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This study proposes a program trading system and applied to Taiwan Stock Market , the trading rules refer to Curtis(2007) -¡yWay of the turtle¡z. In this research , we follow the trend following rule to invest in Taiwan Stock Market and not predict the future price of the market. To see if this system is good enough , we use average earn/loss ratio and average return to measure. Finally , we take trend following strategy compare with four different strategies. This research choose 16 stocks of Taiwan Stock Market , research period is from 1998/1/3 to 2007/12/31. The result show that trend following strategy gain positive return in different states and periods , its performance better than other strategies. We find the¡§Loss Stop¡¨is the key of trend following strategy in this research.

Program Trading has gotten more popular recent years. According to thestatistics, there was about 53.6% of daily volume in the United States, and increased to 73% in 2009. With the universal of program trading, more people have begun to research program trading. The purpose of this paper is constructing a developed platform of program trading for researching or developing. In addition to developed platform, we provide the run-time environment, and three main functions: 1. The job scheduler 2. The high scalability 3. The developed platform In this paper, we use SLURM to implement an economical computing service for program trading. SLURM is a resource management software for some large clusters. However it lacked for an easy interface to the ended users. We modify Xinetd as the external interface for SLURM, and implement the program trading development platform for researching or developing. According to the result, using our scheduler and the external interface that modify from Xinetd can be effective in controlling the server resource and increase the availability.

Thesis (M.Sc.)--Acadia University, 2000. / Includes bibliographical references (leaves 97-98). Also available on the Internet via the World Wide Web.

The results from 17 new experiments and 19 previously reported experiments are compared in an investigation of call and continuous auctions. The call auction used is the computerized PLATO sealed bid/offer (SBO), uniform price auction. The continuous auction used is the PLATO double auction (DA), a computerized version of the "open outcry" double auction. The SBO call auction has temporal consolidation of market orders and has limited information about trading activity. The continuous DA auction is characterized by sequential bilateral trades, and trading information (bids, offers, and prices) is publicly displayed. The paper first explores the effect of multiple crossings per trading period in the SBO call auction. Next, a comparison of SBO and DA is made, based on market experiments using flow supply and demand schedules. The institutional comparison is then extended to experimental asset markets. The results imply the following. First, multiple calls per period increase the efficiency of the SBO call auction, relative to one call per period, but they also induce greater misrepresentation of costs and values in the first crossing each period. Buyers and sellers also withhold units from the first crossing in a further attempt to gain strategic advantage. However, neither the withholding nor the misrepresentation appears to have any substantial influence on price. Second, the SBO auction with two calls per period is as efficient as the DA auction. In markets with a random competitive equilibrium (CE) each period, the SBO auction does a better job than DA at tracking the random CE price. Thus the SBO auction is equally as efficient as the DA, and has the further attributes of lower price volatility and greater privacy. Third, in laboratory asset markets, the SBO auction exhibits price bubbles similar to those observed in DA markets. Price dynamics in the two institutions are comparable, despite the stark differences in order flow and information dissemination.

Wang, Yu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 33-35). / Abstracts in English and Chinese. / Abstract --- p.ii / 摘要 --- p.iii / Chapter I. --- Introduction and Overview --- p.1 / Chapter II. --- Literature Review --- p.6 / Chapter 1. --- Price Impact Literatures --- p.6 / Chapter 2. --- Cost Measurement Literatures --- p.9 / Chapter 3. --- Trading Friction Literatures --- p.11 / Chapter III. --- Sample Description --- p.13 / Chapter 1. --- Data Source --- p.13 / Chapter 2. --- Selection Criteria for Sample Stocks --- p.14 / Chapter 3. --- Summary of Statistics --- p.15 / Chapter i. --- General Description --- p.15 / Chapter ii. --- Shanghai Stock Exchange versus Shenzhen Stock Exchange --- p.16 / Chapter iii. --- Normality Test --- p.17 / Chapter IV. --- Regression Analysis --- p.19 / Chapter 1. --- Sales Premium Estimation --- p.19 / Chapter 2. --- Statistics of the Estimated Sales Premium --- p.20 / Chapter 3. --- Factors that Impact the Sales Premium --- p.22 / Chapter i. --- Panel Data Regression --- p.22 / Chapter ii. --- Results and Interpretations --- p.23 / Chapter iii. --- Sales Premium versus Economic Events --- p.25 / Chapter IV. --- Robustness Tests. --- p.27 / Chapter 1. --- Common Robustness Tests --- p.27 / Chapter i. --- Validity of Fixed-Effect Model --- p.27 / Chapter ii. --- Autocorrelation Problem: Durbin-Watson tests --- p.27 / Chapter iii. --- Heteroskedasticity --- p.28 / Chapter iv. --- Consistency of Estimators --- p.28 / Chapter 2. --- Additional Variable for Sales Premium Estimation in Shenzhen Stock Exchange --- p.29 / Chapter V. --- Conclusion --- p.30 / Bibliography --- p.33 / Chapter Appendix A. --- Graphs --- p.36 / Chapter Appendix B. --- Tables --- p.41

This thesis consists of three parts: a genetic fuzzy trading model for stock trading, incremental intraday information for financial time series forecasting, and intraday effects in conditional variance estimation. Part A investigates a genetic fuzzy trading model for stock trading. This part contributes to use a fuzzy trading model to eliminate undesirable discontinuities, incorporate vague trading rules into the trading model and use genetic algorithm to select an optimal trading ruleset. Technical indicators are used to monitor the stock price movement and assist practitioners to set up trading rules to make buy-sell decision. Although some trading rules have a clear buy-sell signal, the signals are always detected with 'hard' logical. These trigger the undesirable discontinuities due to the jumps of the Boolean variables that may occur for small changes of the technical indicator. Some trading rules are vague and conflicting. They are difficult to incorporate into the trading system while they possess significant market information. Various performance comparisons such as total return, maximum drawdown and profit-loss ratios among different trading strategies were examined. Genetic fuzzy trading model always gave moderate performance. Part B studies and contributes to the literature that focuses on the forecasting of daily financial time series using intraday information. Conventional daily forecast always focuses on the use of lagged daily information up to the last market close while neglecting intraday information from the last market close to current time. Such intraday information are referred to incremental intraday information. They can improve prediction accuracy not only at a particular instant but also with the intraday time when an appropriate predictor is derived from such information. These are demonstrated in two forecasting examples, predictions of daily high and range-based volatility, using linear regression and Neural Network forecasters. Neural Network forecaster possesses a stronger causal effect of incremental intraday information on the predictand. Predictability can be estimated by a correlation without conducting any forecast. Part C explores intraday effects in conditional variance estimation. This contributes to the literature that focuses on conditional variance estimation with the intraday effects. Conventional GARCH volatility is formulated with an additive-error mean equation for daily return and an autoregressive moving-average specification for its conditional variance. However, the intra-daily information doesn't include in the conditional variance while it should has implication on the daily variance. Using Engle's multiplicative-error model formulation, range-based volatility is proposed as an intraday proxy for several GARCH frameworks. The impact of significant changes in intraday data is reflected in the MEM-GARCH variance. For some frameworks, it is possible to use lagged values of range-based volatility to delay the intraday effects in the conditional variance equation. / Ng, Hoi Shing Raymond. / Adviser: Kai-Pui Lam. / Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 107-114). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.