“Deliberated Intuition” in Stock Price Forecasting

Endreß, Tobias

doi:10.14254/2071-789x.2018/11-3/1

Cited by 4 publications

(1 citation statement)

References 51 publications

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“…Chen and He (2018) believed that it is reliable to use deep learning to predict stock prices. Combination of methods believed to have higher prediction accuracy compared to other techniques in stock price forecasting (Jujie & Danfeng, 2018;Endress, 2018;Plastun et al, 2019). Economists are today more unease with the monitoring and prediction of economic sequences due to the influence of the economic crisis in 2007/2008.…”

Section: Introductionmentioning

confidence: 99%

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

Vochоzka

Horák

Krulický

2020

MMI

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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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Section: Introductionmentioning

confidence: 99%

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

Vochоzka

Horák

Krulický

2020

MMI

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Assessing the Effects of Expanded Input Elicitation and Machine Learning-Based Priming on Crowd Stock Prediction

Bhogaraju,

Jain,

Jaiswal

et al. 2023

Communications in Computer and Information Science

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Do their reputations precede them? Stock market reaction to changes in corporate reputation in the context of sector and market maturity

Blajer-Gołębiewska,

Nowak

2024

Journal of International Studies

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Our study extends the existing literature by examining whether the stock market’s maturity and the sector in which a company operates affect the relationship between an improvement in its corporate reputation and its stock returns. This event-study research is based on data from developed and emerging stock markets: the NYSE (US) and the WSE (Poland). The improvement in corporate reputation is proxied by its inclusion in a reputational index. We analysed inclusions in the RESPECT Index and WIG-ESG in Poland from 2009 to 2023. Then we compared the effects of inclusions on stock prices in the Polish market and the US market, in which the Dow Jones Sustainability Index (DJSI) was applied. Our findings suggest that in the emerging Polish market, significant reaction to inclusion in the index persists even when the sectoral circumstances are considered. Contrarily, in the developed US market, the strong positive effect of inclusion disappears in the sectoral context. Hence, significant returns may not be driven by the inclusion in a reputational index but rather result from other events that affect the particular sector.

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“Deliberated Intuition” in Stock Price Forecasting

Cited by 4 publications

References 51 publications

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

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

Assessing the Effects of Expanded Input Elicitation and Machine Learning-Based Priming on Crowd Stock Prediction

Do their reputations precede them? Stock market reaction to changes in corporate reputation in the context of sector and market maturity

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