1,298 research outputs found
European exchange trading funds trading with locally weighted support vector regression
In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the Îľ-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series
A survey on financial applications of metaheuristics
Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness
(TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program
(E-2015-36)
Recommended from our members
Machine Learning Stock Market Prediction Studies: Review and Research Directions
Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. A systematic literature review methodology is used to identify relevant peer-reviewed journal articles from the past twenty years and categorize studies that have similar methods and contexts. Four categories emerge: artificial neural network studies, support vector machine studies, studies using genetic algorithms combined with other techniques, and studies using hybrid or other artificial intelligence approaches. Studies in each category are reviewed to identify common findings, unique findings, limitations, and areas that need further investigation. The final section provides overall conclusions and directions for future research
Machining Performance Analysis in End Milling: Predicting Using ANN and a Comparative Optimisation Study of ANN/BB-BC and ANN/PSO
End milling machining performance indicators such as surface roughness, tool wear and machining time are the principally indicators of machine tool industrial productivity, cost and competitiveness. Since accurate predictions and optimisations are necessary for control purposes, new merit-driven approaches are for good results. The aim of this work is two folds: prediction of machining performance for surface roughness, tool wear and machining time with ANN and the optimisation of these performance indicators using the combined models of ANN-BB-BC and ANN-PSO. However, the optimisation platform is hinged on the fuzzy goal programming model, which facilitates comparisons between the performance of the BB-BC and the PSO algorithms. To demonstrate the approach, optimal tool wear and surface roughness were obtained from a fuzzy goal programme, then converted to a bi-objective non-linear programming model, and solved with the BB-BC and the PSO algorithms. The outputs of the artificial neural network (ANN) were integrated with the optimisation models. The effectiveness of the method was ascertained using extensive literature data. Thus, prediction and optimisation of complex end milling parameters was attained using appropriate selection of parameters with high quality outputs, enhanced by precise prediction and optimisation tools in this proposed approach
Technical and Fundamental Features Analysis for Stock Market Prediction with Data Mining Methods
Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working.
Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks.
In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiersâ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiersâ accuracy.
Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) â ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the modelâs input variables and buy and sell signals are considered as output variables.
To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working.
Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks.
In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiersâ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiersâ accuracy.
Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) â ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the modelâs input variables and buy and sell signals are considered as output variables.
To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.154 - Katedra financĂvyhovÄ
Forecasting: theory and practice
Forecasting has always been in the forefront of decision making and planning.
The uncertainty that surrounds the future is both exciting and challenging,
with individuals and organisations seeking to minimise risks and maximise
utilities. The lack of a free-lunch theorem implies the need for a diverse set
of forecasting methods to tackle an array of applications. This unique article
provides a non-systematic review of the theory and the practice of forecasting.
We offer a wide range of theoretical, state-of-the-art models, methods,
principles, and approaches to prepare, produce, organise, and evaluate
forecasts. We then demonstrate how such theoretical concepts are applied in a
variety of real-life contexts, including operations, economics, finance,
energy, environment, and social good. We do not claim that this review is an
exhaustive list of methods and applications. The list was compiled based on the
expertise and interests of the authors. However, we wish that our encyclopedic
presentation will offer a point of reference for the rich work that has been
undertaken over the last decades, with some key insights for the future of the
forecasting theory and practice
Evolutionary computation for trading systems
2007/2008Evolutionary computations, also called evolutionary algorithms, consist of
several heuristics, which are able to solve optimization tasks by imitating
some aspects of natural evolution. They may use different levels of abstraction, but they are always working on populations of possible solutions for a
given task. The basic idea is that if only those individuals of a population
which meet a certain selection criteria reproduce, while the remaining individuals die, the population will converge to those individuals that best meet
the selection criteria. If imperfect reproduction is added the population can
begin to explore the search space and will move to individuals that have an
increased selection probability and that hand down this property to their
descendants. These population dynamics follow the basic rule of the Darwinian evolution theory, which can be described in short as the âsurvival of the fittestâ.
Although evolutionary computations belong to a relative new research area,
from a computational perspective they have already showed some promising
features such as:
⢠evolutionary methods reveal a remarkable balance between efficiency
and efficacy;
⢠evolutionary computations are well suited for parameter optimisation;
⢠this type of algorithms allows a wide variety of extensions and constraints that cannot be provided in traditional methods;
⢠evolutionary methods are easily combined with other optimization
techniques and can also be extended to multi-objective optimization.
From an economic perspective, these methods appear to be particularly well
suited for a wide range of possible financial applications, in particular in this
thesis I study evolutionary algorithms
⢠for time series prediction;
⢠to generate trading rules;
⢠for portfolio selection.
