# High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method
## 1 Introduction  
As an exploration and innovation of the financial market, the futures market plays an essential role in risk diversification, price discovery, and hedging that cannot be replaced by the spot market [1]. In China, the steel industry has been an essential support for various industries such as agriculture, transportation, and architecture. Currently, China is the world's largest steel producer and trader, and the launch of rebar futures is of great significance to the progress of the Chinese futures market and steel industry. Since its listing on the Shanghai Futures Exchange (SHFE) in 2009, rebar futures have become one of the most active futures, with its trading volume ranking first among the steel-related futures for many years. On the one hand, rebar futures provides market participants with an effective tool for reducing market risk by establishing a profit and loss hedging mechanism by employing the futures and spot markets. On the other hand, steel enterprises can have access to corresponding market price information so they could rationally arrange their steel production and operation activities. Therefore, the study of price-movement-direction forecasting of the rebar futures market has become an essential issue for many scholars, market investors, and steel-related companies. In the last few decades, numerous scholars have developed practical approaches in exploring financial forecasting, such as the traditional econometric methods and artificial intelligence technologies. Among those approaches, Autoregressive Integrated Moving Average (ARIMA) is one of the most commonly used traditional econometric models [2; 3]. However, when referring to the nonlinear characteristics of time series data in financial markets, traditional analysis methods might be unable to show favorable forecasting ability [4; 5]. With the development and successful application of artificial intelligence, many machine learning methods, such as Artificial Neural Network (ANN) [6; 7], Support Vector Machines (SVM) [8; 9], and Random Forest (RF) [10; 11], have been evidenced to be capable of producing excellent performances for financial time series forecasting. For instance, an ANN-based model was proposed by [PERSON] et al. for daily price-movement prediction in the stock market, and it generated great forecasting accuracy [12]. [PERSON] et al. adopted a novel SVM-based method to predict stock price trends by extracting financial news texts, and the results showed that their proposed model was more robust than the benchmark methods [13]. [PERSON] et al. successfully predicted the stock price direction by applying an RF-based method [14]. Recently, since [PERSON] and [PERSON] first proposed the eXtreme Gradient Boosting (XGBoost) algorithm in 2016 [15], an increasing number of researchers have implemented it to solve the forecasting task, and great accuracies have been produced in many applications [16; 17]. 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, [PERSON] et al. predicted the intradaily market trends using an XGBoost-based method, and it successfully produced satisfactory forecasting performance [21; 22; 23]. [PERSON] 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]. However, there are a limited number of studies in the literature that report on the futures market prediction by using the XGBoost method. Hence, this paper fills the gap and applies XGBoost in the rebar futures price prediction to investigate whether XGBoost could be employed as an efficient method and whether it is superior to the machine learning algorithms that other researchers have applied.