Browsing by Author "Kruse, Timm"

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Financial Transaction Tax: Policy Analytics Based on Optimal Trading
(2015-06-01)
Sun, Edward W.
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Kruse, Timm
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Yu, Min-Teh
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Introducing a financial transaction tax (FTT) has recently attracted tremendous global attention, with both proponents and opponents disputing its dampening effects on financial markets. In this paper we present a model to show under some circumstances that there exists a win-win situation via optimal trading when the tax burden can be dispersed. The way to absorb FTT in our model is to adjust the bid-ask spread. In our optimal trading model that considers FTT, the representative traders depend on the liquidity (market depth) they supply to weight their associated transaction cost by adjusting the spread ex post. We illustrate the analytical properties and computational solutions of our model when finding the optimal trading strategy under different market situations in order to offset FTT. We also conduct a simulation study to show the superior performance of our proposed optimal trading strategy in comparison to the alternative strategies that do not consider absorbing FTT. The results demonstrate that there is indeed a win-win situation, because financial institutions will not be worse off if such an optimal trading strategy is applied to offset FTT and reduce their transaction cost.
High frequency trading, liquidity, and execution cost
(2014-12-01)
Sun, Edward W.
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Kruse, Timm
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Yu, Min-Teh
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
We build a model under the framework of discrete optimization to explain how high frequency trading (HFT) can be applied to supply liquidity and reduce execution cost. We derive the analytical properties of our model in finding the optimal solution to minimize the overall execution cost of HFT. We show that the execution cost can be reduced after increasing trading frequency (i.e., the higher the trading frequency, the lower the execution cost) with a simulation study. In addition, we conduct an empirical investigation with tick level data from US equity market through January 2008 to October 2010 to verify our conclusion drawn from the simulation study. Based on the simulation and empirical results we collected, we show that the HFT can reduce the execution cost when supplying liquidity.
Stylized algorithmic trading: satisfying the predictive near-term demand of liquidity
(2019-10-01)
Sun, Edward W.
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Kruse, Timm
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Chen, Yi-Ting
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資訊工程學系
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Department of Computer Science
Regulatory reform enacted (e.g., the Dodd-Frank Act enforced in the U.S.) requires the financial service industry to consider the "reasonably expected near term demand" (i.e., RENTD) in trading. To manage the price impact and transaction cost associated with orders submitted to an order driven market, market makers or specialists must determine their trading styles (aggressive, neutral, or passive) based on the market liquidity in response to RENTD, particularly for trading a large quantity of some financial instrument. In this article we introduce a model considering different trading styles to satisfy the predictive near-term customer demand of market liquidity in order to find an optimal order submission strategy based on different market situations. We show some analytical properties and numerical performances of our model in search of optimal solutions. We evaluate the performances of our model with simulations run over a set of experiments in comparison with two alternative strategies. Our results suggest that the proposed model illustrates superiority in performance.