It is commonly believed that asset prices are not random, but are permeated by complex interrelations that often translate in assets mispricing and
may give rise to potentially profitable opportunities. Classical financial approaches, such as dividend discount models or even capital asset pricing theories, are not able to capture these market complexities. Thus, in the
last decades, researchers have employed intensive econometric and statistical
modeling that examine the effects of a multitude of variables, such as price-
earnings ratios, dividend yields, interest rate spreads and changes in foreign
exchange rates, on a broad and variegated range of stocks at the same time.
However, these models often result in complex functional forms difficult to
manage or interpret and, in the worst case, are solely able to fit a given time
series but are useless to predict it. Parallelly to quantitative approaches,
other researchers have focused on the impact of investor psychology (in particular, herding and overreaction) and on the consequences of considering
informed signals from management and analysts, such as share repurchases
and analyst recommendations. These theories are guided by intuition and
experience, and thus are difficult to be translated into a mathematical environment.
Hence, the necessity to combine together these point of views in order to
develop models that examine simultaneously hundreds of variables, including qualitative informations, and that have user friendly representations, is
urged. To this end, the thesis focuses on the study of methodologies that
satisfy these requirements by integrating economic insights, derived from
academic and professional knowledge, and evolutionary computations.
The main task of this work is to provide efficient algorithms based on the
evolutionary paradigm of biological systems in order to compute optimal
trading strategies for various profit objectives under economic and statistical constraints. The motivations for constructing such optimal strategies
are:
i) the necessity to overcome data-snooping and supervisorship bias in
order to learn to predict good trading opportunities by using market
and/or technical indicators as features on which to base the forecasting;
ii) the feasibility of using these rules as benchmark for real trading
systems;
iii) the capability of ranking quantitatively various markets with respect
to their profitability according to a given criterion, thus making possible portfolio allocations.
More precisely, I present two algorithms that use artificial expert trading
systems to predict financial time series, and a procedure to generate integrated neutral strategies for active portfolio management.
The first algorithm is an automated procedure that simultaneously selects
variables and detect outliers in a dynamic linear model using information
criteria as objective functions and diagnostic tests as constraints for the
distributional properties of errors. The novelties are the automatic implementation of econometric conditions in the model selection step, making
possible a better exploration of the solution space on one hand, and the use
of evolutionary computations to efficiently generate a reduction procedure from a very large number of independent variables on the other hand.
In the second algorithm, the novelty is given by the definition of evolutionary
learning in financial terms and its use in a multi-objective genetic algorithm
in order to generate technical trading systems.
The last tool is based on a trading strategy on six assets, where future
movements of each variable are obtained by an evolutionary procedure that
integrates various types of financial variables. The contribution is given
by the introduction of a genetic algorithm to optimize trading signals parameters and the way in which different informations are represented and
collected.
In order to compare the contribution of this work to âclassicalâ techniques
and theories, the thesis is divided into three parts. The first part, titled
Background, collects Chapters 2 and 3. Its purpose is to provide an introduction to search/optimization evolutionary techniques on one hand, and to
the theories that relate the predictability in financial markets with the concept of efficiency proposed over time by scholars on the other hand. More
precisely, Chapter 2 introduces the basic concepts and major areas of evolutionary computation. It presents a brief history of three major types of evolutionary algorithms, i.e. evolution strategies, evolutionary programming
and genetic algorithms, and points out similarities and differences among
them. Moreover it gives an overview of genetic algorithms and describes
classical and genetic multi-objective optimization techniques. Chapter 3
first presents an overview of the literature on the predictability of financial
time series. In particular, the extent to which the efficiency paradigm is
affected by the introduction of new theories, such as behavioral finance, is
described in order to justify the market forecasting methodologies developed
by practitioners and academics in the last decades. Then, a description of
the econometric and financial techniques that will be used in conjunction
with evolutionary algorithms in the successive chapters is provided. Special
attention is paid to economic implications, in order to highlight merits and
shortcomings from a practitioner perspective.
The second part of the thesis, titled Trading Systems, is devoted to the description of two procedures I have developed in order to generate artificial
trading strategies on the basis of evolutionary algorithms, and it groups
Chapters 4 and 5. In particular, chapter 4 presents a genetic algorithm for
variable selection by minimizing the error in a multiple regression model.
