This paper examines the profitability of technical trading rules in the five Southeast Asian stock markets. The data cover a period of 14 years from January 2000 to December 2013. The instruments investigated are five Southeast Asian stock market indices: SET index (Thailand), FTSE Bursa Malaysia KLC index (Malaysia), FTSE Straits Times index (Singapore), JSX Composite index (Indonesia), and PS...

Keywords: We applied genetic programming with a lambda abstraction module mechanism to learn technical trading rules based on S&P 500 index from 1982 to 2002. The results show strong evidence of excess returns over buy-and-hold after transaction cost. The discovered trading rules can be interpreted easily; each rule uses a combination of one to four widely used technical indicators to make trad...

in this study, we focused on tehran stock exchange market analysis based on applying moving average rules. the tehran stock exchange in the middle east has evolved into an exciting and growing marketplace where individual and institutional investor trade securities of over 420 companies. in an attempt to examine the ability to earn excess return by exploiting moving average rules, the average a...

We implement a multi-agent financial market model simulation in which agents follow technical and fundamental trading rules to determine their speculative investment positions. We consider direct interactions between speculators due to which they may decide to change their trading behaviour. For instance, if a technical trader meets a fundamental trader and they realize that fundamental trading...

This paper presents two methods for increasing comprehensibility in technical trading rules produced by Genetic Programming. For this application domain adding a complexity penalizing factor to the objective fitness function also avoids overfitting the training data. Using pre-computed derived technical indicators, although it biases the search, can express complexity while retaining comprehens...

Genetic algorithms are applied to several simulated data series to study the predicting power of technical trading rules. The two kinds of rules considered are moving averages and filter rules. Genetic algorithms simulate how the best rule is uncovered by analysts over time. The analysis supports the predicting power of technical rules. The predictability is especially strong for data with movi...

.Traders make trade decisions specifying entry, exit, and stop loss prices. Technicians often decide on entry, exit, and stop loss prices based on a predefined set of technical rules. In this paper, we employ a method based on grammatical evolution to coevolve technical rules for entry, exit, and stop loss for trading a London Stock Exchange (LSE) based stock in high frequency. We consider the ...

Numerous studies in the finance literature have investigated technical analysis to determine its validity as an investment tool. Several of these studies conclude that technical analysis does have merit, however, it is noted that the effects of data-snooping are not fully accounted for. In this paper we utilize White’s Reality Check bootstrap methodology (White (1997)) to evaluate simple techni...