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Financial Transaction Tax: Policy Analytics Based on Optimal Trading

Author

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  • Edward Sun
  • Timm Kruse
  • Min-Teh Yu

Abstract

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. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Edward Sun & Timm Kruse & Min-Teh Yu, 2015. "Financial Transaction Tax: Policy Analytics Based on Optimal Trading," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 103-141, June.
    • Handle: RePEc:kap:compec:v:46:y:2015:i:1:p:103-141
    DOI: 10.1007/s10614-014-9473-4
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    References listed on IDEAS

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    Cited by:

    1. Edward W. Sun & Timm Kruse & Yi-Ting Chen, 2019. "Stylized algorithmic trading: satisfying the predictive near-term demand of liquidity," Annals of Operations Research, Springer, vol. 281(1), pages 315-347, October.
    2. Yi-Ting Chen & Edward W. Sun & Min-Teh Yu, 2018. "Risk Assessment with Wavelet Feature Engineering for High-Frequency Portfolio Trading," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 653-684, August.

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    More about this item

    Keywords

    Bid–ask spread; Discrete optimization; Financial transaction tax (FTT); Optimal trading; Price impact; C61; C63; G10;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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