Measures of errors such as ME, RMSE, MAE, Theilâs inequality coefficient
and CDC are analyzed choosing models based on AIC, BIC, ICOMP and
similar criteria. Two components of penalty functions are taken in analysis-
level of significance and Durbin Watson statistics. Asymptotic properties of
functions are tested on several financial variables including stocks, bonds,
returns, composite prices indices from the US and the EU economies. Variables with outliers that distort the efficiency and consistency of estimators
are removed to solve masking and smearing problems that they may cause in
estimations. Two examples complete the chapter. In both cases, models are
designed to produce short-term forecasts for the excess returns of the MSCI
Europe Energy sector on the MSCI Europe index and a recursive estimation-
window is used to shed light on their predictability performances. In the first
application the data-set is obtained by a reduction procedure from a very
large number of leading macro indicators and financial variables stacked
at various lags, while in the second the complete set of 1-month lagged
variables is considered. Results show a promising capability to predict excess sector returns through the selection, using the proposed methodology,
of most valuable predictors. In Chapter 5 the paradigm of evolutionary
learning is defined and applied in the context of technical trading rules for
stock timing. A new genetic algorithm is developed by integrating statistical
learning methods and bootstrap to a multi-objective non dominated sorting
algorithm with variable string length, making possible to evaluate statistical
and economic criteria at the same time. Subsequently, the chapter discusses
a practical case, represented by a simple trading strategy where total funds
are invested in either the S&P 500 Composite Index or in 3-month Treasury
Bills. In this application, the most informative technical indicators are selected from a set of almost 5000 signals by the algorithm. Successively, these
signals are combined into a unique trading signal by a learning method. I
test the expert weighting solution obtained by the plurality voting committee, the Bayesian model averaging and Boosting procedures with data from
the the S&P 500 Composite Index, in three market phases, up-trend, down-
trend and sideways-movements, covering the period 2000â2006.
In the third part, titled Portfolio Selection, I explain how portfolio optimization models may be constructed on the basis of evolutionary algorithms and
on the signals produced by artificial trading systems. First, market neutral
strategies from an economic point of view are introduced, highlighting their
risks and benefits and focusing on their quantitative formulation. Then, a
description of the GA-Integrated Neutral tool, a MATLAB set of functions
based on genetic algorithms for active portfolio management, is given. The
algorithm specializes in the parameter optimization of trading signals for
an integrated market neutral strategy. The chapter concludes showing an
application of the tool as a support to decisions in the Absolute Return
Interest Rate Strategies sub-fund of Generali Investments.Gli âalgoritmi evolutiviâ, noti anche come âevolutionary computationsâ
comprendono varie tecniche di ottimizzazione per la risoluzione di problemi,
mediante alcuni aspetti suggeriti dallâevoluzione naturale. Tali metodologie
sono accomunate dal fatto che non considerano unâunica soluzione alla
volta, bens`Äą trattano intere popolazioni di possibili soluzioni per un dato
problema. Lâidea sottostante `e che, se un algoritmo fa evolvere solamente
gli individui di una data popolazione che soddisfano a un certo criterio di
selezione, e lascia morire i restanti, la popolazione converger`a agli individui
che meglio soddisfano il criterio di selezione. Con una selezione non ottimale,
cio`e una che ammette pure soluzioni sub-ottimali, la popolazione rappresenter`
a meglio lâintero spazio di ricerca e sar`a in grado di individuare in modo
pi`u consistente gli individui migliori da far evolvere. Queste dinamiche interne
alle popolazioni seguono i principi Darwiniani dellâevoluzione, che si
possono sinteticamente riassumere nella dicitura âla sopravvivenza del piĂš
adattoâ.
Sebbene gli algoritmi evolutivi siano unâarea di ricerca relativamente nuova,
dal punto di vista computazionale hanno dimostrato alcune caratteristiche
interessanti fra cui le seguenti:
⢠permettono un notevole equilibrio tra efficienza ed efficacia;
⢠sono particolarmente indicati per la configurazione dei parametri in
problemi di ottimizzazione;
⢠consentono una flessibilit`a nella definizione matematica dei problemi
e dei vincoli che non si trova nei metodi tradizionali;
⢠possono facilmente essere integrati con altre tecniche di ottimizzazione
ed essere essere modificati per risolvere problemi multi-obiettivo.
Dal un punto di vista economico, lâapplicazione di queste procedure pu`o
risultare utile specialmente in campo finanziario. In particolare, nella mia
tesi ho studiato degli algoritmi evolutivi per
⢠la previsione di serie storiche finanziarie;
⢠la costruzione di regole di trading;
⢠la selezione di portafogli.
