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DC Field | Value | Language |
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dc.contributor.advisor | Mulvey, John M. | - |
dc.contributor.author | Fu, Shirley | - |
dc.date.accessioned | 2017-07-19T16:09:20Z | - |
dc.date.available | 2017-07-19T16:09:20Z | - |
dc.date.created | 2017-04-16 | - |
dc.date.issued | 2017-4-16 | - |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/dsp01hx11xh867 | - |
dc.description.abstract | In this study, we approach the long debated question of whether active fund managers are able to beat the market from a regime-based perspective. First, we separate the S&P 500 monthly returns time series into two regimes, growth regime and crash regime, through a trend-filtering algorithm. Then, under each regime, we compare five actively managed mutual fund categories against corresponding indices. These five categories are large-cap, medium-cap, small-cap, growth, and value. We found that in the growth regime, it is optimal to invest in small-cap actively managed funds. If the market is in a transition phase or unclear condition, actively managed large-cap funds or indices are preferred. Proceeding with an attempt to understand underlying drivers of these returns, we choose factor analysis as an explanatory mechanism. Through factor analysis, we found that small-cap and high-momentum factors drive returns in the growth regime and the value factor drives returns in the crash regime. The momentum factor plays a minimal role in driving returns in both regimes. Thus, it should not be the main metric for making investment decisions. | en_US |
dc.language.iso | en_US | en_US |
dc.title | REGIME-BASED ANALYSIS OF ACTIVELY MANAGED MUTUAL FUND RETURNS VERSUS EQUITY INDEX RETURNS | en_US |
dc.type | Princeton University Senior Theses | - |
pu.date.classyear | 2017 | en_US |
pu.department | Operations Research and Financial Engineering | en_US |
pu.pdf.coverpage | SeniorThesisCoverPage | - |
pu.contributor.authorid | 960888559 | - |
pu.contributor.advisorid | 010004005 | - |
pu.certificate | Applications of Computing Program | en_US |
Appears in Collections: | Operations Research and Financial Engineering, 2000-2019 |
Files in This Item:
File | Size | Format | |
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ShirleyFu_Thesis_Final.pdf | 7.05 MB | Adobe PDF | Request a copy |
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