1,286 research outputs found

    The Tobin Tax A Review of the Evidence

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    The debate about the Tobin Tax, and other financial transaction taxes (FTT), gives rise to strong views both for and against. Unfortunately, little of this debate is based on the now considerable body of evidence about the impact of such taxes. This review attempts to synthesise what we know from the available theoretical and empirical literature about the impact of FTTs on volatility in financial markets. We also review the literature on how a Tobin Tax might be implemented, the amount of revenue that it might realistically produce, and the likely incidence of the tax. We conclude that, contrary to what is often assumed, a Tobin Tax is feasible and, if appropriately designed, could make a significant contribution to revenue without causing major distortions. However, it would be unlikely to reduce market volatility and could even increase it.Tobin tax, financial transaction taxes, volatility, revenue, incidence, feasibility

    Market Stability vs. Market Resilience: Regulatory Policies Experiments in an Agent-Based Model with Low- and High- Frequency Trading

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    We investigate the effects of different regulatory policies directed towards high-frequency trading (HFT) through an agent-based model of a limit order book able to generate flash crashes as the result of the interactions between low- and high-frequency (HF) traders. We analyze the impact of the imposition of minimum resting times, of circuit breakers (both ex-post and ex-ante types), of cancellation fees and of transaction taxes on asset price volatility and on the occurrence and duration of ash crashes. In the model, low- frequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. In contrast, high-frequency traders activation is event-driven and depends on price fluctuations. In addition, high-frequency traders employ low-latency directional strategies that exploit market information and they can cancel their orders depending on expected profits. Monte-Carlo simulations reveal that reducing HF order cancellation, via minimum resting times or cancellation fees, or discouraging HFT via financial transaction taxes, reduces market volatility and the frequency of ash crashes. However, these policies also imply a longer duration of flash crashes. Furthermore, the introduction of an ex-ante circuit breaker markedly reduces price volatility and removes ash crashes. In contrast, ex-post circuit breakers do not affect market volatility and they increase the duration of flash crashes. Our results show that HFT-targeted policies face a trade-o between market stability and resilience. Policies that reduce volatility and the incidence of flash crashes also imply a reduced ability of the market to quickly recover from a crash. The dual role of HFT, as both a cause of the flash crash and a fundamental actor in the post-crash recovery underlies the above trade-off

    Computational Models of Algorithmic Trading in Financial Markets.

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    Today's trading landscape is a fragmented and complex system of interconnected electronic markets in which algorithmic traders are responsible for the majority of trading activity. Questions about the effects of algorithmic trading naturally lend themselves to a computational approach, given the nature of the algorithms involved and the electronic systems in place for processing and matching orders. To better understand the economic implications of algorithmic trading, I construct computational agent-based models of scenarios with investors interacting with various algorithmic traders. I employ the simulation-based methodology of empirical game-theoretic analysis to characterize trader behavior in equilibrium under different market conditions. I evaluate the impact of algorithmic trading and market structure within three different scenarios. First, I examine the impact of a market maker on trading gains in a variety of environments. A market maker facilitates trade and supplies liquidity by simultaneously maintaining offers to buy and sell. I find that market making strongly tends to increase total welfare and the market maker is itself profitable. Market making may or may not benefit investors, however, depending on market thickness, investor impatience, and the number of trading opportunities. Second, I investigate the interplay between market fragmentation and latency arbitrage, a type of algorithmic trading strategy in which traders exercise superior speed in order to exploit price disparities between exchanges. I show that the presence of a latency arbitrageur degrades allocative efficiency in continuous markets. Periodic clearing at regular intervals, as in a frequent call market, not only eliminates the opportunity for latency arbitrage but also significantly improves welfare. Lastly, I study whether frequent call markets could potentially coexist alongside the continuous trading mechanisms employed by virtually all modern exchanges. I examine the strategic behavior of fast and slow traders who submit orders to either a frequent call market or a continuous double auction. I model this as a game of market choice, and I find strong evidence of a predator-prey relationship between fast and slow traders: the fast traders prefer to be with slower agents regardless of market, and slow traders ultimately seek the protection of the frequent call market.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120811/1/ewah_1.pd

