Enhanced　indexation　aims　to　construct　a　portfolio　to　track　and　outperform　the　performance　of　a　stock　market　index　by　employing　both　passive　and　active　fund　management　strategies.　This　paper　presents　a　novel　sparse　enhanced　indexation　model　with　chance　and　cardinality　constraints.　Its　goal　is　to　maximize　the　excess　return　that　can　be　attained　with　a　high　probability,　while　the　model　allows　a　fund　manger　to　limit　the　number　of　stocks　in　the　portfolio　and　specify　the　maximum　tolerable　relative　market　risk.　In　particular,　we　model　the　asset　returns　as　random　variables　and　estimate　their　probability　distributions　by　the　Capital　Asset　Pricing　Model　or　Fama-French　3-factor　model,　and　measure　the　relative　market　risk　with　the　coherent　semideviation　risk　function.　We　deal　with　the　chance　constraint　via　distributionally　robust　approach　and　present　a　second-order　cone　programming　and　a　semidefinite　programming　safe　approximation　for　the　model　under　different　sets　of　potential　distribution　functions.　A　hybrid　genetic　algorithm　is　applied　to　solve　the　NP-hard　problem.　Numerical　tests　are　conducted　on　the　real　data　sets　from　major　international　stock　markets,　including　USA,　UK,　Germany　and　China.　The　results　demonstrate　that　the　proposed　model　and　the　method　can　efficiently　solve　the　enhanced　indexation　problem　and　our　approach　can　generally　achieve　sparse　tracking　portfolios　with　good　out-of-sample　excess　returns　and　high　robustness.