As in many other sectors of EU economies, 'artificial intelligence' (AI) has entered the scene of the financial services industry as a game-changer. Trading on capital markets is undoubtedly one of the most promising AI application domains. A growing number of financial market players have in...

This paper studies whether and why algorithmic traders exhibit one of the most broadlydocumented behavioral puzzles - the disposition effect. We use trade data from the NASDAQ Copenhagen Stock Exchange merged with the weather data. We find that on average, the disposition effect for human...

We study liquidity provision by competitive high-frequency trading firms (HFTs) in a dynamic trading model with private information. Liquidity providers face adverse selection risk from trading with privately informed investors and from trading with other HFTs that engage in latency arbitrage...

As in many other sectors of EU economies, 'artificial intelligence' (AI) has entered the scene of the financial services industry as a game-changer. Trading on capital markets is undoubtedly one of the most promising AI application domains. A growing number of financial market players have in...

We examine the soundness of high-frequency trading (HFT) proxies that are widely defined on the limit order book (LOB) information. We use a unique TRTH (Thomson Reuters Tick History) millisecond time-stamped intraday trades and quotes dataset enriched with 10 levels of LOB depth messages for...

In recent years, Bitcoin has received substantial attention as potentially high-earning investment. However, its volatile price movement exhibits great financial risks. Therefore, how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to...

For this research, we implemented a trading system based on the Turtle rules and examined its efficiency when trading selected assets from the Forex, Metals, Commodities, Energy and Cryptocurrency Markets using historical data. Afterwards, we enhanced our Turtle-based trading system with...

In this work we simulate algorithmic trading (AT) in asset markets to clarify its impact. Our markets consist of human and algorithmic counterparts of traders that trade based on technical and fundamental analysis, and statistical arbitrage strategies. Our specific contributions are: (1)...

We investigate electronic trading among customers under normal market conditions and during the Covid-19 crisis using a unique data sample of U.S. corporate bond transactions from UBS Bond Port. We show that electronic customer-to-customer (C-to-C) trading is beneficial in terms of costs for...

High-frequency trading (HFT) has been dominating the activity in developed financial markets in the last two decades. Despite its recent formation, the literature on the impacts of HFT on financial markets and participants is broad. However, there are ongoing debates and unanswered questions...

Determinants of bid-ask spread have been explored significantly for low-frequency datasets in many developed markets. Researchers have identified share price, traded volume, market-capitalization, return volatility, and number of trades as the prime spread drivers. However, the validity of these...

This paper studies the role of trade for the joint uptake of AI-enabled automation in manufacturing and engineering. It develops an agent-based model (ABM) where the agents are heterogeneous manufacturers and engineering firms. The model features two technology-related business models:...

During times of market stress, arbitrage capital cannot be timely deployed, and assets trade away from fundamentals. This gives rise to transitory price volatility, a latent factor that signals difficulties in the market-making process. I propose a market-wide illiquidity measure based on SPY's...

Predicting stock market movements is a well-known problem of interest. Now-a- days social media is perfectly representing the public sentiment and opinion about current events. Especially, Twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market...

Based on granular NASDAQ HFT data, we find that HFT activity in the stock market increases market-making costs in the options markets via two channels: the hedging channel and the arbitrage channel. HFTs' liquidity-demanding orders increase the hedging costs due to a higher stock bid-ask spread...

We develop measures of stock-specific trading activity based on durations of sequences of consecutive trades with fixed cumulative values. Trade sizes and signed-trade imbalances rise with activity, while price impacts generally fall, but not always, due to endogenous variation in liquidity...

This research is based on an empirical analysis of the impact of HFT activity on the stock in the SET50 index trading in the Stock Exchange of Thailand (SET), using publicly-available trade-by-trade tick data for the period between January 01, 2016, and June 30, 2018. The HFT data is illustrated...

The paper analyses the effects of technology-based innovative techniques on Bulgarian capital market -algorithmic trading, in general, and high frequency trading (HFT), in particular - from macroeconomic costs-benefits perspective. Overwhelmingly, empirical studies emphasize that HFT improves...

A later version of this paper has been published in the Journal of Financial Stability (Volume 50, October 2020, Article Number 100776).When trading volumes are high, bitcoin futures on the Chicago Mercantile Exchange (CME) and -- until recently -- the Chicago Board Options Exchange (CBOE) lead...

We measure the extent to which consolidated liquidity in modern fragmented equity markets overstates true liquidity due to a phenomenon that we call Ghost Liquidity (GL). GL exists when traders place duplicate limit orders on competing venues, intending for only one of the orders to execute, and...

This paper proposes an optimal intraday trading strategy to absorb the shock to the stock market when an online portfolio selection algorithm rebalances a portfolio. It considers real-time data of limit order books and splits a very large market order into a number of consecutive market orders...

Latency delays — known as “speed bumps” — slow the execution of orders at an exchange, often to protect market makers against latency arbitrage. We study informed trading in a fragmented market, where one exchange introduces a latency delay on market orders. While liquidity improves at...

We propose the option realized variance as an observable variable to summarize information from high-frequency option data. This variable aggregates intraday option returns from midquote prices to compute the option's total variability for a given day. Using the S&P 500 index time series and...

Using new data from the two U.S. securities information processors (SIPs) between August 6, 2015 and June 30, 2016, we examine claims that high-frequency trading (HFT) firms use direct feeds to exploit traders who rely on SIP prices. Across $3.7 trillion of trades, the SIPs report quote updates...

We model endogenous technology adoption and competition among liquidity providers with access to High-Frequency Trading (HFT) technology. HFT technology provides speed and informational advantages. Information advantages may restore excessively toxic markets. Speed technology may reduce resource...

We study the consequences of high-frequency trading (HFT) — and potential policy responses — via the tradeoff between liquidity and information production. Faster speeds facilitate HFT with consequences for this tradeoff: information production diminishes because informed traders have less...

Are endogenous liquidity providers (ELPs) reliable in times of market stress? We examine the activity of a common ELP type – high frequency traders (HFTs) – around extreme price movements (EPMs). We find that on average HFTs provide liquidity during EPMs by absorbing imbalances created by...

I study empirically how competition among high-frequency traders (HFTs) affects their trading behavior and market quality. The analysis exploits a unique dataset, which allows comparing environments with and without high-frequency competition, and contains an exogenous event - a tick size reform...

This study provides evidence that high-frequency traders (HFTs) identify patterns in past trades and orders that allow them to anticipate and trade ahead of other investors' order flow. Specifically, HFTs' aggressive purchases and sales lead those of other investors, and this effect is stronger...

We show that there cannot be common knowledge trade in a truly decentralised environment, such as in a blockchain. A trade is an agreement to buy and sell a security that pays according to some state of nature; it is settled when the security's value is verified by an oracle, an intermediary who...