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We are running a portfolio of fund managers in our fund. When one of the managers hits the max DD constraint we pull money from this manager. This may happen in the middle of the allocation period and we need to reinvest the money to the other managers. We cannot decrease the allocations for the remaining managers. What is the smart way to allocate the money we have pulled? I suspect it is easiest to answer this question in the MVO framework.

Any ideas and references are really appreciated.


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I voted to close this as it asks for a "smart way". The adjective "smart" points to something open-ended and subjective, all of which, with agreement from @Quantlbex, validates removal. –  madilyn Jul 19 '13 at 5:20
Here is the formal question. Let (w_1, w_2, w_3, ..., w_N) be the optimal mean-variance allocation that solves min w^t \Sigma w subject to <w, r>=c. Assume that we pool money from the manager "N". How to allocate money (v_1, ...,v_{N-1}) to managers 1,..,N-1 in such a way that v_i>w_i and the final portfolio lies on the efficient frontier? The problem is that usually removal of one manager will leave only a small portion of n efficient frontier. How to select the new value of c for optimisation –  vkrouglov Jul 19 '13 at 11:53
@kristine - I feel the better course of action, here, is to edit the user's question to be less subjective, rather than focus on one word. I'm all for closing questions for predefined reasons, but not before resolving linguistic ambiguities and seeing if the question can be useful in clearer context or wording. –  Andrew Cheong Jul 19 '13 at 13:28
@kristine What did Quantlbex write on here? –  chrisaycock Jul 19 '13 at 16:19
Just to clarify, I haven't written here yet, and I did NOT agree to close this question. I guess @kristine is refering to (part of) the justification I gave (based on help center) to close one of her questions which was about conferences and networking events. –  QuantIbex Jul 19 '13 at 17:27

1 Answer 1

You should have a look at chapter 8 (p. 261ff.) of
Hedge Fund Market Wizards by Jack D. Schwager

Excerpt from there (but it is much more detailed in the book):

Perhaps the most potent risk control Platt employs in BlueCrest’s discretionary strategy is maintaining an extremely tight rein on what a trader can lose before capital is withdrawn. A mere 3 percent loss is enough to trigger a 50 percent reduction in a trader’s allocation, and the same small additional percentage loss is all it takes to remove a trader’s entire allocation. These rigid rules seek to prevent any trader from losing more than 5 percent of his initial stake. (The combination of two successive 3 percent losses is less than a 5 percent loss because the second 3 percent loss is incurred on only 50 percent of the starting stake.) In his own trading book, Platt is subject to the same rules as his traders, but he has never approached the 3 percent loss point.

You would think that with such extreme loss limitations, it would be very difficult for individual traders, and in turn the strategy, to make much money. It seems that with only 3 percent leeway before their capital allocation is slashed that traders would be risking too little on their trades to make much of a return. How then has the discretionary strategy managed to average nearly a 14 percent per year net return? The key is that the 3 percent/3 percent risk rule applies to a trader’s starting stake. So certainly, the rule encourages traders to be very cautious at the onset, being highly selective in their trades and tightly limiting the loss on any trade. But as traders get ahead, their cushion widens, as trading gains augment the small initial 3 percent loss allowance. Once they are comfortably in the black, traders can take much more risk, thereby creating the potential to achieve large returns, despite the highly restrictive initial loss limitation. Essentially, the trader allocation risk control strategy assures capital preservation, while at the same time keeping upside potential open-ended by allowing greater risk-taking with profits. It is, effectively, an asymmetric risk management strategy.

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Thanks for the reference. This is not what the question is about but the book seems very interesting –  vkrouglov Jul 16 '13 at 22:57

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