Da un punto di vista pi`u ampio, lo scopo di questa ricerca `e dunque lâanalisi
dellâevoluzione e della complessit`a dei mercati finanziari. In tal senso, dal
momento che i prezzi non seguono andamenti puramente casuali, ma sono
governati da un insieme molto articolato di eventi correlati, i modelli e le
teorie classiche, come i dividend discount model e le varie capital asset pricing
theories, non sono pi`u sufficienti per determinare potenziali opportunit`a di
profitto. A tal fine, negli ultimi decenni, alcuni ricercatori hanno sviluppato
una vasta gamma di modelli econometrici e statistici in grado di esaminare
contemporaneamente le relazioni e gli effetti di centinaia di variabili, come
ad esempio, price-earnings ratios, dividendi, differenziali fra tassi di interesse
e variazioni dei tassi di cambio, per una vasta gamma di assets. Comunque,
questo approccio, che fa largo impiego di strumenti di calcolo, spesso porta
a dei modelli troppo complicati per essere gestiti o interpretati, e, nel peggiore
dei casi, pur essendo ottimi per descrivere situazioni passate, risultano
inutili per fare previsioni. Parallelamente a questi approcci quantitativi, si
`e manifestato un grande interesse sulla psicologia degli investitori e sulle
conseguenze derivanti dalle opinioni di esperti e analisti nelle dinamiche del
mercato. Questi studi sono difficilmente traducibili in modelli matematici
e si basano principalmente sullâintuizione e sullâesperienza. Da qui la necessit`
a di combinare insieme questi due punti di vista, al fine di sviluppare
modelli che siano in grado da una parte di trattare contemporaneamente
un elevato numero di variabili in modo efficiente e, dallâaltra, di incorporare
informazioni e opinioni qualitative. La tesi affronta queste tematiche integrando
le conoscenze economiche, sia accademiche che professionali, con gli
algoritmi evolutivi. Pi`u pecisamente, il principale obiettivo di questo lavoro
`e lo sviluppo di algoritmi efficienti basati sul paradigma dellâevoluzione dei
sistemi biologici al fine di determinare strategie di trading ottimali in termini
di profitto e di vincoli economici e statistici. Le ragioni che motivano
lo studio di tali strategie ottimali sono:
i) la necessit`a di risolvere i problemi di data-snooping e supervivorship
bias al fine di ottenere regole di investimento vantaggiose utilizzando
indicatori di mercato e/o tecnici per la previsione;
ii) la possibilitĂ di impiegare queste regole come benchmark per sistemi
di trading reali;
iii) la capacit`a di individuare gli asset pi`u vantaggiosi in termini di profitto,
o di altri criteri, rendendo possibile una migliore allocazione di
risorse nei portafogli.
In particolare, nella tesi descrivo due algoritmi che impiegano sistemi di trading
artificiali per predire serie storiche finanziarie e una procedura di calcolo
per strategie integrate neutral market per la gestione attiva di portafogli.
Il primo algoritmo `e una procedura automatica che seleziona le variabili
e simultaneamente determina gli outlier in un modello dinamico lineare
utilizzando criteri informazionali come funzioni obiettivo e test diagnostici
come vincoli per le caratteristiche delle distribuzioni degli errori. Le novit`a
del metodo sono da una parte lâimplementazione automatica di condizioni
econometriche nella fase di selezione, consentendo una migliore analisi dello
EVOLUTIONARY COMPUTATIONS FOR TRADING SYSTEMS 3
spazio delle soluzioni, e dallâaltra parte, lâintroduzione di una procedura di
riduzione evolutiva capace di riconoscere in modo efficiente le variabili pi`u
informative.
Nel secondo algoritmo, le novitĂ sono costituite dalla definizione dellâapprendimento
evolutivo in termini finanziari e dallâapplicazione di un algoritmo
genetico multi-obiettivo per la costruzione di sistemi di trading basati
su indicatori tecnici.
Lâultimo metodo proposto si basa su una strategia di trading su sei assets,
in cui le dinamiche future di ciascuna variabile sono ottenute impiegando
una procedura evolutiva che integra diverse tipologie di variabili finanziarie.
Il contributo è dato dallâimpiego di un algoritmo genetico per ottimizzare i
parametri negli indicatori tecnici e dal modo in cui le differenti informazioni
sono presentate e collegate.
La tesi `e organizzata in tre parti. La prima parte, intitolata Background,
comprende i Capitoli 2 e 3, ed è intesa a fornire unâintroduzione alle tecniche
di ricerca/ottimizzazione su base evolutiva da una parte, e alle teorie
che si occupano di efficienza e prevedibilit`a dei mercati finanziari dallâaltra.
