Portfolio management system
Studies by a leading US-based research firm have found that most investors in financial markets do not know what they are doing and are simply chasing ‘hot’ stocks. On average, over a year long holding period, while the S&P 500 Index provided 32% returns, investors were 6% lower in terms of the...
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| 主要作者: | |
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
2019
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| 主題: | |
| 在線閱讀: | http://hdl.handle.net/10356/78376 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Studies by a leading US-based research firm have found that most investors in financial markets do not know what they are doing and are simply chasing ‘hot’ stocks. On average, over a year long holding period, while the S&P 500 Index provided 32% returns, investors were 6% lower in terms of the return on investment. This clearly proves that there is a lack of knowledge and/or tools for uninformed investors to make sure they can beat the market. Every investment is a trade-off between risk and reward and portfolio management is the art and science of balancing the two. In the past, there have been several attempts at designing optimum portfolios across the world. However, they have been limited in scope, in terms of limiting to a single country or not considering the needs of young investors. Students in Singapore have previously attempted to optimize a portfolio using the constituents of the Straits Times Index (STI). Although they were successful at getting reasonable returns in the range of about 20%, the risk associated with the same was nearly 10%-20%. There were also certain other limitations in that approach, and therefore, there is a need to design a new model that can generate higher returns at the same risk levels. This project is primarily targeted at young adults, who wish to beat the market in both bullish and bearish years. The aim of the project is to be able to lay down the best method for young investors to distribute their stock portfolio, based on their risk appetite. By using the powerful Data Solver Add-In for Microsoft Excel, I have met the objectives laid out in the upcoming chapters, by being able to generate as much as 45% Return on Investment in a single year when the index rose by just 5%. Some of the tools used for this are Python, Excel, VBA scripts and knowledge of statistics, matrices and the related calculations. |
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