Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning

In this study, we analyze the term structure of credit default swaps (CDSs) and predict future term structures using the Nelson–Siegel model, recurrent neural network (RNN), support vector regression (SVR), long short-term memory (LSTM), and group method of data handling (GMDH) using CDS term structure data from 2008 to 2019. Furthermore, we evaluate the change in the forecasting performance of the models through a subperiod analysis. According to the empirical results, we confirm that the Nelson–Siegel model can be used to predict not only the interest rate term structure but also the CDS term structure. Additionally, we demonstrate that machine-learning models, namely, SVR, RNN, LSTM, and GMDH, outperform the model-driven methods (in this case, the Nelson–Siegel model). Among the machine learning approaches, GMDH demonstrates the best performance in forecasting the CDS term structure. According to the subperiod analysis, the performance of all models was inconsistent with the data period. All the models were less predictable in highly volatile data periods than in less volatile periods. This study will enable traders and policymakers to invest efficiently and make policy decisions based on the current and future risk factors of a company or country.

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Forecasting the risk at infractions: an ensemble comparison of machine learning approach

Industrial Management & Data Systems ◽

10.1108/imds-10-2020-0603 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Lei Li ◽

Desheng Wu

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Short Term Memory ◽

Model Performance ◽

Large Data ◽

Support Vector ◽

Learning Approaches ◽

Content Type ◽

Day To Day Operations ◽

Prediction Approach

PurposeThe infraction of securities regulations (ISRs) of listed firms in their day-to-day operations and management has become one of common problems. This paper proposed several machine learning approaches to forecast the risk at infractions of listed corporates to solve financial problems that are not effective and precise in supervision.Design/methodology/approachThe overall proposed research framework designed for forecasting the infractions (ISRs) include data collection and cleaning, feature engineering, data split, prediction approach application and model performance evaluation. We select Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machines, Artificial Neural Network and Long Short-Term Memory Networks (LSTMs) as ISRs prediction models.FindingsThe research results show that prediction performance of proposed models with the prior infractions provides a significant improvement of the ISRs than those without prior, especially for large sample set. The results also indicate when judging whether a company has infractions, we should pay attention to novel artificial intelligence methods, previous infractions of the company, and large data sets.Originality/valueThe findings could be utilized to address the problems of identifying listed corporates' ISRs at hand to a certain degree. Overall, results elucidate the value of the prior infraction of securities regulations (ISRs). This shows the importance of including more data sources when constructing distress models and not only focus on building increasingly more complex models on the same data. This is also beneficial to the regulatory authorities.

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A Machine Learning View on Momentum and Reversal Trading

Algorithms ◽

10.3390/a11110170 ◽

2018 ◽

Vol 11 (11) ◽

pp. 170 ◽

Cited By ~ 2

Author(s):

Zhixi Li ◽

Vincent Tam

Keyword(s):

Neural Network ◽

Machine Learning ◽

Stock Market ◽

Short Term Memory ◽

Predictive Ability ◽

Trading Strategies ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Learning Techniques

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.

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Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽

10.3390/computers8010004 ◽

2019 ◽

Vol 8 (1) ◽

pp. 4 ◽

Cited By ~ 4

Author(s):

Jurgita Kapočiūtė-Dzikienė ◽

Robertas Damaševičius ◽

Marcin Woźniak

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Full Dataset ◽

Learning Techniques ◽

Long Short Term Memory

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

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Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems

Advances in Meteorology ◽

10.1155/2018/1824317 ◽

2018 ◽

Vol 2018 ◽

pp. 1-18 ◽

Cited By ~ 5

Author(s):

Xianming Dou ◽

Yongguo Yang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Water Vapor ◽

Data Driven ◽

Machine Learning Techniques ◽

Support Vector ◽

Group Method ◽

Generalized Regression Neural Network ◽

Learning Approaches ◽

Inference System

Remarkable progress has been made over the last decade toward characterizing the mechanisms that dominate the exchange of water vapor between the biosphere and the atmosphere. This is attributed partly to the considerable development of machine learning techniques that allow the scientific community to use these advanced tools for approximating the nonlinear processes affecting the variation of water vapor in terrestrial ecosystems. Three novel machine learning approaches, namely, group method of data handling, extreme learning machine (ELM), and adaptive neurofuzzy inference system (ANFIS), were developed to simulate and forecast the daily evapotranspiration (ET) at four different grassland sites based on the flux tower data using the eddy covariance method. These models were compared with the extensively utilized data-driven models, including artificial neural network, generalized regression neural network, and support vector machine (SVM). Moreover, the influences of internal functions on their corresponding models (SVM, ELM, and ANFIS) were investigated together. It was demonstrated that most developed models did good job of simulating and forecasting daily ET at the four sites. In addition to strengths of robustness and simplicity, the newly proposed methods achieved the estimates comparable to those of the conventional approaches and accordingly can be used as promising alternatives to traditional methods. It was further discovered that the generalization performance of the ELM, ANFIS, and SVM models strongly depended on their respective internal functions, especially for SVM.

