Innovations in Management Forecast: Time Development of Stock Prices with Neural Networks

Marek Vochоzka · Jakub Horák · Tomáš Krulický

35 citations0/0 files storedMarketing and Management of Innovations

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Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

Abstract

Accurate prediction of stock market values is a challenging task for over decades. Prediction of stock prices is associated with numerous benefits including but not limited to helping investors make wise decisions to accumulate profits. The development of the share price is a dynamic and nonlinear process affected by several factors. What is interesting is the unpredictability of share prices due to the global financial crisis. However, classical methods are no longer sufficient for the application of share price development prediction.However, over-relying on prediction data can lead to losses in the case of software malfunction. This paper aims to innovate the prediction management when predicting the share price development over time by the use of neural networks. For the contribution, the data on the prices of CEZ, a.s. shares obtained from the Prague Stock Exchange database. The stock price data are available for the period 2012-2017. In the case of Statistica software, the multilayer perceptron networks (MLP) and the radial basis function networks (RBF) are generated. In the case of Matlab software, the Support Vector Regression (SVR) and the Back-Propagation Neural Network (BPNN) are generated. The networks with the best characteristics are retained and based on the statistical interpretation of the results, and all are applicable in practice. In all data sets, MLP networks show stable performance better than in the case of SVR and BPNN networks. As for the final assessment, the deviation of 2.26% occurs in the most significant differential of the maximal and the minimal prediction. It is not necessarily significant regarding the price of one stock. However, in the case of purchasing or selling a large number of stocks, the difference may seem significant. Therefore, in practice, the application of two networks is recommended: MLP 1-2-1 and MLP 1-5-1. The first network always represents a pessimistic, minimal prediction. The second one of the recommended networks is an optimistic, maximal prediction. The actual situation should correspond to the interval of the difference between the optimistic and pessimistic prediction. Keywords: Statistica software, Matlab software, stock price development, neural networks, prediction.

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2019Systematic analysis and review of stock market prediction techniques 2016Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model 2018Stock Prediction Using Convolutional Neural Network 2016Oil price forecasting using gene expression programming and artificial neural networks 2018Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods 2011CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator 2016Financial Time Series Prediction Using Elman Recurrent Random Neural Networks 2017Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

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