國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
36
Chapter 5. 結論
這裡透過了代理人基股票市場來分析開、收盤資訊揭露對股市可能造成 的影響。我們關注的議題包含了「開、收盤資訊揭露之範圍」及「開、收盤 資訊揭露之頻率」，並從流動性、波動性之影響進行分析。我們模擬的結果，
可以下列的表作為總結。
表 5-1 模擬結果摘要
議題 結果
開收盤資訊揭露之範圍
波動性
1.波動性指標大小：「僅揭露參考 成交價量」低於「僅揭示 5 檔參 考買賣價量」低於「兩者都揭示」
的波動性指標。
2.「僅揭示 5 檔參考買賣價量」此 機制使得股票市場股價之絕對報 酬(波動性指標)之波動程度降到 最低。
流動性
1.開、收盤之流動性指標：「兩者 都揭示」高於「僅揭示參考成交 價量」高於「僅揭示 5 檔參考買 賣價量」。
2.「僅揭示參考成交價量」此機制 使開盤成交量之波動程度降到最 低。
「僅揭示 5 檔參考買賣價量」此 機制使收盤成交量之波動程度降 到最低。
‧
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
38
資訊揭露頻率」優於「低資訊揭露頻率」的結論。而且在實務上，因有計算 與處理速度上的限制，亦不可能將實際資訊揭露頻率無限制地調高。
綜觀模擬結果，針對「開收盤資訊揭露之範圍」這個議題而言：從波動 性與流動性觀察之，對於波動性來說，大多數的模擬結果顯示了「僅揭示參 考成交價量」這個制度優於「僅揭示 5 檔參考買賣價量」及優於「兩者都揭 示」；這個結果顯示了並非資訊揭露越多，股價的波動度越低。造成這種現 象的主要原因，可能是由於資訊過多，會造成投資者在判斷合理價格的差異 過大。如此一來，會造成股價的波動度擴大。但是對此指標的波動程度來說，
「僅揭示 5 檔參考買賣價量」此制度會使波動性指標之波動程度降至最低。
對於「開收盤資訊揭露之範圍」之流動性而言：「兩者都揭示」之開、收盤 流動量均最佳。這個原因可能為資訊越多，交易者在判斷合理價格的差異會 擴大，如此一來套利的機會增加，成交量也會隨著增加。在開盤成交量之波 動程度方面：「僅揭示參考成交價量」此機制，會使開盤成交量之波動程度 降到最低。但針對於收盤成交量之波動程度：「僅揭示 5 檔參考買賣價量」
此機制，使收盤成交量之波動程度降到最低。針對「開收盤資訊揭露之頻率」
這個議題，大多數的模擬結果顯示出：資訊揭露頻率最高的市場其股價之波 動程度是最低。但是對股價絕對報酬之波動程度來說：在「中資訊揭露頻率」
下為最低。在「開收盤資訊揭露之頻率」的流動性方面，亦是資訊揭露頻率 愈高，其開、收盤之成交量愈高。針對成交量之波動程度而言，在「中頻率 資訊揭露」下，其開盤成交量之波動程度為最低。而在「高頻率資訊揭露」
下，其收盤成交量之波動程度為最低。
當然，為了探討更一般化且更真實的情況，本模擬的假設可以透過一些 校準過程，進而逐步放寬。這有待更進一步的研究。
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
39
參考文獻
柯美珠與黃彥聖，2006，「資訊揭露對日內市場流動性之影響：台灣股票市場之 實證研究」，國立台灣科技大學 2006 年管理新思維學術研討會。
Ahn, H. -J., and Cheung, Y. -L., 1999, "The intraday patterns of the spread and depth in a market without market makers: The Stock Exchange of Hong Kong", Pacific-Basin Finance Journal 7, 539–556
Amihud, Y., and Mendelson, H., 1987, "Trading Mechanisms and Stock Returns: An Empirical Investigation", Journal of Finance 42, 3, 533-553.
Amihud, Y., and Mendelson, H., 1991, " Liquidity, Maturity, and the Yields on U.S.
Treasury Securities", Journal of Finance 46, 4, 1411-1425.
Arthur, W. B., Holland, J., LeBaron, B., Palmer, R., and Tayler, P., 1997, "Asset pricing under endogenous expectations in an artificial stock market. In: W. B. Arthur, S. Durlauf & D. Lane (Eds.)", The Economy as an Evolving Complex System II.
Addison-Wesley, 15-44.
Baruch, S., 2005, "Who Benefits from an Open Limit-Order Book?", Journal of Business, Vol. 78, No. 4, 1267-1306.
Biais, B., 1993, "Price Formation and Equilibrium Liquidity in Fragmented and Centralized Markets", Journal of Finance 48, 157-185.
Biais, B., and P. Hillion, C. Spatt, 1999, “Price Discovery and Learning during the Preopening Period in the Paris Bourse”, Journal of Political Economy 107, 1218-1248.
