21 research outputs found
Equity portfolio management with cardinality constraints and risk parity control using multi-objective particle swarm optimization
The financial crisis and the market uncertainty of the last years have pointed out the shortcomings of traditional portfolio theory to adequately manage the different sources of risk of the investment process. This paper addresses the issue by developing an alternative portfolio design, that integrates risk parity into the cardinality constrained portfolio optimization model. The resulting mixed integer programming problem is handled by an improved multi-objective particle swarm optimization algorithm. Three hybrid approaches, based on a repair mechanism and different versions of the constrained-domination principle, are proposed to handle constraints. The efficiency of the algorithm and the effectiveness of the solution approaches are assessed through a set of numerical examples. Moreover, the benefits of adopting the proposed strategy instead of the cardinality constrained mean-variance approach are validated in an out-of-sample experiment
Portfolio Management Using Artificial Trading Systems Based on Technical Analysis
Evolutionary algorithms consist of several heuristics able to solve optimization tasks by
imitating some aspects of natural evolution. In the \ufb01eld of computational \ufb01nance, this type of
procedures, combined with neural networks, swarm intelligence, fuzzy systems and machine
learning has been successfully applied to a variety of problems, such as the prediction of stock
price movements and the optimal allocation of funds in a portfolio.
Nowadays, there is an increasing interest among computer scientists to solve these issues
concurrently by de\ufb01ning automatic trading strategies based on arti\ufb01cial expert systems,
technical analysis and fundamental and economic information. The objective is to develop
procedures able, from one hand, to mimic the practitioners behavior and, from the other, to
beat the market. In this sense, Fernandez-Rodr\uedguez et al. (2005) investigate the pro\ufb01tability
of the generalized moving average trading rule for the General Index of Madrid Stock Market
by optimizing parameter values with a genetic algorithm. They conclude that the optimized
trading rules are superior to a risk-adjusted buy-and-hold strategy if the transaction costs
are reasonable. Similarly, Papadamou & Stephanides (2007) present the GATradeTool, a
parameter optimization tool based on genetic algorithms for technical trading rules. In the
description of this software, they compare it with other commonly used, non-adaptive tools in
terms of stability of the returns and computational costs. Results of the tests on the historical
data of a UBS fund show that GATradeTool outperforms the other tools. Fern\ue1ndez-Blanco
et al. (2008) propose to use the moving average convergence divergence technical indicator
to predict stock indices by optimizing its parameters with a genetic algorithm. Experimental
results for the Dow Jones Industrial Average index con\ufb01rm the capability of evolutionary
algorithms to improve technical indicators with respect to the classical con\ufb01gurations adopted
by practitioners.
An alternative approach to generate technical trading systems for stock timing that combines
machine learning paradigms and a variable length string multi-objective genetic algorithm
is proposed in Kaucic (2010). The most informative technical indicators are selected by
the genetic algorithm and combined into a unique trading signal by a learning method. A
static single-position automated day trading strategy between the S&P 500 Composite Index
and the 3-months Treasury Bill is analyzed in three market phases, up-trend, down-trend
and sideways-movements, covering the period 2000-2006. The results indicate that the near-optimal set of rules varies among market phases but presents stable results and is able to
reduce or eliminate losses in down-trend periods.
As a natural consequence of these studies, evolutionary algorithms may constitute a
promising tool also for portfolio strategies involving more than two stocks. In the \ufb01eld of
portfolio selection, Markowitz and Sharpe models are frequently used as a task for genetic
algorithm optimization. For instance, the problem of \ufb01nding the ef\ufb01cient frontier associated
with the standard mean-variance portfolio is tackled by Chang et al. (2000). They extend
the standard model to include cardinality and composition constraints by applying three
heuristic algorithms based upon genetic algorithms, tabu search and simulated annealing.
Computational results are presented for \ufb01ve data sets involving up to 225 assets.
Wilding (2003) proposes a hybrid procedure for portfolio management based on factor
models, allowing constraints on the number of trades and securities. A genetic algorithm
is responsible for selecting the best subset of securities that appears in the \ufb01nal solution, while
a quadratic programming routine determines the utility value for that subset. Experiments
show the ability of this approach to generate portfolios highly able to track an index.
