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Confronting Machine Learning With Financial Research
[article]
2021
arXiv
pre-print
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We discuss some of the main challenges of machine learning in finance and examine how these could be accounted for. ...
Moreover, we discuss the various applications of machine learning in the research process such as estimation, empirical discovery, testing, causal inference and prediction. ...
Markov-shifting models have been a popular regime switching framework for financial and economic time series. ...
arXiv:2103.00366v2
fatcat:bp4j34cenjf4lii5rgav5o5d6e
Tricks for Time Series
[chapter]
2012
Lecture Notes in Computer Science
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In the last section we focus on tricks related to time series analysis and economic forecasting. ...
As an alternative, the author presents several new smoothing regularizers for both feedforward and recurrent networks that empirically are found to work better. ...
turning points in time series (p. 374). ...
doi:10.1007/978-3-642-35289-8_21
fatcat:xvasculgqzboponywvqp4jloke
Bid Prediction in Repeated Auctions with Learning
[article]
2020
arXiv
pre-print
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This portrays the importance of using structural econometric approaches in predicting how players will respond to changes in the market. ...
We show that the no-regret econometric methods perform comparable to state-of-the-art time-series machine learning methods when there is no co-variate shift, but significantly outperform machine learning ...
the machine-learning methods both in the task of predicting a series of future bids and in the task of predicting one step at every time. ...
arXiv:2007.13193v2
fatcat:ckcrbxdbnbeptbtrke34etozbe
Learning Leading Indicators for Time Series Predictions
[article]
2016
arXiv
pre-print
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We consider the problem of learning models for forecasting multiple time-series systems together with discovering the leading indicators that serve as good predictors for the system. ...
While the first method assumes common structures across the whole system, our second method uncovers model clusters based on the Granger-causality and leading indicators together with learning the model ...
Our work builds on the standard regularized multi-task learning and structured regularization techniques developed outside the time-series settings. ...
arXiv:1507.01978v3
fatcat:awbeye5ghnfhja75lwbajo7p7u
Neural Network Predictive Modeling on Dynamic Portfolio Management—A Simulation-Based Portfolio Optimization Approach
2020
Journal of Risk and Financial Management
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In this paper, we present a simulation-based approach by fusing a number of macroeconomic factors using Neural Networks (NN) to build an Economic Factor-based Predictive Model (EFPM). ...
their portfolio performance in a timely manner. ...
A number of simulated time series are generated for economic factors to combine the time-varying volatility, tail dependence structure, and sample mean together. ...
doi:10.3390/jrfm13110285
fatcat:qce2lcyp4zdobbo75iunlvlyum
P-V-L Deep: A Big Data Analytics Solution for Now-casting in Monetary Policy
2020
Journal of Information Technology Management
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was used for unsupervised learning, data representation, and data reconstruction; moreover, long short-term memory was adopted in order to evaluate now-casting performance of deep nets in time-series ...
In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated where it considers eventuality ...
learning and to determine and evaluate the predicting performance of deep nets' macro-econometrics variables' time-series. ...
doi:10.22059/jitm.2020.293071.2429
doaj:97430d4a1eb04b2f9cc9be5597cd2b26
fatcat:waqwqypolra7zbcxyczuli2zci
Causality Learning from Time Series Data for the Industrial Finance Analysis via the Multi-dimensional Point Process
2020
Intelligent Automation and Soft Computing
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Causality learning has been an important tool for decision making, especially for financial analytics. ...
The expectation-maximization algorithm is used for minimizing the negative loglikelihood with the regularization in order to avoid overfitting in the high dimension and will make the causal inference more ...
The EM Algorithm Zhou et al. have proposed an EM-based learning method for low-rank and sparse regularizations. ...
doi:10.32604/iasc.2020.010121
fatcat:epwypuhuhrhz3ig25a7kgx7jsi
Machine learning methods in finance
2021
SHS Web of Conferences
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, logistic regression, and autoregressive time series models. ...
This article focuses on supervised learning and reinforcement learning. These areas overlap most with econometrics, predictive modelling, and optimal control in finance. ...
