|
on Market Microstructure |
| By: | Rzayev, Khaladdin; Ibikunle, Gbenga; Steffen, Tom |
| Abstract: | Exploiting information transmission latency between stock exchanges in Frankfurt and London, and speed-inducing technological upgrades, we show that when cross-market latency arbitrage opportunities are linked to the arrival of information, high-frequency traders' (HFTs’) activities impair liquidity and enhance price discovery by facilitating the incorporation of public information into prices. Conversely, when cross-market latency arbitrage opportunities are driven by liquidity shocks, HFTs improve liquidity and reduce trading costs, thus incentivizing information acquisition and trading with private information. These findings underscore the complex nature of the association between trading speed and market quality and reconcile mixed evidence in the extant literature. |
| Keywords: | transmission latency; microwave connection; high-frequency trading; liquidity; price discovery; ES/R004021/1 |
| JEL: | G14 G10 |
| Date: | 2023–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:119989&r=mst |
| By: | Raffaele Giuseppe Cestari; Filippo Barchi; Riccardo Busetto; Daniele Marazzina; Simone Formentin |
| Abstract: | Accurately forecasting the direction of financial returns poses a formidable challenge, given the inherent unpredictability of financial time series. The task becomes even more arduous when applied to cryptocurrency returns, given the chaotic and intricately complex nature of crypto markets. In this study, we present a novel prediction algorithm using limit order book (LOB) data rooted in the Hawkes model, a category of point processes. Coupled with a continuous output error (COE) model, our approach offers a precise forecast of return signs by leveraging predictions of future financial interactions. Capitalizing on the non-uniformly sampled structure of the original time series, our strategy surpasses benchmark models in both prediction accuracy and cumulative profit when implemented in a trading environment. The efficacy of our approach is validated through Monte Carlo simulations across 50 scenarios. The research draws on LOB measurements from a centralized cryptocurrency exchange where the stablecoin Tether is exchanged against the U.S. dollar. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.16190&r=mst |
| By: | Ingomar Krohn; Vladyslav Sushko; Witit Synsatayakul |
| Abstract: | This paper finds that trading by non-residents in an emerging financial market reinforces the existence of a momentum anomaly, in an apparent violation of an efficient market hypothesis. Using detailed order flow data in Thai foreign exchange, equity, and fixed income markets, we find that foreign investors engage in momentum trading, which amplifies positive feedback between returns and order flow across all asset classes. Innovations in foreign investor order flow are informative of future returns, but the information is not based on local macro fundamentals. Local financial investors tend to mimic foreign investor trading, reinforcing returns to momentum, while non-financial investors consistently provide liquidity. Further tests suggest that the returns to momentum trading are time-varying and are positively related to the amount of foreign capital flowing into the local financial market. Taken together, the results indicate that a significant presence of foreign investors can alter the trading behaviour of local investors and can reduce the importance of local fundamentals in driving asset prices. |
| Keywords: | international financial markets, heterogeneous trading, disaggregated order flow, foreign investors, emerging markets |
| JEL: | F30 G11 G14 G15 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1154&r=mst |
| By: | Mingxuan He |
| Abstract: | I study the price dynamics of non-fungible tokens (NFTs) and propose a deep learning framework for dynamic valuation of NFTs. I use data from the Ethereum blockchain and OpenSea to train a deep learning model on historical trades, market trends, and traits/rarity features of Bored Ape Yacht Club NFTs. After hyperparameter tuning, the model is able to predict the price of NFTs with high accuracy. I propose an application framework for this model using zero-knowledge machine learning (zkML) and discuss its potential use cases in the context of decentralized finance (DeFi) applications. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.05346&r=mst |