Volatility, dark trading and market quality: evidence from the 2020 COVID-19 pandemic-driven market volatility

We exploit the exogenous shock of the COVID-19 pandemic on financial markets and regulatory restrictions on dark trading to investigate how volatility drives dark market share and trader venue selection. We find that, consistent with theory, excessive volatility on lit exchanges is linked with an economically significant loss of market share by dark pools to lit exchanges. The dynamics of market share loss are driven by the cross-migration of informed and uninformed traders between lit and dark venues. Informed traders migrate from lit venues to dark venues when lit venues' volatility becomes excessive, while uninformed traders, wary of the presence of informed traders in dark pools, shift their trading to lit exchanges rather than delay trading in a volatile market environment. The market quality implications of the cross-migration are mixed: while it improves liquidity on the lit exchange, it results in a loss of informational efficiency

Three essays on the transformative role of technology in financial markets

Financial markets are vital for capital allocation and as a consequence, for the wider economy. They perform two primary functions: liquidity and price discovery. Liquidity refers to the ability to trade large quantities of an instrument quickly, and with relatively little price impact. Therefore, it offers investors the flexibility to make investment decisions. Price discovery encompasses the price formation process in financial markets and is, therefore, critical for efficient capital allocation. Both these functions are linked to the functioning of the wider economy. Over the last decade, financial markets have been transformed with the help of technology and are now a completely different proposition. Specifically, technological advancements, such as high frequency trading (HFT), have altered the structure of financial markets, the strategies of traders, and the liquidity and price discovery processes. These changes and developments have ignited a heated debate among academics and regulators. While some researchers claim that HFTs increase the market efficiency by improving the liquidity and price discovery (see as an example, Brogaard et al., 2014b), others argue that they create adverse selection risks for slow traders and contribute to market instability by exacerbating illiquidity shocks, such as flash crashes (see as an example, Kirilenko et al., 2017). Motivated by these contrasting views, this thesis investigates these issues, and is therefore situated at the intersection of financial markets, technology and regulations. It specifically examines the topical issues around the transformative role of technology in financial markets by adopting novel and unique approaches. In the first study, I present a novel framework illustrating the links between order aggressiveness and flash crashes. My framework involves a trading sequence beginning with significant increases in aggressive sell orders relative to aggressive buy orders until instruments’ prices fall to their lowest levels. Thereafter, a rise in aggressive buy orders propels the prices back to their pre-crash levels. Using a sample of S&P 500 stocks trading during the May 6 2010 flash crash, I show that the framework is correctly specified and provides a basis for linking flash crashes to aggressive strategies, which are found to be more profitable during flash crashes. The second study is a methodological contribution to the financial econometrics literature, in which I propose a state space modelling approach for decomposing a high frequency trading volume into liquidity- and information-driven components. Using a set of high frequency S&P 500 stocks data, I show that the model is empirically relevant, and that informed trading is linked to a reduction in volatility, illiquidity and toxicity/adverse selection. Furthermore, I observe that my estimated informed trading component of volume is a statistically significant predictor of one-second stock returns; however, it is not a significant predictor of one-minute stock returns. I show that this disparity can be explained through the HFT activity, which eliminates pricing inefficiencies at high frequencies. The third study exploits the impact of the international transmission latency on liquidity and volatility by constructing a measure of the transmission latency between exchanges in Frankfurt and London and exploiting speed-inducing technological upgrades. I find that a decrease in the transmission latency increases the liquidity and volatility. In line with the existing theoretical models, I show that the amplification of liquidity and volatility is associated with the variations in adverse selection risk and aggressive trading. I then investigate the net economic effect of high latency, which lead to the finding that the liquidity deterioration effect of high latency dominates its volatility reducing effect. This implies that the liquidity enhancing benefit of increased trading speed in financial markets outweighs its volatility inducing effect

More heat than light:Investor attention and bitcoin price discovery

We investigate how increased attention affects bitcoin’s price discovery process. We first decompose bitcoin price into efficient and noise components and then show that the noise element of bitcoin pricing is driven by high levels of attention. This implies that high levels of attention are linked with an increase in uninformed trading activity in the market for bitcoin, while informed trading activity is driven by arbitrage rather than attention

High-frequency trading in the stock market and the costs of option market making

Using a comprehensive panel of 2,969,829 stock-day data provided by the Securities and Exchange Commission (MIDAS), we find that HFT activity in the stock market increases market-making costs in the options markets. We consider two potential channels - the hedging channel and the arbitrage channel - and find that HFTs' liquidity-demanding orders increase the hedging costs due to a higher stock bid-ask spread and a higher price impact for larger hedging demand. The arbitrage channel subjects the options market-maker to the risk of trading at stale prices. We show that the hedging (arbitrage) channel is dominant for ATM (ITM) options. Given the significant growth in options trading, we believe that our study highlights the need to better understand the costs/risks due to HFT activities in equity markets on derivative markets

Algorithmic trading and investment-to-price sensitivity

Does the increased prevalence of algorithmic trading (AT) produce real economic effects? We find that AT contributes to managerial learning by fostering the production of new information and thereby increases firms' investment-to-price sensitivity. We link AT's impact on the investment-to-price sensitivity to the revelatory price efficiency - extent to which stock prices reveal information for real efficiency. AT-driven investment-to-price sensitivity helps managers make better investment decisions, leading to improved firm performance. While in aggregate AT contributes positively to managerial learning, we also show that there is a subset of AT strategies, namely opportunistic AT that is harmful to managerial learning

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants