532 research outputs found
Optimal Investment in the Development of Oil and Gas Field
Let an oil and gas field consists of clusters in each of which an investor
can launch at most one project. During the implementation of a particular
project, all characteristics are known, including annual production volumes,
necessary investment volumes, and profit. The total amount of investments that
the investor spends on developing the field during the entire planning period
we know. It is required to determine which projects to implement in each
cluster so that, within the total amount of investments, the profit for the
entire planning period is maximum.
The problem under consideration is NP-hard. However, it is solved by dynamic
programming with pseudopolynomial time complexity. Nevertheless, in practice,
there are additional constraints that do not allow solving the problem with
acceptable accuracy at a reasonable time. Such restrictions, in particular, are
annual production volumes. In this paper, we considered only the upper
constraints that are dictated by the pipeline capacity. For the investment
optimization problem with such additional restrictions, we obtain qualitative
results, propose an approximate algorithm, and investigate its properties.
Based on the results of a numerical experiment, we conclude that the developed
algorithm builds a solution close (in terms of the objective function) to the
optimal one
Optimal leverage from non-ergodicity
In modern portfolio theory, the balancing of expected returns on investments
against uncertainties in those returns is aided by the use of utility
functions. The Kelly criterion offers another approach, rooted in information
theory, that always implies logarithmic utility. The two approaches seem
incompatible, too loosely or too tightly constraining investors' risk
preferences, from their respective perspectives. The conflict can be understood
on the basis that the multiplicative models used in both approaches are
non-ergodic which leads to ensemble-average returns differing from time-average
returns in single realizations. The classic treatments, from the very beginning
of probability theory, use ensemble-averages, whereas the Kelly-result is
obtained by considering time-averages. Maximizing the time-average growth rates
for an investment defines an optimal leverage, whereas growth rates derived
from ensemble-average returns depend linearly on leverage. The latter measure
can thus incentivize investors to maximize leverage, which is detrimental to
time-average growth and overall market stability. The Sharpe ratio is
insensitive to leverage. Its relation to optimal leverage is discussed. A
better understanding of the significance of time-irreversibility and
non-ergodicity and the resulting bounds on leverage may help policy makers in
reshaping financial risk controls.Comment: 17 pages, 3 figures. Updated figures and extended discussion of
ergodicit
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Loss-size and Reliability Trade-offs Amongst Diverse Redundant Binary Classifiers
Many applications involve the use of binary classifiers, including applications where safety and security are critical. The quantitative assessment of such classifiers typically involves receiver operator characteristic (ROC) methods and the estimation of sensitivity/specificity. But such techniques have their limitations. For safety/security critical applications, more relevant measures of reliability and risk should be estimated. Moreover, ROC techniques do not explicitly account for: 1) inherent uncertainties one faces during assessments, 2) reliability evidence other than the observed failure behaviour of the classifier, and 3) how this observed failure behaviour alters one's uncertainty about classifier reliability. We address these limitations using conservative Bayesian inference (CBI) methods, producing statistically principled, conservative values for risk/reliability measures of interest. Our analyses reveals trade-offs amongst all binary classifiers with the same expected loss { the most reliable classifiers are those most likely to experience high impact failures. This trade-off is harnessed by using diverse redundant binary classifiers
Commercializing Biomedical Research Through Securitization Techniques
Biomedical innovation has become riskier, more expensive and more difficult to finance with traditional sources such as private and public equity. Here we propose a financial structure in which a large number of biomedical programs at various stages of development are funded by a single entity to substantially reduce the portfolio's risk. The portfolio entity can finance its activities by issuing debt, a critical advantage because a much larger pool of capital is available for investment in debt versus equity. By employing financial engineering techniques such as securitization, it can raise even greater amounts of more-patient capital. In a simulation using historical data for new molecular entities in oncology from 1990 to 2011, we find that megafunds of $5–15 billion may yield average investment returns of 8.9–11.4% for equity holders and 5–8% for 'research-backed obligation' holders, which are lower than typical venture-capital hurdle rates but attractive to pension funds, insurance companies and other large institutional investors