The \u3b2 12 G genetic portfolio algorithm proposed by Oh et al. (2006) selects stocks based on
their market capitalization and optimizes their weights in terms of portfolio \u3b2\u2019s standard
deviation. The performance of this procedure depends on market volatility and tends to
register outstanding performance for short-term applications.
The approach I consider for portfolio management is quite different from the previous models
and is based on technical analysis. In general, portfolio optimizations using technical analysis
are modular procedures where a module employs a set of rules based on technical indicators
in order to classify the assets in the market, while another module concentrates on generating
and managing portfolio over time (for a detailed presentation of the subject, the interested
reader may refer to Jasemi et al. (2011)). An interesting application in this context is the approach developed by Korczak & Lipinski
(2003) that leads to the optimization of portfolio structures by making use of arti\ufb01cial trading
experts, previously discovered by a genetic algorithm (see Korczak & Roger (2002)), and
evolutionary strategies. The approach has been tested using data from the Paris Stock
Exchange. The pro\ufb01ts obtained by this algorithm are higher than those of the buy-and-hold
strategy.
Recently, Ghandar et al. (2009) describe a two-modules interacting procedure where a genetic
algorithm optimizes a set of fuzzy technical trading rules according to market conditions and
interacts with a portfolio strategy based on stock ranking and cardinality constraints. They
introduce several performance metrics to compare their portfolios with the Australian Stock
Exchange index, showing greater returns and lower volatility.
An alternative multi-modular approach has been developed by Gorgulho et al. (2011) that
aims to manage a \ufb01nancial portfolio by using technical analysis indicators optimized by a
genetic algorithm. In order to validate the solutions, authors compare the designed strategy
against the market itself, the buy-and-hold and a purely random strategy, under distinct
market conditions. The results are promising since the approach outperforms the competitors.
As the previous examples demonstrate, the technical module occupies, in general, a
subordinate position relative to the management component. Since transaction costs, cardinality and composition constraints are of primary importance for the rebalancing
purpose, the effective impact of technical signals in the development of optimal portfolios
is not clear. To highlight the bene\ufb01ts of using technical analysis in portfolio management,
I propose an alternative genetic optimization heuristic, based on an equally weighted zero
investment strategy, where funds are equally divided among the stocks of a long portfolio
and the stocks of a short one. Doing so, the trading signals directly in\ufb02uence the portfolio
construction. Moreover, I implement three types of portfolio generation models according to
the risk-adjusted measure considered as the objective, in order to study the relation between
portfolio risk and market condition changes.
The remainder of the chapter is organized as follows. Section 2 explains in detail the proposed
method, focusing on the investment strategy, the de\ufb01nitions of the technical indicators and
the evolutionary learning algorithm adopted. Section 3 presents the experimental results and
discussions. Finally, Section 4 concludes the chapter with some remarks and ideas for future
improvements
Group Risk Parity Strategies for ETFs Portfolios
This research aims to compare different strategies that a non-professional investor in exchange-traded funds (ETFs) could employ to reach a good performance both from profits and from a risk perspective. In recent years, especially after the 2008 crisis, a new technique to evaluate the risk has become more popular, the so-called risk parity, which seeks to equalise the contributions to risk of the portfolio constituents. Our study analyses 17 variants of risk parity portfolio design for groups with the minimum variance strategy and equally weighted portfolio over a pool of 56 ETFs\u2014listed on the Italian Stock Exchange\u2014of eight different categories of specialisation. Empirical results confirm the usefulness of the group risk parity strategies in improving outcomes regarding diversification of risks among classes with good out-of-sample performance with respects to the target models
Multi-Objective Stochastic Optimization Programs for a non-Life Insurance Company under Solvency Constraints
In the paper, we introduce a multi-objective scenario-based optimization approach
for chance-constrained portfolio selection problems. More specifically, a modified version
of the normal constraint method is implemented with a global solver in order to generate a
dotted approximation of the Pareto frontier for bi- and tri-objective programming problems.
