scholarly journals A Quantitative Model for Option Sell-Side Trading with Stop-Loss Mechanism by Using Random Forest

2021 ◽  
Author(s):  
Chi-Fang Chao ◽  
Yu-Chen Wang ◽  
Mu-En Wu
Keyword(s):  
Random Forest ◽  
Predictive Model ◽  
Portfolio Management ◽  
Statistical Approach ◽  
Learning Algorithm ◽  
Quantitative Model ◽  
Machine Learning Algorithm ◽  
Random Forest Algorithm ◽  
Loss Mechanism ◽  
Stop Loss

Abstract Due to the characteristics of high leverage and low margin, option is very suitable for quantitative trading by applying portfolio management to control the profit and risk. The money management is an important issue to build a portfolio especially for option sell-side trader, since the profit is only the premium, while the loss is unlimited. In this research, we propose a model for option sell-side strategy to estimate the win-rate of option by the premium, time to maturity, and volatility based on statistical approach and random forest algorithm. The prediction of the model is visualized through heatmap which can reveal the profitable trading range intuitively, we use the precision score to evaluate the performance in these two models and proof the effectiveness and robustness of predictive model proposed by random forest algorithm. In the future, we plan to apply other machine learning algorithm to propose the predictive model for spread trading.

scholarly journals Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†

2021 ◽  
Vol 8 (3) ◽  
pp. 209-221
Author(s):  
Li-Li Wei ◽  
Yue-Shuai Pan ◽  
Yan Zhang ◽  
Kai Chen ◽  
Hao-Yu Wang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Random Forest Algorithm ◽  
Random Forest Regression ◽  
Data Set

Abstract Objective To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy. Methods This study identified indicators related to GDM through a literature review and expert discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction model was analyzed. Results We obtained 4806 analyzable data from 1625 pregnant women. Among these, 3265 samples with all 67 indicators were used to establish data set F1; 4806 samples with 38 identical indicators were used to establish data set F2. Each of F1 and F2 was used for training the random forest algorithm. The overall predictive accuracy of the F1 model was 93.10%, area under the receiver operating characteristic curve (AUC) was 0.66, and the predictive accuracy of GDM-positive cases was 37.10%. The corresponding values for the F2 model were 88.70%, 0.87, and 79.44%. The results thus showed that the F2 prediction model performed better than the F1 model. To explore the impact of sacrificial indicators on GDM prediction, the F3 data set was established using 3265 samples (F1) with 38 indicators (F2). After training, the overall predictive accuracy of the F3 model was 91.60%, AUC was 0.58, and the predictive accuracy of positive cases was 15.85%. Conclusions In this study, a model for predicting GDM with several input variables (e.g., physical examination, past history, personal history, family history, and laboratory indicators) was established using a random forest regression algorithm. The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy. In addition, there are certain requirements for the proportions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.

scholarly journals Prediction of Heart Stroke using A Novel Framework – PySpark

2021 ◽  
Vol 1 (2) ◽  
pp. 1-4
Author(s):  
Chitluri Sai Harish B ◽  
G gnana krishna vamsi ◽  
G jaya phani akhil ◽  
J n v hari sravan ◽  
V mounika chowdary
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Accuracy Score ◽  
Random Forest Algorithm ◽  
The World

Heart diseases are one of the most challenging problems faced by the Health Care sectors all over the world. These diseases are very basic now a days. With the expanding count of deaths because of heart illnesses, the necessity to build up a system to foresee heart ailments precisely. The work in this paper focuses on finding the best Machine Learning algorithm for identification of heart diseases. Our study compares the precision of three well known classification algorithms, Decision Tree and Naïve Bayes, Random Forest for the prediction of heart disease by making the use of dataset provided by Kaggle. We utilized various characteristics which relate with this heart diseases well, to find the better algorithm for prediction. The result of this study indicates that the Random Forest algorithm is the most efficient algorithm for prediction of heart disease with accuracy score of 97.17%.

scholarly journals A Survey on Heart Attack Prediction Using Machine Learning

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
P.Santhi, Et. al.
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Heart Attack ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Random Forest Algorithm ◽  
Danger Zone

