Ensemble deep learning framework for stock market data prediction (EDLF-DP)

Ingle, Vaishali; Deshmukh, Sachin N.

doi:10.1016/j.gltp.2021.01.008

Cited by 26 publications

(12 citation statements)

References 5 publications

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“…In equation (11), lambda is asymmetric in nature [25]. We turn things around so as to make categorical predictions of pattern from returns.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…When developing an investment strategy and selecting index or stocks for our portfolio, the association can be a very helpful tool. ere are various algorithms and models available in the literature, for predicting the pattern of financial time series [11][12][13][14]. In the modern era, the most trendy pattern prediction technique is artificial intelligence.…”

Section: Objectivementioning

confidence: 99%

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Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ)

2022

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For selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first obtain the buy/sell pattern using an Optimal Band. The analysis of the association between patterns and returns is based on the Goodman–Kruskal prediction error reduction index ( λ ). Empirical analysis suggests that the prediction of returns from patterns is more impressive or of less error as compared to the prediction of patterns from returns. We demonstrated the prediction index for Index NIFTY 50, BANK-NIFTY, and NIFTY-IT of NSE (National Stock Exchange), for the period 2010–2020.

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“…In equation (11), lambda is asymmetric in nature [25]. We turn things around so as to make categorical predictions of pattern from returns.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Objectivementioning

confidence: 99%

Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ)

2022

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“…RNN has only one activation function in the intermediate layer, whereas LSTM and GRU have multiple activation functions with complex advanced operations performed on various gates. [23][24][25][26]. LSTM has variable C t for long-term information storage in its cells or blocks.…”

Section: Lstm and Gru Algorithmsmentioning

confidence: 99%

Implementation of Long Short-Term Memory and Gated Recurrent Units on grouped time-series data to predict stock prices accurately

2022

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Stocks are an attractive investment option because they can generate large profits compared to other businesses. The movement of stock price patterns in the capital market is very dynamic. Therefore, accurate data modeling is needed to forecast stock prices with a low error rate. Forecasting models using Deep Learning are believed to be able to predict stock price movements accurately with time-series data input, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. Unfortunately, several previous studies and investigations of LSTM/GRU implementation have not yielded convincing performance results. This paper proposes eight new architectural models for stock price forecasting by identifying joint movement patterns in the stock market. The technique is to combine the LSTM and GRU models with four neural network block architectures. Then, the proposed architectural model is evaluated using three accuracy measures obtained from the loss function Mean Absolute Percentage Error (MAPE), Root Mean Squared Percentage Error (RMSPE), and Rooted Mean Dimensional Percentage Error (RMDPE). The three accuracies, MAPE, RMSPE, and RMDPE, represent lower accuracy, true accuracy, and higher accuracy in using the model.

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“…Research results have shown that automated stock market trend prediction, which is supported by computational intelligence, can reduce loss risks of the income and bring profit to the investor (Sheta et al 2013). Some of computational methods, which have been employed to solve the stock market prediction problems, are ANN (Bisoi and Dash 2014;Akbilgic et al 2014;Moghar and Hamiche 2020), regression models (Ananthi and Vijayakumar 2020) (Huang 2012), deep learning (Ingle and Deshmukh 2021) (Shen and Shafiq 2020), swarm intelligence (Bagheri et al 2014), evolutionary computation (Sheta et al 2013) (Hsu 2011) (Huang 2012), dynamical Bayesian factor graphs (Wang et al 2015), neuro-fuzzy systems (Rajab and Sharma 2019) (Mahmud and Meesad 2016).…”

Section: Introductionmentioning

confidence: 99%

DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction

Ari

Alagöz

2022

Soft Comput

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Stock markets are one of the most popular financial markets since they can bring high revenues to their investors. In order to reduce the risk factor for investors, intelligent and automated stock market trend prediction tools are developed by using computational intelligence methods. This study presents a hyper-parameter optimal Genetic Programming (GP) model generation framework for a day-ahead prediction of stock market index trends. In order to obtain the best trend prediction model from the stock market data, a Differential Evolution (DE) algorithm is implemented to optimize hyperparameters of a GP algorithm with Orthogonal Least Square (GpOls). Evolutions of GpOls agents within the hyper-parameter search space allow adaptation of GpOls algorithm for a given training dataset by enabling optimal autotuning of user-defined parameters. Authors demonstrated that this structure can significantly improve modeling performances of GpOls algorithms for data-driven modeling applications. In the study, the proposed DE based hyper GpOls (DEHypGpOls) algorithm is implemented for a day-ahead forecast problem of the Istanbul Stock Exchange 100 Index (ISE100) trends. In the experimental study, real trend data from ISE100 and seven other international stock markets are used to extract a day-ahead forecast model of daily ISE100 trends. The forecast model of the DEHypGpOls algorithm can provide 68% prediction accuracy for the test dataset. In ISE100 index market simulations, daily exchanges of ISE100 index according to buy or sell signal of the forecast model of DEHypGpOls provided about 273% more income compared to the income of a long-term investment strategy.

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Ensemble deep learning framework for stock market data prediction (EDLF-DP)

Cited by 26 publications

References 5 publications

Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ)

Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ)

Implementation of Long Short-Term Memory and Gated Recurrent Units on grouped time-series data to predict stock prices accurately

DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction

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