Two robust long short-term memory frameworks for trading stocks

Fister, Dušan; Perc, Matjaž; Jagrič, Timotej

doi:10.1007/s10489-021-02249-x

Cited by 24 publications

(10 citation statements)

References 56 publications

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“…The tenfold cross-validation methods are employed for picking testing and training datasets. Table 2 represents an analysis of correlation coefficients for four types of datasets with different methods such as Kalman and hidden Markov model (HMM) filtering method (Tenyakov and Mamon 2017 ), two robust long short-term memory (TRLSTM) (Fister et al 2021 ), long short–term memory(LSTM) recurrent neural networks (RNN) (Shen et al 2021 ), variational mode decomposition (VMD)-iterated cumulative sums of squares (ICSS)-bidirectional gated recurrent unit (BiGRU) (VMD-ICSS-BiGRU) (Li et al 2021 ), and Deep reinforcement learning (DRL) technique (Taghian et al 2022 ). From this table, the proposed method has high correlation coefficients compared with other methods.…”

Section: Resultsmentioning

confidence: 99%

“…The precious metal price forecast and stock market datasets are considered input datasets. Table 3 denotes the analysis of relative absolute error for four datasets with various methods like RNN-LSTM (Shen et al 2021 ), DRL (Taghian et al 2022 ), TRLSTM (Fister et al 2021 ), Kalman-HMM (Tenyakov and Mamon 2017 ), VMD-ICSS-BiGRU (Li et al 2021 ), and proposed. Among all those methods, the proposed method has a very low relative absolute error value and which shows the good performance of the proposed method.…”

Section: Resultsmentioning

confidence: 99%

“…The analysis of root means square error (RMSE) for different methods such as RNN-LSTM (Shen et al 2021 ), DRL (Taghian et al 2022 ), TRLSTM (Fister et al 2021 ), Kalman-HMM (Tenyakov and Mamon 2017 ), VMD-ICSS-BiGRU [12], and proposed Hybrid HHO-EFDM optimized LightGBM model are established in Table 4 . The proposed method has a very low RMSE value related to other methods.…”

Section: Resultsmentioning

confidence: 99%

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H3O-LGBM: hybrid Harris hawk optimization based light gradient boosting machine model for real-time trading

Gupta

Kumar

2023

Artif Intell Rev

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The gross domestic product (GDP) of a country is mainly dependent on its trade and external sector which improves the country's income. According to FY2021–2022, India's nominal GDP is estimated to be 3.12 trillion US dollars. Overall exports and imports have a year-over-year increase of 49.6% and 68% respectively. Machine learning techniques have the potential to improve India's Current gross value by up to 15% by the year 2035. The integration of data, Technology, and talent helps to create intelligent models that enhance artificial intelligence growth. This paper presents an optimized light gradient boosting machine (Light GBM) model using the hybrid Harris hawk optimization (H 3 O) algorithm for trade forecasting. The overfitting problem in the conventional Harris Hawk Optimization is overcome using the exclusive feature bundling (EFB) and the gradient-based one-side sampling (GOSS) methodologies. The H 3 O optimization algorithm offers fast convergence by optimizing different lightGBM parameters such as a number of training iterations, maximum depth, minimal data in the leaf, etc. To improve the performance, one step further, the residual errors of the optimized lightGBM model are corrected using the Markov Chain model. The main aim of the optimized lightGBM model is to extract the crucial input values of certain variables such as imports and exports of goods and services, service trade, and merchandise trade and predict the price movement decision. The proposed model identifies the interrelationship with the external market and future market growth along with analyzing the variation in market conditions. The prediction decision is mainly to hold, but, or sell the stocks. When evaluated using the precious metal price forecast and stock market datasets, the proposed methodology shows that the hybrid approach can enhance the prediction performance. The results show that the input parameters were efficient in predicting the economic growth regarding the Intermarket trading system (ITS) and services with higher accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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H3O-LGBM: hybrid Harris hawk optimization based light gradient boosting machine model for real-time trading

Gupta

Kumar

2023

Artif Intell Rev

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“…Therefore, this paper builds a retail price of a sporting goods prediction model based on the LSTM network unit and can make full use of its feature that any length sequence can be used as input and apply it to online data recognition. LSTM solves the problem that RNN cannot handle long time dependence by introducing [ 25 ].…”

Section: Lstm Networkmentioning

confidence: 99%

Prediction of Retail Price of Sporting Goods Based on LSTM Network

Ding

2022

Computational Intelligence and Neuroscience

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Commodity prices play a unique role as a lever to regulate the economy. Price forecasting is an important part of macrodecision-making and micromanagement. Because there are many factors affecting the price of goods, price prediction has become a difficulty in research. According to the characteristics that price data are also affected by other factors except for time series, a multifactor LSTM price prediction method is proposed based on the long-term and short-term memory network (LSTM) deep learning algorithm. This method not only makes use of the memory of LSTM to historical data but also introduces the influence of external factors on price through the full connection layer, which provides a new idea for solving the problem of price prediction. Compared with BP neural network, the experimental results show that this method has higher accuracy and better stability. Analyze the commodity description and commodity price characteristics, find out the commodities similar to the target commodity, complete the commodity price data by using the historical price data of similar commodities, and establish the training set to verify the validity of the proposed method.

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“…The prediction of the stock price is becoming difficult due to the complexity of the stock data, and the analysis of stock movement will be on the analysis of previous stock prices [3]. Before the use of computers, people used to do trading based on their gut feelings [12]. As investment increases and the stock market grew, people are searching for tools and methods that would increase their profits while minimizing the risk [7].…”

Section: Introductionmentioning

confidence: 99%

Stock Price Prediction Using LSTM, ARIMA and UCM

Sharma¹,

Pareek²,

Ghosh³

et al. 2022

JDMS

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The stock market is growing abundantly due to the rise of investors for their passive income is This article aims to develop an innovative artificial recurrent neural network approach for better stock market forecasts. The stock 611market is receiving a lot of attention from investors. Capturing the regularity of stock market changes has always been a key point for investors and investment firms. Investors are very interested in the field of stock price forecasting research. To make a successful investment, many investors want to know the future of the stock market. The data is pulled from the livestock market for analysis and visualization and results in analysis in real-time and offline. Predictive methods can be divided into two broad categories: statistical methods and artificial intelligence methods. Statistical methods include the logistic regression model and ARIMA, UCM model. Artificial intelligence techniques include multi-layer perceptron's, accumulative neural networks, Naïve Bayes, back-propagation networks, single-layer LSTMs, vector bearers, cyclic neural networks, and more. From this research, LSTM is achieving less Error percentage than any other model. LSTM helps investors, analysts, or anyone interested in investing in the stock market to get a better understanding of the future state of the stock market.

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Two robust long short-term memory frameworks for trading stocks

Cited by 24 publications

References 56 publications

H3O-LGBM: hybrid Harris hawk optimization based light gradient boosting machine model for real-time trading

H3O-LGBM: hybrid Harris hawk optimization based light gradient boosting machine model for real-time trading

Prediction of Retail Price of Sporting Goods Based on LSTM Network

Stock Price Prediction Using LSTM, ARIMA and UCM

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