While　decision-making　task　is　critical　in　knowledge　transfer,　particularly　from　multi-source　domains,　existing　knowledge　transfer　approaches　are　not　generally　designed　to　be　privacy　preserving.　This　has　potential　legal　and　financial　implications,　particularly　in　sensitive　applications　such　as　financial　market　forecasting.　Therefore,　in　this　paper,　we　propose　a　Privacy-preserving　Multi-party　Knowledge　Transfer　system　(PMKT),　based　on　decision　trees,　for　financial　market　forecasting.　Specifically,　in　PMKT,　we　leverage　a　cryptographic-based　model　sharing　technique　to　securely　outsource　knowledge　reflected　in　decision　trees　of　multiple　parties,　and　design　a　secure　computation　mechanism　to　facilitate　privacy-preserving　knowledge　transfer.　An　encrypted　user-submitted　request　from　the　target　domain　can　also　be　sent　to　the　cloud　server　for　secure　prediction.　Also,　the　use　of　decision　trees　allows　us　to　provide　interpretability　of　the　predictions.　We　then　demonstrate　how　PMKT　can　achieve　privacy　guarantees,　and　empirically　show　that　PMKT　achieves　accurate　forecasting　without　compromising　on　accuracy.　©　2020　Elsevier　B.V.