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

Huang, Xiaoru; Wang, Jiahui; Qin, Zhaohui; Fu, Zhe; Wang, Aiming; Yang, Tianxiang

doi:10.1109/access.2020.3047138

Cited by 16 publications

(11 citation statements)

References 70 publications

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“…To this end, first we consider an investor participating in a general binary prediction market with 𝑈 (𝑤) as their utility function, where 𝑤 represent an amount of wealth. The indifference pricing strategy for such an investor can be achieved by solving (1), which requires that the expected value of the utility remains unchanged [13]:…”

Section: A Binary Prediction Marketmentioning

confidence: 99%

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

Shamsi

Cuffe

2022

IEEE Trans. Sustain. Energy

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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.

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Section: A Binary Prediction Marketmentioning

confidence: 99%

“…Futures markets have a long history of application in various industries (e.g. agriculture products [1]) to hedge against such uncertainties in revenue streams, yet these are not fully exploited for renewable generators. Power futures markets can be used to hedge against volumetric risk [2] or price uncertainty [3] for power plants.…”

Section: Introductionmentioning

confidence: 99%

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

Shamsi

Cuffe

2022

IEEE Trans. Sustain. Energy

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“…The results showed that XGBoost was significantly better than the other two classical machine learning methods [33]. Therefore, considering the excellent performance of XGBoost in practical applications [34,35], it is selected as the base prediction model for forecasting stock price crashes in this research.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

Duan

et al. 2022

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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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“…The most remarkable advantage of XGBoost is that the prediction accuracy and computing speed are significantly enhanced compared to the traditional gradient boosting algorithms [18][19][20]. In the field of financial markets, Huang et al predicted the intradaily market trends using an XGBoost-based method, and it successfully produced satisfactory forecasting performance [21][22][23]. Chen et al built an XGBoost-based portfolio construction method to forecast the price movement of stock market, and the experiment results indicated that their proposed method outperformed all the benchmarks by evaluating the trading returns and transaction risks [24].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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

Cited by 16 publications

References 70 publications

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

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

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

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

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