72 research outputs found
Evolutionary data selection for enhancing models of intraday forex time series
The hypothesis in this paper is that a significant amount of intraday market data is either noise or redundant, and that if it is eliminated, then predictive models built using the remaining intraday data will be more accurate. To test this hypothesis, we use an evolutionary method (called Evolutionary Data Selection, EDS) to selectively remove out portions of training data that is to be made available to an intraday market predictor. After performing experiments in which data-selected and non-data-selected versions of the same predictive models are compared, it is shown that EDS is effective and does indeed boost predictor accuracy. It is also shown in the paper that building multiple models using EDS and placing them into an ensemble further increases performance. The datasets for evaluation are large intraday forex time series, specifically series from the EUR/USD, the USD/JPY and the EUR/JPY markets, and predictive models for two primary tasks per market are built: intraday return prediction and intraday volatility prediction
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
High-Frequency Analysis of Foreign Exchange Interventions: What do we learn?
The high-frequency analysis of foreign exchange dynamics is helpful in order to better identify the impact of central bank interventions. Evidence robustly shows that interventions do indeed move the exchange rate level in the desired direction. Interventions increase volatility in the short run as they are regarded as information; but they can reduce volatility overall. Ways of transmission may reach beyond the signaling channel and also include theportfolio balance and a damping channel. Finally, interventions are more successful if they obey certain conditions, such as being coordinated among central banks and going with the market and fundamentals.foreign exchange, central bank intervention, high-frequency data, transmission channel
Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic
In this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT
FOREX Prediction Using An Artificial Intelligence System
The purpose of this study is to examine the use and applicability of an artificial intelligence system in predicting changes in foreign currency exchange rates. There are algorithms available for that purpose and this study compares several of these algorithms for efficiency and accuracy. This comparison was carried out through the use of the Metlab computer software program. The multi-layer back-propagation neural network was chosen for this research. We use feed-forward topologies, supervised learning and back-propagation learning algorithms on the network. This program allows for training neural networks, thereby producing predictions of future foreign currency exchange rates. This paper builds a model for pattern recognition of foreign currency exchange rate trends. The methodology used in this paper was successful in that neural networks were successfully trained and predictions of future foreign currency exchange rates were produced. A total of eleven algorithms and different exchange rates were compared and tested through the neural network training procedure. The Levenberg-Marquardt algorithm is best suited to deal with a function approximation problem where the network has up to several hundred weights, and the approximation must be very accurate. Over all, of the algorithms considered, the Levenberg-Marquardt algorithm appears to be the most appropriate for the purposes of this paper.Computer Science Departmen
Machine Learning-Driven Decision Making based on Financial Time Series
L'abstract eÌ presente nell'allegato / the abstract is in the attachmen
Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets
This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as âemotional data.â People âproduceâ emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they âconsumeâ it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data â which again lead to new data because decisions may change behaviors or results. âEmotional Data Intelligenceâ ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes.
The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics
Stock Market Prediction via Deep Learning Techniques: A Survey
The stock market prediction has been a traditional yet complex problem
researched within diverse research areas and application domains due to its
non-linear, highly volatile and complex nature. Existing surveys on stock
market prediction often focus on traditional machine learning methods instead
of deep learning methods. Deep learning has dominated many domains, gained much
success and popularity in recent years in stock market prediction. This
motivates us to provide a structured and comprehensive overview of the research
on stock market prediction focusing on deep learning techniques. We present
four elaborated subtasks of stock market prediction and propose a novel
taxonomy to summarize the state-of-the-art models based on deep neural networks
from 2011 to 2022. In addition, we also provide detailed statistics on the
datasets and evaluation metrics commonly used in the stock market. Finally, we
highlight some open issues and point out several future directions by sharing
some new perspectives on stock market prediction
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