The present paper is focused on the solution of optimal control problems such as optimal acquisition, optimal liquidation, and market making in relation to the high-frequency trading market. We have modeled optimal control problems with the price approximated by the diffusion process for the...

Algorithmic trading, so popular nowadays, uses many strategies that are algorithmizable and promise profitability. This research answers the question whether it is possible to successfully use a convexity arbitrage strategy in a bond portfolio in financial practice. It should provide a positive...

Among innovative technologies, Artificial Intelligence (AI) is often avouched as the game changerin the provision of financial services. In this regard, the algorithmic trading domain is no exception.The impact of AI in the industry is a catalyst for transformation in the operations and the...

We propose a framework to study optimal trading policies in a one-tick pro-rata limit order book, as typically arises in short-term interest rate futures contracts. The high-frequency trader chooses to post either market orders or limit orders, which are represented respectively by impulse...

We study a differential Riccati equation (DRE) with indefinite matrix coefficients, which arises in a wide class of practical problems. We show that the DRE solves an associated control problem, which is key to provide existence and uniqueness of a solution. As an application, we solve two...

We construct a Kyle (1985)-type market model in which fast and slow traders are present. We perform numerical calculations after deriving the equilibrium condition, described as a simultaneous equation system. A major finding is that the fast trader, who has an advantage in trade frequency, acts...

Jumps and cojumps are examined in the covariance matrices of high-frequency financial markets. We propose a new method for identifying intraday volatility jumps in the diffusive covariance matrix of asset pairs. Our method avoids model misspecification errors, is able to identify multiple...

Full electronic automation in stock exchanges has recently become popular, generating high-frequency intraday data and motivating the development of near real-time price forecasting methods. Machine learning algorithms are widely applied to mid-price stock predictions. Processing raw data as...

Unbeknownst to the public, most investment funds actually underperform the broader market. Yet, millions of individual investors fare even worse, barely treading water. Algorithmic trading now accounts for over 80% of all trades and is the domain of professionals. Can it also help the small...

The paper deals with the pattern of asset price dynamics as sequence of "bull markets" and "bear markets" and with stabilizing these" long swings" through replacing continuous asset trading with electronic auctions. First, the paper sketches the channels through which the "overshooting" of...

We use recent European restrictions to evaluate how traders substitute across available dark pools. Our findings suggest that restricting dark trading at the most prominent platform has a detrimental effect on dark trading activity. Annual dark trading in a restricted stock decreases by more...

This essay examines three potential arguments against high-frequency trading and offers a qualified critique of the practice. In concrete terms, it examines a variant of high-frequency trading that is all about speed - low-latency trading - in light of moral issues surrounding arbitrage,...

In recent years major financial exchanges have introduced Frequent Batch Auctions (FBAs) as a novel automated auction mechanism for matching buyers and sellers of various types of asset, in contrast to the traditional Continuous Double Auction (CDA) that has been the basis of such exchanges...

We propose a class of execution algorithms that consists of a strategic layer and a speculative layer. The strategic layer is an optimal trading schedule that encodes the trader's objective, her tolerance to risk, and the impact of her own trades in the market. The schedule of the strategic...

This study examines how algorithmic trading (AT) affects forward-looking disclosures in Management Discussion and Analysis (MD&A) of annual reports. We predict and find evidence that AT relates negatively to modifications in year-over-year forward-looking MD&A disclosures. This evidence is...

This study investigates the impact of algorithmic trading strategies on asset price mispricing and relative payoffs of human and algorithmic traders using the Smith- Suchanek-Williams (SSW - (Smith et al. (1988))) framework. A 2x2 treatment design varying algorithmic strategy (market-making or...

We develop a novel experimental paradigm to study the causal impact of trading algorithms on informational efficiency, liquidity, and welfare. In our design, public information about the asset value is revealed during trading, which gives algorithms a reaction speed advantage. We distinguish...

This paper examines evidence on colocation dates and their impact on market efficiency. International colocation dates can be sourced from a number of avenues including: [1] an 'exchange's news announcements and reports, [2] news media, and [3] by direct communication with the officers of an...

Time-series data, which exhibit a low signal-to-noise ratio, non-stationarity, and non-linearity, are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in...

Using high-frequency intraday data sampled on half-a-second frequencies for the Chinese crude oil futures contract (SC), we examine and compare trading dynamics of SC with both WTI and Brent oil futures contracts. We use a modified volume method to create a continuous SC time series,...

We argue that owing to traders' inability to fully express their preferences over the execution times of their orders, contemporary stock market designs are prone to latency arbitrage. In turn, we propose a new order type, which allows traders to specify the time at which their orders are...

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 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...

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...

One of the challenges that economic experiments that use artificial financial markets to explore high-frequency trading face, is the development of a sufficiently sophisticated software. Moreover, it is not trivial to adequately communicate the complex financial market rules to non-experts. The...