Journal of Investment Strategies
ISSN:
2047-1238 (print)
2047-1246 (online)
Editor-in-chief: Ali Hirsa
Volume 9, Number 3 (September 2020)
Editor's Letter
Ali Hirsa
Managing Partner, Sauma Capital LLC & Professor, Columbia University
Welcome to the third issue of the ninth volume of The Journal of Investment Strategies. In this issue you will find three papers. They cover the price of liquidity in there insurance of fund returns, sign prediction and sign regression, and realized profits on the Stationary Offshore Ocean Economy (SOOE).
In our first paper, “The price of liquidity in the reinsurance of fund returns”, David Saunders, Luis Seco and Markus Senn aim to extend downside protection to an investment portfolio beyond the first tranche of losses insured by a first-loss principle. By considering a second tranche, they suggest an up-front premium to a reinsurance party, in exchange for which the investor gains full protection against all losses, not just those occurring in the first tranche. The authors identify a fund’s underlying liquidity as a key parameter in deriving the price for the additional reinsurance. They provide a method for computing the premium using two approaches: an analytic closed-form solution based on the Black–Scholes framework, and a numerical simulation using a Markov-switching model. In addition, they implement a simplified backtesting method to evaluate the practical application of the concept.
In this paper, the authors suggest a total of two tranches. As an extension to their work, they suggest creating a fund structure similar to an asset-backed security containing several tranches, each with a different risk and return profile. The parameters could also be altered to a point where both the reinsurance and the manager earn a fixed fee and a performance-dependent fee. In their approach, the fund’s underlying liquidity is assumed to be homogeneous with daily liquidation steps. Combining this with certain liquidation time distributions, eg, an exponential or Weibull distribution, and the optimization of the liquidation process that comes along with the issue could be considered.
In “Sign prediction and sign regression”, our second paper, Weige Huang proposes a new approach –termed “sign regression” – where the loss function considers errors in predicted signs and the sizes and signs of the residuals in the model prediction simultaneously. Weige shows that sign regression generates lower Sharpe ratios than ordinary least squares (OLS) for most assets. However, sign regression does perform better for some assets.
Weige argues that investors suffer much more from incorrectly predicting the sign of the return of a financial strategy than from simply underestimating or overestimating the return while correctly predicting the sign, as, in the latter case, they at least would not suffer financial losses. The author also argues that the sign of the residual itself matters to investors. To address this problem, a weighted linear regression is introduced so that residual error in the training set is penalized more if the forecasted sign is different to that of the realized one, or if the sign of the residual is negative. This regression, called signed regression, is then tested against OLS and least absolute deviation on both simulated and financial data. It is shown that the signed regression produces a smaller objective function than OLS in all of the cases considered.
In the issue’s third paper, “Realized profits on the Stationary Offshore Ocean Economy: an analysis”, Jeremy Van Dyken, Houshang Habibniya and Maia Chiabrishvili provide a novel perspective on investment opportunities related to the ocean economy. The authors structure an SOOE selection framework that might form a meaningful asset class and an investable index.
This paper analyzes the recent financial performance of the publicly traded companies that employ stationary structures on the open ocean using a comprehensive Thomson Reuters Eikon search. These companies are found in the offshore aquaculture, wind and drilling sectors together with their respective equipment providers, and in combination they comprise the SOOE. The findings indicate that offshore aquaculture, though still in its infancy, is emerging noticeably and showing promise. In this study, the performance of offshore wind providers was generally very favorable, while the performance of offshore wind equipment manufacturers tended to be weak, though this was sometimes mixed. The performance of offshore drilling services also varied, having been heavily influenced by the 2014 oil price declines.
As economic development continues offshore, it is expected that a broad array of new economic opportunities and systems may emerge. These opportunities and systems could lead to greater flourishing and upward mobility in the global population. Future research should focus on identifying industries that may emerge from moving offshore to the extreme, that is, to the point of being outside of the exclusive economic zones of any country.
On behalf of the editorial board, we hope you are doing well during the Covid-19 pandemic. We would like to thank our readers for their continued support of, and keen interest in, The Journal of Investment Strategies. We look forward to sharing with you the growing list of practical papers on a broad variety of topics related to modern investment strategies that we continue to receive from both academics and practitioners.
Papers in this issue
The price of liquidity in the reinsurance of fund returns
The authors consider a new type of contract for insuring the returns of hedge funds and aim to extend downside protection to an investment portfolio beyond the first tranche of losses insured by first-loss fee structures, which have become increasingly…
Realized profits on the Stationary Offshore Ocean Economy: an analysis
This paper analyzes the recent financial performance of the publicly traded companies that employ stationary structures on the open ocean using a comprehensive Thomson Reuters Eikon search.
Sign prediction and sign regression
This paper proposes an approach whereby the loss function regularizes the errors in prediction in different ways.