Andrei A. Kirilenko

Capital Controls; Country Experiences with Their Use and Liberalization

This paper examines country experiences with the use and liberalization of capital controls to develop a deeper understanding of the role of capital controls in coping with volatile capital flows, as well as the issues surrounding their liberalization. Detailed analyses of country cases aim to shed light on the motivations to limit capital flows; the role the controls may have played in coping with particular situations, including in financial crises and in limiting short-term inflows; the nature and design of the controls; and their effectivenes and potential costs. The paper also examines the link between prudential policies and capital controls and illstrates the ways in which better prudential practices and accelerated financial reforms could address the risks in cross-border capital transactions.

Valuation and Control in Venture Finance

This paper presents the model of a relationship between a venture capitalist and an entrepreneur engaged in the formation of a new firm. I assume that the entrepreneur derives private nonpecuniary benefits from having some control over the firm. I show that to separate the entrepreneurs value of control from the firms expected payoff, the venture capitalist demands disproportionately highercontrol rights than the size of his equity investment. The entrepreneur is compensated for a greater loss of control through better terms of financing, ability to extract higher rents from asymmetric information, and improved risk sharing. Copyright The American Finance Association 2001.

Securities Transaction Taxes and Financial Markets

We consider the impact of transaction taxes on financial markets in the context of four questions. How important is trading? What causes price volatility? How are prices formed? How valuable is the volume of transactions? Drawing on the literature on market microstructure, asset pricing, rational expectations, and international finance, we argue that securities transaction taxes “throw sand” not in the wheels, but into the engine of financial markets. We conclude that transaction taxes can have negative effects on price discovery, volatility, and liquidity and lead to a reduction in the informational efficiency of markets.

The Flash Crash: High-Frequency Trading in an Electronic Market

We study intraday market intermediation in an electronic market before and during a period of large and temporary selling pressure. On May 6, 2010, U.S. financial markets experienced a systemic intraday event, known as the Flash Crash, when a large automated sell program was rapidly executed in the E-mini S&P 500 stock index futures market. Using audit trail transaction-level data for the E-mini on May 6 and the previous three days, we find that the trading pattern of the most active non-designated intraday intermediaries (classified as High Frequency Traders) did not change when prices fell during the Flash Crash.

Moore's Law versus Murphy's Law: Algorithmic Trading and Its Discontents

Financial markets have undergone a remarkable transformation over the past two decades due to advances in technology. These advances include faster and cheaper computers, greater connectivity among market participants, and perhaps most important of all, more sophisticated trading algorithms. The benefits of such financial technology are evident: lower transactions costs, faster executions, and greater volume of trades. However, like any technology, trading technology has unintended consequences. In this paper, we review key innovations in trading technology starting with portfolio optimization in the 1950s and ending with high-frequency trading in the late 2000s, as well as opportunities, challenges, and economic incentives that accompanied these developments. We also discuss potential threats to financial stability created or facilitated by algorithmic trading and propose “Financial Regulation 2.0,” a set of design principles for bringing the current financial regulatory framework into the Digital Age.

Risk and Return in High-Frequency Trading

We study performance and competition among high-frequency traders (HFTs). We construct measures of latency and find that differences in relative latency account for large differences in HFTs’ trading performance. HFTs that improve their latency rank due to colocation upgrades see improved trading performance. The stronger performance associated with speed comes through both the short-lived information channel and the risk management channel, and speed is useful for various strategies including market making and cross-market arbitrage. We find empirical support for many predictions regarding relative latency competition.

A Multiscale Model of High-Frequency Trading

We propose and study a stylization of high frequency trading (HFT). Our interest is an order book which consists of orders from slow liquidity traders and orders from high-frequency traders. We would like to frame a model which is amenable to the (seemingly natural) mathematical toolkit of separation of scales and which can be used to address some of the larger issues involved in HFT. The main issue to which we address our model is volatility. An important question is how volatility is affected by HFT. In our stylized model, we show how HFT increases volatility, and can quantify this effect as a function of the parameters in our model and the separation of scales.

