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Journal of Economic Dynamics and Control
, Pages 45-59
We develop a dynamic valuation model of the hedge fund seeding business by solving the consumption and portfolio-choice problem for a risk-averse manager who launches a hedge fund through a seeding vehicle. This vehicle, i.e. fees-for-seed swap, specifies that a strategic partner (seeder) provides a critical amount of capital in exchange for participation in the funds revenue. Our results indicate that the new swap not only solves the serious problem of widespread financing constraints for new and early-stage funds (ESFs) managers, but can be highly beneficial to both the manager and the seeder if structured properly.
There has been a significant increase in both the number of hedge fund seeders and the amount of capital available for hedge fund seeding since the aftermath of 2008׳s market upheaval.1 However, there still remains a tremendous shortage of capital for new and early-stage funds (ESFs). This is mainly because most capital providers or institutional investors increasingly focus on larger established hedge funds whose assets under management (AUM) are usually larger than 1 billion and who are considered highly credible. Additionally a larger talent pool of ESFs managers is now competing for the scarce available seed capital. Worse still, barriers to entry for ESFs are much higher today than in the period before the 2008 financial crisis.2
Therefore, navigating the terrain to a successful launch of a hedge fund has become more difficult and the financing constraint faced by ESFs managers nowadays is much more serious than before. In order to reach the initial AUM target and cover organizational expenses, more and more ESFs managers are likely to turn to seed investors for early stage of capital through a seeding vehicle. This is an arrangement to which we refer as fees-for-seed swap that specifies that a seed investor (or seeder) commonly commits to providing a remarkable amount of seed capital to an ESFs manager as an “anchor investor” in a new fund in exchange for a share of “enhanced economics” which is usually the fees that the ESFs manager generates from the entire pool of assets in the fund. If structured properly, the seeding approach can be highly beneficial to the ESFs manager and to investors who provide the seed capital. It is not uncommon that the hedge fund seeder receives a portion of the hedge fund׳s revenue stream to get greater return potential than an ordinary investor.
In general a seeder can expect about 1% of revenues for each$1 million of seed capital for seed transactions no larger than$50 million. However, seed arrangements can vary substantially based on factors such as the experience of the manager, the alpha record, the amount of seed capital provided, the withdrawal and lock-up period terms, and the relative negotiating power of each party.3
While the seed investor will often demand the flexibility to redeem her4 investment as soon as possible, the manager needs (and should require) the seed capital to remain invested for a period sufficient to set its strategy, create a track record, and procure other investors. Generally, during the lock-up period, the seed investor should be prohibited from redeeming the investment if, in the reasonable judgment of the manager, doing so could adversely affect the interests of the other investors in the fund.5
The ordinary investors may withdraw capital if the fund shows poor performance. For simplicity, we assume that the withdrawal rate is constant. This is a common assumption in the hedge fund literature and has been employed by Goetzmann et al. (2003) and Lan et al. (2013). Also, depending on the terms of the deal, the seeder will generally commit to keep the investment in the fund for a defined lock-up period, typically two to four years. It makes sense to assume that during this initial phase, idiosyncratic risks take a more pronounced role as compared to later stages in the fund׳s life, possibly due to ordinary investors entering (or leaving) the fund and/or the fund manager experimenting with different asset classes in order to set up a successful strategy. As seed commitments expire, AUM will be divided among the ordinary investors, the seeder, and the ESFs manager according to a ”waterfall” schedule.6 After the initial seeding stage, the fund becomes more stable and in our idealized setup we assume that the ESFs manager no longer bears any idiosyncratic risk after the lock-up period has been completed. Therefore, we can apply Goetzmann-Ingersoll-Ross׳ (Goetzmann et al., 2003) model to calculate the market value of the fund at termination of the lock-up period. While the manager׳s performance incentives during the lock-up period are implemented through a waterfall schedule, performance incentives after the lock-up period are provided by a high-water-mark (HWM) incentive, compare (Goetzmann et al., 2003).
