Abstract The impenetrable movement and crash of the stock market is always the most intriguing research task of any financial researcher. Nowadays, it has been proved that the movements of financial asset have the property of non-linearity or near-chaos and shows some tendency within a given period. We used the R/S analysis as the tool to indicate the tendency, and those stocks as our researching objects. We then combined purely price technical analysis indicators and genetic algorithms to form a predicting model. Then we compared our genetic predicting model with the traditional ARIMA analysis and hope to find out the invisible pattern under price volatility. And we hope our model could assist investors in assessing the stock markets more objectively and reduce the risk of stock investment. The researching target is TSMC(2330). We covered the period from 5 September 1994 to 28 December 1999, resulting in 1490 trading days. Historical data are available from Taiwan Economic Journal (TEJ). We execute the researching comparison by bear-market, bull-market, and bull-then-bear market and concluded as follows. 1. After the R/S analysis, we got the Hurst exponent of TSMC to be 0.849855 and the trending cycle was 940. It has proved that the market has tendency and indirectly showed that the Taiwan stock market was not efficient. 2. According to directional precision, our predicting model apparently outpaced the ARIMA model in these three periods. The reason was that our model grabbed more information than the ARIMA model. 3. If we only think about the inputs and outputs, our model seems to be a proper framework for explaining the relationships among variables in comparison with the neural network model having the same input and output variables. 4. We can deduce the invisible relationships of price technical indicators and the closing price. 5. Genetic predicting model can detect the prevailing trend of the learning periods. 6. The shorter the learning period, the better the predicting effects. As a whole and conservatively speaking, we have 70% confidence in directional precision. 7. If we combine proper trading strategy with genetic predicting model and deduct the transaction cost, we still get a better profit than buy-and-hold strategy and have some maneuvering flexibility. 8. After hypothesis testing, our predicting model seems to have some potential of ex ante prediction, but the stability and usability still need further study. In short, we proposed the ex post stock price movement learning model and the viable direction of ex ante prediction. Investors can take advantage of the flexibility of the predicting model and avoid using the over-complex and rigid trading strategies.

The aim of this graduation thesis is the descriptiton of R/S analysis and it's aplication on chosen time series of share prices and exchange rates. Some main models of financial time series will be mentioned in the second chapter. There will described basic linear models of stationary and non stationary time series and models of volatility. Then we will focus on the main theme of this thesis, R/S analysis. The algorithm of R/S analysis and the interpretation of the Hurst exponent will be described in the forth chapter. In the fifth chapter, the R/S analysis will by applied on real data sets. There will be two data sest of share prices of Telefónica O2 and Philip Morris and two data sets of exchange rates CZK/EUR and CZK/USD. The results will be interpreted and compared.