86 research outputs found
Study on stock trading and portfolio optimization using genetic network programming
制度:新 ; 報告番号:甲3002号 ; 学位の種類:博士(工学) ; 授与年月日: 2010/3/15 ; 早大学位記番号:新525
Agent-based artificial financial market with evolutionary algorithm
In traditional financial studies, existing approaches are unable to
address increasingly complex problems. In this paper, an artificial
financial market is proposed, in accordance with the adaptation
market hypothesis, using artificial intelligence algorithms. This
market includes three types of agents with different investments
and risk preferences, representing the heterogeneity of traders.
Genetic network programming is combined with a state-actionreward-state-action (SARSA)(k) algorithm for designing the market
to reflect the adaptation of technical agents. A pricing mechanism
is taken into consideration, based on the auction mechanism of
the Chinese securities market. The characteristics of price time
series are analyzed to determine whether excessive volatility
exists in four different markets. Explanations are provided for the
corresponding financial phenomena considering the hypotheses
under the proposed novel artificial financial market
Reinforcement Learning Applied to Trading Systems: A Survey
Financial domain tasks, such as trading in market exchanges, are challenging
and have long attracted researchers. The recent achievements and the consequent
notoriety of Reinforcement Learning (RL) have also increased its adoption in
trading tasks. RL uses a framework with well-established formal concepts, which
raises its attractiveness in learning profitable trading strategies. However,
RL use without due attention in the financial area can prevent new researchers
from following standards or failing to adopt relevant conceptual guidelines. In
this work, we embrace the seminal RL technical fundamentals, concepts, and
recommendations to perform a unified, theoretically-grounded examination and
comparison of previous research that could serve as a structuring guide for the
field of study. A selection of twenty-nine articles was reviewed under our
classification that considers RL's most common formulations and design patterns
from a large volume of available studies. This classification allowed for
precise inspection of the most relevant aspects regarding data input,
preprocessing, state and action composition, adopted RL techniques, evaluation
setups, and overall results. Our analysis approach organized around fundamental
RL concepts allowed for a clear identification of current system design best
practices, gaps that require further investigation, and promising research
opportunities. Finally, this review attempts to promote the development of this
field of study by facilitating researchers' commitment to standards adherence
and helping them to avoid straying away from the RL constructs' firm ground.Comment: 38 page
A study on the memory schemes for genetic network programming
制度:新 ; 報告番号:甲3376号 ; 学位の種類:博士(工学) ; 授与年月日:2011/9/15 ; 早大学位記番号:新569
Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm
Evolutionary computation and data mining are two fascinating
fields that have attracted many researchers. This paper proposes
a new rule mining method, named genetic network programming
(GNP), to solve the prediction problem using the evolutionary
algorithm. Compared with the conventional association rule methods
that do not consider the weight factor, the proposed algorithm
provides many advantages in financial prediction, since it
can discover relationships among the attributes of different transactions.
Experimental results on data from the New York
Exchange Market show that the new method outperforms other
conventional models in terms of both accuracy and profitability,
and the proposed method can establish more important and
accurate rules than the conventional methods. The results confirmed
the effectiveness of the proposed data mining method in
financial prediction
Hierarchical reinforcement learning for trading agents
Autonomous software agents, the use of which has increased due to the recent growth in computer power, have considerably improved electronic commerce processes by facilitating automated trading actions between the market participants (sellers, brokers and buyers). The rapidly changing market environments pose challenges to the performance of such agents, which are generally developed for specific market settings. To this end, this thesis is concerned with designing agents that can gradually adapt to variable, dynamic and uncertain markets and that are able to reuse the acquired trading skills in new markets. This thesis proposes the use of reinforcement learning techniques to develop adaptive trading agents and puts forward a novel software architecture based on the semi-Markov decision process and on an innovative knowledge transfer framework. To evaluate my approach, the developed trading agents are tested in internationally well-known market simulations and their behaviours when buying or/and selling in the retail and wholesale markets are analysed. The proposed approach has been shown to improve the adaptation of the trading agent in a specific market as well as to enable the portability of the its knowledge in new markets
Machine Learning
Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience
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