5,611 research outputs found
An academic review: applications of data mining techniques in finance industry
With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance
The Bayesian Context Trees State Space Model for time series modelling and forecasting
A hierarchical Bayesian framework is introduced for developing rich mixture
models for real-valued time series, along with a collection of effective tools
for learning and inference. At the top level, meaningful discrete states are
identified as appropriately quantised values of some of the most recent
samples. This collection of observable states is described as a discrete
context-tree model. Then, at the bottom level, a different, arbitrary model for
real-valued time series - a base model - is associated with each state. This
defines a very general framework that can be used in conjunction with any
existing model class to build flexible and interpretable mixture models. We
call this the Bayesian Context Trees State Space Model, or the BCT-X framework.
Efficient algorithms are introduced that allow for effective, exact Bayesian
inference; in particular, the maximum a posteriori probability (MAP)
context-tree model can be identified. These algorithms can be updated
sequentially, facilitating efficient online forecasting. The utility of the
general framework is illustrated in two particular instances: When
autoregressive (AR) models are used as base models, resulting in a nonlinear AR
mixture model, and when conditional heteroscedastic (ARCH) models are used,
resulting in a mixture model that offers a powerful and systematic way of
modelling the well-known volatility asymmetries in financial data. In
forecasting, the BCT-X methods are found to outperform state-of-the-art
techniques on simulated and real-world data, both in terms of accuracy and
computational requirements. In modelling, the BCT-X structure finds natural
structure present in the data. In particular, the BCT-ARCH model reveals a
novel, important feature of stock market index data, in the form of an enhanced
leverage effect.Comment: arXiv admin note: text overlap with arXiv:2106.0302
Analysis and modeling a distributed co-operative multi agent system for scaling-up business intelligence
Modeling A Distributed Co-Operative Multi Agent System in the area of Business Intelligence is the newer topic. During the work carried out a software Integrated Intelligent Advisory Model (IIAM) has been develop, which is a personal finance portfolio ma
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
We study the problem of learning Granger causality between event types from
asynchronous, interdependent, multi-type event sequences. Existing work suffers
from either limited model flexibility or poor model explainability and thus
fails to uncover Granger causality across a wide variety of event sequences
with diverse event interdependency. To address these weaknesses, we propose
CAUSE (Causality from AttribUtions on Sequence of Events), a novel framework
for the studied task. The key idea of CAUSE is to first implicitly capture the
underlying event interdependency by fitting a neural point process, and then
extract from the process a Granger causality statistic using an axiomatic
attribution method. Across multiple datasets riddled with diverse event
interdependency, we demonstrate that CAUSE achieves superior performance on
correctly inferring the inter-type Granger causality over a range of
state-of-the-art methods
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