Use of Econometric Predictors and Artificial Neural Networks for the Construction of Stock Market Investment Bots

Nametala, Ciniro Aparecido Leite; Souza, Jonas Villela de; Pimenta, Alexandre; Carrano, Eduardo G.

doi:10.1007/s10614-021-10228-0

Cited by 5 publications

(2 citation statements)

References 64 publications

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“…Taking into account the influence of the two explanatory variables, x 2 and x 4 , on the explanatory variable, y, the econometric model is a more or less faithful reflection of the studied reality. The forecasting process is illustrated by the following equation: -961.6908951 -315.2413292 x2 + 3.298623389 x4 One major challenge in stock markets is the use of the tools which are needed in the process of forecasting (Nametala et. al, 2023).…”

Section: Methodsmentioning

confidence: 99%

Knowledge of financial management practices of listed SMEs

Ejsmont,

Sandal

2023

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RESEARCH OBJECTIVE: Small and medium businesses are constantly driving economic prosperity. The SME sector accounts for the overwhelming majority of enterprises, i.e. 99.8%, both in Poland and in the EU as a whole. Therefore, the aim of this article is to verify the research hypothesis assuming that Knowledge Management (KM) plays a key role in the financial management of small and medium-sized listed companies, especially in terms of investment decisions. THE RESEARCH PROBLEM AND METHODS: The research method used in this article will be the analysis of stock market data describing as a not very satisfied financial condition of small and medium listed companies in times of broad economic crises, which mainly include the pandemic time and the armed conflict in Ukraine. On the basis of selected financial data, estimate an econometric model was created to help explain the mechanism of changes occurring the financial condition of the listed business entities. THE PROCESS OF ARGUMENTATION: A literature review confirming the important role of knowledge management including the Balanced Scorecard (BSC) methodology in financial situation of the joint-stock companies. The current state of the global economy is leading to a dangerous situation in which it is becoming increasingly difficult to maintain a secure financial cushion. RESEARCH RESULTS: Estimation of an econometric model is expected to confirm the research hypothesis that KM plays a key role in the financial management of small and medium-sized listed companies, especially in terms of their investment decisions in range of using innovative method as BSC. CONCLUSIONS, INNOVATIONS, AND RECOMMENDATIONS: The article explains the key role of KM in the financial management of small and medium-sized listed companies during crises. The results of the estimated model can serve the more effective management of SMEs listed on NewConnect and other alternative trading floors around the world.

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Section: Methodsmentioning

confidence: 99%

Knowledge of financial management practices of listed SMEs

Ejsmont,

Sandal

2023

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“…Subsequent research was carried out with two classes of Artificial Neural Networks (ANN) combined with three classes of econometric predictors by Nametala et al [4] with a stock dataset in Brazil. Analysis of this method with econometric predictors, ANN trend and the candlestick, Bovespa index, and Buy and Hold strategy) results in Shapiro-Wilk and Fligner-Killen evaluations obtaining satisfactory values, respectively, 0.85 and 0.75, which makes this method good.Stocks and Stock Portfolio.…”

Section: Literature Reviewmentioning

confidence: 99%

Portfolio Optimization Based on Return Prediction and Semi Absolute Deviation (SAD)

Mulyono,

Saepudin,

Rohmawati

2023

ijoict

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A portfolio is a collection of investment financial assets managed by financial institutions or individuals. In investment activities, investors expect minimal loss risk and optimal stock portfolio weight to get maximum profit. Investors can monitor changes in stock index values to compare portfolio performance. This research has discussed how to build a portfolio based on stock datasets with the LQ45 index using return predictions from the artificial neural network (ANN) method with semi-absolute deviation (SAD). Furthermore, the portfolio is optimized by looking for weights that match it. After that, a comparison of portfolio performance was carried out using the Sharpe ratio (SR) method between the semi-absolute deviation (SAD) portfolio and the portfolio resulting from the formation of the equal weight (EW) portfolio. Portfolio performance with ANN prediction and SAD is better than equal-weight portfolios in terms of mean return, standard deviation, and sharpe ratio for portfolios with few stocks, namely 2 and 3 stocks. In addition, a portfolio with a higher number of stocks can make the portfolio value from the ANN close prediction algorithm process and the selection of weights based on SAD is better than portfolios with equal weight for each list of stocks in the portfolio.

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