    Understanding How the Flash Clashes are Affected in an Asymmetric Informational Market with Agent-based Modelling

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    This thesis explores the impact of ïŹ‚ash crashes on the dynamics of ïŹnancial markets with asymmetric information. We built, implemented, and analysed an agent-based model of an extended information-sequential trading framework inspired by the models of Das and Glosten-Milgrom, where an exogenous fake shock is added into the system to disturb the actions of some traders where there is informational asymmetry. The key modelled agents include fundamental traders, who place orders at preferred prices; zero-intelligence traders, who place orders randomly; a market maker, who provides liquidity; and an exchange matching all orders under continuous auctions or batch auctions. To this end, by Monte-Carlo methods, we implement the model and examine the dynamics of the market under information asymmetry in the following aspects: the market structure, market risk, the network topology of agents and market mechanisms. Our results demonstrate that, an uninformed fundamental trader (UFT) in a messy network is highly likely to suffer a major loss due to the signiïŹcant price crash in a strongly UFT-dominated market (the informed traders only account for less than 20%), in which case the market efïŹciency is also negatively affected; Applying batch auctions helps reallocate the proïŹts among the agents to reduce the information advantage between informed and uninformed traders, but it has limited effect on mitigating ïŹ‚ash crashes; Building an information-sharing connection between agents is effective to reducing ïŹ‚ash crashes and narrows the information advantage gap between informed and uninformed traders, but a complete network with full information exposure could mislead uninformed traders to make biased decisions. These ïŹndings generated by an agent-based simulation model give us insights into real-world ïŹnancial markets under asymmetric information, and the framework proposed in this thesis can be extended for future studies of asymmetric-information markets

    Asymmetric Information, Trading Volume, and Portfolio Performance

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    In dealership markets, asymmetric information feeds through to higher transaction costs as dealers adjust their bid-ask spreads to compensate for anticipated losses. In this paper, we show that the presence of asymmetric information can also provide a positive externality to those market participants who operate in multiple markets-portfolio managers. Specifically, insiders lower the estimation errors of portfolio selection methods, thus improving asset allocation. We develop multiple artificial markets, in which portfolio managers trade alongside informed and uniformed speculators, and we contrast the performance of 'volatility timing'-a method that relies on efficient price discovery - with that of 'naive diversification'. Volatility timing is shown to consistently outperform naive diversification on a risk-adjusted basis

