| dc.description.abstract | Pairs trading is one of famous strategy in investment, the way of trading is being chosen by the two similar stocks, and being calculated on their historical price data’s gap of mean and standard deviation. If the gap of new price data is exceed the boundary, we will buy the stock and short selling the other stock. The purpose of it is removing the systemic risk by the market of changing, but it also be effected by idiosyncratic risk. If we can reduce the loss of making fault decision, we will get higher ROI.
We propose a new trading way which combine clustering analysis and pairs trading is named “Cluster trading”. The limit of pairs trading which only can trade two stocks (one pair) at each time is broken. It can be traded with more than two stocks at each time. More stock’s information can be considered and the loss can be shared by other stocks when making the fault decision to let the loss be decreased. Thus, we will have higher and more robust in the profit of making decision by comparing with pairs trading. However, we also find out the famous similarity which is proposed by Gatev, Goetzmann, and Rouwenhorst (2006) in pairs trading has a drawback about iterating, and it makes the similarity inaccurate. We use EWMA and stock’s trend to improve the similarity to get the better results and get higher ROI.
We use the stocks FTSE TWSE Taiwan 50 Index during recent four years and classify some of stocks into different industries. And then, compare TAIEX, pairs trading, cluster trading with original similarity and cluster trading with new similarity which is better. Finally, find the setting which have high ROI in cluster trading with new similarity in different industries. | en_US |