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Title: | 關於選擇權市場處置效果與相似度衡量期貨交易策略的兩篇論述 Two Essays on the Disposition Effect of the Options Market and Similarity-based Futures Trading Strategies |
Authors: | 邱信瑜 Chiu, Hsin Yu |
Contributors: | 江彌修 Chiang, Mi Hsiu 邱信瑜 Chiu, Hsin Yu |
Keywords: | 處置效果 選擇權市場 Delat避險選擇權報酬 未實現資本利得 技術交易 相似度衡量交易策略 dipsosition effect options market delta-hedged option returns capital gains overhang technical trading rules similarity-based trading rules |
Date: | 2016 |
Issue Date: | 2016-08-02 17:13:35 (UTC+8) |
Abstract: | 第一篇論述討論處置效果於選擇權市場的實證。處置效果係指投資人在處分資產時,傾向盡快賣出有未實現利得的投資部位,並且繼續持有有未實現損失的投資部位的行為偏誤現象。文獻上有關處置效果的實證多半集中在股票市場而少有於選擇權市場的實證。選擇權市場一般認為是具有私有資訊及較具備金融知識與經驗的投資人會選擇交易的市場。本文實證處置效果在指數選擇權市場上的影響。我們認為對於選擇權投資人來說,價內外程度是最重要且顯而易見的資訊,是很直觀可以衡量可能利得及損失的參考點。相較於傳統衡量根據過去交易價格所形成的未實現損益指標,價內外程度更能吸引投資人的注意力。以本文所提出的基於價內外程度衡量之賣出傾向指標(Moneyness-based Propensity to Sell, MPS)以及根據Grinblatt and Han (2005)所形成的調整後未實現資本利得指標(adjusted Capital Gains Overhang, ACGO),每周將買權(賣權)排序成五等分後,我們發現持有最高等分的MPS或ACGO的買權(賣權)並賣出最低等分的買權(賣權)所形成的投資組合能夠產生超額報酬,顯示處置效果在指數選擇權市場亦存在。利用雙重排序(double sorting)的方法,我們發現MPS相較於ACGO,是較能夠在選擇權市場捕捉處置效果的指標。第二篇論述討論相似度衡量策略在期貨市場獲利的可能性。文獻上對於技術交易是否能產生顯著的報酬結果並不一致,然而實務上分析過去的價格走勢並使用技術指標所產生的訊號,是廣泛被接受的。現有測試技術交易指標獲利能力的文獻,通常假設投資人在實證測試的樣本期間一致性的參考某個交易指標產生的交易訊號並依此交易。然而實務上投資人可能同時參考不同的交易指標,每次交易可能根據不同交易指標所產生的訊號,且投資人會從歷史交易價格走勢中尋找類似於現有走勢的狀況,以這些歷史走勢接續的報酬率做為現有走勢未來報酬率的預期值。本文中我們提出一個較符合實際狀況的決策過程來描述技術交易投資人的行為,並重新檢視技術交易的獲利能力。我們提出的決策過程包含三個步驟。首先投資人建立一個特徵向量,包含投資人所認為足以預測未來報酬率並足以描述現況的指標。第二個步驟,投資人從過去某段期間中尋找相似於現有特徵向量的歷史狀況,並以這些歷史狀況接續的報酬率來作為預測的根據。最後,投資人依照過去的歷史狀況與現在有多相似,作為接續報酬率的加權權重,並以相似度權重加權平均報酬來做為未來報酬率的預測值,我們將依照相似度加權報酬所產生交易訊號所形成的策略稱為相似度衡量交易策略(Similarity-based trading rules)。我們檢視相似度衡量交易策略在九個不同的期貨市場中的獲利能力,在考量data-snooping及交易成本後,每日相似度衡量交易策略仍在其中六個市場中獲得顯著的報酬率。 The disposition effect, which refers to the tendency of investors to selling their winning investments too soon and to hold losing investments too long, has been well-documented in the extant literature. However, while empirical researches focus on examining the behavioral bias in the stock market, little attention is paid to the option market, where most informed investors and sophisticated traders gather. This essay tests for the disposition effect on the index options market. We argue that moneyness, the most salient and readily available information for option investors, is a natural reference point for potential gains and losses, which likely attracts market participants’ attention more than traditional measures that are based on past trading prices. Based on the Moneyness-based Propensity to Sell (MPS) measure that we introduce and an adjusted capital gains overhang (ACGO) measure of Grinblatt and Han (2005), we find that a strategy formed by buying calls/puts in the highest MPS or ACGO quintile and selling those in the lowest quintile would generate significant abnormal returns, suggesting the presence of the disposition effect. Using double sorting method, we find that the MPS is better as a measure in capturing the disposition effect on the options market than the ACGO. While the literature documents mixed results for the profitability of technical trading rules, the use of technical buy/sell signals based on analyzing past prices is widely accepted by practitioners. The existing literature on testing the predictive ability of technical trading mostly assumes that a technical investor consistently makes investment decisions based on the buy/sell signals according to one particular trading rule during the entire sample period. However this may be far from reality. Technical investors may simultaneously make predictions based on different technical