• An innovative learning framework for predicting crude oil prices is proposed. • Sustainable management in oil markets is surveyed. • Intensive verifications with other challenging approaches are implemented. • The superior performance of the proposed approach is verified in the experiments. • A case study is proposed to illustrate the rationality of the proposed approach. Crude oil price predictability has continually been considered as a fundamental argument of finance literature, given its critical propositions for risk management, investment decisions, and commercial and financial policymaking. This work presents an innovative learning framework for efficient predictive modeling of daily and weekly crude oil price (COP) information, which aims to enable sustainable management in oil markets. Firstly, an optimized version of variation mode decomposition (OVMD) is proposed to adaptively decompose the original COP time series into multiple modes based on a set of optimized parameters calculated with a Tree-structured Parzen Estimator (TPE) algorithm. Secondly, an AdaBoost algorithm is redesigned using random forest (RF) to model the future price information in the modes with the high frequency. Thirdly, a new deep network is presented to develop automatically learn spatial-temporal representations from decomposed COP data, where a novel Conv-former module is designed to efficiently extract local as well as global spatial representations without incurring extra computational costs. Followingly, Multiple Long short-term Memory (LSTM) networks are stacked to learn temporal representations from input modes. To further empower the representation power of our framework, a new bidirectional learning module is presented to stack the LSTM layer to learn from COP data in the forward and backward directions. To validate the efficiency of the proposed framework, this work performs experimental simulations and analyses based on a case study from Brent crude oil prices at both daily and weekly scales. The experimental findings show up the competent predictive modeling capabilities of the proposed framework over the cutting-edge methods rendering it as a promising solution to enable sustainable management in crude oil markets. The proposed framework can be generalized to different predictive modeling tasks and hence qualified to be used as a valuable tool for oil portfolio creation, property pricing, and risk management in Crude Oil Markets . 

Deep Learning Framework for Predictive Modeling of Crude Oil Price for Sustainable Management in Oil Markets

Different from traditional algorithms and model, machine learning is a systematic and comprehensive application of computer algorithms and statistical models, and it has been widely used in many fields. In the field of finance, machine learning is mainly used to study the future trend of capital market price. In this paper, to predict the time-series data of stock, we applied the traditional models and machine learning models for forecasting the linear and non-linear problem, respectively. First, stock samples that occurred from year 2010 to 2019 at the New York Stock Exchange are collected. Next, the ARIMA (autoregressive integrated moving average model) model and LSTM (long short-term memory) neural network model are applied to train and predict stock price and stock price subcorrelation. Finally, we evaluate the proposed model by several indicators, and the experiment results show that: (1) Stock price and stock price correlation are accurately predicted by the ARIMA model and LSTM model; (2) compared with ARIMA, the LSTM model performance better in prediction; and (3) the ensemble model of ARIMA-LSTM significantly outperforms other benchmark methods. Therefore, our proposed method provides theoretical support and method reference for investors about stock trading in China stock market.

/pdf/research-on-stock-price-time-series-prediction-based-on-deep-6qwrnc2n.pdf

Research on Stock Price Time Series Prediction Based on Deep Learning and Autoregressive Integrated Moving Average

Evaluating and understanding the financial impacts of COVID-19 has emerged as an urgent research agenda. Nevertheless, the impacts of government interventions on stock markets remain poorly understood. This study explores, for the first time, the impact of COVID-19 related government intervention policies on different stock market sectors using explainable machine learning-based prediction models. The empirical findings suggest that the LightGBM model provides excellent prediction accuracy while preserving computationally efficient and easy explainability of the model. We also find that COVID-19 government interventions are better predictors of stock market volatility than stock market returns. We further show that the observed effects of government intervention on the volatility and returns of ten stock market sectors are heterogeneous and asymmetrical. Our findings have important implications for policymakers and investors in terms of promoting balance and sustaining prosperity across industry sectors through government interventions. 

/pdf/an-interpretable-system-for-predicting-the-impact-of-covid-em1d9985.pdf

An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

The future carbon price is crucial to relevant companies, investors, and carbon policymakers, and the significance of carbon price prediction research is self-evident. However, existing study usually predicts actual carbon prices, rarely considering price trends and lacking reasonable interpretations for the prediction model. Thus, in this study, an interpretable machine learning model is proposed to predict carbon price trends. It integrates five methods, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), two-stage feature selection (TFS), light gradient boosting machine (LightGBM) optimized by Bayesian optimization algorithm (BOA), and SHapley Additive exPlanations (SHAP). The effectiveness of the proposed model is validated with the carbon prices of the Hubei carbon trading market, which has the largest volume among other markets in China. The experimental results showed that the proposed model outperforms other benchmark models under five evaluation criteria, including AUC, Accuracy, Precision, Recall, and F1 score, on multiple-step predictions. For one-step-ahead prediction, the average hit ratio results are 0.8342, 77.32%, 77.87%, 76.83%, and 76.88% respectively; for five-step-ahead prediction, the average hit ratio results are 0.7641, 69.25%, 71.17%, 71.97%, and 71.00% respectively; and for ten-step-ahead prediction, the average hit ratio results are 0.7519, 69.11%, 73.80%, 69.61%, and 71.16% respectively. The SHAP model interpretation results indicated that the high-frequency intrinsic mode function (IMF) components of the historical carbon price are the most important features for predicting carbon price trends. This study contributes by forecasting both the upward and downward trends of carbon prices through multi-step-ahead forecasting with the LightGBM model and further interpreting the model’s predictions with the SHAP approach. Therefore, the proposed model has excellent forecasting performance with interpretability, which is an effective tool for forecasting carbon price trends. 