Bloomfield, R., and M. O'Hara, 1999, "Market Transparency: Who Wins and Who Loses?", The review of Financial Studies 12, 5-35.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
40
Bloomfield, R., and M. O'Hara, 2000, "Can transparent markets survive?", Journal of Financial Economics 55, 425-429.
Boehmer, E., Saar, G., and Yu, L., 2005, "Lifting the Veil: An Analysis of Pre-trade Transparency at the NYSE", Journal of Finance, 60, 783–815.
Brock, W., Dechert, W., J. Scheinkman, and Lebaron, B., 1996, "A Test for Independence Based on the Correlation Dimension", Econometric Reviews 15, 197-235
Brock, W., and Hommes, C., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model.", Journal of Economic Dynamics and Control 22, 1235-1274.
Chen, S.-H, Chang, C.-L., and Du, Y-.R. 2012, “Agent-Based Economic Models and Econometrics.” , Knowledge Engineering Review, forthcoming.
Chen, S.-H., and Yeh, C.-H., 2001, "Evolving traders and the business school with genetic programming: a new architecture of the agent-based artificial stock market.", Journal of Economic Dynamics and Control 25, 363-393.
Chen, S.-H., and Yeh, C.-H., 2002, "On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis.", Journal of Economic Behavior and Organization 49, 217-239.
Chiarella, C., Dieci, R., and He, X.-Z., 2007, "Heterogeneous expectations and speculative behavior in a dynamic multi-asset framework.", Journal of Economic Behavior and Organization 62, 408-427.
Chiarella, C., and He, X.-Z., 2001, "Asset pricing and wealth dynamics under heterogeneous expectations.", Quantitative Finance 1, 509-526.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
41
Chiarella, C., and He, X.-Z., 2002, "Heterogeneous beliefs, risk and learning in a simple asset pricing model.", Computational Economics 19, 95-132.
Chiarella, C., and He, X.-Z., 2003, "Heterogeneous beliefs, risk and learning in a simple asset pricing model with a market maker.", Macroeconomic Dynamics 7, 503-536.
Chiarella, C., He, X.-Z., and Hommes, C., 2006, "A dynamic analysis of moving average rules.", Journal of Economic Dynamics and Control 30, 1729-1753.
Eom, K.-S., Ok, J., and Park, J. -H., 2007, "Pre-trade transparency and market quality", Journal of Financial Markets 10, 319–341.
Flood, M. D., Huisman, R., Koedijk, K. G., and Mahieu, R. J.,1999, "Quote disclosure and price discovery in multiple-dealer financial markets", The Review of Financial Studies 12(1), 37-59.
Frutos, M. Angeles de, and Manzano, C., 2002, "Risk Aversion, Transparency, and Market Performance", Journal of Finance 57(2), 959-984.
He, X.-Z., and Li, Y., 2007, "Power-law behaviour, heterogeneity, and trend chasing.", Journal of Economic Dynamics and Control 31, 3396-3426.
He, X.-Z., and Li, Y., 2008, "Heterogeneity, convergence, and autocorrelations. "
Quantitative Finance 8, 59-79.
Hendershott, T., and Jones, C. M., 2005, "Island Goes Dark: Transparency, Fragmentation, and Regulation" , The Review of Financial Studies 18(3), 743-793.
Kang, J., and Lee, D., 2007, "The Effects of a Transparency Change in the Pre-opening Session on Price discovery", (Working paper of Korean Securities Association).
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
42
Kirman, A., 2006, "Heterogeneity in economics.", Journal of Economic Interaction and Coordination 1, 89-117.
Koza, J. R., 1992, "Genetic Programming: On the Programming of Computers by Means of Natural Selection.", Cambridge: MIT Press.
LeBaron, B., Arthur, W. B., and Palmer, R., 1999, "Time series properties of an artificial stock market.", Journal of Economic Dynamics and Control 23, 1487-1516.
LeBaron, B., 2006, "Agent-based computational finance. In: L. Tesfatsion and K. L.
Judd (Eds.)", Handbook of Computational Economics, vol. 2. Amsterdam: Elsevier, 1187-1233.
Lux, T., 1995, "Herd behavior, bubbles and crashes.", The Economic Journal 105, 881-896.
Lux, T., 1997, "Time variation of second moments from a noise trader/infection model.", Journal of Economic Dynamics and Control 22, 1-38.
Lux, T., 1998, "The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions.", Journal of Economic Behavior and Organization 33, 143-165.
Lux, T., and Marchesi, M., 1999, "Scaling and criticality in a stochastic multi-agent model of a financial market.", Nature 397, 498-500.
Lux, T., and Marchesi, M., 2000, "Volatility clustering in financial markets: a microsimulation of interacting agents.", International Journal of Theoretical and Applied Finance 3, 675-702.
