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|Title:||Application of statistical methods and genetic programming in pair trading||Authors:||Tjoa Justin.||Keywords:||DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence||Issue Date:||2010||Abstract:||Pair trading is a form of Statistical Arbitrage that exploits deviation of the expected price relationship between two similar assets. Based on Mean-Reversion principle, difference of price between 2 assets that statistically exhibit price relationship would revert back to the expected value in 'short' term. For Equity trading, this implies that trader would earn profit by 'going long' the underpriced stock and 'short' the overpriced stock. Theoretically pair trading would grant profit without risk assuming that the statistical price relationship holds true. However,in practice, it is difficult to determine which pairs contain statistical price relationship, and decide which stocks to short or long and at what prices to enter or exit trades. The position such that in the long run , profit is optimised. The complex nature of seeking statistical arbitrage or pair tradings need heavy time consuming, quantitative and computational approaches to the trading strategy. The aim of this project is to create software that helps the process of decision making in pair trading. The software allows free daily update for equity data online and perform computation within the mean of most user's computing devices. With simple user interface and user making input of his trading preferences the software generate a list of pairs that holds statistical price relationship and have statistically deviated from their means (good indication to open trade position), and generate appropriate indicators that trader can use as exit signals as he monitors his various pairs positions. This software however do not keep track of the trader's portfolio or perform automated trading.||URI:||http://hdl.handle.net/10356/39963||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Student Reports (FYP/IA/PA/PI)|
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Updated on Dec 5, 2020
Updated on Dec 5, 2020
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