indicators and follow different technical signals. Furthermore, they analyze historical price patterns that are similar to the current market condition and make assessment of future returns based on the subsequent returns of these similar patterns. The process is known as charting. We attempt to propose a more realistic decision-making process that incorporates the similarity-based predictors to account for technical investors’ decisions in the real world and reexamine the profitability of technical trading rules. The proposed process includes three steps. First, the investor attempts to predict future returns based on a vector of current characteristics that is sufficient for his assessment of the future returns and to depict the present scenario of the stock market. Second, the investor searches for the similar patterns in a specific time window prior to the current date and make an assessment of the future returns based on how similar these past patterns and the current pattern are and how rewarding the subsequent returns of the similar patterns are. Third, the investor is assumed to form a similarity-based indicator which is an assessment of the future returns depended on the similarity-weighted average of all previously observed values of the subsequent returns. The technical investor is then assumed to buy/sell according to the signals generated by the similarity-based trading rules (SBTR). We examine the profitability of the SBTR in nine futures markets and find significantly positive and robust returns after considering the data-snooping adjustments and transaction costs in six of the nine markets. |
Reference: | Essay 1 References Baker, M., B. Bradley and J. Wurgler, 2011, “Benchmarks as limits to arbitrage: Understanding the low-volatility anomaly”, Financial Analysts Journal 67 (1), pp. 40-54 Bakshi, G. and N. Kapadia, 2003, “Delta-hedged gains and the negative market volatility risk premium”, Review of Financial Studies 16 (2), pp. 527-566. Bali, T. G., N. Cakici and R. F. Whitelaw, 2011, “Maxing out: Stocks as lotteries and the cross-section of expected returns”, Journal of Financial Economics 99 (2), pp. 427-446. Bali, T. G. and S. Murray, 2013, “Does risk-neutral skewness predict the cross section of equity option portfolio returns?”, Journal of Financial and Quantitative Analysis 48 (4), pp. 1145-1171. Barber, B. M. and T. Odean, 2000, “Trading is hazardous to your wealth: The common stock investment performance of individual investors”, Journal of Finance 55 (2), pp. 773-806. Barberis, N. and M. Huang, 2008, “Stocks as lotteries: The implications of probability weighting for security prices”, American Economic Review 98 (5), pp. 2066-2100. Bates, D. S., 2000, “Post-’87 crash fears in the S&P 500 futures option market”, Journal of Econometrics 94 (1-2), pp. 181-238. Bhootra, A. and J. Hur, 2014, “High idiosyncratic volatility and low returns: A prospect theory explanation”, Financial Management, forthcoming. Black, F., 1975, “Fact and fantasy in the use of options”, Financial Analysts Journal 31 (4), pp. 36-41. Bondarenko, O., 2014, “Why are put options so expensive?”, Quarterly Journal of Finance, forthcoming. Boyer, B. H. and K. Vorkink, 2014, “Stock options as lotteries”, Journal of Finance 69 (4), pp. 1485-1527. Broadie, M., M. Chernov and M. Johannes, 2009, “Understanding index option returns”, Review of Financial Studies 22 (11), pp. 4443-4529. Carhart, M. M., 1997, “On persistence in mutual fund performance”, Journal of Finance 52 (1), pp. 57-82. Cao, J. and B. Han, 2013, “Cross section of option returns and idiosyncratic stock volatility”, Journal of Financial