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研究生:李世欽
研究生(外文):Shih-Chin Lee
論文名稱:台灣樂透市場投注者選號行為之研究
論文名稱(外文):Selection Behavior of Taiwan Lotto Players
指導教授:何淮中何淮中引用關係
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:財務金融學研究所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:英文
論文頁數:70
中文關鍵詞:賭徒謬誤台灣樂透彩第二型賭徒謬誤
外文關鍵詞:Taiwan Lotto
相關次數:
  • 被引用被引用:2
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  • 收藏至我的研究室書目清單書目收藏:1
Although lotto games are better suited for testing the nature of human rationality than stock markets or laboratory designs in experimental psychology, insufficient attention has been given to the quantitative analysis of the lotto players’ behavior. This may be due to the fact that analyzing the behavior of lotto participants requires the exact frequencies of numbers chosen by the players and, unfortunately, lottery operators seldom release such data.
The most well-known cognitive bias exhibited by lotto players is the gambler’s fallacy, which infers that people underestimate the repetition of recent signals from a random binary series. In the first part, we introduce a method that enables us to test whether the numbers drawn in the past have any impact on the players’ selection of numbers without using the exact distribution of the numbers chosen. We apply this method to the Taiwan 6/42 lotto game and obtain two main findings. First, we show that the short horizon betting behavior of Taiwan lotto players is strongly consistent with the gambler’s fallacy. Second, consistent with the notion of Type II gambler’s fallacy (Keren and Lewis, 1994), these same players tend to pick those numbers that have been drawn most frequently in the past.
The gambler’s fallacy can be explained by the representativeness heuristic, while the type II gambler’s fallacy in number selection may be resulted by the availability heuristic, since winning numbers with higher occurrence rates come to mind more easily than those with low occurrence frequencies. Our finding is the first in the literature that presents statistically significant evidence of lotto players falling in two types of fallacies both.
The purpose of the second part is to study the behavior of the Taiwan lotto players by developing various dynamic regression models. The data collected for our analysis are accurate and precise since we exhaust a large database of lotto players choices of the number combinations maintained by the only lottery operator in Taiwan. There are three main results in this study. First, the gambler’s fallacy temporarily influences players’ selection of lotto numbers. Second, such negative influence can be partially offset by picking the numbers that appeared more frequently in the past. Third, the players using the system bet strategy have more misconceptions about random processes than the players using the ordinary bet strategy. The first two findings are related to Rabin and Vayanos (2005) model, which states that people judge the performance of a signal depending not only on the luck with reversals, but also on the underlying state with persistence.
Contents

1 Introduction 1
2 Gambler’s Fallacy 4
2.1 Introduction 5
2.2 Data and methodology 10
2.3 Non-random selection behavior 12
2.3.1 Proportions of prize winners 12
2.3.2 Estimated probability of players’ picking an individual number: the winning chance aspect 13
2.4 Short horizon: gambler’s fallacy 15
2.4.1 Draws without replacement 15
2.4.2 Perspective of previous winning frequencies in short term 17
2.5 Long horizon: type II gamblers’ fallacy 18
2.6 Simulation 21
2.7 Conclusions 24

3 Dynamic analyses of number selection 35
3.1 Introduction 36
3.2 Determinants of conscious selection 41
3.3 Methodology 44
3.3.1 Average probability of individual numbers 44
3.3.2 Reaction to hits of winning numbers 45
3.3.3 A dynamic model 46
3.3.4 Nonconsecutive combinations 50
3.4 Misconception across betting types 52
3.4.1 Reaction of average picking frequency across betting types 52
3.4.2 Dynamic model across betting types 54
3.4.3 Nonconsecutive combinations for system bets 55
3.5 Conclusions 56
References 66
References