Machine learning focuses on finding structure in large data sets, and the main tools for predictor selection are regularization and dropout. ...
doi:10.1051/shsconf/202111005012
fatcat:fll3dsvghnfddab2mtkj7nuz4q
Analysis of Causality in Stock Market Data
2012
2012 11th International Conference on Machine Learning and Applications
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While there is a rich literature in estimating implied and stochastic volatility in financial time series using traditional econometric methods, the application of machine learning methods such as sparse ...
In this paper, we propose a sparse, smooth regularized regression model to infer the volatility of the data while explicitly accounting for dependencies between different companies. ...
Estimation and Learning To estimate and learn the β w k coefficients for each company k for time window w specified in equation 4, Algorithm 1 is used and it defines how we tune the regularization terms ...
doi:10.1109/icmla.2012.56
dblp:conf/icmla/HendahewaP12
fatcat:fzbyzg4cpvdz3ovl2enggdsmeu
Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models
2021
Applied Sciences
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The purpose of this study is to improve the prediction of container volumes in Busan ports by applying external variables and time-series data decomposition methods to deep learning prediction models. ...
In this study, a novel deep learning prediction model is proposed that combines prediction and trend identification of container volume. ...
Section 2 provides an overview of time-series analysis, deep learning prediction, and deep learning in container volume prediction. ...
doi:10.3390/app11198995
fatcat:xboky2pwo5h6lk3h6w64m2goiq
Using Elman Neural Network Model to Forecast and Analyze the Agricultural Economy
2022
Journal of Mathematics
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To improve the accuracy of agricultural economic time series forecasting under the condition of complexity and diversity, this paper proposes an agricultural economic forecasting method based on Elman ...
Secondly, this paper designs an efficient Elman neural network topology and sends the selected important data into the neural network for data learning and neural network parameter optimization, to achieve ...
Elman neural network structure and its topological structure are mainly for processing nonlinear time series data. e characteristic of this type of network structure is that the data feedback connection ...
doi:10.1155/2022/8374696
fatcat:s275lafccbe4jlsjkonuqnfsva
Predictive Analysis of Economic Chaotic Time Series Based on Chaotic Genetics Combined with Fuzzy Decision Algorithm
2021
Complexity
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model for economic chaotic time series, performed parameter synchronization optimization and moderate function construction, analyzed the prediction processes of economic chaotic time series, conducted ...
The irreversibility in time, the multicausality on lines, and the uncertainty of feedbacks make economic systems and the predictions of economic chaotic time series possess the characteristics of high ...
In this way, the traditional algorithm for solving the embedded dimension and time delay is no longer suitable for the changed phase space structure, so a multistep prediction of economic chaotic time ...
doi:10.1155/2021/5517502
fatcat:ix3qqyos7bendfmtefzgc7qwva
Regularized Dynamic Self Organized Neural Network Inspired by the Immune Algorithm for Financial Time Series Prediction
[chapter]
2014
Lecture Notes in Computer Science
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In this work, the average values of 30 simulations generated from 10 financial time series are examined. ...
The simulation results indicated that the proposed network showed average improvement using the annualized return for all signals of 0.491, 8.1899 and 1.0072 in comparison to the benchmarked networks, ...
Modelling DSMIA for Financial Time Series Prediction: In this section the structure of the neural network models is explained. ...
doi:10.1007/978-3-319-09330-7_8
fatcat:vt5nquqok5bjhii6mx3dxcjlj4
Regularized dynamic self-organized neural network inspired by the immune algorithm for financial time series prediction
2016
Neurocomputing
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The file type is application/pdf.
In this work, the average values of 30 simulations generated from 10 financial time series are examined. ...
The simulation results indicated that the proposed network showed average improvement using the annualized return for all signals of 0.491, 8.1899 and 1.0072 in comparison to the benchmarked networks, ...
Modelling DSMIA for Financial Time Series Prediction: In this section the structure of the neural network models is explained. ...
doi:10.1016/j.neucom.2015.01.109
fatcat:n7f6vewv5zf35fjuhuvotppqlm
Neural Network Techniques for Time Series Prediction: A Review
2019
JOIV: International Journal on Informatics Visualization
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Neural Networks (NN) have appeared as an effective tool for forecasting of time series. ...
The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN. ...
It creates the overfitting problem in the structure and needs more training time for learning. ...
doi:10.30630/joiv.3.3.281
fatcat:uhqrmqth35a6hkf26raf3rt7lu
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