Machine Learning Algorithm is used for many different diseases. Machine Learning is a learning of machine by own itself. And it is a part of AI that deals with to learn a machine according to their own. Now-a-days most are affected due to Heart attack it becomes head ache for doctors. In order to reduce the count of death we need to predict the Heart attack. For this problem Machine Learning play a major role in this paper. This prediction takes a people from the danger zone of their life. In this paper we use KNN algorithm and Random forest algorithm can predict the heart attack in advance.

scholarly journals Prediction of Heart Stroke using A Novel Framework – PySpark

2021 ◽  
pp. 1-4
Author(s):  
Mr. Chitluri Sai Harish ◽  
◽  
Mr. G gnana krishna vamsi ◽  
Mr. G jaya phani akhil ◽  
Mr. J n v hari sravan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Accuracy Score ◽  
Random Forest Algorithm ◽  
The World

Heart diseases are one of the most challenging problems faced by the Health Care sectors all over the world. These diseases are very basic now a days. With the expanding count of deaths because of heart illnesses, the necessity to build up a system to foresee heart ailments precisely. The work in this paper focuses on finding the best Machine Learning algorithm for identification of heart diseases. Our study compares the precision of three well known classification algorithms, Decision Tree and Naïve Bayes, Random Forest for the prediction of heart disease by making the use of dataset provided by Kaggle. We utilized various characteristics which relate with this heart diseases well, to find the better algorithm for prediction. The result of this study indicates that the Random Forest algorithm is the most efficient algorithm for prediction of heart disease with accuracy score of 97.17%.

scholarly journals Crime Analysis using Machine Learning Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 2195-2198
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
Crime Analysis ◽  
Machine Learning Algorithm ◽  
Prediction System ◽  
Random Forest Algorithm

Counteraction is better that Cure. Forestalling a wrongdoing from happening is superior to examining what or how the wrongdoing had happened. When I pick out do expand this venture the fundamental hassle is growing the centralized server. Awful conduct scene want has relies mostly on the certain awful conduct record and various geospatial and part data. In existing machine they're proposed only getting the crime from the consumer most effective until now they didn’t have system for prediction the crime. Wrongdoing that happens nowadays are have following key qualities, for example, violations rehashing in an occasional style, wrongdoings happening because of some other action and event of violations pre shown by some other data .In our proposed system we overcome that answer and we enforce the Prediction System. We need to accumulate raw facts and method in addition. We use Random forest Algorithm

scholarly journals Corporate distress prediction using random forest and tree net for india

2020 ◽  
Vol 10 (1) ◽  
pp. 1-11
Author(s):  
Arvind Shrivastava ◽  
Nitin Kumar ◽  
Kuldeep Kumar ◽  
Sanjeev Gupta
Keyword(s):  
Random Forest ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Data Mining Tool ◽  
Distress Prediction ◽  
Out Of Sample ◽  
Mining Tool ◽  
Corporate Distress

The paper deals with the Random Forest, a popular classification machine learning algorithm to predict bankruptcy (distress) for Indian firms. Random Forest orders firms according to their propensity to default or their likelihood to become distressed. This is also useful to explain the association between the tendency of firm failure and its features. The results are analyzed vis-à-vis Tree Net. Both in-sample and out of sample estimations have been performed to compare Random Forest with Tree Net, which is a cutting edge data mining tool known to provide satisfactory estimation results. An exhaustive data set comprising companies from varied sectors have been included in the analysis. It is found that Tree Net procedure provides improved classification and predictive performance vis-à-vis Random Forest methodology consistently that may be utilized further by industry analysts and researchers alike for predictive purposes.

Detecting Cognitive Distraction Using Random Forest by Considering Eye Movement Type

2018 ◽  
pp. 1587-1599
Author(s):  
Hiroaki Koma ◽  
Taku Harada ◽  
Akira Yoshizawa ◽  
Hirotoshi Iwasaki
Keyword(s):  
Machine Learning ◽  
Eye Movements ◽  
Random Forest ◽  
Decision Trees ◽  
Eye Movement ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Still Images ◽  
Cognitive Distraction ◽  
Movement Type

Detecting distracted states can be applied to various problems such as danger prevention when driving a car. A cognitive distracted state is one example of a distracted state. It is known that eye movements express cognitive distraction. Eye movements can be classified into several types. In this paper, the authors detect a cognitive distraction using classified eye movement types when applying the Random Forest machine learning algorithm, which uses decision trees. They show the effectiveness of considering eye movement types for detecting cognitive distraction when applying Random Forest. The authors use visual experiments with still images for the detection.

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