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As an important reflection of the national economy system, stock market is closely related to the development of a country, which has received widespread attention of researchers in the economics. With the daily trading of the stock market, stock price forecasting has gradually been one of the common concerns in the economic analysis. Compared with traditional forecasting task, the stock price is interval data which can be handled by interval data regression or multi-output regression. Previous stock forecasting merely considers the stock price in homogeneous scenarios. However, the price distributions from different stocks may be heterogeneous. It is a challenging task to analyze the relationship between different stocks which follow heterogeneous distributions. In order to forecast stocks in heterogeneous scenarios, this paper introduces multi-output transfer learning into stock price forecasting. Compared with traditional regression or multi-output regression models, the multi-output transfer regression can predict opening price, closing price, highest price and lowest price of stocks and utilize source domain of a known stock to enhance the prediction of target stock price which may have limited known data in training set. The experimental results on four public market indices demonstrate the effectiveness of multi-output transfer regression for stock price forecasting.

Accurate forecasting of stock prices not only guides investor behavior but also assesses financial risk and promotes balanced economic and social development. This paper uses a dynamic factor-enhanced model averaging method to forecast the daily closing price of the Shanghai Composite Index, maximizing the use of valid information by weighting the forecast values of different models. Firstly, the common factor is extracted from the smoothed original explanatory variables; then the dynamic factor augmented model selection method and the model averaging method based on different criteria are used to predict different lag orders of the common factor and the explanatory variables, and the effectiveness of the dynamic factor augmented censored group cross-validation model averaging method is verified using multiple predictor error indicators as well as the DM test. The experimental results show that the dynamic factor augmented censored group cross-validation model averaging method has better prediction results and is more robust.

Literature abounds with various statistical and machine learning techniques for stock market forecasting. However, Reinforcement Learning (RL) is conspicuous by its absence in this field and is little explored despite its potential to address the dynamic and uncertain nature of the stock market. In a first-of-its-kind study, this research precisely bridges this gap, by forecasting stock prices using RL, in the static as well as streaming contexts using deep RL techniques. In the static context, we employed three deep RL algorithms for forecasting the stock prices: Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimisation (PPO) and Recurrent Deterministic Policy Gradient (RDPG) and compared their performance with Multi-Layer Perceptron (MLP), Support Vector Regression (SVR) and General Regression Neural Network (GRNN). In addition, we proposed a generic streaming analytics-based forecasting approach leveraging the real-time processing capabilities of Spark streaming for all six methods. This approach employs a sliding window technique for real-time forecasting or nowcasting using the above-mentioned algorithms. We demonstrated the effectiveness of the proposed approach on the daily closing prices of four different financial time series dataset as well as the Mackey–Glass time series, a benchmark chaotic time series dataset. We evaluated the performance of these methods using three metrics: Symmetric Mean Absolute Percentage (SMAPE), Directional Symmetry statistic (DS) and Theil’s U Coefficient. The results are promising for DDPG in the static context and GRNN turned out to be the best in streaming context. We performed the Diebold–Mariano (DM) test to assess the statistical significance of the best-performing models.

In this study, a hybrid decomposition and ensemble framework incorporating Ensemble empirical mode decomposition (EEMD) and selected modeling methodologies are proposed for stock price forecasting. Under the framework, the original stock price series was first decomposed into several subseries including a number of intrinsic mode functions (IMFs) and a residue using EEMD technique. Then, extracted subseries was modeled to generate forecasts respectively. Finally, the forecasts of all extracted subseries were aggregated to produce an ensemble forecasts for the original stock price series. An extensive experiment was conducted to compare the feasibility and validity of the proposed hybrid framework employing different modeling methodologies, such as support vector machines (SVMs) (in the formulation of support vector regression (SVR), feed forward neural networks (FNN), and autoregressive integrated moving average (ARIMA). The real daily closing price series of Thirty Dow Jones industrial stocks from New York Stock Exchange (NYSE) was used for experimental evaluation. The results demonstrate that significant improvement can be achieved with the proposed hybrid decomposition and ensemble framework across all the three modeling methodologies, particularly, hybrid EEMD-based FNN modeling framework achieved the most significant improvement but hybrid EEMD-based SVMs modeling framework performed best in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE), and directional symmetry (DS).

Determining stock price movements is a challenging problem because stock prices are often influenced by multiple factors such as economic, political, business, and human behavior. In this paper, we will attempt different modeling methods for two types of data, a total of 40 Dow Jones Industrial Index components, to verify the effectiveness of daily and high-frequency data for stock price prediction. Furthermore, we will attempt to validate the performance of LSTM model in stock price prediction, and also try to improve its performance by incorporating an attention mechanism. We assume that adding an attention layer to LSTM model would improve model performance in our data sets, especially in high-frequency data, since the data set would contain a huge amount of noise. Our results indicate that the simple LSTM performs better than the attention-based LSTM for both data types of prediction tasks with a benchmark of the number of stock prediction outcomes that outperform the number of those in other model, which is 24 out 40 stocks, which refutes our initial assumptions and does not validate whether adding attention mechanism is useful for solving the shallow layers and gradient vanishing problem and thus improving the LSTM model performance.

The stock market is a complex, evolving, and nonlinear dynamic system. Forecasting stock prices has been regarded as one of the most challenging applications of modern time series forecasting. This paper proposes a novel multi-input Hamacher-ANFIS (adaptive network-based fuzzy inference system based on Hamacher operator) ensemble model to forecast stock prices in China’s stock market and achieve good prediction performance. We selected five stocks with the largest total market capitalization from the Shanghai and Shenzhen Stock Exchanges, measured their historical volatility over the same time period, and weighed the performance of each stock forecasting model based on the above volatility. Then, the experiment was repeated 100 times for each data set, and we calculated the comprehensive $R^2$ of the testing set according to the weight that we obtained earlier. The statistical test of the experimental results shows that: (1) In terms of comprehensive $R^2$ of the stock price, the multi-input Hamacher-ANFIS model is superior to other conventional models; (2) when compared with the nonensemble forecasting strategy, the ensemble strategy of the Hamacher-ANFIS model has significant advantages.