scholarly journals Investigation of decision making support in digital trading

2020 ◽  
Author(s):  
Ilona Stalovinaitė ◽  
Nijolė Maknickienė ◽  
Raimonda Martinkutė-Kaulienė
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Financial Markets ◽  
Short Term Memory ◽  
Short Term ◽  
Learning Technique ◽  
Technical Indicator ◽  
Long Short Term Memory ◽  
Investment Portfolios ◽  
Decision Making Support

In order to trade successfully investors are looking for the best method to determine possible directions of the price changes of financial means. The main objective of this paper is to evaluate the results of digital trading using different decision-making techniques. The paper examines deep learning technique known as Long Short – Term Memory (LSTM) neural network and parabolic stop and reverse (SAR) technical indicator as possible means for decision-making support. Based on an investigation of theoretical and practical aspects of digital trading and its support possibilities, investment portfolios in real-time “IQ Option” digital trading platform were created. Short-term results show that investment portfolios created using LSTM neural network performed better compared to the ones that were created using technical analysis. The study contributes to the development of new decision-making algorithms that can be used for forecasting of the results in the financial markets.

scholarly journals The Application of Stock Index Price Prediction with Neural Network

2020 ◽  
Vol 25 (3) ◽  
pp. 53
Author(s):  
Penglei Gao ◽  
Rui Zhang ◽  
Xi Yang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Financial Markets ◽  
Short Term Memory ◽  
Historical Data ◽  
Stock Index ◽  
Main Task ◽  
Short Term ◽  
Price Prediction ◽  
Long Short Term Memory

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. The main task is to predict the next day’s index price according to the historical data. The dataset consists of the SP500 index, CSI300 index and Nikkei225 index from three different financial markets representing the most developed market, the less developed market and the developing market respectively. Seven variables are chosen as the inputs containing the daily trading data, technical indicators and macroeconomic variables. The results show that the attention-based model has the best performance among the alternative models. Furthermore, all the introduced models have better accuracy in the developed financial market than developing ones.

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

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