DEVELOPMENT OF INTELLIGENT DECISION MAKING MODEL FOR STOCK MARKETS

This paper is focused on the development of intelligent decision making model which is based on the application of artificial neural networks (ANN) and swarm intelligence technologies. The proposed model is used to generate one-step forward investment decisions. The ANN are used to make the analysis of historical stock returns and to calculate one day forward possible profit, which could be get while following the model proposed decisions, concerning the purchase of the stocks. Subsequently the Particle Swarm Optimization (PSO) algorithm is applied in order to select the „global best” ANNs for the future investment decisions and to adapt the weights of other networks towards the weights of the best network. The experimental investigations were made considering different number of neural networks, moving time intervals and commission fees. The experimental results presented in the paper show that the application of our proposed methodology lets to achieve better results than the average of the market

Akcijų kainų kitimo rangavimo metodas vertybinių popierių prekybos sistemose

The goal of this paper was to introduce a stock ranking method and to show how this method could be incorporated into stock trading systems. The proposed method analyses a large number of securities and rankes them according relative change in price during the defined interval of time. Then the values of ranks arc being normalized and assume the values from -1 to +1. The securities with ranking values close to -1 are good candidates for an investor portfolio, because these securities historically had shown a statistically significant price increase by the following days. The proposed method was tested using experimental data from US security markets. Two groups of securities (30 companies from Dow Jones Industrial Index and 292 companies from SP500 Index) were tested during time interval between 1992-2002. First investigations had shown efficiency of the proposed method. The stock’s rank indicator exhibits a mean reverting behavior. For the stocks with strong negative rank a positive stock price change in mean is followed. Furthermore, a negative stock price change on average is followed for the high ranked stocks. So these results contradict the statements of the effective market hypothesis and motivate the creating of stock trading systems, based on stock’s price rank. In this paper we analyze the usefulness of this method while applying it in a virtual stock trading system. The trading simulation is executed using historical data from USA stock market (1992-2002). The trading system has given significantly higher return. relative the benchmark (SP500 index). However, there are some practical problems that must be overcome if we arc going to apply the stock’s rank method in real stock trading process. The most important of these is the taxes rate during stock trading operations. In the paper we analyze the influence of taxes rate on the systems profit and we give some suggestions how to make this system more suitable for the practical applications.Straipsnyje nagrinėjamas autoriaus pasiūlytas akcijų kainų kitimo rangavimo metodas ir šio metodo taikymas vertybinių popierių prekybos sistemose. Atlikti eksperimentiniai tyrimai parodė, kad taikant siūlomą metodą istoriniams akcijų kainų kitimo duomenims įvertinti, virtualūs investavimo rezultatai ilgą laiką yra ženkliai geresni negu rinkos vidurkis. Taigi šie rezultatai prieštarauja efektyvios rinkos hipotezės teiginiams ir motyvuoja prekybos sistemų, pagrįstų akcijų kainų kitimo rangavimu, kūrimą ir eksperimentinius tyrimus. Straipsnyje analizuojami praktiniai šio metodo taikymo realioje rinkoje aspektai ir komisinių mokesčių už vertybinių popierių pirkimo-pardavimo operacijas dydžio įtaka investavimo rezultatams

Adaptive set-point control system for microbial cultivation processes

A control system for set-point control of microbial cultivation process parameters is developed, in which a tendency model is applied for controller adaptation to process nonlinearity and time-varying operating conditions. The tendency model is updated on-line and introduced into control algorithm for prediction of steady-state control action and returning of feedback controller. The control system was tested for controlling dissolved oxygen concentration in batch operating mode bioreactor under extreme operating conditions. In simulation experiments, the control system demonstrates fast adaptation, robust behaviour and significant improvement in control performance compared to that of fixed gain controller

User Adaptive Text Predictor for Mentally Disabled Huntington’s Patients

This paper describes in detail the design of the specialized text predictor for patients with Huntington’s disease. The main aim of the specialized text predictor is to improve the text input rate by limiting the phrases that the user can type in. We show that such specialized predictor can significantly improve text input rate compared to a standard general purpose text predictor. Specialized text predictor, however, makes it more difficult for the user to express his own ideas. We further improved the text predictor by using the sematic database to extract synonym, hypernym, and hyponym terms for the words that are not present in the training data of the specialized text predictor. This data can then be used to compute reasonable predictions for words that are originally not known to the text predictor