PiĂš precisamente, il Capitolo 2 introduce i concetti base e i maggiori
campi di studio della computazione evolutiva. In tal senso, si dĂ una breve
presentazione storica di tre dei maggiori tipi di algoritmi evolutivi, ciò e le
strategie evolutive, la programmazione evolutiva e gli algoritmi genetici,
evidenziandone caratteri comuni e differenze. Il capitolo si chiude con una
panoramica sugli algoritmi genetici e sulle tecniche classiche e genetiche di
ottimizzazione multi-obiettivo. Il Capitolo 3 affronta nel dettaglio la problematica
della prevedibilit`a delle serie storiche finanziarie mettendo in luce,
in particolare, quanto il paradigma dellâefficienza sia influenzato dalle pi`u
recenti teorie finanziarie, come ad esempio la finanza comportamentale. Lo
scopo è quello di dare una giustificazione su basi teoriche per le metodologie
di previsione sviluppate nella tesi. Segue una descrizione dei metodi
econometrici e di analisi tecnica che nei capitoli successivi verrano impiegati
assieme agli algoritmi evolutivi. Una particolare attenzione è data alle implicazioni
economiche, al fine di evidenziare i loro meriti e i loro difetti da
un punto di vista pratico.
La seconda parte, intitolata Trading Systems, raggruppa i Capitoli 4 e 5 ed
è dedicata alla descrizione di due procedure che ho sviluppato per generare
sistemi di trading artificiali sulla base di algoritmi evolutivi. In particolare,
il Capitolo 4 presenta un algortimo genetico per la selezione di variabili attraverso
la minimizzazione dellâerrore in un modello di regressione multipla.
Misure di errore, quali il ME, il RMSE, il MAE, il coefficiente di Theil e
il CDC sono analizzate a partire da modelli selezionati sulla scorta di criteri
informazionali, come ad esempio AIC, BIC, ICOMP. A livello di vincoli
diagnostici, ho considerato una funzione di penalitĂ a due componenti e la
statistica di Durbin Watson. Il programma impiega variabili finanziarie di
vario tipo, come rendimenti di titoli, bond e prezzi di indici composti ottenuti
dalle economie Statunitense ed Europea. Nel caso le serie storiche
4 MASSIMILIANO KAUCIC
considerate presentino outliers che distorcono lâefficienza e la consistenza
degli stimatori, lâalgoritmo `e in grado di individuarle e rimuoverle dalla serie,
risolvendo il problema di masking and smearing. Il capitolo si conclude
con due applicazioni, in cui i modelli sono progettati per produrre previsioni
di breve periodo per lâextra rendimento del settore MSCI Europe Energy sullâindice
MSCI Europe e una procedura di tipo recursive estimation-window è
utilizzata per evidenziarne le performance previsionali. Nel primo esempio,
lâinsieme dei dati `e ottenuto estraendo le variabili di interesse da un considerevole
numero di indicatori di tipo macro e da variabili finanziarie ritardate
rispetto alla variabile dipendente. Nel secondo esempio ho invece considerato
lâintero insieme di variabili ritardate di 1 mese. I risultati mostrano una
notevole capacitĂ previsiva per lâextra rendimento, individuando gli indicatori
maggiormente informativi. Nel Capitolo 5, il concetto di apprendimento
evolutivo viene definito ed applicato alla costruzione di regole di trading su
indicatori tecnici per lo stock timing. In tal senso, ho sviluppato un algoritmo
che integra metodi di apprendimento statistico e di boostrap con un
particolare algoritmo multi-obiettivo. La procedura derivante è in grado di
valutare contemporaneamente criteri economici e statistici. Per descrivere
il suo funzionamento, ho considerato un semplice esempio di trading in cui
tutto il capitale è investito in un indice (che nel caso trattato è lâindice S&P
500 Composite) o in un titolo a basso rischio (nellâesempio, i Treasury Bills
a 3 mesi). Il segnale finale di trading `e il risultato della selezione degli indicatori
tecnici pi`u informativi a partire da un insieme di circa 5000 indicatori
e la loro conseguente integrazione mediante un metodo di apprendimento
(il plurality voting committee, il bayesian model averaging o il Boosting).
Lâanalisi è stata condotta sullâintervallo temporale dal 2000 al 2006, suddiviso
in tre sottoperiodi: il primo rappresenta lâindice in un
Decision-making between rationality and intuition: effectiveness conditions and solutions to enhance decision efficacy
Decision-making, one process, many theories: a multidisciplinary literature review. How individual and environmental factors interact and influence the effectiveness of strategic decisions through rational and intuitive dynamics. Mentoring and the promotion of self-confidence in decision-making: the role of cognitive awareness and expertise building through the lenses of rationality and intuition
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