Economics 108 (1), pp. 231-249. Chakravarty, S., H. Gulen and S. Mayhew, 2004, “Informed trading in stock and option markets”, Journal of Finance 59 (3), pp. 1235-1258. Choe, H. and Y. Eom, 2010, “The disposition effect in the KOSPI 200 option market”, working paper. Constantinides, G. M., J. C. Jackwerth and A. Savov, 2013,“The puzzle of index option returns”, Review of Asset Pricing Studies 3 (2), pp. 229-257. Coval, J. D. and T. Shumway, 2001, “Expected option returns”, Journal of Finance 56 (3), pp. 983-1009. Coval, J. D. and T. Shumway, 2005, “Do behavioral biases affect prices”, Journal of Finance 60 (1), pp. 1-34. Daniel, K. and S. Titman, 1999, “Market efficiency in an irrational world”, Financial Analysts Journal 55 (6), pp. 28-40. De Bondt, W. F. M. and R. Thaler, 1985, “Does the stock market overreact?”, Journal of Finance 40 (3), pp. 793-805. Dhar, R. and N. Zhu, 2006, “Up close and personal: Investor sophistication and the disposition effect”, Management Science 52 (5), pp. 726-740. Driessen, J. and P. Maenhout, 2013, “The world price of jump and volatility risk”, Journal of Banking and Finance 37 (2), pp. 518-536. Fama, E. F. and K. R. French, 1993, “Common risk factors in the returns on stocks and bonds”, Journal of Financial Economics 33 (1), pp. 3-56. Fang, Y., 2012, “Aggregate investor preferences and beliefs in stock market: A stochastic dominance analysis”, Journal of Empirical Finance 19 (4), pp. 528-547 Feng, L. and M. S. Seasholes, 2005, “Do investor sophistication and trading experience eliminate behavioral biases in financial markets”, Review of Finance 9 (3), pp. 305-351. Frazzini, A., 2006, “The disposition effect and underreaction to news”, Journal of Finance 61 (4), pp. 2017-2046. Frazzini, A. and L. H. Pedersen, 2014, “Betting against beta”, Journal of Financial Economics 111 (1), pp. 1-25. Genesove, D. and C. Mayer, 2001, “Loss aversion and seller behavior: Evidence from the housing market”, Quarterly Journal of Economics 116 (4), pp. 1233-1260. Goyal, A. and A. Saretto, 2009, “Cross-section of option returns and volatility”, Journal of Financial Economics 94 (2), pp. 310-326. Grinblatt, M. and B. Han, 2005, “Prospect theory, mental accounting and momentum”, Journal of Financial Economics 78 (2), pp. 311-339. Grinblatt, M. and M. Keloharju, 2001, “What makes investors trade”, Journal of Finance 56 (2), pp. 589-616. Heath, C., S. Huddart and M. Lang, 1999, “Psychological factors and stock option exercise”, Quarterly Journal of Economics 114 (2), pp.601-627. Hirshleifer, D. and S. H. Teoh, 2003, “Limited attention, information disclosure, and financial reporting”, Journal of Accounting and Economics 36 (1-3), pp. 337-386. Hur, J., M. Pritamani and V. Sharma, 2010, “Momentum and the disposition effect: the role of individual investors”, Financial Management 39 (3), pp. 1155-1176. Jackwerth, J. C., 2000, “Recovering risk aversion from option prices and realized returns”, Review of Financial Studies 13 (2), pp. 433-451. Jegadeesh, N. and S. Titman, 1993,“Returns to buying winners and selling losers: implications for stock market efficiency”, Journal of Finance 48 (1), pp. 65-91. Jegadeesh, N. and S. Titman, 2001, “Profitability of momentum strategies: An evaluation of alternative explanations”, Journal of Finance 56 (2), pp. 699-720. Jones, C. S., 2006, “A nonlinear factor analysis of S&P 500 index option returns”, Journal of Finance 61 (5), pp. 2325-2363. Kahneman, D., 1973, “Attention and effort”, Prentice-Hall, Englewood Cliffs, NJ. Kahneman, D. and A. Tversky, 1979, “Prospect theory: An analysis of decision under risk”, Econometrica 47 (2), pp. 