Correctly predicting the stock price movement direction is of immense importance in the financial market. In recent years, with the expansion of dimension and volume in data, the nonstationary and nonlinear characters in finance data make it difficult to predict stock movement accurately. In this article, we propose a methodology that combines technical analysis and sentiment analysis to construct predictor variables and then apply the improved LASSO-LASSO to forecast stock direction. First, the financial textual content and stock historical transaction data are crawled from websites. Then transfer learning Finbert is used to recognize the emotion of textual data and the TTR package is taken to calculate the technical indicators based on historical price data. To eliminate the multi-collinearity of predictor variables after combination, we improve the long short-term memory neural network (LSTM) model with the Absolute Shrinkage and Selection Operator (LASSO). In predict phase, we apply the variables screened as the input vector to train the LASSO-LSTM model. To evaluate the model performance, we compare the LASSO-LSTM and baseline models on accuracy and robustness metrics. In addition, we introduce the Wilcoxon signed rank test to evaluate the difference in results. The experiment result proves that the LASSO-LSTM with technical and sentiment indicators has an average 8.53% accuracy improvement than standard LSTM. Consequently, this study proves that utilizing historical transactions and financial sentiment data can capture critical information affecting stock movement. Also, effective variable selection can retain the key variables and improve the model prediction performance.
Regional innovation output is influenced by many factors such as macroeconomic environments, residents consumption, fixed asset investment, foreign trade, fiscal revenue and expenditure, education, and research and development (R&D) input. Correctly predicting regional innovation output is an important subject in the economic field. In this paper, we propose four regularized Poisson regressions to forecast regional innovation output for 31 provinces in China. Firstly, we screen out 20 important factors and combine with four penalties: ridge penalty , least absolute shrinkage and selection operator penalty (LASSO), smoothly clipped absolute deviation penalty (SCAD), and minimax concave penalty (MCP) to construct four regularized Poisson regressions. Secondly, we introduce the cyclic coordinate descent (CCD) algorithm and the training set to complete variable selection and obtain the least squares weighted iterative estimators and make model selection by introducing three criterions to compare goodness of fit to different models. Finally, we apply the testing set and the learned regressions to exhibit the prediction performances and found that SCAD/MCP regularized Poisson regression predicts better than /LASSO regularized Poisson regression in terms of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In particular, MCP regularized Poisson regression outperforms the other three regularized Poisson regressions in predicting the number of granted patents in the three regions.
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