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Abstract

The thesis presents four studies in the field of financial microstructure, specifically trading and manipulation in financial markets. The first two studies aim to investigate the effect of spoofing manipulation on intraday market quality and forecast the market state with high spoofing manipulation risk. The latest two studies focus on informed trading and information incorporation into asset prices.

The first paper studies the intraday relationship between spoofing manipulation activity and market quality in the automated equity market on the Moscow Exchange (MOEX).We find that higher spoofing activity is associated with lower intraday market quality (greater quoted and effective spreads and greater volatility). This effect is economically significant and robust to different specifications, endogeneity, and alternative spoofing measures. Our results hold after controlling for volatility, day trading volume, and intensive trading periods during the day.

The second study introduces a data-driven approach to forecast the market state with high spoofing risk. The approach reduces model selection’s importance through forecasts combining different machine learning predictors. We apply the algorithm to a unique dataset of suspicious spoofing cases detected on MOEX. We show that learning from the limit order book using machine learning techniques generates an effective manipulation prediction measure. Our study introduces an indicator of real-time risk to trade in a manipulative environment that exchanges and regulators could utilise for their surveillance systems. Our approach achieves significant forecasting accuracy in a high-frequency environment.

The third study is an empirical investigation of informed trading in the futures market. Using the comprehensive data with the customer type indication from MOEX, we examine trading by different customer groups and find that retail traders forecast intraday returns in a high-frequency time dimension. Institutional traders effectively predict short-term returns while losing their forecasting power after four trading days. We find that different customer groups systematically trade in opposite directions, and their order flows are highly informative about intraday returns. Finally, the fourth study examines price discovery dynamics between Bitcoin exchange-traded products (ETPs) and spot markets on centralised cryptocurrency exchanges. We apply four popular price discovery measures to ETP and spot transaction data. Our results show that price discovery is dominated by the spot market across all measures and sampling frequencies. This implies that ETP markets play a smaller role in incorporating new information about Bitcoin prices and that informed investors largely prefer to trade on spot markets.

Four essays form coherent research motivated by the increasing speed of development of the electronic markets, the rise of high-frequency trading with the introduction of new possibilities for market destabilisation, and natural demand from investors for a higher-quality trading environment and diversification strategies in new asset classes and markets.

Publication Type:	Thesis (Doctoral)
Subjects:	H Social Sciences > HG Finance
Departments:	Bayes Business School > Bayes Business School Doctoral Theses Bayes Business School > Finance Doctoral Theses

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