263-292. Kang, J., M. H. Liu and S. X. Ni, 2002, “Contrarian and momentum strategies in the China stock market: 1993-2000”, Pacific-Basin Finance Journal 10 (3), pp. 243-265. Levy, M. and Z. Wiener, 1998, “Stochastic dominance and prospect dominance with subjective weighting functions”, Journal of Risk and Uncertainty 16 (2), pp. 147-163. Ni, S. X., J. Pan and A. M. Poteshman, 2008, “Volatility information trading in the option market”, Journal of Finance 63 (3), pp. 1059-1091. Odean, T., 1998, “Are investors reluctant to realize their losses”, Journal of Finance, 53 (5), pp. 1775-1798. Odean, T., 1999, “Do investors trade too much?”, American Economic Review 89 (5), pp. 1279-1298. Pan, J., 2002, “The jump-risk premia implicit in options: evidence from an integrated time-series study”, Journal of Financial Economics 63 (1), pp. 3-50. Pan, J. and A. M. Poteshman, 2006, “The information in option volume for future stock prices”, Review of Financial Studies 19 (3), pp. 871-908. Peng, L. and W. Xiong, 2006, “Investor attention, overconfidence and category learning”, Journal of Financial Economics 80 (3), pp. 563-602. Polkovnichenko, V. and F. Zhao, 2013, “Probability weighting functions implied in options prices”, Journal of Financial Economics 107 (3), pp. 580-609. Poteshman, A. M., 2001, “Underreaction, Overreaction, and increasing misreaction to information in the options market”, Journal of Finance 56 (3), pp. 851-876. Poteshman, A. M. and V. Serbin, 2003, “Clearly irrational financial market behavior: Evidence from the early exercise of exchange traded stock options”, Journal of Finance 58 (1), pp. 37-70. Rouwenhorst, K. G., 1998, “International momentum strategies”, Journal of Finance 53 (1), pp. 267-284. Shefrin, H. and M. Statman, 1985, “The disposition to sell winners too early and ride losers too long: Theory and evidence”, Journal of Finance 40 (3), pp. 777-790. Statman, M., S. Thorley and K. Vorkink, 2006, “Investor overconfidence and trading volume”, Review of Financial Studies 19 (4), pp. 1531-1565. Thaler, R., 1980, “Toward a positive theory of consumer choice”, Journal of Economic Behavior and Organization 1 (1), pp. 39-60. Tversky, A. and D. Kahneman, 1992, “Advances in prospect theory: Cumulative representation of uncertainty”, Journal of Risk and Uncertainty 5 (4), pp. 297-323. Xing, Y., X. Zhang and R. Zhao, 2010, “What does the individual option volatility smirk tell us about future equity returns?”, Journal of Financial and Quantitative analysis 45 (3), pp. 641-622. Essay 2 References Bajgrowicz, P. and O. Scaillet, 2012, “Technical trading revisited: False discoveries, persistence tests, and transaction costs”, Journal of Financial Economics 106 (3), pp. 473-491. Brock, W., J. Lakonishok and B. LeBaron, 1992, “Simple technical trading rules and the stochastic properties of stock returns”, Journal of Finance 47 (5), pp. 1731-1764. Chan, L. K. C., and J. Lakonishok, 1993, “Institutional trades and intraday stock price behavior”, Journal of Financial Economics 33 (2), pp. 173-199. Elton, E. J., M. J. Gruber and J. C. Rentzler, 1987, “Professionally managed, public traded commodity funds”, Journal of Business 60 (2), pp. 175-200. Fama, E. F. and M. E. Blume, 1966, “Filter rules and stock-market trading”, Journal of Business 39 (1), pp. 226-241. Fong, W. M. and L. H. M. Yong, “Chasing trends: Recursive moving average trading rules and internet stocks”, Journal of Empirical Finance 12 (1), pp. 43-76. Gencay, R., 1998, “Optimization of technical trading strategies and the profitability in security markets”, Economics Letters 59 (2), pp. 249-254. Gilboa, I., O. Lieberman and