هزینه نقدینگی سفارشات بازار در بازار سهام تایوان : مطالعه بر اساس بازار سهام مصنوعی بنگاه مدار سفارش محور
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|15690||2012||9 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Review of Financial Analysis, Volume 23, June 2012, Pages 72–80
We developed an order-driven agent-based artificial stock market to analyze the liquidity costs of market orders in the Taiwan Stock Market (TWSE). The agent-based stock market was based on the DFGIS model proposed by Daniels, Farmer, Gillemot, Iori and Smith (Daniels et al., 2003). We also improved the DFGIS model by using two average order size parameters. When tested on 10 stocks and securities in the market, the model-simulated liquidity costs were higher than those of the TWSE data. We identified some possible factors that have contributed to this result: 1) the overestimated effective market order size, which can be improved by using two average order size parameters; 2) the random market order arrival time designed in the DFGIS model; 3) the zero-intelligence of the artificial agents in our model; and 4) the price of the effective market order. We continued improving the model so that it could be used to study liquidity costs and to devise liquidation strategies for stocks and securities traded in the Taiwan Stock Market.
Market liquidity, or the ability of an asset to be sold without causing a significant amount of price movement and with minimum loss of value, plays an important role in financial investment and in securities trading. One recent event that highlighted the impact of asset liquidity on financial institutions was the collapse of Bear Stearns. Bear Stearns was involved in securitization and issued a huge amount of asset-backed securities, mostly mortgage-backed assets. Due to the subprime crisis in 2007, the company issued subprime hedge funds that had very low market liquidity and subsequently lost most of their value. In March 2008, the Federal Reserve Bank of New York provided an emergency loan to try to avert a sudden collapse of the company. However, the company could not be saved and was subsequently sold to JP Morgan Chase in 2008. In large investment institutions, the liquidation of a large block of assets within a given time constraint to obtain cash flow arises frequently. For example, a financial institution may need to liquidate part of its portfolio to pay for its immediate cash obligations. One possible liquidation strategy is to sell the entire block of assets at once. However, this high-volume trading can cause the price of the share to drop between the time the trade is decided and the time the trade is completed. This implicit cost (due to the price decline) is known as the market impact cost (MIC) or liquidity cost (the numerical definition is given in Section 3). To minimize such cost, a better strategy is to divide the block of assets into chunks and sell them one chunk at a time. However, in what way should those chunks be sold so that the liquidity cost is minimized? In Algorithmic Trading, where computer programs are used to perform asset trading including deciding the timing, price, or the volume of a trading order, this liquidation problem is characterized as an optimization problem. With a smooth and differentiable utility function, the problem can be solved mathematically (Almgren & Chriss, 2000) (Kalin & Zagst, 2004). However, this mathematical approach to find an optimal liquidation strategy has some shortcomings, such as the imposed assumption that risk has a linear impact on prices. In this paper, we adopt a different approach by devising an agent-based artificial stock market, which has more relaxed assumptions (explained in Section 3). By performing simulations and analyzing liquidity costs induced under different market scenarios, we hope to understand the dynamics of liquidity costs, and hence to devise a more realistic optimal liquidation strategy. The rest of this paper is organized as follows. In Section 2, we provide the background and summarize related works. Section 3 explains the agent-based artificial stock market we developed based on the DFGIS model and the data from the Taiwan Stock Market (TWSE). In Section 4, the 10 securities and stocks that we selected to conduct our study are presented. Section 5 provides the model parameters used to perform our simulation. We analyze the simulation results in Section 6 and present our discussions in Section 7. Finally, Section 8 concludes the paper with an outline of our future work.
نتیجه گیری انگلیسی
The market liquidity of a security plays an important role in financial investment decisions and in the liquidation strategies of the security. As an alternative to Algorithmic Trading, this study has developed an agent-based model to examine the liquidity costs of stocks and securities traded in the Taiwan Stock Market. For the 10 TWSE stocks and securities that we studied, the model-simulated liquidity costs are higher than those for the TWSE data. We identified four possible factors that contribute to this result: • The overestimated effective market order size, which can be improved by using two average order size parameters. • The random market order arrival time designed in the DFGIS model, which might be improved by incorporating the ACD model in our system. • The zero-intelligence of the artificial agents in our model. • The price of the effective market order. We can continue improving the model by addressing the above-mentioned issues. A model that behaves in a similar way to the TWSE in terms of the liquidity costs can be used to study liquidity costs and to devise liquidation strategies for stocks and securities traded on the TWSE.