scholarly journals Applications of Machine Learning to Estimating the Sizes and Market Impact of Hidden Orders in the BRICS Financial Markets

2020 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Witness MAAKE ◽  
Terence VAN ZYL
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Financial Markets ◽  
Random Forests ◽  
Price Impact ◽  
Average Price ◽  
Market Impact ◽  
Far Right ◽  
Hidden Orders

The research aims to investigate the role of hidden orders on the structure of the average market impact curves in the five BRICS financial markets. The concept of market impact is central to the implementation of cost-effective trading strategies during financial order executions. The literature is replicated using the data of visible orders from the five BRICS financial markets. We repeat the implementation of the literature to investigate the effect of hidden orders. We subsequently study the dynamics of hidden orders. The research applies machine learning to estimate the sizes of hidden orders. We revisit the methodology of the literature to compare the average market impact curves in which true hidden orders are added to visible orders to the average market impact curves in which hidden orders sizes are estimated via machine learning. The study discovers that: (1) hidden orders sizes could be uncovered via machine learning techniques such as Generalized Linear Models (GLM), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF); and (2) there exist no set of market features that are consistently predictive of the sizes of hidden orders across different stocks. Artificial Neural Networks produce large R2 and small Mean Squared Error on the prediction of hidden orders of individual stocks across the five studied markets. Random Forests produce the most appropriate average price impact curves of visible and estimated hidden orders that are closest to the average market impact curves of visible and true hidden orders. In some markets, hidden orders produce a convex power-law far-right tail in contrast to visible orders which produce a concave power-law far-right tail. Hidden orders may affect the average price impact curves for orders of size less than the average order size; meanwhile, hidden orders may not affect the structure of the average price impact curves in other markets. The research implies ANN and RF as the recommended tools to uncover hidden orders.

scholarly journals Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burdens at the national sub-national scale in Colombia

2020 ◽  
Author(s):  
Naizhuo Zhao ◽  
Katia Charland ◽  
Mabel Carabali ◽  
Elaine Nsoesie ◽  
Mathieu Maher-Giroux ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Local Model ◽  
Disease Forecasting ◽  
Forecast Horizon ◽  
Demographic Predictors ◽  
National Model ◽  
Artificial Neural

AbstractThe robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed for the national and departmental levels in Colombia to predict weekly dengue cases at 12-weeks ahead. A national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, population counts, income inequality, and education. Our RF model trained on the national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department’s data. Additionally, the forecast errors of the national RF model were smaller to those of the national ANN model and were increased with the forecast horizon increasing from one-week ahead (mean absolute error, MAE: 5.80; root mean squared error, RMSE: 11.10) to 12-weeks ahead (MAE: 13.38; RMSE: 26.82). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study showed the potential of RF in dengue forecasting with also demonstrating the feasibility of using a national model to forecast at finer spatial scales. Furthermore, sociodemographic predictors are important to include to capture longer-term trends in dengue.Author summaryDengue virus has the highest disease burden of all mosquito-borne viral diseases, infecting 390 million people annually in 128 countries. Forecasting is an important warning mechanism that can help with proactive planning and response for clinical and public health services. In this study, we compare two different machine learning approaches to dengue forecasting: random forest (RF) and neural networks (NN). National and local (departmental-level) models were compared and used to predict dengue cases in the future. The results showed that the counts of future dengue cases were more accurately estimated by RF than by NN. It was also shown that environmental and meteorological predictors were more important for forecast accuracy for shorter-term forecasts while socio-demographic predictors were more important for longer-term forecasts. Finally, the national model applied to local data was more accurate in dengue forecasting compared to the local model. This research contributes to the field of disease forecasting and highlights different considerations for future forecasting studies.

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

Adaptive kinetic structural behavior through machine learning: Optimizing the process of kinematic transformation using artificial neural networks

2015 ◽  
Vol 29 (4) ◽  
pp. 371-391 ◽  
Author(s):  
Odysseas Kontovourkis ◽  
Marios C. Phocas ◽  
Ifigenia Lamprou
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Behavior ◽  
Physical Environment ◽  
Transformation Process ◽  
Artificial Neural ◽  
Kinematic Transformation ◽  
Alternative Solutions ◽  
Digital Or

AbstractNowadays, on the basis of significant work carried out, architectural adaption structures are considered to be intelligent entities, able to react to various internal or external influences. Their adaptive behavior can be examined in a digital or physical environment, generating a variety of alternative solutions or structural transformations. These are controlled through different computational approaches, ranging from interactive exploration ones, producing alternative emergent results, to automate optimization ones, resulting in acceptable fitting solutions. This paper examines the adaptive behavior of a kinetic structure, aiming to explore suitable solutions resulting in final appropriate shapes during the transformation process. A machine learning methodology that implements an artificial neural networks algorithm is integrated to the suggested structure. The latter is formed by units articulated together in a sequential composition consisting of primary soft mechanisms and secondary rigid components that are responsible for its reconfiguration and stiffness. A number of case studies that respond to unstructured environments are set as examples, to test the effectiveness of the proposed methodology to be used for handling a large number of input data and to optimize the complex and nonlinear transformation behavior of the kinetic system at the global level, as a result of the units’ local activation that influences nearby units in a chaotic and unpredictable manner.

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