The purpose of this paper is to demonstrate that by changing the underlying data used in technical analysis and technical trading systems the performance of these techniques can be greatly improved. We present two techniques – one for real-time (intraday) data and one for standard daily (end-of-day) data. For high frequency data we present a dynamic sampling technique that can generate a time s...

Genetic programming (GP) is increasingly investigated in finance and economics. One area of study is its use to discover effective rules for technical trading in the context of a portfolio of equities (or an index). Early work in this area used GP to find rules that were profitable, but were nevertheless outperformed by the simple “buy and hold” (B&H) strategy. Attempts since then tend to repor...

In this paper we try to apply Technical Analysis methodology into the Behavior Theory for the large capitalization firms of the Athens Stock Exchange (ASE). In Behavioural and in Technical Theory we observe a combination between fundamental (rational) and psychological – emotional (irrational) factors. We use standards tests in combination with bootstrap methodology under the AR(1) & GARCH(1,1)...

Weconduct an extensive examination of the profitability of technical analysis in ten emerging foreign exchange markets. Studying 25,988 trading strategies for emerging foreign exchangemarkets, we find that the best rules can sometimes generate an annualmean excess return of more than 30%. Based on standard tests, we find hundreds to thousands of seemingly significant profitable strategies. Howe...

Genetic network programming (GNP) as an evolutionary computation method has been used for stock trading recently. Former researches confirm the efficiency of trading rules which are created by GNP. In this paper, GNP has been applied for stock portfolio optimization by generating risk-adjusted trading rules. There are two main novelties in this paper: 1) we use conditional Sharp ratio as a risk...

Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a functio...

The Santa Fe Arti cial Stock Market consists of a central computational market and a number of arti cially intelligent agents The agents choose be tween investing in a stock and leaving their money in the bank which pays a xed interest rate The stock pays a stochastic dividend and has a price which uc tuates according to agent demand The agents make their investment decisions by attempting to f...

Evolutionary Algorithms (EA) consist of several heuristics, which are able to solve optimisation tasks by imitating some aspects of natural evolution. They may use different levels of abstraction, but they are always working on whole populations of possible solutions for a given task. EAs are an approved set of heuristics, which are flexible to use and postulate only neglectible requirements on...

This study intends to mine reasonable trading rules using genetic algorithms for Korea Stock Price Index 200 (KOSPI 200) futures. We have found trading rule which would have yielded the highest return over a certain time period using historical data. Simulated results of buying and selling of trading rules were outstanding. These preliminary results suggest that genetic algorithms are promising...

The objective of this research is to examine the trends in the exchange rate markets of the ASEAN-5 countries (Indonesia (IDR), Malaysia (MYR), the Philippines (PHP), Singapore (SGD), and Thailand (THB)) through the application of dynamic moving average trading systems. This research offers evidence of the usefulness of the time-varying volatility technical analysis indicator, Adjustable Moving...