2,267 research outputs found
Dynamic Generation of Investment Recommendations Using Grammatical Evolution
The attainment of trading rules using Grammatical Evolution traditionally follows a static approach. A single rule is obtained and then used to generate investment recommendations over time. The main disadvantage of this approach is that it does not consider the need to adapt to the structural changes that are often associated with financial time series. We improve the canonical approach introducing an alternative that involves a dynamic selection mechanism that switches between an active rule and a candidate one optimized for the most recent market data available. The proposed solution seeks the flexibility required by structural changes while limiting the transaction costs commonly associated with constant model updates. The performance of the algorithm is compared with four alternatives: the standard static approach; a sliding window-based generation of trading rules that are used for a single time period, and two ensemble-based strategies. The experimental results, based on market data, show that the suggested approach beats the rest
Dynamic generation of investment recommendations using grammatical evolution
The attainment of trading rules using Grammatical Evolution traditionally follows a static approach. A single
rule is obtained and then used to generate investment recommendations over time. The main disadvantage of
this approach is that it does not consider the need to adapt to the structural changes that are often associated
with financial time series. We improve the canonical approach introducing an alternative that involves a
dynamic selection mechanism that switches between an active rule and a candidate one optimized for the most
recent market data available. The proposed solution seeks the flexibility required by structural changes while
limiting the transaction costs commonly associated with constant model updates. The performance of the
algorithm is compared with four alternatives: the standard static approach; a sliding window-based generation
of trading rules that are used for a single time period, and two ensemble-based strategies. The experimental
results, based on market data, show that the suggested approach beats the rest.The authors would like to acknowledge the financial support of the Spanish Ministry of Science, Innovation and Universities under grant PGC2018-096849-B-I00 (MCFin). This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3MXX), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)
Risk Management using Model Predictive Control
Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%
Computational Intelligence Applied to Financial Price Prediction: A State of the Art Review
The following work aims to review the most important research from computational intelligence applied to the financial price prediction problem. The article is organized as follows: The first section summarizes the role of predictability in the Neoclassical financial world. This section also criticizes the zero predictability framework. The second section presents the main computational intelligence techniques applied to financial price prediction. The third section depicts common features of revised works
Risk Management using Model Predictive Control
Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%
Evolution of trading strategies with flexible structures: A configuration comparison
Evolutionary Computation is often used in the domain of automated discovery of trading rules. Within this area, both Genetic Programming and Grammatical Evolution offer solutions with similar structures that have two key advantages in common: they are both interpretable and flexible in terms of their structure. The core algorithms can be extended to use automatically defined functions or mechanisms aimed to promote parsimony. The number of references on this topic is ample, but most of the studies focus on a specific setup. This means that it is not clear which is the best alternative. This work intends to fill that gap in the literature presenting a comprehensive set of experiments using both techniques with similar variations, and measuring their sensitivity to an increase in population size and composition of the terminal set. The experimental work, based on three S&P 500 data sets, suggest that Grammatical Evolution generates strategies that are more profitable, more robust and simpler, especially when a parsimony control technique was applied. As for the use of automatically defined function, it improved the performance in some experiments, but the results were inconclusive. (C) 2018 Elsevier B.V. All rights reserved.The authors acknowledge financial support granted by the Spanish Ministry of Science and Innovation under grant ENE2014-56126-C2-2-R
Evolutionary algorithms for financial trading
Genetic programming (GP) is increasingly popular as a research tool for applications in
finance and economics. One thread in this area is the use of GP to discover effective
technical trading rules. In a seminal article, Allen & Karjalainen (1999) used GP to find
rules that were profitable, but were nevertheless outperformed by the simple “buy and
hold” trading strategy. Many succeeding attempts have reported similar findings. This
represents a clear example of a significant open issue in the field of GP, namely,
generalization in GP [78]. The issue of generalisation is that GP solutions may not be
general enough, resulting in poor performance on unseen data. There are a small
handful of cases in which such work has managed to find rules that outperform buyand-
hold, but these have tended to be difficult to replicate. Among previous studies,
work by Becker & Seshadri (2003) was the most promising one, which showed
outperformance of buy-and-hold. In turn, Becker & Seshadri’s work had made several
modifications to Allen & Karjalainen’s work, including the adoption of monthly rather
than daily trading. This thesis provides a replicable account of Becker & Seshadri’s
study, and also shows how further modifications enabled fairly reliable outperformance
of buy-and-hold, including the use of a train/test/validate methodology [41] to evolve
trading rules with good properties of generalization, and the use of a dynamic form of
GP [109] to improve the performance of the algorithm in dynamic environments like
financial markets. In addition, we investigate and compare each of daily, weekly and
monthly trading; we find that outperformance of buy-and-hold can be achieved even for
daily trading, but as we move from monthly to daily trading the performance of evolved
rules becomes increasingly dependent on prevailing market conditions. This has
clarified that robust outperformance of B&H depends on, mainly, the adoption of a
relatively infrequent trading strategy (e.g. monthly), as well as a range of factors that
amount to sound engineering of the GP grammar and the validation strategy. Moreover,
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we also add a comprehensive study of multiobjective approaches to this investigation
with assumption from that, and find that multiobjective strategies provide even more
robustness in outperforming B&H, even in the context of more frequent (e.g. weekly)
trading decisions. Last, inspired by a number of beneficial aspects of grammatical
evolution (GE) and reports on the successful performance of various kinds of its
applications, we introduce new approach for (GE) with a new suite of operators
resulting in an improvement on GE search compared with standard GE. An empirical
test of this new GE approach on various kind of test problems, including financial
trading, is provided in this thesis as well
Revisión del Estado del Arte en Métodos de Redes Neuronales, Máquinas de Kernel y Computación Evolutiva para Predicción de Precios Financieros
A review of the representative models of machine learning research applied to the foreign exchange rate and stock price prediction problem is conducted. The article is organized as follows: The first section provides a context on the definitions and importance of foreign exchange rate and stock markets. The second section reviews machine learning models for financial prediction focusing on neural networks, SVM and evolutionary methods. Lastly, the third section draws some conclusions.El siguiente artículo revisa algunos de los trabajos de investigación mas representativos relacionados con aprendizaje computacional aplicado al problema de predicción de tipos de cambio y precios de acciones. El artículo esta organizado de la siguiente forma: La primera sección se concentra en contextualizar definiciones relevantes y la importancia del problema de predicción en el mercado de acciones y de tasa de cambio. La segunda sección contiene la revisión de modelos de aprendizaje computacional para predicción de precios financieros enfocándose en tres subareas: Redes Neuronales, SVM y métodos evolutivos. La tercera sección presenta las conclusiones
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