The management of financial portfolios or funds constitutes a widely known problematic in financial markets which normally requires a rigorous analysis in order to select the most profitable assets. The presented paper proposes a new approach, based on Intelligent Computation, in particular genetic algorithms, which aims to manage a financial portfolio by using technical analysis indicators (EMA, HMA, ROC, RSI, MACD, TSI, OBV). In order to validate the developed solution an extensive evaluation was performed, comparing the designed strategy against the market itself and several other investment methodologies, such as Buy and Hold and a purely random strategy. The time span (2003–2009) employed to test the approach allowed the performance evaluation under distinct market conditions, culminating with the most recent financial crash. The results are promising since the approach clearly beats the remaining approaches during the recent market crash.
The fast technology evolution together with the massive evolvement of financial markets in modern societies leads, nowadays, to an increasing interest to the field of computational finance.
This field is becoming popular among computer scientists, especially to computational intelligence specialists who try to combine elements of learning, evolution and adaptation in order to create intelligent software. In particular, subjects such as neural networks, swarm intelligence, fuzzy systems and evolutionary computation are becoming extremely notorious on market’s domain. The mentioned techniques can be applied to financial markets in a variety of ways; as to predict the future movement of a stock’s price, or to optimize a collection of investment assets, such as a fund or a portfolio. This innovation is of special importance due to the high volume of securities (financial instruments) involved, normally, it is very hard for a simple investor to optimize his profits without requiring the skills of financial markets specialists. The goal of this work is to provide an application which tries to partially replace those specialists in order to help an investor or an investment company to achieve a significant profit on buying and selling (trading) financial instruments. In order to apply such procedures we must accept that the historical data related to stocks and markets gives appropriate signals about the market future performance. This premise constitutes the basis of technical analysis which simply tries to analyze the securities past performance in order to evaluate investments at the present time. This philosophy relies on three bases (Murphy, 1999); the fact that market action discounts everything, the fact that price moves in trends, and that history tends to repeat itself. These considerations allow, through the study of charts and financial data, the recognition of which way the market is more likely to go. Despite the fact that technical analysis is becoming widely used, there are still some criticisms to this perception on the market evolution. For instance, Burton Malkiel (Malkiel, 1973) stated that the “past movement or direction of the price of a stock, or overall market cannot be used to predict its future movement”. His findings become popular, leading to a new investment theory called The Random Walk Theory where the author stipulates that if we cannot beat the market, then the best investment strategy we can apply is Buy and Hold in which an investor buys stocks and holds them for a long period of time, regardless of market fluctuations. For the technical community, this idea of purely random movements of prices is totally rejected, and more recent studies (Lo and MacKinlay, 2001 and Park and Irwin, 2007) try to evidence their beliefs. For instance, in (Lo & MacKinlay, 2001) the author demonstrated the validity of technical analysis using more than seventy technical indicators which showed that market movements can be predicted at a certain degree. Also, if we consider the price movement as unpredictable, how can we explain that price moves in trends? If we observe several stock charts considering a predefined period we can easily detect an uptrend or a downtrend.
The presented paper provides a detailed discussion on a new approach for intelligent portfolio management. The paper is structured as follows: Section 2 addresses the theory behind the developed work, namely the concepts of financial portfolio, portfolio management, and technical analysis. Also, in this section, it is given a brief overview about different methodologies which can be used to address the portfolio management problematic. Section 3 illustrates the system architecture. Section 4 proposes the validation procedure used to evaluate the developed strategy. Section 5 summarizes the provided document and supplies the respective conclusion.
This work proposes capable new approach to automatically manage a portfolio by using a GA conjugated with technical analysis rules. As observed under the previous sections, the system shows a good adaptive degree to different market trends achieving outstanding return rates. Although, several management rules were defined to increase the system performance, evolutionary computation plays a fundamental role to provide a correct balance between several types of technical indicators in order to pick the most promising stocks for portfolio composition.
