Prognosticate of the financial market utilizing ensemble-based conglomerate model with technical indicators

Padhi, Dushmanta Kumar; Padhy, Neelamadhab

doi:10.1007/s12065-020-00528-z

Cited by 7 publications

(8 citation statements)

References 28 publications

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“…As a benchmark, we took the mean absolute error (MAE) of both the models. The model which gave less MAE value was the best predictive model [40]. As shown in Table 28, our proposed model showed a lower MAE value than the WLSTM+Attention model.…”

Section: Forecast Accuracy Comparison With Past Workmentioning

confidence: 78%

“…The specialised investigators attempt to foresee the securities exchange through the learning of graphs that depict the historical market costs and technical indicators [37][38][39]. Technical indicators are statistical techniques that are calculated with the help of mathematical formulas using historical prices [40]. The development of artificial intelligence techniques and the increased number of datasets that are easily publicly available brings about new opportunities for researchers to explore something new from the market.…”

Section: Related Workmentioning

confidence: 99%

“…Machine learning technique commonly uses two approaches to predict the stock market (a) using a single model to predict the stock market, and (b) using an ensemble of machine learning models [13,60,63,65,66]. The use of ensemble models reported by some researchers found that ensemble models provide better performance than a single predictive model [40,67,68]. According to Fatih et al [69], there still is little research that has been done to predict the stock market using ensemble models.…”

Section: Machine Learning Techniquementioning

confidence: 99%

“…There are so many machine learning algorithms that have been applied in previous studies [70]. Examples are logistic regression, support vector machine, k-nearest neighbours, random forest, decision tree [40], and neural networks [49,52,60,71]. As seen in the literature, the most commonly used algorithms for stock market forecasting are support vector machines and artificial neural networks [72].…”

Section: Machine Learning Techniquementioning

confidence: 99%

See 3 more Smart Citations

A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators

et al. 2021

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People continuously hunt for a precise and productive strategy to control the stock exchange because the monetary trade is recognised for its unbelievably different character and unpredictability. Even a minor gain in predicting performance will be extremely profitable and significant. Our novel study implemented six boosting techniques, i.e., XGBoost, AdaBoost, Gradient Boosting, LightGBM, CatBoost, and Histogram-based Gradient Boosting, and these boosting techniques were hybridised using a stacking framework to find out the direction of the stock market. Five different stock datasets were selected from four different countries and were used for our experiment. We used two-way overfitting protection during our model building process, i.e., dynamic reduction technique and cross-validation technique. For model evaluation purposes, we used the performance metrics, i.e., accuracy, ROC curve (AUC), F-score, precision, and recall. The aim of our study was to propose and select a predictive model whose training and testing accuracy difference was minimal in all stocks. The findings revealed that the meta-classifier Meta-LightGBM had training and testing accuracy differences that were very low among all stocks. As a result, a proper model selection might allow investors the freedom to invest in a certain stock in order to successfully control risk and create short-term, sustainable profits.

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Section: Forecast Accuracy Comparison With Past Workmentioning

confidence: 78%

Section: Related Workmentioning

confidence: 99%

Section: Machine Learning Techniquementioning

confidence: 99%

Section: Machine Learning Techniquementioning

confidence: 99%

See 2 more Smart Citations

A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators

et al. 2021

Self Cite

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“… 2021 ) 0.47 0.52 23.36 0.19 74.12 0.49 0.53 13.86 0.18 45.12 0.8 0.82 2.82 0.18 8.28 RANSACRegressor (Aziz et al. 2020 ) 0.46 0.51 23.51 0.08 71.43 0.41 0.47 14.8 0.08 40.97 0.75 0.78 3.11 0.07 6.85 Ridge (McDonald 2009 ) 0.52 0.56 22.25 0.01 73.68 0.51 0.55 13.59 0.02 44.81 0.77 0.79 3.02 0.01 8.26 RidgeCV (Padhi and Padhy 2021 ) 0.54 0.58 21.73 0.01 73.64 0.55 0.59 12.92 0.01 44.81 0.78 0.8 2.94 0.01 8.26 …”

Section: Discussionmentioning

confidence: 99%

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Stephan

Al‐Turjman²,

Wang

et al. 2022

Environ Sci Pollut Res

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India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country’s immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India’s renewable energy transition and air quality. India’s economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand. A clean energy transition is a key step in enabling the integration of energy and climate. Millions in India are affected owing to fossil fuel pollution and the increasing climate heating that has led to inconceivable health impacts. This paper attempts to study the impact of COVID-19 on India’s climate and renewable energy transitions through machine learning algorithms. India is observing a massive collapse in energy demand during the lockdown as its coal generation is suffering the worst part of the ongoing pandemic. During this current COVID-19 crisis, the renewable energy sector benefits from its competitive cost and the Indian government’s must-run status to run generators based on renewable energy sources. In contrast to fossil fuel-based power plants, renewable energy sources are not exposed to the same supply chain disruptions in this current pandemic situation. India has the definite potential to surprise the global community and contribute to cost-effective decarbonization. Moreover, the country has a good chance of building more flexibility into the renewable energy sector to avoid an unstable future.

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Stock Market Prediction Performance Analysis by Using Machine Learning Regressor Techniques

Padhy,

Dharmireddi,

Padhy

et al. 2024

Communications in Computer and Information Science

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Prognosticate of the financial market utilizing ensemble-based conglomerate model with technical indicators

Cited by 7 publications

References 28 publications

A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators

A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators

Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality

Stock Market Prediction Performance Analysis by Using Machine Learning Regressor Techniques

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