Study of statistical correlations in intraday and daily financial return time series
The aim of this article is to briefly review and make new studies of
correlations and co-movements of stocks, so as to understand the
"seasonalities" and market evolution. Using the intraday data of the CAC40, we
begin by reasserting the findings of Allez and Bouchaud [New J. Phys. 13,
025010 (2011)]: the average correlation between stocks increases throughout the
day. We then use multidimensional scaling (MDS) in generating maps and
visualizing the dynamic evolution of the stock market during the day. We do not
find any marked difference in the structure of the market during a day. Another
aim is to use daily data for MDS studies, and visualize or detect specific
sectors in a market and periods of crisis. We suggest that this type of
visualization may be used in identifying potential pairs of stocks for "pairs
trade".Comment: 22 pages, 11 figures, Springer-Verlag format. To appear in the
conference proceedings of Econophys-Kolkata VI: "Econophysics of systemic
risk and network dynamics", Eds. F. Abergel, B.K. Chakrabarti, A. Chakraborti
and A. Ghosh, to be published by Springer-Verlag (Italia), Milan (2012
Quantifying the behavior of stock correlations under market stress
Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios
Portfolio selection problems in practice: a comparison between linear and quadratic optimization models
Several portfolio selection models take into account practical limitations on
the number of assets to include and on their weights in the portfolio. We
present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset
Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional
Value-at-Risk (LACVaR) models, where the assets are limited with the
introduction of quantity and cardinality constraints. We propose a completely
new approach for solving the LAM model, based on reformulation as a Standard
Quadratic Program and on some recent theoretical results. With this approach we
obtain optimal solutions both for some well-known financial data sets used by
several other authors, and for some unsolved large size portfolio problems. We
also test our method on five new data sets involving real-world capital market
indices from major stock markets. Our computational experience shows that,
rather unexpectedly, it is easier to solve the quadratic LAM model with our
algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of
the best commercial codes for mixed integer linear programming (MILP) problems.
Finally, on the new data sets we have also compared, using out-of-sample
analysis, the performance of the portfolios obtained by the Limited Asset
models with the performance provided by the unconstrained models and with that
of the official capital market indices
Asset pricing with downside liquidity risks
© 2016 INFORMS. We develop a parsimonious liquidity-adjusted downside capital asset pricing model to investigate whether phenomena such as downward liquidity spirals and flights to liquidity impact expected asset returns.We find strong empirical support for the model. Downside liquidity risk (sensitivity of stock liquidity to negative market returns) has an economically meaningful return premium that is 10 times larger than its symmetric analogue. The expected liquidity level and downside market risk are also associated with meaningful return premiums. Downside liquidity risk and its associated premium are higher during periods of low marketwide liquidity and for stocks that are relatively small, illiquid, volatile, and have high book-to-market ratios. These results are consistent with investors requiring compensation for holding assets susceptible to adverse liquidity phenomena. Our findings suggest that mitigation of downside liquidity risk can lower firms' cost of capital
The role of government co-investment funds in the supply of entrepreneurial finance: An assessment of the early operation of the UK Angel Co-investment Fund
Co-investment funds – which invest alongside private investors, especially business angels – thereby leveraging their networks and experience and minimizing public sector transaction costs – are a recent approach by governments in various countries to address the early stage entrepreneurial funding gap which is perceived as a barrier to the ability of firms to scale-up. However, little literature exists on their operation, impact and effectiveness. This paper assesses the early operation of the UK’s Angel Co-investment Fund, established in 2011. Interview evidence from angels and business managers suggests that the Angel Co-investment Fund is improving the availability of finance by enabling firms to raise funding rounds of between £500,000 and £2 m, hence addressing some aspects of the broken finance escalator model. However, our evidence suggests that it is not yet impacting the supply side, either in terms of stimulating the formation of new angel groups or enhancing learning amongst less experienced angels. Some aspects of the operation of the investment process have attracted criticism from angels and entrepreneurs which need to be addressed. Nevertheless, there is sufficient evidence for positive impact to justify the scheme’s expansion
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