Map-reduced based approach for mining group stock portfolio

[[abstract]]In this paper, the map-reduce technique is utilized for speeding up the mining process and derived as similar results as our previous approach. The chromosome representation consists of four parts that are a mapper number, grouping part, stock part and portfolio part. According to mapper number, chromosomes in population are divided into subsets and sent to respective mappers. Fitness evaluation and genetic operations are the same with our previous approach, and executed on reducers. The evolution process is repeated until reaching the terminal conditions. Experiments are conducted on a real dataset to show the performance of proposed approach.[[notice]]補正完

Taktisen allokaation signaalien tunnistaminen Yhdysvaltojen ja Euroopan osakemarkkinoilla

Extensive research has been done to discover historically profitable tactical asset allocation strategies. Researchers have found a wide range of different market signals; valuation levels, company fundamentals, macroeconomic variables, sentiment indicators and seasonal patterns have been suggested to predict the market movements. However, it seems that most findings in published papers have been obtained with datamining. Especially multiple tests have been performed without adjusting the significance level appropriately. In a typical journal article, predictors have also been tested only on in-sample periods, i.e., with the same dataset that was used for fitting the model. The thesis re-evaluates the forecasting ability of the most potential stock market predictors found in the tactical asset allocation and equity market timing literature. The out-of-sample test results show that the equity premium has not been predictable in real-time after the turn of the millennium. The thesis thereby recommends the passive "buy and hold" strategy for both private and professional investors. However, if investors still want to try to "time the stock market" or actively adjust the strategic asset allocation mix, the inflation rate and ETF fund flows seem to be the most potential signals to follow.Taktisen allokaation ja markkinoiden ajoittamisen tutkimuksessa tutkijat ovat löytäneet laajalti erilaisia osakemarkkinoita ennustavia tekijöitä; arvostustasojen, yrityksen fundamenttien, makrotalouden muuttujien, sentimentti-indikaattoreiden ja kausivaihteluiden on havaittu ennustavan osakkeiden riskipreemiota. Vaikuttaa kuitenkin siltä, että suuri osa aikaisemmin julkaistuista löydöksistä on saatu tiedonlouhinnalla. Erityisesti useita tilastollisia testejä on tehty huomioimatta merkitsevyystason asianmukaisesta korjaamista. Useissa tutkimuksissa testit on lisäksi tehty samalla ajanjaksolla, jota käytettiin alkuperäisen mallin sovittamiseen. Diplomityö arvioi uudelleen aikaisemmat taktisen allokaation ja markkinoiden ajoittamisen tutkimuksessa esitetyt löydökset osakemarkkinoiden ennustamisesta. Otoksen ulkopuolella tehdyt testit osoittavat, ettei osakeriskipreemioita ole ollut mahdollista ennustaa reaaliajassa vuosituhannen vaihteen jälkeen. Diplomityö siten suosittelee passiivista osta ja pidä –strategiaa sekä yksityisille että ammattimaisille sijoittajille. Mikäli sijoittajat kuitenkin haluavat yrittää taktista allokaatioita, potentiaalisimpia seurattavia markkinasignaaleita näyttävät olevan inflaatio ja ETF-pääomavirrat

Modeling financial risk: from uni- to bi-directional.

Yeung Kin Bong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 69-73).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Credit risk modeling --- p.3Chapter 1.2 --- Uniqueness of bi-directional: hybrid system --- p.4Chapter 1.3 --- Scope of the study --- p.5Chapter 2 --- Literature Review --- p.6Chapter 2.1 --- Statistical / Empirical approach --- p.6Chapter 2.2 --- Structural approach --- p.8Chapter 3 --- Background --- p.10Chapter 3.1 --- Merton structural default model --- p.10Chapter 3.2 --- Cross-sectional regression analysis (CRA) --- p.15Chapter 3.3 --- Neural network learning (NN) --- p.16Chapter 3.3.1 --- Single-layer network --- p.17Chapter 3.3.2 --- Multi-layer perceptron (MLP) --- p.20Chapter 3.3.3 --- Back-propagation network --- p.22Chapter 3.3.4 --- "Supervised, unsupervised and combine unsupervised-supervised learning" --- p.23Chapter 3.4 --- Weaknesses of uni-directional modeling --- p.23Chapter 4 --- Methodology --- p.26Chapter 4.1 --- Bi-directional modeling --- p.26Chapter 4.2 --- Asset price estimation --- p.31Chapter 4.3 --- Quantifying accounting data noise --- p.33Chapter 5 --- Proposed Model --- p.37Chapter 5.1 --- Core of the model --- p.37Chapter 5.2 --- Feature selection --- p.41Chapter 5.3 --- Bi-directional default neural system --- p.44Chapter 6 --- Implementations --- p.49Chapter 6.1 --- Data preparation --- p.50Chapter 6.2 --- Experiment --- p.51Chapter 6.3 --- Empirical results --- p.61Chapter 6.3.1 --- Predicted spreads from the uni-directional models --- p.61Chapter 6.3.2 --- Predicted spreads from the proposed bi-directional model --- p.63Chapter 6.3.3 --- Performance comparison --- p.64Chapter 7 --- Conclusions --- p.67Bibliography --- p.6

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Integration of Renewables in Power Systems by Multi-Energy System Interaction

This book focuses on the interaction between different energy vectors, that is, between electrical, thermal, gas, and transportation systems, with the purpose of optimizing the planning and operation of future energy systems. More and more renewable energy is integrated into the electrical system, and to optimize its usage and ensure that its full production can be hosted and utilized, the power system has to be controlled in a more flexible manner. In order not to overload the electrical distribution grids, the new large loads have to be controlled using demand response, perchance through a hierarchical control set-up where some controls are dependent on price signals from the spot and balancing markets. In addition, by performing local real-time control and coordination based on local voltage or system frequency measurements, the grid hosting limits are not violated

SIS 2017. Statistics and Data Science: new challenges, new generations

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data