Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks

Laboissiere, Leonel A.; Fernandes, Ricardo Augusto Souza; Lage, Guilherme G.

doi:10.1016/j.asoc.2015.06.005

Cited by 116 publications

(45 citation statements)

References 27 publications

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“…Los resultados arrojados por ambos estudios confirmaron el hecho que las RNA (ya sea de propagación hacia atrás o la diferencial), son una herramienta poderosa para hacer pronósticos a corto plazo, especialmente diario y semanal. Laboissiere et al, utilizan las RNA para pronosticar rangos de precios mínimos y máximos de las acciones de tres compañías de energía brasileras, y concluyen que esta metodología es de gran utilidad tanto para los inversores, quienes a partir de esta tendrían la habilidad para definir valores umbrales en la bolsa, así como para los brokers quienes poseen poco conocimiento de dichas empresas (Laboissiere, Fernandes, & Lage, 2015).…”

Section: Algunos Casos Aplicadosunclassified

Modelo Log-Normal Para Predicción Del Precio De Las Acciones Del Sector Bancario // Log-Normal Model for Predicting the Price of Shares of the Banking Sector // Log-Normal Modelo De Predição Do Preço Das Ações Do Setor Bancário

Camargo¹,

Fontanilla²,

Luna³

2015

Dempresarial

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El objetivo de este artículo de investigación es la predicción de las cotizaciones de las acciones del sector bancario que cotizaron en el índice general de la bolsa de valores de Colombia (IGBC) durante el periodo 2008 – 2012. Para tal efecto, metodológicamente se hizo uso del modelo Log-normal complementado con simulaciones de Monte-Carlo a fin de determinar pruebas de bondad de ajuste del modelo mediante la raíz del error métrico cuadrado (RMSE). Los resultados encontrados indican que si bien es cierto el modelo sirve para tener una aproximación a los posibles valores mínimos y máximos que puede tomar las acciones, sus resultados carecen de la suficiente precisión para inducir la compra certera de este tipo de activo financiero, razón por la cual se recomienda en próximas investigaciones la aplicación de modelos con promedios móviles de suavizamiento exponencial y modelos de la familia Arch y Garch que generan mayor capacidad de predicción.

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Section: Algunos Casos Aplicadosunclassified

Modelo Log-Normal Para Predicción Del Precio De Las Acciones Del Sector Bancario // Log-Normal Model for Predicting the Price of Shares of the Banking Sector // Log-Normal Modelo De Predição Do Preço Das Ações Do Setor Bancário

Camargo¹,

Fontanilla²,

Luna³

2015

Dempresarial

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“…But ANN performed in an average a slight better in performance as compared to SVM [11]. Laboissiere et al proposed a model based [12] on ANN with an objective to solve the difficulty of calculation of maximum and minimum day stock prices using NASDAQ stock exchange indices for Brazilian Distribution Company was found to be effective. The prediction's performance was measured by involving Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) calculations.…”

Section: Annmentioning

confidence: 99%

Applications of neural network based methods on stock market prediction: survey

Mohapatra¹,

Das²,

Das³

et al. 2018

IJET

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Financial forecasting is one of the domineering fields of research, where investor's money is at stake due to the rise or fall of the stock prices which unpredictable and fluctuating. Basically as the demand for stock markets has been rising at an unprecedented rate so its prediction becomes all the more exciting and challenging. Prediction of the forthcoming stock prices mostly Artificial Neural Network (ANN) based models are taken into account. The other models such as Bio-inspired Computing, Fuzzy network model etc., considering statistical measures, technical indicators and fundamental indicators are also explored by the researchers in the field of financial application. Ann's development has led the investors for hoping the best prediction because networks included great capability of machine learning such as classification and prediction. Most optimization techniques are being used for training the weights of prediction models. Currently, various models of ANN-based stock price prediction have been presented and successfully being carried to many fields of Financial Engineering. This survey aims to study the mostly used ANN and related representations on Stock Market Prediction and make a proportional analysis between them.

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“…Its accuracy in RMSE was 50.44%, MAD was 39.78%, MAPE was 0.242% and RMSPE was 0.302%.The propose prediction model was the capability to extract the feature into independent source from simply observed nonlinear mixture data, there is no relevant data mixing methodology are available. Laboissiere et al (2015) proposed a methodology for the prediction of max-min stock price of Brazilian power distribution companies. In this technique multilayer-perception-architecture merges with ANN, while Levenberg Marquardt was used for estimating Max-Min day price.…”

Section: Literature Reviewmentioning

confidence: 99%

Pakistan stock exchange prediction using RIDOR classifier

Akram¹,

Imran

2017

Int. j. adv. appl. sci.

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Now-a-days stock market prediction is important activity and interesting topic for professional analyst. The stock market is biggest investment platform, however investment in stock market need accurate and complete information. The accurate and fast-prediction of stock market attracted the investor for profitable output. The stock market prediction is complex task because uncertainty involves in the market movement up/down. Mostly the machine learning techniques (MLT) are used for accurate prediction of stock market, because of its capability of partitioning; extract hidden information form raw data, monitors the fluctuation rate of stock market, suitable for nonlinear data etc. This research work is about to review the strength and weakness of existing stock market prediction techniques. This research work proposed a Ripple-down-rule-learner (RIDOR) classifier based technique. The RIDOR rule base classifier generates default value and work like if-else statement for uncertainties. The other contribution is a prepared data-set using technical indicator to predict stock market trend. The output of the propose model is outperformed as compare to the exiting techniques.

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Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks

Cited by 116 publications

References 27 publications

Modelo Log-Normal Para Predicción Del Precio De Las Acciones Del Sector Bancario // Log-Normal Model for Predicting the Price of Shares of the Banking Sector // Log-Normal Modelo De Predição Do Preço Das Ações Do Setor Bancário

Modelo Log-Normal Para Predicción Del Precio De Las Acciones Del Sector Bancario // Log-Normal Model for Predicting the Price of Shares of the Banking Sector // Log-Normal Modelo De Predição Do Preço Das Ações Do Setor Bancário

Applications of neural network based methods on stock market prediction: survey

Pakistan stock exchange prediction using RIDOR classifier

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