As there is no publicly available data on the historical performance of seeding strategies, there are only very few simple models in practice focusing on hedge fund seeding return, volatility and liquidity profile.7 To our knowledge, this paper provides the first dynamic framework on valuation of the hedge fund seeding business by solving the portfolio-choice problem for a risk-averse manager.
Several other studies evaluate the performance of hedge funds focusing on different aspects. Goetzmann et al. (2003) provide the first quantitative inter-temporal valuation framework of investors׳ payoff and managers׳ fees in a setting where the fund׳s value follows a log-normal process and the fund managers have no discretion over the choice of portfolio. Carpenter (2000) shows that it is optimal for hedge fund managers who face no explicit downside risk to choose infinite volatility as asset value goes to zero. This behavior is referred to as risk-shifting. Basak et al., 2007, Hodder and Jackwerth, 2007 and Aragon and Nanda (2012) argue though that a manager׳s convex payoff structure does not necessarily induce risk shifting when the fund shows poor performance as long as the manager is exposed to downside risk, either through her ownership of fund share or through her annual fees. Panageas and Westerfield (2009), and Lan et al. (2013) analyze the impact of management fees and high-water mark based incentive fee on leverage and valuation. None of these studies, however, model the hedge fund seeding innovation in the context of the ESFs manager׳s portfolio choice problem, and hence they do not assess the costs of illiquidity and unspanned risk of hedge fund seeding investments.
Our article also relates to the literature about valuation and portfolio choice with illiquid assets, such as restricted stocks, executive compensation, illiquid entrepreneurial businesses, and private equity (PE) investments. For example, Kahl et al. (2003) analyze a continuous-time portfolio choice model with restricted stocks. BothChen et al. (2010) and Wang et al. (2012) study entrepreneurial firms with unspanned idiosyncratic risks under incomplete markets. For PE investments, Sorensen et al. (2014) develop a dynamic valuation model of PE investments by solving the portfolio-choice problem for a risk-averse investor, who invests in a private equity fund, managed by a general partner. We are unaware, though, of any existing models that capture the illiquidity, managerial skill (alpha), risk attitude and compensation of the hedge fund seeding business. Capturing these important features in a model that is sufficiently tractable to determine the subjective value of fees in the hedge fund seeding business is one of the main contributions of this study.
The paper is organized as follows. Section 2 presents a dynamic valuation framework for modeling hedge fund seeding innovation and the impact of incentive contracts, managerial stake and hedge fund liquidation on a risk-averse ESFs manager׳s consumption and portfolio-choice behavior. A solution for this model is derived in Section 3. 4 Breakeven alphas and seed costs, 5 Seeding investments with lock-up period, subjective value of fees, and idiosyncratic risk discuss numerical results for breakeven alphas, seed costs and subjective value of management compensation. The main conclusions are summarized in Section 6. The appendix provides detailed computations relating to the market value of the hedge fund after the initial seeding stage.
Hedge fund seeding investment opportunities
We consider an infinitely-lived risk-averse ESFs manager who has the opportunity to launch a take-it-or-leave-it hedge fund at present time 0, which requires to raise the target AUM S0. All sources of uncertainty arise from two independent standard Brownian motions B and Z defined on a filtered probability space , where describes the flow of information available to the seeder.
In addition to the opportunity of launching a fund, the manager has access to standard
In this section, we first derive seed costs with fees-for-seed swaps for the ESFs manager and the breakeven alpha for the ordinary investors. Then we analyze the manager׳s consumption and portfolio choice in a dynamic valuation model taking account of illiquidity, ESFs managers׳ value-adding skills (alpha), incentive compensation, and the fees-for-seed swap. However, the idiosyncratic risk which is present in the hedge funds seeding business invalidates the standard two-step complete-markets
Breakeven alphas and seed costs
We have derived the solution of the fees-for-seed swap portion ψ referred to as the seed cost and the subjective value of the ESFs manager׳s compensation in Section 3 In this section we provide some numerical results in order to develop more economic intuition. Baseline breakeven parameters are chosen according to Sorensen et al. (2014). Table 1 summarizes the parameter values used in our baseline breakeven case.
