| dc.description.abstract | When there are funds with excellent performance in the market, how to search for their existence is the doubt in the minds of most investors and economists. However, no matter what method is used to test, as long as people try to gradually verify the performance of each fund with multiple null hypotheses, they will face a difficult problem needed to overcome, that is, the problem of multiple testing. In the vast fund market, the problem of multiple testing will be significantly worsened with the expansion of the sample size. Therefore, most of the targets found in the process are full of false discoveries, which means that the fund is not an excellent one but is sometimes certified as a fund with good performance, affecting the correctness of the overall testing result. With an aim of rigorously searching for the wanted target while controlling the possibility of false discovery, we need to make a series of adjustments to moderately balance the tradeoff of improving the testing power and controlling the false discovery rate. First, limit the duration of the fund. Next, use machine learning to make up for the lack of fund data. Then, use wild bootstrap to establish a sample distribution close to the matrix. Finally, employ Benjamini and Hochberg procedure to control the occurrence of errors. Eventually, we can discover funds that really have significantly positive returns from the vast fund market. A total of 138 funds has been identified as excellent funds by at least one model, and the highest internal rate of return among all portfolios is as high as 9.76%, surpassing the returns of the US market portfolio. | en_US |