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.

/pdf/stock-price-crash-warning-in-the-chinese-security-market-2k3xhh2i.pdf

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

Ichimoku Kinkohyo or Ichimoku Cloud Chart is one of the most popular technical indicators used by traders all over the world. However, its profitability is heavily influenced by the market environment, to which it is applied. Furthermore, the COVID-19 outbreak may have an impact on the market environment as well as the performance of all technical indicators. This study is the first to look into the profitability of Ichimoku-based trading rules in the Vietnamese stock market in the context of the COVID-19 outbreak. More particularly, the COVID-19 outbreak has a positive influence on the performance of this strategy when considering the entire market as well as a variety of industries including real estate industry, food and beverage industry, resource industry, and automotive and electronic components industry. Compared to the pre-pandemic period, the return on investment obtained per each transaction using the Ichimoku-based strategy increased by roughly $$8-9\%$$ in the pandemic period. Compared to the Buy-and-hold method, the Ichimoku-based strategy could slightly increase Accumulated return while posing a lower risk. The findings indicate that the Ichimoku-based strategy is applicable to the Vietnam stock market, regardless of the adverse effects of the pandemic on the industries. 

/pdf/profitability-of-ichimoku-based-trading-rule-in-vietnam-2fpjhv5v.pdf

Profitability of Ichimoku-Based Trading Rule in Vietnam Stock Market in the Context of the COVID-19 Outbreak

The article presents a new scalable iterative method for linear programming, called the apex method. The key feature of this method is constructing a path close to optimal on the surface of the feasible region from a certain starting point to the exact solution of the linear programming problem. The optimal path refers to a path of minimum length according to the Euclidean metric. The apex method is based on the predictor-corrector framework and proceeds in two stages: Quest (predictor) and Target (corrector). The Quest stage calculates a rough initial approximation of the linear programming problem. The Target stage refines the initial approximation with a given precision. The main operation used in the apex method is an operation that calculates the pseudoprojection, which is a generalization of the metric projection to a convex closed set. This operation is used both in the Quest stage and in the Target stage. A parallel algorithm using a Fejér mapping to compute the pseudoprojection is presented. An analytical estimation of the parallelism degree of this algorithm is obtained. Also, an algorithm implementing the Target stage is given. The convergence of this algorithm is proven. The results of applying the apex method for solving various linear programming problems are presented. 

/pdf/on-new-version-of-the-apex-method-for-solving-linear-3mzryxqr.pdf

On New Version of the Apex Method for Solving Linear Programming Problems

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

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