Hedge Funds: Traditional vs Decentralize
Putting aside legislative issues, hedge funds may be presented as a pool of deposits managed by so called fund managers. Commonly, hedge funds manage securities of different firms, thus creating an overall pool. Since none of the securities dominates in the pool, all risks are reduced guaranteeing reliability and safety.
Hedge funds are different. Their main idea is that fund managers have information about market fluctuations and stock volatility. They are able to consider different opportunities finding the ones that fit best. After the first investment is made, additional investors will pay attention to the fund policy. The main difference among funds here is what exactly information a manager has and how the whole fund is managed.
All decisions can be generated and accepted in two ways, traditional and decentralized.
The traditional strategy to generate decisions is the personnel management. Several professionals are hired and asked to generate an investment strategy. Depending on their proposal efficiency those managers will receive bonuses and benefits.
The better the proposed solution is, the more bonuses a manager receives. Basically, a single hedge fund has more than two hundred managers generating profitable solutions both.
Is it possible to increase the number of managers? The answer is in the decentralized decision-making strategy.
Blockchain implementation proved it is possible to create a distributed protocol that could unite people that do not have to trust to each other but still can fairly share a profit, manage voting, protect themselves from malicious behavior, etc. When it comes to hedge funds, this innovation works perfectly. Let’s illustrate this.
Many people make market research and then place bets (or votes) on different proposals that can be accepted by the fund. All those bets are accumulated and combined. The most popular proposal will be accepted by the hedge and all the participants will get their rewards in proportion to their stakes values.
At first sight, making decisions this way is very simple, considering economic issues. Unfortunately, things are more complex. Old-fashioned hedge funds use predictions based on unique insight. This approach might be inherited by their decentralized descendants. It is crucially important to use data unknown by the most market players.
A few interesting examples exist. These are the Numerai project that provides a protocol for organizing data scientists, and the God Token startup that allows organizing miners all over the globe. Regardless of the fact that distributed protocols are complex in real life, this approach is applicable to hedge funds. The innovative technology in this case is supported by economic features, which are networking, membership, and profit distribution.
The main purpose of opening a decision-making process is to involve as many participants as possible. The more people enter the community, the better. It is important to remove competition among participants, otherwise one group of people may start doing its best to hinder the activity of others. As a result, both funds and investors will lose opportunities. The best option is to make participants to attract new members instead of messing with the old ones.
Network participants are not equal. Apparently, if they differently contribute to the system, their actions should be treated in different ways. This issue is resolved by implementing so called popularity rating. Thus, all votes made by participants are to be evaluated and estimated. The higher the reputation is, the more valuable the vote of a particular member. Zero reputation means that the person has just joined the community. At the same time, the idea of adding a confidence level to the bets is much more difficult. It means that each participant must support its stake with additional bets.
As we mentioned above, the more reliable participants are, the more profit they can gain. The profit is to be calculated with the same function. It could be a function based on the reputation or some other features. Specific implementation is irrelevant and may vary.