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Causal Analysis for Supporting Users' Understanding of Investment Trusts

While many governments have introduced financial schemes to encourage people to invest, it is difficult to understand investment trusts and decide which one to buy. To address that difficulty of understanding, a method for extracting causalities from monthly reports of investment trusts and visualizing them to support a potential investor's understanding of a trust is proposed. First, CRF is used to extract causalities from monthly reports. Note that features of financial reports other than linguistic features are also considered. Next, a causal network is constructed and visualized in consideration of the degrees of influence, frequency, and newness of the extracted causalities. The LOD control method is then applied to present causalities in consideration of the granularity of events appearing in the causal network. The results of a user evaluation demonstrate that proposed method performed better than a baseline method in terms of helping a user's understanding of investment trusts.