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The management of prediction method in the system of investment decisions making

Wiliński A., Rejer I.

Management and Production Engineering Review

2011

Vol. 2, No. 2

46-52

The aim of the paper is to find a method of using prediction rules in time series in such a way to maximize the profit considering the risk. To deal with this task, a regression approach to prediction was chosen. Hence, the paper refers to relation between autoregression of a chosen time series and investment strategies. The time series under consideration is the most important polish financial instrument, a future contract on WIG20. Usually, it is rather easy to prove statistically that the autoregression of a single time series cannot be considered as an effective method for forecasting WIG20 quotations for investment purpose. However, the authors find the relation between the autoregression (and also multi-regression) and real future values of WIG20 which can be the source of effective strategies. The paper presents both - the theoretical description of the proposed strategies and results of their application for monthly data of WIG20, unemployment rate and money supply in Poland (data from years 1995-2007).

Application of Cluster Analysis Performed by SOM Neural Network to the Creation of Financial Transaction Strategies

Morajda J., Domaradzki R.

Journal of Applied Computer Science

2005

Vol. 13, nr 1

87-98

The authors propose a method of construction of transaction strategies in capital markets, which utilizes a Kohonen neural network called also self-organizing map (SOM). This network is applied for cluster analysis of input vectors that describe particular states of a stock market. The presented approach is quite different than typical neural techniques used for financial markets modeling or prediction, as it utilizes a network trained with an unsupervised method. The paper submits also the research concerning the effectiveness of the described method, based on analysis of active investment strategy for stocks of KGHM (Polish mining and metallurgy corporation).