Does high frequency trading affect technical analysis and market efficiency? And if so, how? V Manahov, R Hudson, B Gebka Journal of International Financial Markets, Institutions and Money 28, 131-157, 2014 | 88 | 2014 |
Cryptocurrency liquidity during extreme price movements: is there a problem with virtual money? V Manahov Quantitative Finance 21 (2), 341-360, 2021 | 62 | 2021 |
A note on the relationship between market efficiency and adaptability–New evidence from artificial stock markets V Manahov, R Hudson Expert Systems with Applications 41 (16), 7436-7454, 2014 | 55 | 2014 |
Front‐running scalping strategies and market manipulation: why does high‐frequency trading need stricter regulation? V Manahov* Financial Review 51 (3), 363-402, 2016 | 53 | 2016 |
The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets V Manahov, A Urquhart International Review of Financial Analysis 73, 101629, 2021 | 32 | 2021 |
Herd behaviour experimental testing in laboratory artificial stock market settings. Behavioural foundations of stylised facts of financial returns V Manahov, R Hudson Physica A: Statistical Mechanics and its Applications 392 (19), 4351-4372, 2013 | 25 | 2013 |
High‐frequency trading from an evolutionary perspective: Financial markets as adaptive systems V Manahov, R Hudson, A Urquhart International Journal of Finance & Economics 24 (2), 943-962, 2019 | 22 | 2019 |
Return predictability and the ‘wisdom of crowds’: Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis V Manahov, R Hudson, H Hoque Journal of International Financial Markets, Institutions and Money 37, 85-98, 2015 | 22 | 2015 |
Islamic and conventional equity market movements during and after the financial crisis: Evidence from the newly launched MSCI indices H Hoque, SH Kabir, EK Abdelbari, V Manahov Financial Markets, Institutions & Instruments 25 (4), 217-252, 2016 | 21 | 2016 |
Forecasting financial markets using high-frequency trading data: Examination with strongly typed genetic programming V Manahov, H Zhang International Journal of Electronic Commerce 23 (1), 12-32, 2019 | 20 | 2019 |
A note on the relationship between high-frequency trading and latency arbitrage V Manahov International review of financial analysis 47, 281-296, 2016 | 17 | 2016 |
Can high‐frequency trading strategies constantly beat the market? V Manahov International Journal of Finance & Economics 21 (2), 167-191, 2016 | 16 | 2016 |
The implications of high-frequency trading on market efficiency and price discovery V Manahov, R Hudson Applied Economics Letters 21 (16), 1148-1151, 2014 | 16 | 2014 |
High‐frequency trading order cancellations and market quality: Is stricter regulation the answer? V Manahov International Journal of Finance & Economics 26 (4), 5385-5407, 2021 | 15 | 2021 |
The rise of the machines in commodities markets: new evidence obtained using strongly typed genetic programming V Manahov Annals of Operations Research 260, 321-352, 2018 | 13 | 2018 |
Investigation of institutional changes in the UK housing market using structural break tests and time-varying parameter models H Zhang, R Hudson, H Metcalf, V Manahov Empirical economics 53, 617-640, 2017 | 11 | 2017 |
New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming V Manahov, R Hudson, P Linsley Journal of International Financial Markets, Institutions and Money 33, 299-316, 2014 | 10 | 2014 |
The great crypto crash in September 2018: why did the cryptocurrency market collapse? V Manahov Annals of Operations Research 332 (1), 579-616, 2024 | 8 | 2024 |
Identification of house price bubbles using user cost in a state space model H Zhang, R Hudson, H Metcalf, V Manahov Applied economics 47 (56), 6088-6101, 2015 | 7 | 2015 |
The implications of trader cognitive abilities on stock market properties V Manahov, M Soufian, R Hudson Intelligent Systems in Accounting, Finance and Management 21 (1), 1-18, 2014 | 7 | 2014 |