Forecasting carbon price trends based on an interpretable light gradient boosting machine and Bayesian optimization

It has been evidenced by numerous studies that irrational investor sentiment is one of the critical factors leading to dramatic volatility in financial market prices. Therefore, how to effectively predict market prices by information on investor sentiment is a popular but complex topic for researchers, market investors, and financial regulators. In this research, we aim to investigate the effectiveness of stock index prediction using multiple investor sentiment features, and we propose an advanced price trend prediction and trading simulation approach for the Shanghai Stock Exchange index and the Shenzhen Component index by integrating the Boosting, Bagging, and NSGA-II methods. Additionally, the SHAP method is employed as a model interpretation approach to analyze the importance of the sentiment variables and quantify their contributions to the predictions from both local and global perspectives. According to the experimental results, it can be found that the proposed method outperforms the benchmark methods in terms of the hit ratio, accumulated return, and maximum drawdown. It indicates that the proposed method is capable of achieving high accuracy, low risk, and stable profit in price trend prediction and trading simulation of the Chinese stock indexes. Moreover, the SHAP approach incorporated in the proposed method improved the interpretability of the proposed model, which can provide a beneficial reference for market participants to clarify the important sentiment factors and make relative decisions. 

An Integrated Approach of Ensemble Learning methods for Stock Index Prediction using Investor Sentiments

AIM
To evaluate the performance of chat generative pre-trained transformer (ChatGPT) in key domains of clinical pharmacy practice, including prescription review, patient medication education, adverse drug reaction (ADR) recognition, ADR causality assessment, and drug counseling.


METHODS
Questions and clinical pharmacist' answers were collected from real clinical cases and clinical pharmacist competency assessment. ChatGPT's responses were generated by inputting the same question into the "New Chat" box of "ChatGPT Mar 23 Version". Five licensed clinical pharmacists independently rated these answers on a scale of 0 (completely incorrect) to 10 (completely correct). The mean scores of ChatGPT and clinical pharmacists were compared using a paired two-tailed Student's t-test. The text content of the answers was also descriptively summarized together.


RESULTS
The quantitative results indicated that ChatGPT was excellent in drug counseling (ChatGPT: 8.77 vs. clinical pharmacist: 9.50, p=0.0791) and weak in prescription review (5.23 vs. 9.90, p=0.0089), patient medication education (6.20 vs. 9.07, p=0.0032), ADR recognition (5.07 vs. 9.70, p=0.0483), and ADR causality assessment (4.03 vs. 9.73, p=0.023). The capabilities and limitations of ChatGPT in clinical pharmacy practice were summarized based on the completeness and accuracy of the answers. ChatGPT revealed robust retrieval, information integration, and dialogue capabilities. It lacked medicine-specific datasets as well as the ability for handling advanced reasoning and complex instructions.


CONCLUSIONS
While ChatGPT holds promise in clinical pharmacy practice as a supplementary tool, the ability of ChatGPT to handle complex problems needs further improvement and refinement.

Evaluating the performance of ChatGPT in clinical pharmacy: A comparative study of ChatGPT and clinical pharmacists.

Stock Index Direction Forecasting using an Explainable eXtreme Gradient Boosting and Investor Sentiments

In the last decade, High-Frequency Trading (HFT) has become a popular issue in the futures market, which has attracted much attention from numerous researchers. In this study, an intelligent decision support system is proposed for apple futures high-frequency trading. First, three eXtreme Gradient Boosting (XGBoost) based models use the feature inputs from multiple time scales for return and direction prediction. Then, based on a pre-designed trading rule, the signals of long and short-selling are determined, and corresponding transactions are executed. In order to retain considerable profits in time and to avoid serious losses possibly caused by sudden and huge price changes toward the opposite direction as predictions, a position closing function is implemented in the trading rule. Meanwhile, Particle Swarm Optimization (PSO) is employed to optimize the parameters of the trading rule as well as the XGBoost parameters. By evaluating the experimental results, we observed that the proposed approach successfully achieved the best performance in terms of direction prediction accuracy, transaction returns, as well as return/risk ratio. It could be inferred from the experimental results that the proposed approach could provide decision support and beneficial reference for market traders involved in high-frequency trading of the apple futures.

/pdf/a-decision-support-system-for-trading-in-apple-futures-3ie5ppujhg.pdf

A Decision Support System for Trading in Apple Futures Market Using Predictions Fusion

A novel hybrid method for direction forecasting and trading of Apple Futures

Futures price-movement-direction forecasting has always been a significant and challenging subject in the financial market. In this paper, we propose a combination approach that integrates the XGBoost (eXtreme Gradient Boosting), SMOTE (Synthetic Minority Oversampling Technique), and NSGA-II (Non-dominated Sorting Genetic Algorithm-II) methods. We applied the proposed approach on the direction prediction and simulation trading of rebar futures, which are traded on the Shanghai Futures Exchange. Firstly, the minority classes of the high-frequency rebar futures price change magnitudes are oversampled using the SMOTE algorithm to overcome the imbalance problem of the class data. Then, XGBoost is adopted to construct a multiclassification model for the price-movement-direction prediction. Next, the proposed approach employs NSGA-II to optimize the parameters of the pre-designed trading rule for trading simulation. Finally, the price-movement direction is predicted, and we conducted the high-frequency trading based on the optimized XGBoost model and the trading rule, with the classification and trading performances empirically evaluated by four metrics over four testing periods. Meanwhile, the LIME (Local Interpretable Model-agnostic Explanations) is applied as a model explanation approach to quantify the prediction contributions of features to the forecasting samples. From the experimental results, we found that the proposed approach performed best in terms of direction prediction accuracy, profitability, and return–risk ratio. The proposed approach could be beneficial for decision-making of the rebar traders and related companies engaged in rebar futures trading.

/pdf/high-frequency-direction-forecasting-of-the-futures-market-12vpensj.pdf

High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method

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