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Prediction of the Hardness of Cu-Ti-Co Alloy Using Machine Learning Techniques

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.777.372 ◽

2018 ◽

Vol 777 ◽

pp. 372-376 ◽

Cited By ~ 1

Author(s):

Shan Feng Fang

Keyword(s):

Machine Learning ◽

Copper Alloys ◽

Least Square ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Forecasting Accuracy ◽

Learning Techniques ◽

Vector Machines ◽

Forecasting Performance

Diverse machine learning approaches were employed to build regression models for predicting mechanical property of Cu-Ti-Co alloy. The forecasting performance of the least-square support vector machines (LSSVM) model has been compared with other artificial intelligence methods such as GRNN, RBF-PLS and RBFNN. The models were developed and validated utilizing a cross-validation (CV) procedure to improve the forecasting accuracy and generalization ability. The result demonstrates that the generalization performance of the new LSSVM is slightly better or superior to those acquired using GRNN, RBF-PLS and RBFNN. In future, it would be expected that the relatively new model based on machine learning is used as an especially helpful implement to accelerate materials design of copper alloys.

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Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning

Sensors ◽

10.3390/s20071813 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1813 ◽

Cited By ~ 3

Author(s):

Alexander L. Bowler ◽

Serafim Bakalis ◽

Nicholas J. Watson

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Real Time ◽

Short Term Memory ◽

Support Vector ◽

Learning Approaches ◽

Learning Models ◽

Ultrasonic Sensors ◽

Multisensor Data Fusion ◽

Machine Learning Models

Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-cost, real-time, in-line, and applicable to characterise opaque systems. In this study, a non-invasive, reflection-mode ultrasonic measurement technique was used to monitor two model mixing systems. The two systems studied were honey-water blending and flour-water batter mixing. Classification machine learning models were developed to predict if materials were mixed or not mixed. Regression machine learning models were developed to predict the time remaining until mixing completion. Artificial neural networks, support vector machines, long short-term memory neural networks, and convolutional neural networks were tested, along with different methods for engineering features from ultrasonic waveforms in both the time and frequency domain. Comparisons between using a single sensor and performing multisensor data fusion between two sensors were made. Classification accuracies of up to 96.3% for honey-water blending and 92.5% for flour-water batter mixing were achieved, along with R2 values for the regression models of up to 0.977 for honey-water blending and 0.968 for flour-water batter mixing. Each prediction task produced optimal performance with different algorithms and feature engineering methods, vindicating the extensive comparison between different machine learning approaches.

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Machine Learning Blunts the Needle of Advanced SQL Injections

MENDEL ◽

10.13164/mendel.2019.1.023 ◽

2019 ◽

Vol 25 (1) ◽

pp. 23-30

Author(s):

Marina Volkova ◽

Petr Chmelar ◽

Lukas Sobotka

Keyword(s):

Machine Learning ◽

Short Term Memory ◽

Detection System ◽

Support Vector ◽

Learning Approaches ◽

Sensitive Data ◽

Machine Learning Approach ◽

Query String ◽

Gated Recurrent Units ◽

Sequential Models

SQL injection is one of the most popular and serious information security threats. By exploiting database vulnerabilities, attackers may get access to sensitive data or enable compromised computers to conduct further network attacks. Our research is focused on applying machine learning approaches for identication of injection characteristics in the HTTP query string. We compare results from Rule-based Intrusion Detection System, Support Vector Machines, Multilayer Perceptron, Neural Network with Dropout layers, and Deep Sequential Models (Long Short-Term Memory, and Gated Recurrent Units) using multiple string analysis, bag-of-word techniques, and word embedding for query string vectorization. Results proved benets of applying machine learning approach for detection malicious pattern in HTTP query string.

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

Current Drug Targets ◽

10.2174/1389450119666180809122244 ◽

2019 ◽

Vol 20 (5) ◽

pp. 488-500 ◽

Cited By ~ 6

Author(s):

Yan Hu ◽

Yi Lu ◽

Shuo Wang ◽

Mengying Zhang ◽

Xiaosheng Qu ◽

...

Keyword(s):

Machine Learning ◽

Drug Design ◽

Anticancer Drugs ◽

Nearest Neighbor ◽

Cost Effective ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Activity Prediction ◽

Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

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Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

Military Medicine ◽

10.1093/milmed/usaa418 ◽

2021 ◽

Vol 186 (Supplement_1) ◽

pp. 445-451

Author(s):

Yifei Sun ◽

Navid Rashedi ◽

Vikrant Vaze ◽

Parikshit Shah ◽

Ryan Halter ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Real World ◽

Short Term Memory ◽

Model Performance ◽

Learning Technologies ◽

Machine Learning Algorithms ◽

Support Vector ◽

K Nearest Neighbor ◽

Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

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