Modeling the Glucose Concentration for the Recombinant E.coli Bioprocesses

This article presents the sophisticated-to-date carbon mass balance for fed-batch E.coli bioprocesses. The model originates from the distribution of carbon mass from glucose in biomass, off-gas, and hypothetical solutes. The suggested model complements Pirt's equation as a particular case scenario. The approach uses the linear relationship between biomass carbon content per carbon grams in glucose and average cell population age. The carbon balance brings two potential practical benefits. First, it has the potential to assess the type of cell metabolism pathway and to have a soft sensor for the concentration of dissolved products such as acetates. The measure of glucose concentration suggests another finding, assuring the reliance on off-gas information only. The paper introduces an average carbon content ratio in biomass and off-gas, with numerical values of 0.5 in growth-limiting experiments and 0.27 in nonlimiting ones, which may serve as a decision-making criterion for metabolic pathway detection in the future

Improving the batch-to-batch reproducibility in microbial cultures during recombinant protein production by guiding the process along a predefined total biomass profile

In industry Escherichia coli is the preferred host system for the heterologous biosynthesis of therapeutic proteins that do not need posttranslational modifications. In this report, the development of a robust high-cell-density fed-batch procedure for the efficient production of a therapeutic hormone is described. The strategy is to guide the process along a predefined profile of the total biomass that was derived from a given specific growth rate profile. This profile might have been built upon experience or derived from numerical process optimization. A surprisingly simple adaptive procedure correcting for deviations from the desired path was developed. In this way the batch-to-batch reproducibility can be drastically improved as compared to the process control strategies typically applied in industry. This applies not only to the biomass but, as the results clearly show, to the product titer also

Stock trading systems based on stock’s price ranks

Straipsnyje nagrinėjamas autoriaus pasiūlytas akcijų kainų kitimo rangavimo metodas ir šio metodo taikymas vertybinių popierių prekybos sistemose. Atlikti eksperimentiniai tyrimai parodė, kad taikant siūlomą metodą istoriniams akcijų kainų kitimo duomenims įvertinti, virtualūs investavimo rezultatai ilgą laiką yra ženkliai geresni negu rinkos vidurkis. Taigi šie rezultatai prieštarauja efektyvios rinkos hipotezės teiginiams ir motyvuoja prekybos sistemų, pagristų akcijų kainų kitimo rangavimu, kūrimą ir eksperimentinius tyrimus. Straipsnyje analizuojami praktiniai šio metodo taikymo realioje rinkoje aspektai ir komisinių mokesčių už vertybinių popierių pirkimo-pardavimo operacijas dydžio jtaka investavimo rezultatams.The goal of this paper was to introduce a stock rahking method and to show how this method could be incorporated into stock trading systems. The proposed method analyses a large number of securities and rankes them according relative change in price during the defined interval of time. Then the values of ranks arc being normalized and assume the values from -1 to +1. The securities with ranking values close to -1 arc good candidates for an investor portfolio, because these securities historically had shown a statistically significant price increase by the following days. The proposed method was tested using experimental data from US security markets. Two groups of securities (30 companies from Dow Jones Industrial Index and 292 companies from SP500 Index) were tested during time interval between 1992- 2002. First investigations had shown efficiency of the proposed method. The stock’s rank indicator exhibits m mean reverting behavior. For the stocks with strong negative rank a positive stock price change in mean is followed. Furthermore, a negative stock price change on average is followed for the high ranked stocks. So these results contradict the statements of the effective market hypothesis and motivate the creating of stock trading systems, based on stock’s price rank. In this paper we analyze the usefulness of this method while applying it in a virtual stock trading system. The trading simulation is executed using historical data from USA stock market (1992-2002). The trading system has given significantly higher returns relative the benchmark (SP500 index). However, there arc some practical problems that must be overcome if we arc going to apply the stock’s rank method in real stock trading process. The most important of these is the taxes rate during stock trading operations. In the paper we analyze the influence of taxes rate on the systems profit and we give some suggestions how to make this system more suitable for the practical applications

DECISION-MAKING MODEL FOR STOCK MARKETS BASED ON PARTICLE SWARM OPTIMIZATION ALGORITHM

The objective of this paper is to introduce the decision-making model for stock markets. The proposed model is based on the study of historic data and the application of Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) algorithm. In the proposed decision-making model the ANN are applied in order to make the analysis of historical daily stock returns and to calculate the recommendations concerning the purchase of the stocks. Subsequently, the application of PSO algorithm is made. The core idea of this algorithm application is to select the "global best" ANN for future investment decisions and to adapt the weights of other ANN towards the weights of the best network. The experimental investigation results presented in this paper show the potentiality of PSO algorithm applications for the decision-making in the stock markets.Stock markets, genetic algorithms, swarm intelligence