# A Machine Learning View on Momentum and Reversal Trading
## 1 Introduction  
Momentum and reversal effects are common and interesting phenomena in stock markets. The momentum effect means that the stocks that have performed well, i.e., given higher returns, in the past (winners) will probably continue to outperform those that have performed poorly in the past (losers) in the future. On the contrary, the reversal effect represents that the past losers may convert to the winners in the future. The reversal effect was first observed by [1], in which it was found that buying losers and selling winners might acquire superior returns on the US stock market, because the US market easily overreacts to some events, which results in abnormal price movements. The momentum effect, which claims that buying winners and selling losers at the same time could earn significant positive returns over holding periods of 3-12 months on the US stock market, was discovered by [2]. Up until recently, many relevant studies have been conducted. In addition to the US market, researchers stated that stock markets in different regions have varying degrees of momentum and/or reversal effect(s). For example, Reference [3] observed the momentum effect in the Latin American emerging markets. Reference [4] found evidence of a substantial momentum effect in the China Shanghai stock market over the period from 1995 to 2005. Reference [5] proposed a contrarian portfolio strategy that could obtain profits on the Malaysian stock market based on the short-term reversal effect. Reference [6] pointed out short-term reversal and mid-term momentum effects in weekly stock returns in the European markets. Reference [7] presented profitable arbitrage strategies built on the short-term reversal effect on the Hong Kong stock market. On top of these observations, various studies [8; 9; 10; 11; 12] have been trying to explain the mechanisms behind the effects. For instance, Reference [8] showed that the momentum effect may be correlated to the past trading volume. Reference [9] concluded that the fundamental finance factors have important links with the reversal effect for stocks traded on the Australian Stock Exchange. Reference [5] argued that the market state has a strong relationship with the momentum effect on the Indian equity market. In addition, some researchers have sought to explain the phenomena via behavioral finance models, such as [11; 12]. The existence of momentum and reversal effects have challenged the Efficient Markets Hypothesis (EMH). In other words, investors may take the extra yield if they can predict which effect may happen in the next market period. Unfortunately, the concluded results of most of the existing studies are highly dependent on human experience and settings, e.g., within specific market observation and holding periods. Their findings tend to be unrepeatable in other periods. As a result, the effects they observed indeed existed in the past may disappear in the future. Similarly, the summarized factors that explain the effects are not very robust. These links may not be persistent when applied to other market periods. Thus, it is difficult to transfer these research outputs to real-world investment trading. Nowadays, machine learning, as one of the most important approaches in artificial intelligence, is a very hot research topic in academia as well as in industry. Many pieces of evidence report that machine learning has been applied widely to diverse domains [13; 14]. Machine learning is capable of automatically recognizing potentially useful patterns in financial data [15]. The purpose of this paper is to propose the use of machine learning approaches instead of the traditional statistical methods (e.g., the Causality Test and Hypothesis Test) that have been used in previous studies to investigate the momentum and reversal effects on the stock market. To the best of our knowledge, little research has applied machine learning to this problem. In this research, we regard the problem as a supervised machine learning task.