The Rates and Revenue of Bank Transaction Taxes

This paper provides cross-country empirical evidence on the productivity of bank transaction taxes (BTTs). Our data set comprises six Latin American countries that have levied BTTs since the late 1980s: Argentina, Brazil, Colombia, Ecuador, Peru and Venezuela. We find that, for a given tax rate, revenue declines over time. Therefore, in order to meet a fixed revenue target in real terms, the tax rate needs to be raised repeatedly. However, we also find that successive increases in the tax rate erode the tax base by more than they raise revenue yield and that the higher the increase in the tax rate, the more and faster the tax base is eroded. We conclude that BTTs do not provide a reliable source of revenue, especially over the medium term. Ce document fournit une etude empirique de comparaison internationale sur la productivite des impots sur les transactions bancaires (ITB). Notre base de donnees correspond a 6 pays d’Amerique latine qui ont un impot sur les transactions bancaires: Argentine, Bresil, Colombie, Equateur, Perou et Venezuela. Nous trouvons que le revenu diminue au fil du temps pour un taux d’imposition donne. Pour cette raison, le taux d’imposition doit etre augmente regulierement en vue d’atteindre une cible de revenu en terme reel. Cependant, nous voyons que les augmentations successives des taux d’imposition reduisent l’assiette d’imposition plus que le rendement obtenu, et plus grande est la hausse du taux d’imposition, plus rapide est l’erosion de l’assiette d’imposition. Nous concluons que l’imposition des transactions bancaires ne fournit pas une source de revenu fiable, particulierement sur le moyen terme.

Gaussian process-based algorithmic trading strategy identification

Many market participants now employ algorithmic trading, commonly defined as the use of computer algorithms, to automatically make certain trading decisions, submit orders and manage those orders after submission. Identifying and understanding the impact of algorithmic trading on financial markets has become a critical issue for market operators and regulators. Advanced data feeds and audit trail information from market operators now allow for the full observation of market participants’ actions. A key question is the extent to which it is possible to understand and characterize the behaviour of individual participants from observations of trading actions. In this paper, we consider the basic problems of categorizing and recognizing traders (or, equivalently, trading algorithms) on the basis of observed limit orders. These problems are of interest to regulators engaged in strategy identification for the purposes of fraud detection and policy development. Methods have been suggested in the literature for describing trader behaviour using classification rules defined over a feature space consisting of summary trading statistics of volume and inventory, along with derived variables that reflect the consistency of buying or selling behaviour. Our principal contribution is to suggest an entirely different feature space that is constructed by inferring key parameters of a sequential optimization model that we take as a surrogate for the decision-making process of the traders. In particular, we model trader behaviour in terms of a Markov decision process. We infer the reward (or objective) function for this process from observations of trading actions using a process from machine learning known as inverse reinforcement learning (IRL). The reward functions learned through IRL then constitute a feature space that can be the basis for supervised learning (for classification or recognition of traders) or unsupervised learning (for categorization of traders). Making use of a real-world data-set from the E-Mini futures contract, we compare two principal IRL variants, linear IRL and Gaussian Process IRL, against a method based on summary trading statistics. Results suggest that IRL-based feature spaces support accurate classification and meaningful clustering. Further, we argue that, because they attempt to learn traders’ underlying value propositions under different market conditions, the IRL methods are more informative and robust than the summary statistic-based approach and are well suited for discovering new behaviour patterns of market participants.

Discovering the Ecosystem of an Electronic Financial Market with a Dynamic Machine-Learning Method

Not long ago securities were traded by human traders in face-to-face markets. The ecosystem of an open outcry market was well-known, visible to a human eye, and rigidly prescribed. Now trading is increasingly done in anonymous electronic markets where traders do not have designated functions or mandatory roles. In fact, the traders themselves have been replaced by algorithms (machines) operating with little or no human oversight. While the process of electronic trading is not visible to a human eye, machine-learning methods have been developed to recognize persistent patterns in the data. In this study, we develop a dynamic machine-learning method that designates traders in an anonymous electronic market into five persistent categories: high frequency traders, market makers, opportunistic traders, fundamental traders, and small traders. Our method extends a plaid clustering technique with a smoothing framework that filters out transient patterns. The method is fast, robust, and suitable for a discovering trading ecosystems in a large number of electronic markets