Application of Ensemble Learning for Views Generation in Meucci Portfolio Optimization Framework

Modern Portfolio Theory assumes that decisions are made by individual agents. In reality most investors are involved in group decision-making. In this research we propose to realize group decision-making process by application of Ensemble Learning algorithm, in particular Random Forest. Predicting accurate asset returns is very important in the process of asset allocation. Most models are based on weak predictors. Ensemble Learning algorithms could significantly improve prediction of weak learners by combining them into one model, which will have superiority in performance. We combine technical fundamental and sentiment analysis in order to generate views on different asset classes. Purpose of the research is to build the model for Meucci Portfolio Optimization under views generated by Random Forest Ensemble Learning algorithm. The model was backtested by comparing with results obtained from other portfolio optimization frameworks

Application of Ensemble Learning for Views Generation in Meucci Portfolio Optimization Framework

Modern Portfolio Theory assumes that decisions are made by individual agents. In reality most investors are involved in group decision-making. In this research we propose to realize group decision-making process by application of Ensemble Learning algorithm, in particular Random Forest. Predicting accurate asset returns is very important in the process of asset allocation. Most models are based on weak predictors. Ensemble Learning algorithms could significantly improve prediction of weak learners by combining them into one model, which will have superiority in performance. We combine technical fundamental and sentiment analysis in order to generate views on different asset classes. Purpose of the research is to build the model for Meucci Portfolio Optimization under views generated by Random Forest Ensemble Learning algorithm. The model was backtested by comparing with results obtained from other portfolio optimization frameworks

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population

Modeling causes of death: an integrated approach using CODEm

Background: Data on causes of death by age and sex are a critical input into health decision-making. Priority setting in public health should be informed not only by the current magnitude of health problems but by trends in them. However, cause of death data are often not available or are subject to substantial problems of comparability. We propose five general principles for cause of death model development, validation, and reporting.Methods: We detail a specific implementation of these principles that is embodied in an analytical tool - the Cause of Death Ensemble model (CODEm) - which explores a large variety of possible models to estimate trends in causes of death. Possible models are identified using a covariate selection algorithm that yields many plausible combinations of covariates, which are then run through four model classes. The model classes include mixed effects linear models and spatial-temporal Gaussian Process Regression models for cause fractions and death rates. All models for each cause of death are then assessed using out-of-sample predictive validity and combined into an ensemble with optimal out-of-sample predictive performance.Results: Ensemble models for cause of death estimation outperform any single component model in tests of root mean square error, frequency of predicting correct temporal trends, and achieving 95% coverage of the prediction interval. We present detailed results for CODEm applied to maternal mortality and summary results for several other causes of death, including cardiovascular disease and several cancers.Conclusions: CODEm produces better estimates of cause of death trends than previous methods and is less susceptible to bias in model specification. We demonstrate the utility of CODEm for the estimation of several major causes of death

The optimally diversified equity portfolio in South Africa: an artificial intelligence approach

A thesis presented to the School of Economic and Business Sciences, Faculty of Commerce, Law and Management, University of Witwatersrand in fulfilment of the requirements for the degree of Master of Commerce (M.Com) in Business Finance, January 2017Diversification has remained a central tenet in investment theory over multiple decades due to its demonstrated value as a risk mitigation technique. Increasing the number assets in a portfolio, where the magnitude of correlation is relatively slim, increases the amount of diversification while also encountering increased costs in the form of transaction costs, taxes and the like. Thus, it is imperative to solve for the optimal point of diversification to ensure an investor does not encounter unnecessary costs. This study aims to solve for the point of optimal diversification in an equity portfolio, focusing on the South African environment. This is achieved by employing a framework using both the traditional simulation method as well as more advanced mathematical techniques, namely: genetic programming and particle swarm optimisation. Marked improvements are realised in this study with regards to the methodology and results through the application of advanced mathematical approaches in addition to removing the restriction of equal weightings being applied to each share in the portfolio. The results revealed that an optimal portfolio can be constructed using up to only 15 shares. Secondly, the genetic programming approach demonstrated increased strength compared to the traditional simulation and particle swarm optimisation approaches, obtaining a greater level of diversification with fewer shares. Finally, although the aim of the study is focused on modelling the relationship between the number of shares in a portfolio and the achievable diversification benefits, it is also established that the portfolios indicated as being optimally diversified achieved market beating returns.XL201

Flexible Neural Trees Ensemble for Stock Index Modeling

The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using Flexible Neural Tree (FNT) ensemble technique. We considered the Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index. We analyzed 7-year Nasdaq-100 main index values and 4-year NIFTY index values. This paper investigates the development of novel reliable and e#cient techniques to model the seemingly chaotic behavior of stock markets. The structure and parameters of FNT are optimized using Genetic Programming (GP) like tree structure based evolutionary algorithm and Particle Swarm Optimization (PSO) algorithms, respectively. A good ensemble model is formulated by the Local Weighted Polynomial Regression (LWPR). This paper investigates whether the proposed method can provide the required level of performance, which is sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experimental results show that the model considered could represent the stock indices behavior very accurately

Flexible Neural Trees Ensemble for Stock Index Modeling

The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using Flexible Neural Tree (FNT) ensemble technique. To this end, we considered the Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index. We analyzed 7-year Nasdaq-100 main index values and 4-year NIFTY index values. This paper investigates the development of novel reliable and efficient techniques to model the seemingly chaotic behavior of stock markets. The structure and parameters of FNT are optimized by using Genetic Programming (GP) and particle Swarm Optimization (PSO) algorithms, repectively. A good ensemble model is formulated by the Local Weighted Polynomial Regression (LWPR). This paper investigates whether the proposed method can provide the required level of performance, which is sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results shown that the model considered could represent the stock indices behavior very accurately