Research on a Prediction Model and Influencing Factors of Cross-Regional Price Differences of Rebar Spot Based on Long Short-Term Memory Network

Wu, Sen; Liu, Shuaiqi; Zong, Huimin; Sun, Yifeng; Wang, Wei

doi:10.3390/su15064951

Cited by 3 publications

(1 citation statement)

References 47 publications

(54 reference statements)

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“…At present, the existing domestic research is mainly based on the mean reversion principle of spreads. There are fewer studies on using neural network for arbitrage, and most of them have the disadvantages of single model and low prediction accuracy [ 21 , 22 ]. Therefore, to develop scientific and efficient arbitrage strategies, it is extremely important to accurately predict the arbitrage spreads between futures.…”

Section: Introductionmentioning

confidence: 99%

Multi-strategy modified sparrow search algorithm for hyperparameter optimization in arbitrage prediction models

Cheng,

Qin,

et al. 2024

PLoS ONE

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Deep learning models struggle to effectively capture data features and make accurate predictions because of the strong non-linear characteristics of arbitrage data. Therefore, to fully exploit the model performance, researchers have focused on network structure and hyperparameter selection using various swarm intelligence algorithms for optimization. Sparrow Search Algorithm (SSA), a classic heuristic method that simulates the sparrows’ foraging and anti-predatory behavior, has demonstrated excellent performance in various optimization problems. Hence, in this study, the Multi-Strategy Modified Sparrow Search Algorithm (MSMSSA) is applied to the Long Short-Term Memory (LSTM) network to construct an arbitrage spread prediction model (MSMSSA-LSTM). In the modified algorithm, the good point set theory, the proportion-adaptive strategy, and the improved location update method are introduced to further enhance the spatial exploration capability of the sparrow. The proposed model was evaluated using the real spread data of rebar and hot coil futures in the Chinese futures market. The obtained results showed that the mean absolute percentage error, root mean square error, and mean absolute error of the proposed model had decreased by a maximum of 58.5%, 65.2%, and 67.6% compared to several classical models. The model has high accuracy in predicting arbitrage spreads, which can provide some reference for investors.

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Section: Introductionmentioning

confidence: 99%

Multi-strategy modified sparrow search algorithm for hyperparameter optimization in arbitrage prediction models

Cheng,

Qin,

et al. 2024

PLoS ONE

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Analyzing time series to forecast hot rolled coil steel price in Spain by means of neural non-linear models

Alcalde,

GarcÍa,

Manzanedo

et al. 2024

Logic Journal of the IGPL

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In the industrial context, steel is a broadly-used raw material with applications in many different fields. Due to its high impact in the activity of many industries all over the world, forecasting its price is of utmost importance for a huge amount of companies. In this work, non-linear neural models are applied for the first time to different datasets in order to validate their suitability when predicting the price of this commodity. In particular, the NAR, NIO and NARX neural network models are innovatively applied for the first time to forecast the price of hot rolled steel in Spain. Besides these variety of models, different datasets consisting of a set of heterogenous variables from the last seven years and related to the price of this commodity are benchmarked and analyzed. The results showed that NARX is the best performing model when the price of raw materials used to produce steel and the stock market prices of three major global steel producing companies are employed as input to this predictive model. Consequently, this result may boost the application of Machine Learning in companies, in order to schedule the supplying operations according to the price forecasting.

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An optimized LSTM network for improving arbitrage spread forecasting using ant colony cross-searching in the K-fold hyperparameter space

Zeng,

Qin,

Zhang

et al. 2024

PeerJ Computer Science

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Arbitrage spread prediction can provide valuable insights into the identification of arbitrage signals and assessing associated risks in algorithmic trading. However, achieving precise forecasts by increasing model complexity remains a challenging task. Moreover, uncertainty in the development and maintenance of model often results in extremely unstable returns. To address these challenges, we propose a K-fold cross-search algorithm-optimized LSTM (KCS-LSTM) network for arbitrage spread prediction. The KCS heuristic algorithm incorporates an iterative updating mechanism of the search space with intervals as the basic unit into the traditional ant colony optimization. It optimized the hyperparameters of the LSTM model with a modified fitness function to automatically adapt to various data sets, thereby simplified and enhanced the efficiency of model development. The KCS-LSTM network was validated using real spread data of rebar and hot-rolled coil from the past three years. The results demonstrate that the proposed model outperforms several common models on sMAPE by improving up to 12.6% to 72.4%. The KCS-LSTM network is shown to be competitive in predicting arbitrage spreads compared to complex neural network models.

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Research on a Prediction Model and Influencing Factors of Cross-Regional Price Differences of Rebar Spot Based on Long Short-Term Memory Network

Cited by 3 publications

References 47 publications

Multi-strategy modified sparrow search algorithm for hyperparameter optimization in arbitrage prediction models

Multi-strategy modified sparrow search algorithm for hyperparameter optimization in arbitrage prediction models

Analyzing time series to forecast hot rolled coil steel price in Spain by means of neural non-linear models

An optimized LSTM network for improving arbitrage spread forecasting using ant colony cross-searching in the K-fold hyperparameter space

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