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A review of portfolio planning: Models and systems
In this chapter, we first provide an overview of a number of portfolio planning models
which have been proposed and investigated over the last forty years. We revisit the
mean-variance (M-V) model of Markowitz and the construction of the risk-return
efficient frontier. A piecewise linear approximation of the problem through a
reformulation involving diagonalisation of the quadratic form into a variable
separable function is also considered. A few other models, such as, the Mean
Absolute Deviation (MAD), the Weighted Goal Programming (WGP) and the
Minimax (MM) model which use alternative metrics for risk are also introduced,
compared and contrasted. Recently asymmetric measures of risk have gained in
importance; we consider a generic representation and a number of alternative
symmetric and asymmetric measures of risk which find use in the evaluation of
portfolios. There are a number of modelling and computational considerations which
have been introduced into practical portfolio planning problems. These include: (a)
buy-in thresholds for assets, (b) restriction on the number of assets (cardinality
constraints), (c) transaction roundlot restrictions. Practical portfolio models may also
include (d) dedication of cashflow streams, and, (e) immunization which involves
duration matching and convexity constraints. The modelling issues in respect of these
features are discussed. Many of these features lead to discrete restrictions involving
zero-one and general integer variables which make the resulting model a quadratic
mixed-integer programming model (QMIP). The QMIP is a NP-hard problem; the
algorithms and solution methods for this class of problems are also discussed. The
issues of preparing the analytic data (financial datamarts) for this family of portfolio
planning problems are examined. We finally present computational results which
provide some indication of the state-of-the-art in the solution of portfolio optimisation
problems
Who are the Best? Local Versus Foreign Analysts on the Latin American Stock Markets
This paper investigates the relative performance of local and foreign financial analysts on Latin American emerging markets. There is strong evidence that foreign financial analysts outperform local analysts on these markets. Foreign analysts produce more timely and more accurate forecasts. A significant price reaction is observed following their downward forecast revisions. Therefore foreign investors do not necessarily need to open relations with local financial analysts when they want to trade on these markets. The results are consistent with previous evidence that documents a better information and greater sophistication on the part of foreign investors on overseas markets.analysts’ forecasts; home bias; international diversification; emerging markets; herding behaviour
On flexibility, capital structure, and investment decisions for the insured bank
Most models of deposit insurance assume that the volatility of a bank's assets is exogenously provided. Although this framework allows the impact of volatility on bankruptcy costs and deposit insurance subsidies to be explored, it is static and does not incorporate the fact that equityholders can respond to market events by adjusting previous investment and leverage decisions. This paper presents a dynamic model of a bank that allows for such behavior. The flexibility of being able to respond dynamically to market information has value to equityholders. The impact and value of this flexibility option are explored under a regime in which flat-rate deposit insurance is provided.Deposit insurance ; Bank capital
Montecarlo simulation of long-term dependent processes: a primer
As a natural extension to León and Vivas (2010) and León and Reveiz (2010) this paper briefly describes the Cholesky method for simulating Geometric Brownian Motion processes with long-term dependence, also referred as Fractional Geometric Brownian Motion (FBM). Results show that this method generates random numbers capable of replicating independent, persistent or antipersistent time-series depending on the value of the chosen Hurst exponent. Simulating FBM via the Cholesky method is (i) convenient since it grants the ability to replicate intense and enduring returns, which allows for reproducing well-documented financial returns’ slow convergence in distribution to a Gaussian law, and (ii) straightforward since it takes advantage of the Gaussian distribution ability to express a broad type of stochastic processes by changing how volatility behaves with respect to the time horizon. However, Cholesky method is computationally demanding, which may be its main drawback. Potential applications of FBM simulation include market, credit and liquidity risk models, option valuation techniques, portfolio optimization models and payments systems dynamics. All can benefit from the availability of a stochastic process that provides the ability to explicitly model how volatility behaves with respect to the time horizon in order to simulate severe and sustained price and quantity changes. These applications are more pertinent than ever because of the consensus regarding the limitations of customary models for valuation, risk and asset allocation after the most recent episode of global financial crisis.Montecarlo simulation, Fractional Brownian Motion, Hurst exponent, Long-term Dependence, Biased Random Walk. Classification JEL: C15, C53, C63, G17, G14.