Seeding investments with lock-up period, subjective value of fees, and idiosyncratic risk
In this section, we further analyze the effects of the lock-up period, seeding investment, and idiosyncratic risk on management compensation, illiquidity discount, and the economic value of the fund.
In this article, we developed a dynamic valuation model for the hedge fund seeding business by solving the consumption and portfolio-choice problem of a risk-averse manager who launches a hedge fund through a seeding vehicle. As traditional approaches to attract the initial AUM and covering of organizational expenses becomes much harder for ESFs managers in a much tighter financial landscape, nowadays more and more ESFs managers are likely to turn to seed investors for early stages of capital
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- M. Kahl et al.Paper millionaires: how valuable is stock to a stockholder who is restricted from selling it?
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Out of the dark hedge fund reporting biases and commercial databases
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Tournament behavior in hedge funds high-water marks, fund liquidation, and managerial stake
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There are more references available in the full text version of this article.
- Robust leverage decision under locked wealth and high-water mark contract
2022, Finance Research Letters
This paper investigates the effects of ambiguity on the optimal fund leverage and the private risk-taking of a hedge fund manager with a high-water mark contract and locked wealth in the fund. We derive a closed-form solution that predicts an increase in ambiguity about the market risk has a U-shaped effect on the optimal fund leverage but a monotonic decreasing effect on the private risk-taking. An increase in ambiguity about the idiosyncratic risk reduces the optimal fund leverage and raises the private risk-taking. According to the simulation, ambiguity erodes the fund wealth and the private wealth of the manager. Consumption-wealth ratio and welfare of the manager are both reduced as ambiguity increases.
- Real options, risk aversion and markets: A corporate finance perspective
2022, Journal of Corporate Finance
We analyze how the presence of financial markets affects the optimal exercise of real options for a risk averse agent. Extending the results of Shackleton and Sodal (2005), we characterize the optimal exercise rule in terms of a benchmark portfolio, even for the case of an incomplete market, facilitating the minimal martingale measure. We unambiguously characterize the effect of idiosyncratic risk on the speed of exercise of the option. We further show that systematic risk can accelerate execution and reduce the value of a call-type option, in contrast with the standard view that both value and execution threshold are increasing in volatility.(Video) Giovanni de Francisci: The "unconventional" hedge fund investor
Hedge fund seeding with fees-for-guarantee swaps
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Journal of Economic Dynamics and Control, Volume 70, 2016, pp. 101-123
Inspired by the α-maxmin expected utility, we propose a new class of mean-variance criterion, called α-maxmin mean-variance criterion, and apply it to the reinsurance-investment problem. Our model allows the insurer to have different levels of ambiguity aversion (rather than only consider the extremely ambiguity-averse attitude as in the literature). The insurer can purchase proportional reinsurance and also invest the surplus in a financial market consisting of a risk-free asset and a risky asset, whose dynamics is correlated with the insurance surplus. Closed-form equilibrium reinsurance-investment strategy is derived by solving the extended Hamilton–Jacobi–Bellman equation. Our results show that the equilibrium reinsurance strategy is always more conservative if the insurer is more ambiguity-averse. When the dependence between insurance and financial risks are weak, the equilibrium investment strategy is also more conservative if the insurer is more ambiguity-averse. However, in order to diversify the portfolio, a more ambiguity-averse insurer may adopt a more aggressive investment strategy if the insurance market is very ambiguous. For an ambiguity-neutral insurer, the investment strategy is identical to the non-robust investment strategy.
