440211, January.
   4. Ruan, Yong-Ping & Song, Xin & Zheng, Kai, 2018. "Do large shareholders collude with institutional investors? Based on the data of the private placement of listed companies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 242-253.
   5. Tihana Škrinjarić, 2018. "Testing for Seasonal Affective Disorder on Selected CEE and SEE Stock Markets," Risks, MDPI, vol. 6(4), pages 1-26, December.

10. Dan Anghel, 2013. "How Reliable is the Moving Average Crossover Rule for an Investor on the Romanian Stock Market?," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 5(2), pages 089-115, December.

    Cited by:

    1. Dan Gabriel Anghel, 2015. "Market Efficiency and Technical Analysis in Romania," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(2), pages 164-177, April.
    2. Andreea Săseanu & Hosney (Harry) Zurub & Gurgen Ohanyan & Natalia Bob, 2014. "The effects of IMF conditionality on Romanian economy: evidence from the Bucharest Stock Exchange," Management & Marketing, Economic Publishing House, vol. 9(3), Autumn.
    3. Victor Dragota & Dragos Stefan Oprea, 2014. "Informational Efficiency Tests on the Romanian Stock Market: A Review of the Literature," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 6(1), pages 015-028, June.

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