19,233 research outputs found
Forecasting with deep learning: S&P 500 index
Stock price prediction has been the focus of a large amount of research but an acceptable solution has so far escaped academics. Recent advances in deep learning have motivated researchers to apply neural networks to stock prediction. In this paper, we propose a convolution-based neural network model for predicting the future value of the S&P 500 index. The proposed model is capable of predicting the next-day direction of the index based on the previous values of the index. Experiments show that our model outperforms a number of benchmarks achieving an accuracy rate of over 55%
Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction
Stock market plays an important role in the economic development. Due to the
complex volatility of the stock market, the research and prediction on the
change of the stock price, can avoid the risk for the investors. The
traditional time series model ARIMA can not describe the nonlinearity, and can
not achieve satisfactory results in the stock prediction. As neural networks
are with strong nonlinear generalization ability, this paper proposes an
attention-based CNN-LSTM and XGBoost hybrid model to predict the stock price.
The model constructed in this paper integrates the time series model, the
Convolutional Neural Networks with Attention mechanism, the Long Short-Term
Memory network, and XGBoost regressor in a non-linear relationship, and
improves the prediction accuracy. The model can fully mine the historical
information of the stock market in multiple periods. The stock data is first
preprocessed through ARIMA. Then, the deep learning architecture formed in
pretraining-finetuning framework is adopted. The pre-training model is the
Attention-based CNN-LSTM model based on sequence-to-sequence framework. The
model first uses convolution to extract the deep features of the original stock
data, and then uses the Long Short-Term Memory networks to mine the long-term
time series features. Finally, the XGBoost model is adopted for fine-tuning.
The results show that the hybrid model is more effective and the prediction
accuracy is relatively high, which can help investors or institutions to make
decisions and achieve the purpose of expanding return and avoiding risk. Source
code is available at
https://github.com/zshicode/Attention-CLX-stock-prediction.Comment: arXiv admin note: text overlap with arXiv:2202.1380
Feature engineering for mid-price prediction with deep learning
Mid-price movement prediction based on limit order book (LOB) data is a
challenging task due to the complexity and dynamics of the LOB. So far, there
have been very limited attempts for extracting relevant features based on LOB
data. In this paper, we address this problem by designing a new set of
handcrafted features and performing an extensive experimental evaluation on
both liquid and illiquid stocks. More specifically, we implement a new set of
econometrical features that capture statistical properties of the underlying
securities for the task of mid-price prediction. Moreover, we develop a new
experimental protocol for online learning that treats the task as a
multi-objective optimization problem and predicts i) the direction of the next
price movement and ii) the number of order book events that occur until the
change takes place. In order to predict the mid-price movement, the features
are fed into nine different deep learning models based on multi-layer
perceptrons (MLP), convolutional neural networks (CNN) and long short-term
memory (LSTM) neural networks. The performance of the proposed method is then
evaluated on liquid and illiquid stocks, which are based on TotalView-ITCH US
and Nordic stocks, respectively. For some stocks, results suggest that the
correct choice of a feature set and a model can lead to the successful
prediction of how long it takes to have a stock price movement
Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study
In 2022, the Indonesia stock exchange (IDX) listed 825 companies, making it challenging to identify low-risk companies. Stock price forecasting and price movement prediction are vital issues in financial works. Deep learning has previously been implemented for stock market analysis, with promising results. Because of the differences in architecture and stock issuers in each study report, a consensus on the best stock price forecasting model has yet to be reached. We present a methodology for comparing the performance of convolutional neural networks (CNN), gated recurrent units (GRU), long short-term memory (LSTM), and graph convolutional networks (GCN) layers. The four layers types combination yields 11 architectures with two layers stacked maximum, and the architectures are performance compared in stock price predicting. The dataset consists of open, highest, lowest, closed price, and volume transactions and has 2,588,451 rows from 727 companies in IDX. The best performance architecture was chosen by a vote based on the coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute percent error (MAPE), and f1-score. TFGRU is the best architecture, producing the finest results on 315 companies with an average score of RMSE is 553.327, MAPE is 0.858, and f1-score is 0.456
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