Research articleEnvelope condition method with an application to default risk models
Journal of Economic Dynamics and Control, Volume 69, 2016, pp. 436-459
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Journal of Economic Dynamics and Control, Volume 69, 2016, pp. 350-374
There is strong empirical evidence for Cobb–Douglas matching functions. We show in this paper that this widely found relation between matches on the one hand and unemployment and vacancies on the other hand can be the result of different underlying mechanisms. Obviously, it can be generated by assuming a Cobb–Douglas matching function. Less obvious, the same relationship results from a vacancy free-entry condition and idiosyncratic productivity shocks. A positive aggregate productivity shock leads to more vacancy posting, a shift of the idiosyncratic selection cutoff and thereby more hiring. We calibrate a model with both mechanisms to administrative German labor market data and show that idiosyncratic productivity for new contacts is an important driver of the elasticity of the job-finding rate with respect to the market tightness. Accounting for idiosyncratic productivity can explain the observed negative time trend in estimated matching efficiency and asymmetric business cycle responses to large aggregate shocks.
Research articleDoes joint modelling of the world economy pay off? Evaluating global forecasts from a Bayesian GVAR
Journal of Economic Dynamics and Control, Volume 70, 2016, pp. 86-100
We analyze how modeling international dependencies improves forecasts for the global economy based on a Bayesian GVAR with SSVS prior and stochastic volatility. To analyze the source of performance gains, we decompose the predictive joint density into its marginals and a copula term capturing the dependence structure across countries. The GVAR outperforms forecasts based on country-specific models. This performance is solely driven by superior predictions for the dependence structure across countries, whereas the GVAR does not yield better predictive marginal densities. The relative performance gains of the GVAR model are particularly pronounced during volatile periods and for emerging economies.
Research articleTesting for time variation in an unobserved components model for the U.S. economy
Journal of Economic Dynamics and Control, Volume 69, 2016, pp. 179-208
This paper analyzes the amount of time variation in the parameters of a reduced-form empirical macroeconomic model for the U.S. economy. We decompose output, inflation and unemployment in their stochastic trend and business cycle gap components, with the latter linked through the Phillips curve and Okun׳s law. A novel Bayesian model selection procedure is used to test which parameters vary over time and which components exhibit stochastic volatility. Using data from 1959Q2 to 2014Q3 we find substantial time variation in Okun׳s law, while the Phillips curve slope appears to be stable. Stochastic volatility is found to be important for cyclical shocks to the economy, while the volatility of permanent shocks remains stable.
Research articleAsymmetric Effects of Exogenous Tax Changes
Journal of Economic Dynamics and Control, Volume 69, 2016, pp. 268-300
We study whether output responds symmetrically to tax increases and decreases in postwar US data, using the identification strategy in Romer and Romer (2010). We find evidence of important asymmetries: the output response to a tax increase is statistically insignificant, but output shows a significantly positive and permanent increase following a tax decrease. We show that this asymmetry appears to be driven by individual-income tax changes, and is transmitted to the economy through asymmetric response in aggregate consumption to tax increases and tax decreases. We also present a simple model that rationalizes our empirical findings, and illustrates how asymmetric output and consumption responses to sign-based tax changes can be generated by plausible consumption-adjustment costs.
© 2016 Elsevier B.V. All rights reserved.
A seeding vehicle commits capital to individual hedge fund managers for a certain number of years and as those commitment periods expire, money is available to be reinvested or returned to investors in the seed vehicle. If reinvested, the money may be subject to the standard liquidity terms of the seeded hedge fund.
A seed deal is essentially an agreement by an investor to invest an agreed (often significant) amount of capital in a manager's fund for a locked-up period in exchange for participation in the manager's business and/or certain other beneficial investment terms.
A hedge fund raises its capital from a variety of sources, including high net worth individuals, corporations, foundations, endowments, and pension funds.