Lyons, R. K., 1996, "Optimal transparency in a dealership market with an application to foreign market", Journal of Finance 45, 2, 591-601.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
43
Ma, T., Lin, Y., and Chen, H. -K., 2008, "Are Investors more Aggressive in Transparent Markets?" , Asia-Pacific Journal of Financial Studies 37, No.2, 343-380
Madhavan, A., 1992, "Trading Mechanisms in Securities Markets", The Journal of Finance 47,2, 607-641.
Madhaven, A., 1996, "Securities Prices and Market Transparency", Journal of Financial Intermediation 5, 255-283.
Madhavan, A., and Panchapagesan, V., 2000, "Price Discovery in Auction Markets: A Look Inside the Black Box", Review of Financial Studies 13(3), 627~658
Madhavan A., Porter, D., and Weaver, D. , 2005, "Should Securities Markets be Transparent? ", Journal of Financial Markets 8, 266-288.
McInish, T. H., and Wood, R. A., 1995, "Hidden limit orders on the NYSE", Journal of Portfolio Management 21, 3, 19-26.
Pagano, M., Roell, A. , 1996, "Transparency and Liquidity: A Comparison of Auction and Dealer Markets with Informed Trading", The Journal of Finance, 51(2), 579-611.
Palmer R., Arthur, W. B., Holland, J. H., LeBaron, B., and Tayler, P., 1994, "Artificial economic life: a simple model of a stock market.", Physica D 75, 264-274.
Chen, S.-H, Lux, T., and Marchesi, M., 2001, "Testing for non-linear structure in an artificial stock market", Journal of Economic Behavior & Organization 46,327-342.
Yamamoto, R., 2011, " Order aggressiveness, pre-trade transparency, and long memory in an order-driven market ", Journal of Economic Dynamics & Control 35, 1938-1963.
‧ 國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
44
Yeh, C.-H., 2007, "The role of intelligence in time series properties.", Computational Economics 30, 95-123.
Yeh, C.-H., 2008, "The effects of intelligence on price discovery and market efficiency.", Journal of Economic Behavior and Organization 68, 613-625.
Yeh, C.-H., and Yang, C.-Y., 2010, "Examining the effectiveness of price limits in an artificial stock market.", Journal of Economic Dynamics and Control 34, 2089-2108.
‧
A1 mean A1 Variance A2 mean A2 Variance t-statistics p-value 1 0.000594 0.00093 0.000562 0.00099 6.4 <.0001
‧
A1 mean A1 Variance A3 mean A3 Variance t-statistics p-value 1 0.000594 0.00093 0.000651 0.00211 -3.03 0.0024
‧
A2 mean A2 Variance A3 mean A3 Variance t-statistics p-value 1 0.000524 0.000966 0.000651 0.00211 -6.72 <.0001
‧
B1 mean B1 Variance B2 mean B2 Variance t-statistics p-value 1 0.000651 0.00211 0.000681 0.00145 -1.44 0.1493
‧
B1 mean B1 Variance B3 mean B3 Variance t-statistics p-value 1 0.000651 0.00211 0.00073 0.00151 -3.72 0.0002
‧
B2 mean B2 Variance B3 mean B3 Variance t-statistics p-value 1 0.000681 0.00145 0.00073 0.00151 -2.85 0.0043
‧
A1 mean A1 Variance A2 mean A2 Variance t-statistics p-value
1 209.9 144.6 277.3 128.7 -42.63 <.0001
‧
A1 mean A1 Variance A3 mean A3 Variance t-statistics p-value
1 209.9 144.6 301.4 116 -60.48 <.0001
‧
A2 mean A2 Variance A3 mean A3 Variance t-statistics p-value
1 277.3 128.7 301.4 116 -17.09 <.0001
‧
B1 mean B1 Variance B2 mean B2 Variance t-statistics p-value
1 301.4 116 283.6 124.1 12.89 <.0001
‧
B1 mean B1 Variance B3 mean B3 Variance t-statistics p-value
1 301.4 116 302 115.2 -0.45 0.652
‧
B2 mean B2 Variance B3 mean B3 Variance t-statistics p-value
1 283.6 124.1 302 115.2 -13.37 <.0001
‧
A1 mean A1 Variance A2 mean A2 Variance t-statistics p-value
1 81.3224 140.4 200.2 177.6 -64.3 <.0001
‧
A1 mean A1 Variance A3 mean A3 Variance t-statistics p-value
1 81.3224 140.4 236.1 164 -87.81 <.0001
‧
A2 mean A2 Variance A3 mean A3 Variance t-statistics p-value
1 200.2 177.6 236.1 164 -18.22 <.0001
‧
B1 mean B1 Variance B2 mean B2 Variance t-statistics p-value
1 236.1 164 206.8 169.2 15.22 <.0001
‧
B1 mean B1 Variance B3 mean B3 Variance t-statistics p-value
1 236.1 164 227.2 168.9 4.63 <.0001
‧
B2 mean B2 Variance B3 mean B3 Variance t-statistics p-value
1 206.8 169.2 227.2 168.9 -10.44 <.0001