Numerical experiments are carried out on a set of portfolios to be optimized for an EU-based
non-life insurance company. Both performance indicators and risk measures are managed
as objectives. Results show that this procedure is effective and readily applicable to achieve
suitable risk-reward tradeoff analysis
Interval-valued upside potential and downside risk portfolio optimisation
A novel interval optimisation approach is developed to include
imprecise forecasts into the portfolio selection process for investors
measuring upside potential and downside risk as deviations from a
target return. Crisp scenarios are substituted by interval scenarios and
the resulting interval optimisation problem is solved in a tractable
manner by means of a bi-objective formulation exploiting a partial
order relation between intervals. Four utility case studies involving
assets from the F.T.S.E. M.I.B. Index are considered to illustrate how
impreciseness can be efficiently handled in portfolio management
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 “survival of the fittest”.
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 “classical” 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, Theil’s 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 2000–2006.
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 “algoritmi evolutivi”, noti anche come “evolutionary computations”
comprendono varie tecniche di ottimizzazione per la risoluzione di problemi,
mediante alcuni aspetti suggeriti dall’evoluzione naturale. Tali metodologie
sono accomunate dal fatto che non considerano un’unica soluzione alla
volta, bens`ı trattano intere popolazioni di possibili soluzioni per un dato
problema. L’idea 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 l’intero 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 dell’evoluzione, che si
possono sinteticamente riassumere nella dicitura “la sopravvivenza del più
adatto”.
Sebbene gli algoritmi evolutivi siano un’area 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, l’applicazione 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 l’analisi
dell’evoluzione 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 sull’intuizione e sull’esperienza. 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, dall’altra, 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 dell’evoluzione 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 l’implementazione automatica di condizioni
econometriche nella fase di selezione, consentendo una migliore analisi dello
EVOLUTIONARY COMPUTATIONS FOR TRADING SYSTEMS 3
spazio delle soluzioni, e dall’altra parte, l’introduzione di una procedura di
riduzione evolutiva capace di riconoscere in modo efficiente le variabili pi`u
informative.
Nel secondo algoritmo, le novità sono costituite dalla definizione dell’apprendimento
evolutivo in termini finanziari e dall’applicazione di un algoritmo
genetico multi-obiettivo per la costruzione di sistemi di trading basati
su indicatori tecnici.
L’ultimo 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 dall’impiego 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 un’introduzione 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 dall’altra.
PiĂą 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 dell’efficienza 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 dell’errore 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 l’efficienza e la consistenza
degli stimatori, l’algoritmo `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 l’extra rendimento del settore MSCI Europe Energy sull’indice
MSCI Europe e una procedura di tipo recursive estimation-window è
utilizzata per evidenziarne le performance previsionali. Nel primo esempio,
l’insieme 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
l’intero insieme di variabili ritardate di 1 mese. I risultati mostrano una
notevole capacità previsiva per l’extra 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 l’indice S&P
500 Composite) o in un titolo a basso rischio (nell’esempio, 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).
L’analisi è stata condotta sull’intervallo temporale dal 2000 al 2006, suddiviso
in tre sottoperiodi: il primo rappresenta l’indice in un
The Information Content of Earnings Announcements in the European Insurance Market: An Event Study Analysis
We contribute to the extensive literature on the relationship between earnings announcements and market reactions by investigating the European insurance sector, which presents a wide difference in transparency between quarterly and semi-annual/annual reports. We design an event-study analysis comprising over 900 events in the period 2009-2016, testing if investors react differently to the two subsets of reports. We find that quarterly reports result in signals on stock prices\u2019 limited to few trading days, whereas semi-annual and annual reports produce bigger and more persistent impacts. Instead, we do not find evidence of differences impacting trading volumes. Our results are supportive of the recent EU decision to remove the obligation to publish quarterly reports to avoid short-termism and suggest that costs are greater than benefits for companies considering to adopt voluntary quarterly reports
A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization
In this paper we present a multi-start particle swarm optimization algorithm for the global optimization of a function subject to bound constraints. The procedure consists of three main steps. In the initialization phase, an opposition learning strategy is performed to improve the search efficiency. Then a variant of the adaptive velocity based on the differential operator enhances the optimization ability of the particles. Finally, a re-initialization strategy based on two diversity measures for the swarm is act in order to avoid premature convergence and stagnation. The strategy uses the super-opposition paradigm to re-initialize particles in the swarm. The algorithm has been evaluated on a set of 100 global optimization test problems. Comparisons with other global optimization methods show the robustness and effectiveness of the proposed algorithm
Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm
Genetic algorithm, Penalty function method, Model selection, Excess return, Information criteria, C32, C52, C53, C61, C63,