An incubated fund is a fund that is first offered privately in an incubation period. Investors in this type of fund are usually employees associated with the fund and their family members. Hedge funds also commonly use incubated funds to test new strategies and offerings.
If it is a small enough amount of money, you'll be able to pay them back over time even if the venture fails. If the venture succeeds, you can pay them back quickly and you have not given up any stake in the company.
The term seed capital refers to the type of financing used in the formation of a startup. Funding is provided by private investors—usually in exchange for an equity stake in the company or for a share in the profits of a product.
The TLDR; seed investors shoot for a 100x return; Series A investors need an investment to return 10x to 15x and later stage investors aim for 3x to 5x multiple of money. This translates into portfolio returns from 20% to 35% targeted IRRs.
Yes, you could start with much less capital, or go through a hedge fund incubator, or use a “friends and family” approach, or target only high-net-worth individuals. But if you start with, say, $5 million, you will not have enough to pay yourself anything, hire others, or even cover administrative costs.
- Identifying the firm.
- Investment strategy.
- Performance results.
- Marketing strategy.
- Firm management.
- Equity knowledge.
How much does it cost to establish an incubator fund? One of the primary catalysts for the growing popularity of the incubator fund is its cost effectiveness. Generally, an incubator hedge fund can be created for $2,500 – $3,500, plus state filing fees to establish the fund and the management company.
Most accelerators and incubators provide mentorship and funding to startups in exchange for some equity, while others do it for a separate programme fee. Both programmes mentor startups and help turn promising ideas into sustainable businesses.
An incubator fund is generally set up using a pass-through fund entity (typically a Delaware limited partnership). The fund's realized income and gains are only taxed at the individual level for federal income tax purposes. Taxation at the state level will vary depending on state.
Seed capital rounds: (founders, F&F, employees and angel investors): expect anywhere from 10 percent to 25 percent as a normal range, with a median 15 percent dilution to be realistically expected. Series A round: 25 percent to 50 percent dilution is the typical range.
"There is no set standard, the amount of equity will depend upon the valuation and amount raised. However, as a target figure, founders shouldn't share more than 33% of the equity in a seed round."
|Pre-Seed Funding||Seed Funding|
|Funding Amount||Within $100 – $250k range||Less than $5 million.|
Corporate Seed Funds
Usually, mega-corporations and tech giants are looking for a way to invest in new innovation that they may spot in the market. This source of funding brings big visibility for the startup brand and is usually an early indication of an acquisition in the future.
In a series A round, founders are advised to give up around 20-25% of equity to investors. These equity investments are often dependent on the kind of startup or business. Some businesses may give up more, while others must give out less equity.
Your balancesheet will reflect the seed money as your equity (ownership) in thecompany. It isn't income. Income is money that comes into thebusiness as a result of sales or interest on invested money. Yourseed money is investment capital, and you're the investor.
The general rule of thumb for angel/seed stage rounds is that founders should sell between 10% and 20% of the equity in the company. These parameters weren't plucked out of thin air, they're based on what an early equity investor is looking for in terms of return.
- Fund it yourself. It might not sound ideal, but dipping into your personal savings is probably the easiest way to raise capital for a startup. ...
- Business loan. ...
- Crowdfunding. ...
- Angel investment. ...
- Personal contacts. ...
- Venture capitalist.
Raising a fund can take substantially longer than raising money for a single investment. Depending on interest from investors and the timeline to complete compliance requirements, a sponsor should expect to spend at least six months on a fund, and the process can often take more than a year from concept to close.
- Seed-capital providers.
- Family and friends.
- High net-worth individuals.
- Financial advisors.
- Wealth-management offices and RIAs.
- Single- and multi-family offices.
- Fund of hedge funds.
- Define your strategy. The first thing you need to do is define your investment strategy as clearly as possible. ...
- Incorporate. ...
- Complete the proper registrations. ...
- Write your investment agreement. ...
- Get your team together. ...
- Market yourself. ...