D. Schmeidler, 2006, “Empirical similarity”, Review of Economics and Statistics 88 (3), pp. 433-444. Gilboa, I., O. Leiberman and D. Schmeidler, 2011, “A similarity-based approach to prediction”, Journal of Econometrics 162 (1), pp. 124-131. Gilboa, I. and D. Schmeidler, 1995, “Case-based decision theory”, Quarterly Journal of Economics 110 (3), pp. 605-639. Hansen, P. R., 2005, “A test for superior predictive ability”, Journal of Business and Economic Statistics 23 (4), pp. 365-380. Hsu, P. H. and C. M. Kuan, 2005, “Reexamining the profitability of technical analysis with data snooping checks”, Journal of Financial Econometrics 3 (4), pp. 606-628. Hume, D., 1748, “Enquiry into the human understanding”, Oxford, Clarendon Press. Jensen, M. C. and G. A. Benington, 1970, “Random walks and technical theories: some additional evidence”, Journal of Finance 25 (2), pp. 469-482. Lo, A. W., H. Mamaysky and J. Wang, 2000, “Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation”, Journal of Finance 55 (4), pp. 1705-1765. Lukac, L. P. and B. W. Brorsen, 1990, “A comprehensive test of futures market disequilibrium”, Financial Review 25 (4), pp. 593-622. Lukac, L. P., B. W. Brorsen and S. H. Irwin, 1988, “A test of futures market disequilibrium using twelve different technical trading systems” Applied Economics 20 (5), pp. 623-639. Lui, Y. H. and D. Mole, 1998, “The use of fundamental and technical analyses by foreign exchange dealers: Hong Kong evidence”, Journal of International Money and Finance 17 (3), pp. 535-545. Menkhoff, L., 2010, “The use of technical analysis by fund managers: International evidence”, Journal of Banking and Finance 34 (11), pp. 2573-2586. Park, C. H. and S. H. Irwin, 2010, “A reality check on technical trading rule profits in the U.S. futures markets”, Journal of Futures Markets 30 (7), pp. 633-659. Qi, M. and Y. Wu, 2006, “Technical trading-rule profitability, data snooping, and reality check: Evidence from the foreign exchange market”, Journal of Money, Credit and Banking 38 (8), pp. 2135-2158. Savin, G., P. Weller and J. Zvingelis, 2007, “The predictive power of “Head-and-Shoulders” price patterns in the U.S. stock market”, Journal of Econometrics 5 (2), pp. 243-265. Shynkevich, A., 2012, “Performance of technical analysis in growth and small cap segments of the US equity market”, Journal of Banking and Finance 36 (1), pp. 193-208. Sullivan, R., A. Timmermann and H. White, 1999, “Data-snooping, technical trading rule performance, and the bootstrap”, Journal of Finance 54 (5), pp. 1647-1691. Sweeney, R. J., 1988, “Some new filter rule tests: Methods and results”, Journal of Financial and Quantitative Analysis 23 (3), pp. 285-300. Taylor, M. P. and H. Allen, 1992, “The use of technical analysis in the foreign exchange market”, Journal of International Money and Finance 11 (3), pp. 304-314. Tversky, A. and D. Kahneman, 1973, “Availability: A heuristic for judging frequency and probability”, Cognitive Psychology 5 (2), pp. 207-232. White, H., 2000, “A reality check for data snooping”, Econometrica 68 (5), pp. 1097-1126. Wong, W. K., M. Manzur and B. K. Chew, 2003, “How rewarding is technical analysis? Evidence from Singapore stock market”, Applied Financial Economics 13 (7), pp. 543-551. Zhu, Y. and G. Zhou, 2009, “Technical analysis: An asset allocation perspective on the use of moving averages”, Journal of Financial Economics 92 (3), pp. 519-544. |
Description: | 博士 國立政治大學 金融學系 98352511 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0983525113 |
Data Type: | thesis |
Appears in Collections: | [金融學系] 學位論文
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