Implementation of Artificial Neural Network to Predict S&amp;P 500 Stock Closing Price

Fitriyaningsih, Ike; Tampubolon, Anthon Roberto; Lumbanraja, Harry L.; Pasaribu, Grace E.; Sitorus, Pita S. A.

doi:10.1088/1742-6596/1175/1/012107

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ANNs refer to as a black box because it is impossible to know in detail the internal structure of the system (Privara & Rievajova, 2021;Stefancik et al, 2021). However, neural networks can reveal non-linear relationships in data and learn (Fitriyaningsih et al, 2018;Grumstrup et al, 2021;Sahoo & Pradhan, 2021). Moreover, neural network models exhibit non-linear nonparametric properties.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Gavurová

Jenčová

Bačík

et al. 2022

View full text Add to dashboard Cite

Research background: In a modern economy, full of complexities, ensuring a business' financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company's financial situation and predicting its future development becomes important. Assessing the financial health of business entities using various models is an important area in not only scientific research, but also business practice. Purpose of the article: This study aims to predict the bankruptcy of companies in the engineering and automotive industries of the Slovak Republic using a multilayer neural network and logistic regression. Importantly, we develop a novel an early warning model for the Slovak engineering and automotive industries, which can be applied in countries with undeveloped capital markets. Methods: Data on the financial ratios of 2,384 companies were used. We used a logistic regression to analyse the data for the year 2019 and designed a logistic model. Meanwhile, the data for the years 2018 and 2019 were analysed using the neural network. In the prediction model, we analysed the predictive performance of several combinations of factors based on the industry sector, use of the scaling technique, activation function, and ratio of the sample distribution to the test and training parts. Findings & value added: The financial indicators ROS, QR, NWC/A, and PC/S reduce the likelihood of bankruptcy. Regarding the value of this work, we constructed an optimal network for the automotive and engineering industries using nine financial indicators on the input layer in combination with one hidden layer. Moreover, we developed a novel prediction model for bankruptcy using six of these indicators. Almost all sampled industries are privatised, and most companies are foreign owned. Hence, international companies as well as researchers can apply our models to understand their financial health and sustainability. Moreover, they can conduct comparative analyses of their own model with ours to reveal areas of model improvements.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Gavurová

Jenčová

Bačík

et al. 2022

View full text Add to dashboard Cite

show abstract

A novel risk-control model for the online portfolio selection of high-frequency transactions

Wang

et al. 2022

Knowledge-Based Systems

View full text Add to dashboard Cite

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

Vochоzka

Horák

Krulický

2020

MMI

View full text Add to dashboard Cite

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.

show abstract

Implementation of Artificial Neural Network to Predict S&P 500 Stock Closing Price

Cited by 3 publications

References 0 publications

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Artificial intelligence in predicting the bankruptcy of non-financial corporations

A novel risk-control model for the online portfolio selection of high-frequency transactions

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

Contact Info

Product

Resources

About