Stock market prediction using Firefly algorithm with evolutionary framework optimized feature reduction for OSELM method

Das, Smruti Rekha; Mishra, Debahuti; Rout, Minakhi

doi:10.1016/j.eswax.2019.100016

Cited by 43 publications

(48 citation statements)

References 39 publications

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“…e authors in [41] did not discuss selection of the best structure of the network in terms of NHL, NNPHL, ACTFUN, and LR. e authors in [42] presented a new stock market prediction method and applied the firefly algorithm with an evolutionary method. ey implemented their method to the Online Sequential Extreme Learning Machine (OSELM).…”

Section: Related Workmentioning

confidence: 99%

A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

Jamous

ALRahhal

El-Darieby

2021

Scientific Programming

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Since the declaration of COVID-19 as a pandemic, the world stock markets have suffered huge losses prompting investors to limit or avoid these losses. The stock market was one of the businesses that were affected the most. At the same time, artificial neural networks (ANNs) have already been used for the prediction of the closing prices in stock markets. However, standalone ANN has several limitations, resulting in the lower accuracy of the prediction results. Such limitation is resolved using hybrid models. Therefore, a combination of artificial intelligence networks and particle swarm optimization for efficient stock market prediction was reported in the literature. This method predicted the closing prices of the shares traded on the stock market, allowing for the largest profit with the minimum risk. Nevertheless, the results were not that satisfactory. In order to achieve prediction with a high degree of accuracy in a short time, a new improved method called PSOCoG has been proposed in this paper. To design the neural network to minimize processing time and search time and maximize the accuracy of prediction, it is necessary to identify hyperparameter values with precision. PSOCoG has been employed to select the best hyperparameters in order to construct the best neural network. The created network was able to predict the closing price with high accuracy, and the proposed model ANN-PSOCoG showed that it could predict closing price values with an infinitesimal error, outperforming existing models in terms of error ratio and processing time. Using S&P 500 dataset, ANN-PSOCoG outperformed ANN-SPSO in terms of prediction accuracy by approximately 13%, SPSOCOG by approximately 17%, SPSO by approximately 20%, and ANN by approximately 25%. While using DJIA dataset, ANN-PSOCoG outperformed ANN-SPSO in terms of prediction accuracy by approximately 18%, SPSOCOG by approximately 24%, SPSO by approximately 33%, and ANN by approximately 42%. Besides, the proposed model is evaluated under the effect of COVID-19. The results proved the ability of the proposed model to predict the closing price with high accuracy where the values of MAPE, MAE, and RE were very small for S&P 500, GOLD, NASDAQ-100, and CANUSD datasets.

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Section: Related Workmentioning

confidence: 99%

A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

Jamous

ALRahhal

El-Darieby

2021

Scientific Programming

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“…Das et al [22], explored the novel work for stock market future trends forecasting of the by firefly algorithm with evolutionary frame work. They adopted the prediction models Extreme Learning Machine (ELM), Online Sequential Extreme Learning Machine (OSELM) and Recurrent Back Propagation Neural Network (RBPNN).…”

Section: Literature Surveymentioning

confidence: 99%

An Optimized Machine Learning Model for Stock Trend Anticipation

Deepika¹,

Nirupamabhat²

2020

ISI

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Security market is an economical-volatile in nature as it is driven by not only based on historical prices various unpredictable external factors like financial news, changes in socio-political issues and natural calamities happened in real world; hence its forecasting is a challenging task for traders. To gain profits and to overcome any crisis in financial market, it is essential to have a very accurate calculation of future trends by for the investors. The trend prediction results can be used as recommendations for investors as to whether they should buy or sell. Feature selection, dimensionality reduction and optimization techniques can be integrated with emerging advanced machine learning models, to get improvised prediction in terms of quality, performance, security and for effective assessment external factors role in stock market nonlinear signals. In this empirical research work, a set of hybrid models were built and their predictive abilities were compared to find consistent model. This work implies the base model, boosted model and deep learning model along with optimization techniques. From the experimental result, the optimized deep learning model, ABC-LSTM was turned out superior to all other considered financial models LSSVM, Gradient Boost, LSTM, ABC-LSSVM, ABC-Gradient Boost by showing best Mean Absolute Percentage Error (MAPE) value, which was low.

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“…Reducing the CPU resources will reduce the prediction speed of ELM and this is the major limitation of this model. In another work, Das et al [25] proposed a stock market prediction using ELM, Online Sequential Extreme Learning Machine (OSELM) and Recurrent Back Propagation Neural Network (RBPNN) with Firefly algorithm based feature reduction method.…”

Section: Related Workmentioning

confidence: 99%

Stock Market Forecasting from Multi-Source Data using Tolerance Based Multi-Agent Deep Reinforcement Learning

Bhuvaneshwari,

Beena

2020

IJEAT

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Analyzing and forecasting the future trends in stock market is challenging due to the ever increasing size of stock data. Modern techniques extract the stock indicators from the web data to forecast the stock movements. However, most previous studies were based on single source of data for extracting these indicators. This might not be effective in obtaining all the possible diverse factors that influence the market movements. Multi-source data has been rarely applied for stock prediction and even those techniques have limitations in handling larger data. In an attempt to utilize multi-source data more effectively for extracting stock indicators and improve the forecasting accuracy of stock movements, this paper developed a stock market forecasting model using Tolerance based Multi-Agent Deep Reinforcement Learning (TMA-DRL) model. The TMA-DRL model effectively combines the quantitative stock data with the indicators i.e. the events extracted from news data and sentiments extracted from tweets. This forecasting model utilizes Random forests to extract the twitter opinions and Restricted Boltzmann Machine (RBM) for event extraction from news data. Combining these indicators, the TMA-DRL model leads to improved data learning and provides highly accurate prediction of future stock trends. Datasets for evaluation were collected from three sources namely Twitter, Market News and Stock exchange, for 12 months period. Evaluation results illustrate the effectiveness of the proposed TMA-DRL stock market forecasting model which makes predictions with high accuracy and less time complexity.

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Stock market prediction using Firefly algorithm with evolutionary framework optimized feature reduction for OSELM method

Cited by 43 publications

References 39 publications

A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

An Optimized Machine Learning Model for Stock Trend Anticipation

Stock Market Forecasting from Multi-Source Data using Tolerance Based Multi-Agent Deep Reinforcement Learning

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