European floating strike lookback options: Alpha prediction and generation using unsupervised learning

• Main Authors: LIM, Tristan, GUNAWAN, Aldy, ONG, Chin Sin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Options; volatility measures; statistical methods; simulations; machine learning; MITB student; Data Science; Finance and Financial Management; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/5988; https://ink.library.smu.edu.sg/context/sis_research/article/6991/viewcontent/059_070_Lim_JFDS.pdf
• Institution: Singapore Management University

This research utilized the intrinsic quality of European floating strike lookback call options, alongside selected return and volatility parameters, in a K-means clustering environment, to recommend an alpha generative trading strategy. The result is an elegant easy-to-use alpha strategy based on the option mechanisms which identifies investment assets with high degree of significance. In an upward trending market, the research had identified European floating strike lookback call option as an evaluative criterion and investable asset, which would both allow investors to predict and profit from alpha opportunities. The findings will be useful for (i) buy-side investors seeking alpha generation and/or hedging underlying assets, (ii) sell-side product manufacturers looking to structure the European floating strike lookback call options, and (iii) market trading platforms looking to introduce new products and enhance liquidity of the product.