Complex and noisy financial eco-system requires reliable models and proven techniques to predict the market movements and investor decisions. This study uses competent soft computing techniques: rough set theory (RST) and formal concept analysis (FCA) to study the investors' preferences, behavioural drivers and their actual behaviour in Gold-ETF (G-ETF) market. G-ETF, though a safe-haven and an alternate for reducing portfolio risks, inherits all complexities of financial markets. The employed RST helps in generating decision rules; and FCA to identify key factors affecting investment decision. This study is first of its kind, as integration of the foresaid techniques was not employed to study financial behaviour, earlier. The study has analysed 250 responses of G-ETF investors, in 12 listed G-ETFs, to conclude with a rich insight on the investment decisions discretised by different decision rules, strongly recommending the combined use of RST and FCA for data driven decisions. © 2020 Inderscience Enterprises Ltd.