    Essays in Microstructure Liquidity, Asset Pricing, and Short Selling

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    This thesis consists of three standalone studies in the fields of market microstructure liquidity, asset pricing, and short selling. The first study examines whether microstructure illiquidity is priced in security returns in the presence of buyers and sellers having identical preferences and facing symmetric liquidity costs. Commencing with a Lucas (1978)-type representative investor but with differing endowments, we develop a new theoretical model of counterparty trading inclusive of frictions to show that symmetric liquidity costs, which could arise either from exogenous costs or from order-flow asymmetric information, are not priced. This is because seller costs cancel out the buyer costs correctly identified in Amihud and Mendelson's (1986a) seminal theoretical model. We test our generalization of the Lucas model utilizing NYSE (US) equity market microstructure data to show that we cannot reject our main hypothesis concerning the absence of liquidity pricing effect on stock returns. We split transaction costs into their buy (upside) and sell (downside) components to find they are priced with similar magnitudes in contemporaneous returns. Based on our NYSE sample, the balanced effect of buy and sell lambda price impact does not generate a downside lambda premium in future stock returns. We further report a positive pricing effect of the bid-ask spread on future returns on the extreme quintile of lambda asymmetry. The second study examines liquidity asymmetry under variations in short selling regimes. I show a near symmetrical adverse effect of shorting flow impediments (caused by an exchange driven short-sale ban or securities lending market-driven constraints) on the buy and sell order flow price impact and liquidity supply dynamics. Overall, I find that the liquidity cost asymmetry is lower than the previously reported outcome with the US 2008 banned stocks in an extreme liquidity crisis. The differential effect is tilted towards sell-initiated order flow impact and bid side liquidity. Utilizing tick-by-tick microstructure data (including depth data) in the Hong Kong market, I conduct ordinary least squares (OLS) and regression discontinuity design (RDD) tests on the Hong Kong market to corroborate my findings. In contrast to Diamond and Verrecchia (1987), my study: a) argues for the importance of informed short sellers (as liquidity suppliers) on the bid and ask side of the market, and b) highlights the juxtaposition between the imperfect competition channel and increased adverse selection due to endogenous information acquisition under an informed short-selling ban. I further report a lower differential effect in buy versus sell under stronger mean reversion properties, a profitable setting for contrarian liquidity provisioning strategies. In the third study, I utilize a novel data panel of institutional short-sell transactions (with identification flags for hedgers and non-hedgers), equity covered put warrant data, and securities lending data based on the Taiwan market to show that put warrant derivatives hedge rebalancing raises borrowing costs (loan fees). The short-sell hedging demand is inelastic to fees. The positive fee effect with increased hedging becomes significantly strong for expensive-to-borrow stocks that have liquid at-the-the-money warrants. Traders who engage in such hedging have a solid motivation to manage downside risk due to price fluctuations and active hedge rebalancing requirements because of the sensitive delta. This risk management requirement is reflected in fees charged by lenders. My analysis provides insights into whether regulators and investors should be wary of increased bearish trading strategies in the derivatives market, which could inflate short-selling costs in the lending market. I further find that warrant hedgers’ demand is sensitive to fees before negative earnings announcements, i.e., hedgers’ short-selling demand declines with higher loan fees. This effect reflects the fact that such hedgers short when they expect higher selling pressure, i.e., they sell low. In contrast, I find that the short-selling demand of traders who are not hedgers is positively associated with costs before the negative earnings information because they feel an urgency to generate profit with overvalued stocks; in other words, they sell high when in receipt of private bad news

    Testing the "weak form efficient market" hypothesis: an analysis on european and italian equity markets

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    The purpose of the thesis is testing the EMH in the weak form on Ftse Mib and Stoxx Europe 600 indexes using econometric and statistical tools. a comparison among the methodologies and a critical analysis of the results lead to empirical evidence that both indexes are weak efficent in the examined time frame ( jan. 1999 to feb. 2016)ope

    Market Manipulation in Stock and Power Markets: A Study of Indicator-Based Monitoring and Regulatory Challenges

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    In recent years, algorithmic-based market manipulation in stock and power markets has considerably increased, and it is difficult to identify all such manipulation cases. This causes serious challenges for market regulators. This work highlights and lists various aspects of the monitoring of stock and power markets, using as test cases the regulatory agencies and regulatory policies in diverse regions, including Hong Kong, the United Kingdom, the United States and the European Union. Reported cases of market manipulations in the regions are examined. In order to help establish a relevant digital regulatory system, this work reviews and categorizes the indicators used to monitor the stock and power markets, and provides an in-depth analysis of the relationship between the indicators and market manipulation. This study specifically compiles a set of 10 indicators for detecting manipulation in the stock market, utilizing the perspectives of return rate, liquidity, volatility, market sentiment, closing price and firm governance. Additionally, 15 indicators are identified for detecting manipulation in the power market, utilizing the perspectives of market power (also known as pricing power or market structure), market conduct and market performance. Finally, the study elaborates on the current challenges in the regulation of stock and power markets in terms of parameter performance, data availability and technical requirements.publishedVersio
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