Portfolio Optimization and Long-Term Dependence
Whilst emphasis has been given to short-term dependence of financial returns, long-term dependence remains overlooked. Despite financial literature provides evidence of long-term’s memory existence, serial-independence assumption prevails. This document’s long-term dependence assessment relies on rescaled range analysis (R/S), a popular and robust methodology designed for Geophysics but extensively used in financial literature. Results correspond to most of the previous evidence of significant long-term dependence, particularly for small and illiquid markets, where persistence is its most common kind. Persistence conveys that the range of possible future values of the variable will be wider than the range of purely random and independent variables. Ahead of R/S financial literature, authors estimate an adjusted Hurst exponent in order to properly estimate the covariance matrix at higher investment horizons, avoiding the traditional -independence reliant- square-root-of-time rule. Ignoring long-term dependence within the mean-variance portfolio optimization results in concealed risk taking; conversely, by adjusting for long-term dependence the weight of high (low) persistence risk factors decreases (increases) as the investment horizon widens. This alleviates some well-known shortcomings of conventional portfolio optimization for long-term investors (e.g. central banks, pension funds and sovereign wealth managers), such as excessive risk taking in long-term portfolios, extreme weights, home bias, and reluctance to hold foreign currency-denominated assets.Portfolio optimization, Hurst exponent, long-term dependence, biased random walk, rescaled range analysis. Classification JEL: G11, G32, G20, C14.
Excel Based Financial Modeling for Making Portfolio Management Decisions
The Excel based financial model proposed in this paper provides a very simple but powerful method for portfolio selection. Apart from a simple and powerful tool for making portfolio management decisions, the paper also proposes an easy to use technique for calculating portfolio standard deviation without using correlation coefficients. The model uses “Excel Solver Add-In†to create an optimum portfolio by maximizing the Sharpe ratio. Benefits of Sharpe style optimization are demonstrated using data on monthly returns from 1999 to 2010 covering 30 stocks
Which heuristics can aid financial-decision-making?
© 2015 Elsevier Inc. We evaluate the contribution of Nobel Prize-winner Daniel Kahneman, often in association with his late co-author Amos Tversky, to the development of our understanding of financial decision-making and the evolution of behavioural finance as a school of thought within Finance. Whilst a general evaluation of the work of Kahneman would be a massive task, we constrain ourselves to a more narrow discussion of his vision of financial-decision making compared to a possible alternative advanced by Gerd Gigerenzer along with numerous co-authors. Both Kahneman and Gigerenzer agree on the centrality of heuristics in decision making. However, for Kahneman heuristics often appear as a fall back when the standard von-Neumann-Morgenstern axioms of rational decision-making do not describe investors' choices. In contrast, for Gigerenzer heuristics are simply a more effective way of evaluating choices in the rich and changing decision making environment investors must face. Gigerenzer challenges Kahneman to move beyond substantiating the presence of heuristics towards a more tangible, testable, description of their use and disposal within the ever changing decision-making environment financial agents inhabit. Here we see the emphasis placed by Gigerenzer on how context and cognition interact to form new schemata for fast and frugal reasoning as offering a productive vein of new research. We illustrate how the interaction between cognition and context already characterises much empirical research and it appears the fast and frugal reasoning perspective of Gigerenzer can provide a framework to enhance our understanding of how financial decisions are made
Using Future Value Analysis to Select an Optimal Portfolio of Force Protection Initiatives
With the recent increase in terrorist activity, force protection has become a key issue for the Department of Defense, Leading the research for new ideas and concepts in force protection for the US Air Force is the Air Force Force Protection Battlelab (FPB). The FPB is charged with searching out force protection ideas and selecting those most worthy for future consideration. In 2002, a Value-Focused Thinking (VFT) hierarchy was created to help the FPB select those ideas that provided the most value to the Air Force and its force protection goals. This research effort uses the Future Value Analysis (FVA) approach, a decision-making methodology, to provide a more accurate project selection tool to the FPB. FVA incorporates the ideals of multi-attribute utility theory, specifically using the VFT process, as well as linear programming optimization techniques, to provide an optimal portfolio of initiatives for the FPB to pursue. FVA provides a solution that optimizes the value of initiatives selected, while remaining within the organizational constraints of the FPB. This research provides a proof of implementation for the FVA process in the force protection environment
Comment: Classifier Technology and the Illusion of Progress
Comment on Classifier Technology and the Illusion of Progress
[math.ST/0606441]Comment: Published at http://dx.doi.org/10.1214/088342306000000042 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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