Barber, B.M. and T. Odean (2003), All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors, Working Paper.
Barber, B.M., T. Odean, and L. Zheng (2005), Out of sight, out of mind: The effects of expenses on mutual fund flows, Journal of Business, 78: 2095-2119.
Bersabe, R. and R.M. Arias (2000), Superstition in gambling, Psychology in Spain, 4, 28-34.
Boynton, D.M. (2003), Superstitious responding and frequency matching in the positive bias and gambler''s fallacy effects, Organizational Behavior and Human Decision Processes, 91, 119-127.
Burns, B.D. and B. Corpus (2004), Randomness and inductions from streaks: Gambler''s fallacy” versus “Hot hand,” Psychonomic Bulletin & Review, 11, 179-184.
Clotfelter, C. and P. Cook (1989), Selling hope: State lotteries in America. Cambridge: Harvard University Press.
Clotfelter, C. and P. Cook (1993), The ‘Gambler’s Fallacy’ in lottery play, Management Science, 39, 1521-1525.
Coleman, L. (2004), New light on the longshot bias, Applied Economics, 36, 315-326.
Cook, P. and C. Clotfelter (1993), The Peculiar Scale Economies of Lotto, American Economic Review, 83, 634-643.
Cox, S.J., G.J. Daniell, and D.A. Nicole (1998), Using maximum entropy to double one''s expected winnings in the UK National Lottery, The Statistician, 47, 629-641.
Croson, R. and J. Sundali (2005), The Gambler''s fallacy and the hot hand: Empirical data from casinos, Journal of Risk and Uncertainty, 30, 195-209.
Durham, G.R., M.G. Hertzel and J.S. Martin (2005), The market impact of trends and sequences in performance: New evidence, Journal of Finance, 60, 2551-2569
Farrell, L., G. Lanot, R. Hartley, and I. Walker (2000), The demand for lotto: the role of conscious selection, Journal of Business and Economic Statistics, 18, 228-241.
Farrell, L. and I. Walker (1999), The welfare effects of lotto: evidence from the UK, Journal of Public Economics, 72, 99–120.
Forrest, D., R. Simmons, and N. Chesters (2002), Buying a dream: alternative models of demand for lotto, ECONOMIC INQUIRY, 485-496.
Gilovich, T., R. Vallone and A. Tversky (1985), The hot hand in basketball: On the misperception of random sequences, Cognitive Psychology, 17, 295-314.
Haigh, J. (1995), Inferring gamblers’ choice of combinations in the National Lottery, Bull. IMA, 31, 132-136. Oxford:Butterworth-Heinemann.
Haigh, J. (1997), The statistics of the national lottery, Journal of Royal Statistical Society A, 160, 187-206.
Henze, N. (1997), A statistical and probabilistic analysis of popular lottery tickets, Statistica Neerlandica, 51, 155-163.
Hill, E. and J. Williamson (1998), Choose six numbers, any numbers, The Psychologist, 11, 17–21.
Jain, P.C. and J.S. Wu (2000), Truth in mutual fund advertising: Evidence on future performance and fund flows, Journal of Finance, 55:937–58.
Jarque, C.M. and A.K. Bera (1987), A test for normality of observations and regression residuals, International Statistical Review, 55, 163-172.
Keele, L. and N.J. Kelly (2005), Dynamic models for dynamic theories: the ins and outs of lagged dependent variables, Political Analysis, 14, 186-205.
Keren, G. and C. Lewis (1994), The 2 fallacies of gamblers: type I and type II, Organizational Behavior and Human Decision Processes, 60, 75-89.
Langer, E.J. (1975), The illusion of control, Journal of Personality and Social Psychology, 32, 311-328.
Lee, Y. K and C. T. Chang (2005), The social impacts of the public welfare lottery: An empirical study in Taiwan, Modern Asian studies, 39, 133-153.
Montier, J. (2002), Behavioural Finance: Insights into irrational minds and markets: Wiley.
Moore, P.G. (1997), The development of the UK national lottery: 1992-96, Journal of the Royal Statistical Society Series A-Statistics in Society, 160,169-185.
Mullainathan, S. (2000), Thinking through categories, Working paper, Department of Economics, Massachusetts Institute of Technology.
Papachristou, G. (2004), The British gambler''s fallacy, Applied Economics, 36, 2073-2077.
Price, D. and E.S. Novak (2000), The income redistribution effects of Texas lottery games, Public Finance Review, 28, 82–92.
Rabin, M. (2002), Inference by believers in the law of small numbers, Quarterly Journal of Economics, 117, 775-816.
Rabin, M. and D. Vayanos (2005), The gambler''s and hot-hand fallacies in a dynamic-inference model, Working Paper.
Riniolo, T.C. and L.A. Schmidt (1999), Demonstrating the gambler''s fallacy in an introductory statistics class. Teaching of Psychology, 26, 198-200.
Rogers, P. (1998), The cognitive psychology of lottery gambling: A theoretical review, Journal of Gambling Studies, 14, 111-134.
Roney, C.J.R. and L.M. Trick (2003), Grouping and gambling: a gestalt approach to understanding the gambler''s fallacy, Canadian Journal of Experimental Psychology, 57, 69-75.
Simon, J. (1999), An analysis of the distribution of combinations chosen by UK National Lottery players, Journal of Risk and Uncertainty, 17, 243-276.
Statman, M. (2002), Lottery players/stock traders, Financial Analysts Journal, 58, 14-21.
Stranahan, H. and M.O. Borg (1998a), Separating the decisions of lottery expenditures and participation: A truncated Tobit approach, Public Finance Review, 26, 99–117.
Stranahan, H. and M.O. Borg (1998b), Horizontal equity implications of the lottery tax, National Tax Journal, 51, 71–82.
Strickland, L.H., R.J. Lewicki, and A.M. Katz (1966), Temporal orientation and perceived control as determinants of risk-taking, Journal of Experimental and Social Psychology, 2,143-151.
Terrell, D. (1994), A test of the gambler’s fallacy – Evidence from pari-mutuel games, Journal of Risk and Uncertainty, 8, 309-317.
Terrell, D. (1998), Biases in assessments of probabilities: New evidence from greyhound races, Journal of Risk and Uncertainty, 17, 151-166.
Thaler, R.H. (1992), The Winner''s Curse: Paradoxes and Anomalies of Economic Life, New York: The Free Press.
Tversky, A. and D. Kahneman (1971), Belief in the law of small numbers, Psychological Bulletin, 76, 105-110.
Tversky, A. and D. Kahneman (1974), Judgment under uncertainty: heuristics and biases, Science, 185, 1124-1131.
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