Hedge funds involve a higher level of complexity than other funds. As a result, hedge funds require more analytical prowess to address the complexity as well as asymmetric returns. Further, hedge funds analysis is not alien to the rapid expansion of data in the past few years. The complex hedge fund operations can be managed via advanced data analytics. Such an analytics solution will consider a number of aspects that can impact the performance of the hedge fund. The data analytics will also help to unite the large amounts of consumer and public data with numerical optimization.
Hedge funds are evaluated for both relative as well as absolute return performance. While the above evaluation is also done for mutual funds, the process is complex for hedge funds as there a variety of strategies whose effectiveness may differ based on the uniqueness of the hedge funds. For instance, absolute returns enable the investor to categorize the fund in comparison to the more conventional types of investments. Absolute returns are also known as total returns that monitor the loss or gain experienced by a fund. On the other hand, relative returns enable an investor to estimate a fund’s attractiveness as compared to other investments. Determination of performance over certain timer periods like five-year annualized returns is the key to evaluating relative returns. Therefore, an advanced analytics solution is much needed to capture the above complexities associated with hedge funds.
Data analytics is largely used for quantitative funds. Quantitative funds or quant funds select securities by using the capabilities of the latest quantitative analytics. While analysts and investment managers comprise the major part of the team along with traders in standard hedge funds, quant hedge funds require additional talent in programming and quant. Such a team might include programmers and statisticians who develop analytical tools to help the traders. The main objective of the analytics solutions is to assist the traders in unraveling patterns, signals, and correlations that can enable them to outperform the market.
While analytics is an important aspect of trading, its essence can be largely felt in hedge fund management. Data-driven analytics solution will allow the hedge fund managers to beat the market to gain better returns.
Hedge Funds are now dictated by Technology:
Financial market generates a humungous amount of data on a daily basis. This has augmented the information base of stock market about many industry verticals that were previously unknown. The data deluge in conjunction with the rise of cloud computing has inspired hedge fund organizations to devise strategies that can boost their return on investment. Most large hedge funds already have powerful computing infrastructure in place to adapt to the technology evolution. From high-frequency trading to implementing machine learning or data-science and other quantitative methods, such corporations have it all. Although machine and data can take care of all the processing and analysis requirements, the onus of finding the right technology is on humans.
Technology can offer a variety of options to hedge funds to fabricate methods that benefit investors. According to a report, a few years ago, owing to the proliferation of high trade computing, many organizations built networks of microwave towers and even reserved space on trans-Atlantic fiber optic cables to eliminate competition by a fraction of seconds. In the last couple of years, hedge funds have driven the wheel of innovation and have incorporated machine learning in their systems, and hand over key decisions to data scientists. This will go a long way in building a different breed of hedge funds that is completely independent of human interventions and is controlled by codes and machines.
With such developments, many organizations have begun to build investment algorithms that eliminate the need for manual intervention in hedge fund ecosystem. If this continues, most organizations in the trade sector are bound to embark on a financial era wherein they have to entrust their money to machines instead of a managers.
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