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

Stock price crashes have occurred frequently in the Chinese security market during the last three decades. They have not only caused substantial economic losses to market investors but also seriously threatened the stability and financial safety of the security market. To protect against the price crash risk of individual stocks, a prediction and explanation approach has been proposed by combining eXtreme Gradient Boosting (XGBoost), the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and SHapley Additive exPlanations (SHAP). We assume that financial indicators can be adopted for stock crash risk prediction, and they are utilized as prediction variables. In the proposed method, XGBoost is used to classify the stock crash and non-crash samples, while NSGA-II is employed to optimize the hyperparameters of XGBoost. To obtain the essential features for stock crash prediction, the importance of each financial indicator is calculated, and the outputs of the prediction model are explained by SHAP. Compared with the results of benchmarks using traditional machine learning methods, we found that the proposed method performed best in terms of both prediction accuracy and efficiency. Especially for the small market capitalization samples, the accuracy of classifying all samples reached 78.41%, and the accuracy of identifying the crash samples was up to 81.31%. In summary, the performance of the proposed method demonstrates that it could be employed as a valuable reference for market regulators engaged in the Chinese security market.

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Stock Price Crash Warning in the Chinese Security Market Using a Machine Learning-Based Method and Financial Indicators

Renewable energy sources face volumetric risk in their revenue streams, in any electricity sale structure, due to changeability associated with weather conditions. Weather/power derivatives are often employed to hedge against such financial risks. This letter proposes that a binary prediction market, if is adequately liquid, has the potential to resemble the function of such derivatives to improve the financial profile of renewable sources. The size and the price of shares (contracts) to be purchased by the renewable generator are determined analytically in the methodology of this paper. To this end, two different approaches have been considered: the indifference utility condition and the maximisation of utility function to reflect different risk preferences of investors.

/pdf/using-binary-prediction-markets-as-hedging-instruments-39pqc9g8.pdf

Using Binary Prediction Markets as Hedging Instruments: Strategies for Renewable Generators

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

Abstract Accurate soybean futures price prediction is critical to related agricultural production, warehousing, and trading. Interval forecasting can avoid the loss of fluctuation information and evaluate the uncertainty of futures prices. However, most previous studies only consider the single‐type auxiliary variable, which will cause the deficiency of valued information. Moreover, the research concentrating on internet search index ignores the search habits of investors, resulting in subjectivity in keyword selection. Therefore, a novel multisource data‐driven combined forecasting model is proposed that consists of four parts: unstructured data processing, interval multi‐scale decomposition, interval combination forecasting, and model evaluation. First, sentiment analysis technology is used to convert news text into sentiment scores. The internet search keyword screening method based on latent Dirichlet allocation is then constructed to achieve the quantification of investor attention. Second, a decomposition method is applied to decompose the original interval‐valued series into finite more stationary components. Third, interval prediction results are obtained by machine learning‐based multiple predictors. Finally, the model evaluation module comprising error evaluation indicators and comparison experiments is presented to verify the effectiveness. The experimental results show that the proposed model has higher prediction accuracy, which indicates that multisource data and designed keyword screening methods can enhance the forecasting performance of the model. 

A multisource data‐driven combined forecasting model based on internet search keyword screening method for interval soybean futures price

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

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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