A portfolio construction framework using <scp>LSTM</scp>‐based stock markets forecasting

Yıldız, Zeynep Çipiloğlu; Yildiz, Selim Baha

doi:10.1002/ijfe.2277

Cited by 21 publications

(5 citation statements)

References 21 publications

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“…Hochreiter et al improve RNN and put forward the LSTM model [20]. LSTM adds the cell state that can maintain the long-term state and gated technologies such as forget gate, input gate and output gate so that LSTM can memorize and learn the input information for a long time [21][22]. As shown in Figure 1, GRU simplifies the internal structure based on LSTM [23][24].…”

Section: Igrumentioning

confidence: 99%

A PCA-IGRU Model for Stock Price Prediction

Wang¹,

Wang²,

Liu³

et al. 2023

Journal of Internet Technology

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<p>Accurate stock price prediction is significant for investors to avoid risks and improve the return on investment. Stock price prediction is a typical nonlinear time-series problem, which many factors affect. Still, too much analysis of influencing factors will lead to input redundancy and a large amount of computation in the model. Although the stock prediction model based on Recurrent Neural Network (RNN) has a good prediction effect, it has the problem of oversaturation. This paper proposes a prediction model of stock closing price based on Principal Component Analysis (PCA) and Improved Gated Recurrent Unit (IGRU), PCA-IGRU. PCA can reduce the redundancy of input information without destroying the correlation of original data, thus reducing the time of model training and prediction. IGRU is an improved Gated Recurrent Unit (GRU) model, which prevents oversaturation by introducing the Anti-oversaturation Conversion Module (ACM) and enhances the sensitivity of model learning. This paper selects the stock trading data of the Shanghai Composite Index (SCI) of China as experimental data. The PCA-IGRU is compared with seven baseline models. The experimental results show that the model has better prediction accuracy and shorter training time.</p> <p> </p>

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Section: Igrumentioning

confidence: 99%

A PCA-IGRU Model for Stock Price Prediction

Wang¹,

Wang²,

Liu³

et al. 2023

Journal of Internet Technology

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“…In practical use, more and more DLs have achieved remarkable results. In financial risk management, a long short-term memory (LSTM) model has been used for predicting the trend of stock prices (Cipiloglu Yildiz & Yildiz, 2022;Fischer & Krauss, 2018); In economic forecasting, a novel LSTM model has been used for predicting exchange rates compared with other traditional time series models (Ito et al, 2022;Sun et al, 2020). Besides, a convolutional neural network-LSTM (CNN-LSTM) model makes a significant boost in improving the performance of gold price forecasting (Livieris et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Deep learning on mixed frequency data

Wang

Jiang

et al. 2023

Journal of Forecasting

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In deep learning, it is common to encounter data observed at different frequencies. Mixed data sampling (MIDAS) is an efficient technique for handling mixed frequency data, where a high frequency predictor is converted into a set of low frequency variables using the frequency alignment approach and parametric function constraints. This efficiently prevents the proliferation of parameters, ensuring the consistency of data frequency. We introduce the MIDAS technique into the deep learning architecture and develop a novel deep learning‐MIDAS (DL‐MIDAS) model, which enables to conduct deep learning on raw mixed frequency data directly. Its efficacy is then illustrated through extensive Monte Carlo simulations and a real‐world application. The simulation experiments show that the DL‐MIDAS model is able to explore nonlinear patterns in mixed frequency data and achieves more stable and accurate prediction results than several competing models, such as the artificial neural network for mixed frequency data (ANN‐MIDAS), long short‐term memory (LSTM), and MIDAS regressions. Additionally, the real‐world application of predicting the inflation rate of China also confirms the strength of DL‐MIDAS. The model can exploit high frequency information contained in financial market to produce timely and accurate prediction results on the inflation rate.

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“…Ref. [9] presented infiltrating future return predictions into portfolio construction strategies framework that the LSTM model trained using monthly stock price data, and the resultant predictions were utilised in portfolio weighting. A CNN and LSTM‐based stock prediction framework called stock sequence array convolutional LSTM is employed in Ref.…”

Section: Introductionmentioning

confidence: 99%

Stock index forecasting using DACLAMNN: A new intelligent highly accurate hybrid ACLSTM/Markov neural network predictor

Safari,

Badamchizadeh

2023

Cognitive Comp and Systems

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The authors present the investigation of a new hybrid predictive model of Duplex Attention‐based Coupled LSTM Markov Averaged Neural Network, known as DACLMANN. The financial field, particularly the stock market, heavily relies on accurate predictive models. DACLMANN comprises four essential components: two LSTM blocks, an Averagiser and a Markov Neural Network block. The first LSTM block is composed of two hidden layers, each containing 50 neurons and a dense layer with 25 neurons. The second LSTM block consists of two hidden layers, each with 100 neurons, and a dense layer with 50 neurons. The Averagiser plays a crucial role by averaging the closing prices and predicted values from the first LSTM block, resulting in a 90% gain. These averaged values are then fed into the second LSTM block for further prediction. Finally, the predictions undergo evaluation using the Markov model, yielding the final prediction. To assess the performance of DACLMANN, it was tested on 22 years of stock prices for the AMZN index. The evaluation metrics used by the authors include an R2 of 0.76, mean absolute error of 6.81216, root mean square error of 8.6040, Precision of 1, Accuracy of 1, Recall of 1 and F1 of 1. Additionally, DACLMANN achieved a Mean Absolute Percentage Error of less than 0.043% and an RMSPE of less than 2.1%. These results not only demonstrate the effectiveness of the proposed model but also authenticate the prediction outcomes. DACLMANN offers several advantages over traditional predictive models in the stock market. By combining the strengths of Duplex Attention‐based Coupled LSTM, Averagiser, and Markov Neural Network, DACLMANN leverages the power of deep learning, attention mechanisms, and sequential modelling. This hybrid approach enables DACLMANN to capture intricate patterns and dependencies present in stock market data, leading to more accurate and reliable predictions. The robust evaluation metrics further validate the superiority of DACLMANN in predicting stock prices.

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A portfolio construction framework using LSTM‐based stock markets forecasting

Cited by 21 publications

References 21 publications

A PCA-IGRU Model for Stock Price Prediction

A PCA-IGRU Model for Stock Price Prediction

Deep learning on mixed frequency data

Stock index forecasting using DACLAMNN: A new intelligent highly accurate hybrid ACLSTM/Markov neural network predictor

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