Adaptive Portfolio Management using Evolutionary Algorithm Essay

Adaptive Portfolio Management using Evolutionary Algorithm - Essay Example Introduction: Portfolio management is the process of managing assets i.e. stocks, bonds, etc., such that a large return with a low risk is obtained. Forecasting price movements in financial markets is an important part of constructing portfolios. Most traders believe that the financial markets are not fully efficient and that there exist temporary predictability, which could be exploited for collecting excess returns above the market average [1]. Consequently, many financial institutions have developed decision support systems to help traders and analysts make decisions about portfolio management more quickly and more effectively. Technical indicators use statistics to determine trends in security prices and are often used by financial markets and private traders to assist with portfolio management. A survey of foreign exchange traders in London [2] estimates that up to 90% of traders use some form of technical indicators and trading rules in their daily trading. Technical indicators assume that securities move according to trends and patterns that are continued over a short periods of time until another trend is triggered by the change in the market condition. The success of technical indicators depends on how one interprets the signals. Expert human traders are capable of combining several technical indicators and trading rules to arrive at composite strategies which are used in portfolio selection, execution and risk management. The process of arriving at such strategies requires high experience, expertise and often long and tidies hours of observation of historical and current market data to test and fine-tune different combinations of technical indicators and trading rules. Although there are agreements that financial markets do sometimes show periods where certain trading rules work [3], it is very hard to find evidence that a single trading strategy can function over an extended period of time. This can be due to the fact that financial markets are const antly evolving, and that when a trading rule is found to work it would not take long before it is exploited until it no longer harvests a significant profit. This forces the traders and technical analyst to constantly create new strategies or retune the existing strategies so that they would work under the new market conditions. The goal of my research would be to create a system that emulates human behaviour in combining a set of simple rules and technical indicators to create sophisticated trading strategies. The system then would constantly evolve those strategies or creating new strategies that would adapt to changing market conditions. 2. Motivation: In the past several years, there has been a notable increase in the use of financial modeling and optimization tools such as algorithmic trading and automated portfolio management in financial industries. In addition to the pressure on asset management firms to reduce costs and maintain a more stable and predictable performance in the aftermath of the downturn in the world’s markets in recent years, three other general trends have contributed to this increase. First, there has been an increase of interest in predictive models for asset returns. Predictive models assume that it is possible to make conditional forecasts of future returns—an objective that was previously considered not achievable by classical financial theory. Second, the wide availability of sophisticated and specialized software packages has enabled generating and exploiting