32,877 research outputs found
Measuring Effectiveness of Quantitative Equity Portfolio Management Methods
In this paper, I use quantitative computer models to measure the effectiveness of Quantitative Equity Portfolio Management in predicting future stock returns using commonly accepted industry valuation factors. Industry knowledge and practices are first examined in order to determine strengths and weaknesses, as well as to build a foundation for the modeling. In order to assess the accuracy of the model and its inherent concepts, I employ up to ten years of historical data for a sample of stocks. The analysis examines the historical data to determine if there is any correlation between returns and the valuation factors. Results suggest that the price to cash flow and price to EBITDA exhibited significant predictors of future returns, while the price to earnings ratio is an insignificant predictor
Beyond Biomass: Valuing Genetic Diversity in Natural Resource Management
Strategies for increasing production of goods from working and natural systems have raised concerns that the diversity of species on which these services depend may be eroding. This loss of natural capital threatens to homogenize global food supplies and compromise the stability of human welfare. We assess the trade off between artificial augmentation of biomass and degradation of biodiversity underlying a populations' ability to adapt to shocks. Our application involves the augmentation of wild stocks of salmon. Practices in this system have generated warnings that genetic erosion may lead to a loss of the âportfolio effectâ and the value of this loss is not accounted for in decision making. We construct an integrated bioeconomic model of salmon biomass and genetic diversity. Our results show how practices that homogenize natural systems can still generate positive returns. However, the substitution of more physical capital and labor for natural capital must be maintained for gains to persist, weakens the capacity for adaptation should this investment cease, and can cause substantial loss of population wildness. We apply an emerging optimization methodâapproximate dynamic programmingâto solve the model without simplifying restrictions imposed previously
Modelling and trading the Greek stock market with gene expression and genetic programing algorithms
This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter
Using attribute construction to improve the predictability of a GP financial forecasting algorithm
Financial forecasting is an important area in computational finance. EDDIE 8 is an established Genetic Programming financial forecasting algorithm, which has successfully been applied to a number of international datasets. The purpose of this paper is to further increase the algorithmâs predictive performance, by improving its data space representation. In order to achieve this, we use attribute construction to create new (high-level) attributes from the original (low-level) attributes. To examine the effectiveness of the above method, we test the extended EDDIEâs predictive performance across 25 datasets and compare it to the performance of two previous EDDIE algorithms. Results show that the introduction of attribute construction benefits the algorithm, allowing EDDIE to explore the use of new attributes to improve its predictive accuracy
Assessment of the effect of the financial crisis on agentsâ expectations through symbolic regression
Agentsâ perceptions on the state of the economy can be affected during economic crises.
Tendency surveys are the main source of agentsâ expectations. The main objective of this study
is to assess the impact of the 2008 financial crisis on agentsâ expectations. With this aim, we
evaluate the capacity of survey-based expectations to anticipate economic growth in the United
States, Japan, Germany and the United Kingdom. We propose a symbolic regression (SR) via
genetic programming approach to derive mathematical functional forms that link survey-based
expectations to GDP growth. By combining the main SR-generated indicators, we generate
estimates of the evolution of GDP. Finally, we analyse the effect of the crisis on the formation
of expectations, and we find an improvement in the capacity of agentsâ expectations to anticipate
economic growth after the crisis in all countries except Germany.Peer ReviewedPostprint (author's final draft
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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
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