Stock Price Change Rate Prediction by Utilizing Social Network Activities

Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques

NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC

Crude Oil Spot Price Forecasting Based on Multiple Crude Oil Markets and Timeframes

This study proposes a multiple kernel learning (MKL)-based regression model for crude oil spot price forecasting and trading. We used a well-known trend-following technical analysis indicator, the moving average convergence and divergence (MACD) indicator, for extracting features from original spot prices. Additionally, we factored in the possibility that movements of target crude oil prices may be related to other important crude oil markets besides the target market for the prediction time horizon since traders may find price movement information within other relevant crude oil markets useful. We also considered multiple timeframes in this study since trends may differ across different timeframes and, in fact, traders may use their own timeframes. Therefore, for forecasting target crude oil prices, this study emphasizes on features pertaining to other important crude oil markets and different timeframes in addition to features of the target crude oil market and target timeframe. Moreover, the MKL framework has been used to fuse information extracted from different sources and timeframes of the same data source. Experimental results show that out-of-sample forecasting using the MKL method is superior to benchmark methods in terms of root mean square error (RMSE) and average percentage profit (APP). They also show that the information from multiple timeframes is useful for prediction, but that from another crude oil market is not

Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading

Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI), while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX) currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits

Zeolite-based catalytic membrane reactors for thermo-catalytic conversion of CO2

Summary: Zeolite-based catalytic membrane reactors have been successfully applied in overcoming the thermodynamic limitations of CO2 hydrogenations and dry reforming of methane (DRM) reactions. This review summarizes the zeolites as membrane reactor components regarding the permeance, permselectivity, durability, conversion, selectivity, and stability by referring to the synergy of catalyst and membrane. Also, five operation parameters (temperature, pressure, feed ratio, sweeping gas flow rate, and gas hourly space velocity) are introduced regarding their impacts on the performance of membrane reactor. Besides, synthesis methods and conditions for zeolite membranes are critically illustrated in the category. Moreover, representative surface and structure properties of zeolite membranes are discussed by relating to the synthesis-structure-performance relationships. Finally, conclusive remarks are demonstrated and possible solutions to existing challenges are proposed. So far, this is the first time to discuss the applications of zeolite membrane reactors in the CO2 adsorption, separation, activation, and conversion in reforming and hydrogenation processes

Financial Futures Prediction Using Fuzzy Rough Set and Synthetic Minority Oversampling Technique

In this research, a novel approach called SMOTE-FRS is proposed for movement prediction and trading simulation of the Chinese Stock Index 300 (CSI300) futures, which is the most crucial financial futures in the Chinese A-share market. First, the SMOTE- (Synthetic Minority Oversampling Technique-) based method is employed to address the sample unbalance problem by oversampling the minority class and undersampling the majority class of the futures price change. Then, the FRS- (fuzzy rough set-) based method, as an efficient tool for analyzing complex and nonlinear information with high noise and uncertainty of financial time series, is adopted for the price change multiclassification of the CSI300 futures. Next, based on the multiclassification results of the futures price movement, a trading strategy is developed to execute a one-year simulated trading for an out-of-sample test of the trained model. From the experimental results, it is found that the proposed method averagely yielded an accumulated return of 6.36%, a F1-measure of 65.94%, and a hit ratio of 62.39% in the four testing periods, indicating that the proposed method is more accurate and more profitable than the benchmarks. Therefore, the proposed method could be applied by the market participants as an alternative prediction and trading system to forecast and trade in the Chinese financial futures market

Identification of Insider Trading Using Extreme Gradient Boosting and Multi-Objective Optimization

Illegal insider trading identification presents a challenging task that attracts great interest from researchers due to the serious harm of insider trading activities to the investors’ confidence and the sustainable development of security markets. In this study, we proposed an identification approach which integrates XGboost (eXtreme Gradient Boosting) and NSGA-II (Non-dominated Sorting Genetic Algorithm II) for insider trading regulation. First, the insider trading cases that occurred in the Chinese security market were automatically derived, and their relevant indicators were calculated and obtained. Then, the proposed method trained the XGboost model and it employed the NSGA-II for optimizing the parameters of XGboost by using multiple objective functions. Finally, the testing samples were identified using the XGboost with optimized parameters. Its performances were empirically measured by both identification accuracy and efficiency over multiple time window lengths. Results of experiments showed that the proposed approach successfully achieved the best accuracy under the time window length of 90-days, demonstrating that relevant features calculated within the 90-days time window length could be extremely beneficial for insider trading regulation. Additionally, the proposed approach outperformed all benchmark methods in terms of both identification accuracy and efficiency, indicating that it could be used as an alternative approach for insider trading regulation in the Chinese security market. The proposed approach and results in this research is of great significance for market regulators to improve their supervision efficiency and accuracy on illegal insider trading identification

Platinum–Water Interaction Induced Interfacial Water Orientation That Governs the pH-Dependent Hydrogen Oxidation Reaction

Understanding the electrode–water interface structure in acid and alkali is crucial to unveiling the underlying mechanism of pH-dependent hydrogen oxidation reaction (HOR) kinetics. In this work, we construct the explicit Pt(111)–H2O interface models in both acid and alkali to investigate the relationship between the HOR mechanism and electrode–electrolyte interface structure using ab initio molecular dynamics and density functional theory. We find that the interfacial water orientation in the outer Helmholtz layer (OHP) induced by the Pt–water interaction governs the pH-dependent HOR kinetics on Pt(111). In alkali, the strong Pt–interfacial water electrostatic interaction behaves as a narrow OHP, which increases the proportion of “H-down” interfacial water and leads to less adsorbed water entering the inner Helmholtz plane (IHP), decreasing the work function of Pt(111). Furthermore, the more “H-down” interfacial water stabilizes the Had adsorption, prevents Had desorption, and suppresses the Volmer step of HOR by forming the solvated [Had···H2O···H2O] complex. Our work provided a visualized molecular-level mechanism to understand the nature of pH-dependent HOR kinetics