Technical analysis strategy optimization using a machine learning approach in stock market indices

Ayala, Jordan; García-Torres, Miguel; Noguera, José Luis Vázquez; Gómez-Vela, Francisco; Divina, Federico

doi:10.1016/j.knosys.2021.107119

Cited by 87 publications

(39 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI-based modes have been developed recently to improve the performance of regression problems 17 such as the prediction of the hydro-power production capacity 18 . They have confirmed the powerful approach for solving complex problems 19 – 21 such as the prediction of stock market indices 22 , and hydro-power production capacity 18 . Seyedzadeh et al 23 developed a machine learning model for predicting building energy loads to support building design and retrofit planning.…”

Section: Introductionmentioning

confidence: 78%

Developing a hybrid time-series artificial intelligence model to forecast energy use in buildings

Ngo

Pham

Truong³

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The development of a reliable energy use prediction model is still difficult due to the inherent complex pattern of energy use data. There are few studies developing a prediction model for the one-day-ahead energy use prediction in buildings and optimizing the hyperparameters of a prediction model is necessary. This study aimed to propose a hybrid artificial intelligence model for forecasting one-day ahead time-series energy consumption in buildings. The proposed model was developed based on the integration of the Seasonal Autoregressive integrated Moving average, the Firefly-inspired Optimization algorithm, and the support vector Regression (SAMFOR). A large dataset of energy consumption in 30-min intervals, temporal data, and weather data from six real-world buildings in Vietnam was used to train and test the model. Sensitivity analyses were performed to identify appropriate model inputs. Comparison results show that the SAMFOR model was more effective than the others such as the seasonal autoregressive integrated moving average (SARIMA) and support vector regression (SVR), SARIMA-SVR, and random forests (RF) models. Evaluation results on real-world building depicted that the proposed SAMFOR model achieved the highest accuracy with the root-mean-square error (RMSE) of 1.77 kWh in, mean absolute percentage error (MAPE) of 9.56%, and correlation coefficient (R) of 0.914. The comparison results confirmed that the SAMFOR model was effective for forecasting one-day-ahead energy consumption. The study contributes to (1) the knowledge domain by proposing the hybrid SAMFOR model for forecasting energy consumption in buildings; and (2) the state of practice by providing building managers or users with a powerful tool for analyzing and improving building energy performance.

show abstract

Section: Introductionmentioning

confidence: 78%

Developing a hybrid time-series artificial intelligence model to forecast energy use in buildings

Ngo

Pham

Truong³

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…e data employed in this study are also historical, and closing prices are predicted using machine learning algorithms. Ayala et al [28] suggested several machine learning approaches, including linear regression, support vector regression, artificial neural networks (ANNs), and moving average-based methods. ey tested the trading data from three indices which included Ibex35, DAX, and Dow Jones Industrial.…”

Section: Stock Market Analysis Using Traditional Methodsmentioning

confidence: 99%

An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning

et al. 2022

View full text Add to dashboard Cite

Stock markets accurately reflect countries’ economic health, and stock returns are tightly related to economic indices. One popular area of financial research is the factors that influence stock returns. Several investigations have frequently cited macroeconomic factors, among numerous elements. Therefore, this study focuses on the empirical analysis of the relationship between macroeconomic factors and stock market returns. When a stock market becomes increasingly volatile, it becomes susceptible to economic uncertainty news, and information on social media platforms. Thus, we incorporated a new dimension of economic uncertainty news sentiment (EUNS) for stock return predictions. We employed the daily data ofgold index, crude oil price, interest rate, exchange rate, and stock returns for a set of countries from January 2010 to December 2020. Subsequently, to compute coefficients, we conducted a regression analysis using one of the more sophisticated approaches: single-layer neural networks and ordinary least square regression. In addition, we only computed EUNS for the period of the fiscal budget announcement for the US, Turkey, and Hong Kong. The results indicate that the gold index, interest rate, and exchange rate are highly significant and negative macroeconomic factors for all analyzed countries. These findings also indicate that EUNS is important and detrimental for projecting stock returns.

show abstract

“…In a similar manner, various hybrid machine learning models were developed by different studies to check their efficiency in predicting stock market movement. For example, the performance of different machine learning models, consisting of the linear model, ANN, random forests (RFs), and SVM, was tested by Ayala et al (2021). Their results exhibit that the linear model and ANN were the best performers.…”

Section: Introductionmentioning

confidence: 99%

Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period

Omar

Huang

Salameh

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

Stock market forecasting is considered the most challenging problem to solve for analysts. In the past 2 years, Covid-19 has severely affected stock markets globally, which, in turn, created a great problem for investors. The prime objective of this study is to use a machine learning model to effectively forecast stock index prices in three time frames: the whole period, the pre-Covid-19 period, and the Covid-19 period. The model accuracy testing results of mean absolute error, root mean square error, mean absolute percentage error, and r2 suggest that the proposed machine learning models autoregressive deep neural network (AR-DNN(1, 3, 10)), autoregressive deep neural network (AR-DNN(3, 3, 10)), and autoregressive random forest (AR-RF(1)) are the best forecasting models for stock index price forecasting for the whole period, for the pre-Covid-19 period, and during the Covid-19 period, respectively, under high stock price fluctuations compared to traditional time-series forecasting models such as autoregressive moving average models. In particular, AR-DNN(1, 3, 10) is suggested when the number of observations is large, whereas AR-RF(1) is suggested for a series with a low number of observations. Our study has a practical implication as they can be used by investors and policy makers in their investment decisions and in formulating financial decisions and policies, respectively.

show abstract

Technical analysis strategy optimization using a machine learning approach in stock market indices

Cited by 87 publications

References 36 publications

Developing a hybrid time-series artificial intelligence model to forecast energy use in buildings

Developing a hybrid time-series artificial intelligence model to forecast energy use in buildings

An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning

Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period

Contact Info

Product

Resources

About