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Dr Viktor Manahov
Reader in Finance

Profile

Biography

I received my PhD titled “An investigation of the behaviour of financial markets using agent-based computational models” from Newcastle University before I went to Queen Mary University in London. I joined the University of York in 2014, first as a Lecturer until 2016, then as Senior Lecturer until 2022 and from October 2022 as Reader in Finance.


I am a Senior Fellow of the UK Higher Education Academy and a Certified Management and Business Educator. My teaching and research focus is on Fintech, cryptocurrency trading and the role of Artificial Intelligence and machine learning in financial markets. I supervise PhD students researching different areas of worldwide financial and cryptocurrency markets.


I am an editorial member of the Review of Behavioral Finance, Cambridge Scholars Publishing, Journal of Computer Science Research, and Artificial Intelligence Advances.

Research

Overview

FinTech and cryptocurrency trading

Artificial Intelligence and machine learning trading algorithms

High-frequency trading

Asset pricing and portfolio management

Stock market forecasts and valuation of securities

Issues in behavioural finance

Publications

Selected publications

Manahov, V. & Li, M. (2024). The implications of virtual money on travel and tourism. Annals of Tourism Research (4*), 105: 103686.

Manahov,V.(2023). The Great Crypto Crash in September 2018. Why did the cryptocurrency market collapse? Annals of Operations Research (3*).  DOI 10.1007/s10479-023-05575-0

Manahov,V.(2023). The rapid growth of cryptocurrencies: How profitable is trading in digital money? International Journal of Finance and Economics (3*). https://doi.org/10.1002/ijfe.2778

Yalaman,A., Manahov,V.(2021). Analysing emerging market returns with high-frequency data during the global financial crisis of 2007-2009. The European Journal of Finance (3*), DOI: 10.1080/1351847X.2021.1957698

Manahov,V.(2021). Cryptocurrency liquidity during extreme price movements: is there a problem with virtual money? Quantitative Finance (3*), 21(2): 341-360.

Manahov,V.,Urquhart,A.(2021). The efficiency of bitcoin: a strongly typed genetic programming approach to smart electronic bitcoin markets. International Review of Financial Analysis (3*), 73: 101629.

Manahov,V.(2020). High‐frequency trading order cancellations and market quality: Is stricter regulation the answer? International Journal of Finance and Economics (3*), Accepted, available online: https://doi.org/10.1002/ijfe.2071

Ergün.H.O,Yalaman,A,Manahov.V.,Zhang,H.(2020). Stock market manipulation in an emerging market of Turkey: how do market participants select stocks for manipulation? Applied Economics Letters (1*). Accepted, available online: https://doi.org/10.1080/13504851.2020.1753874

Manahov, V., Zhang,H. (2019). Forecasting Financial Markets Using High-Frequency Trading Data: Examination with Strongly Typed Genetic Programming. International Journal of Electronic Commerce 23 (1), 12-32 (3* ABS).

Manahov, Hudson,R., Urquhart, A. (2018). High‐frequency trading from an evolutionary perspective: Financial markets as adaptive systems. International Journal of Finance and Economics 24 (2), 943-962 (3* ABS).

Manahov, V. (2016). Scalping strategies and market manipulation. Why does high-frequency trading need stricter regulation? The Financial Review (3* ABS), 51:363-402.

Won the best paper award by the Eastern Finance Association USA

Manahov,V. (2016). Can high-frequency strategies constantly beat the market? International Journal of Finance & Economics (3* ABS), 21(2): 167-191. 

Manahov,V.(2016). A note on the relationship between high-frequency trading and latency arbitrage. International Review of Financial Analysis (3* ABS),47:281-296.

Manahov,V.(2016). The rise of the machines in commodities markets: New evidence obtained using Strongly Typed Genetic Programming. Annals of Operations Research (3* ABS), 1-32.

Hoque,H.,Kabir,S.H.,Abdelbari,E.L.,Manahov,V.(2016). Islamic and conventional equity market movements during and after the financial crisis: Evidence from newly launched MSCI indices. Financial Markets, Institutions and Instruments (3* ABS),25(4):217-252.

Manahov,V.,Hudson,R.,Hoque,H.(2015). Return predictability and the ‘wisdom of crowds’: Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis. Journal of International financial Markets, Institutions and Money (3* ABS),37: 85-98.  

Manahov, V., Hudson, R.,Gebka,B. (2014). Does high frequency trading affect technical analysis and market efficiency? And if so, how? Journal of International Financial Markets, Institutions and Money (3* ABS), 28: 131-157.

Manahov,V.,Hudson,R.,Linsley,P.(2014). New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming. Journal of International Financial Markets, Institutions and Money (3* ABS), 33: 299-316. 

Manahov,V.,Hudson,R.(2014). A note on the relationship between market efficiency and adaptability- New evidence from artificial stock markets. Expert Systems with Applications (3* ABS), 41: 7436-7454.

Manahov, V., Hudson, R. (2013). Herd behaviour experimental testing in laboratory artificial stock market settings. Behavioural foundations of stylized facts of financial returns. Physica A: Statistical Mechanics and its Applications (2* ABS), 392: 4352-4372.

Prasetyo,D.,Manahov,V.,Obiosa,N.(2014). Does capital market reaction to non-economic factors generate abnormal returns? Investment Management and Financial Innovations (2* ABS), 11(4): 66-76.

Zhang,H.,Manahov,V.,Hudson,R.,Metkalf,H.(2015). Do house prices overreact to relevant information? New evidence from UK housing market. Investment Management and Financial Innovations (2* ABS), 12(3): 26-39.

Prasetyo,D.,Manahov,V.,Obiosa,N. (2016). Investigating the determinants of dividend policy in emerging markets using a combination of explanatory variables. Investment Management and Financial Innovations (2* ABS), 2(2): 14-28.

Zhang,H.,Manahov,V.,Hudson,R.,Metkalf,H.(2015).Identification of house price bubbles using user cost in a state space model. Applied Economics (2* ABS), 47(56): 6088-6101.

Zhang,H.,Manahov,V.,Hudson,R.,Metcalf,H. (2016). Investigation of institutional changes in the UK housing market using structural break tests and time varying parameter models. Empirical Economics (2* ABS). 1-24.

Manahov, V., Hudson, R., Soufian, M. (2013). The implications of trader cognitive abilities on stock market properties. Intelligent Systems in Accounting, Finance and Management (1* ABS), 21(1): 1-18.

Manahov, V., Hudson, R. (2014). The implications of high frequency trading on market efficiency and price discovery. Applied Economics Letters (1* ABS), 21(16):1148-1151.

Manahov,V., Hudson, R. (2013). New evidence of technical trading profitability. Economics Bulletin, 33(4): 2493-2503.

School for Business and Society
University of York
Church Lane Building
York Science Park
Heslington
York YO10 5ZF

Telephone: +44 (0) 1904 325847
Email: viktor.manahov@york.ac.uk
Room: CL/A/119A

Subject Group

Accounting and Finance

 

Feedback & Support hours

Please contact your tutor to find out when they are running their virtual office hours or to make an appointment for a virtual meeting

Teaching

Other teaching

FinTech and Advanced Investment Management – PG module

Corporate Financial Strategy – PG online module