Construction and Evaluation of Trading Systems: Warsaw Index Futures
Witkowska, Dorota, Marcinkiewicz, Edyta, International Advances in Economic Research
This paper presents and compares 15 trading systems constructed for the Warsaw Stock Exchange futures contracts. These trading systems are constructed applying technical analysis and artificial neural networks (ANN). The efficiency of constructed trading systems is measured by the profit, which could be gained on the analyzed market when an investor uses various methods of buy and sell signals generating. Investigation is conducted for daily observations of stock index WIG20 futures from December 1, 1999 to November 28, 2003. The conclusion is that the combination of the technical analysis and artificial intelligence in order to gain profit from trading on the Polish futures market can bring much better investment results than trade in the traditional way. (JEL G10, C45)
The stock exchange market in Poland was created from scratch after the political system transformation. The Warsaw Stock Exchange (WSE) started operation in 1991 with five companies listed. The Polish derivatives market came into existence recently, as the first futures were launched in 1998. The law frames for short sale trading of stock was just set in 2003. Before that it was impossible to make short sales on the Warsaw Stock Exchange. This, of course, did not refer to financial futures, which have been present on the WSE since 1998.
Investors apply different strategies either to maximize profit (or rate of return) or to minimize risk of loss (or probability of loss). These strategies are constructed using a great variety of quantitative methods. The question is to what extent can these methods be applied to financial markets to gain profit? To answer this question, it is necessary to test different trading systems using actual data. The future contracts are chosen to analyze the possibility of making profits not only when the prices increase but also when they decrease.
The exact forecast of prices on the stock exchange would allow earning without any risk and losses. However, most of the available prices forecasting methods, such as econometric models or artificial neural networks, give the outcomes that are of a mostly inaccurate. Therefore, relying on the indication of such forecasts is still very risky and can increase losses. Hence, the concept of reducing the inaccuracy of forecasts by filtering them, in a sense, through investment strategies, in particular by the technical analysis, are indicators that the trading systems are based on.
Thus, the aim of the paper is to answer the question of whether including the ANN forecasts of stock market prices to trading systems makes them more efficient and whether this kind of combination is more profitable than investing based only on the forecast indications.
Materials and Methods
In the research, technical analysis and neural nets are used to construct trading systems. Numerous authors recommend both methods. Technical analysis is widely described in literature by Murphy  and Schwager . Neural network applications for financial markets have been discussed by Azoff , Bosarge [1993, pp. 371-402], Chen [1994, pp. 1199-202], Dutta and Shekhar [1993, pp. 257-73], Gately , Goonatilake and Treleaven , Hawley et al. [1993, pp. 27-46), Hoptroff [1993, pp. 59-66], Hsieg [1993, pp. 12-5], Lowe [1994, pp. 3623-28], Refenes , and Witkowska .
Technical analysis comes from the assumption that stock price is set by the intersection of supply and demand, and an investor can make money by frequently buying and selling stocks. A technical analyst does not look at income statements, balance sheets, company policies, or anything fundamental about the company. The technician looks at the actual history of trading and price in a security or index. This is usually done in the form of a chart. The security can be a stock, future, index, or a sector. It is flexible enough to work on anything that is traded in the financial markets. …