scholarly journals Research on Risk Identification System Based on Random Forest Algorithm-High-Order Moment Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li-Jun Liu ◽  
Wei-Kang Shen ◽  
Jia-Ming Zhu
Keyword(s):  
Random Forest ◽  
Stock Market ◽  
Factor Model ◽  
Risk Identification ◽  
Order Moment ◽  
Identification System ◽  
Random Forest Algorithm ◽  
High Order Moment ◽  
Portfolio Change ◽  
Identification Tool

With the continuous development of the stock market, designing a reasonable risk identification tool will help to solve the irrational problem of investors. This paper first selects the stocks with the most valuable investment value in the future through the random forest algorithm in the nine-factor model and then analyzes them by using the higher-order moment model to find that different investors’ preferences will make the weight of the portfolio change accordingly, which will eventually make the optimal return and risk set of the composition of the portfolio change. The risk identification system designed in this paper can provide an effective risk identification tool for investors and help them make rational judgments.

scholarly journals Stock Market Prediction using Machine Learning Algorithms

2019 ◽  
Vol 8 (2S4) ◽  
pp. 280-283
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Stock Market ◽  
Stock Price ◽  
Machine Learning Algorithms ◽  
Random Forest Algorithm ◽  
News Values ◽  
Logical Regression ◽  
Bayes Algorithm ◽  
The One

The purpose of this work is to predict the stock price fluctuation and find the best usable algorithm for predicting stock price by comparing the outcomes of various algorithms of machine learning considering various factors. The algorithms we are proposing to use are linear regression, logical regression, LSTM, random forest algorithm, SVM and naive Bayes' algorithm. We are aiming to apply various algorithms and predict the one with best outcome. The factor which we have used as an attribute in our model is news that we assume will affect the price of the stock market. We have taken the top 25 news of the day and each news will be evaluated as if it’s positive or negative and then the positive new is assigned with +1 value and so as negative with -1. For the particular day the sum of the news values are taken if the sum is positive then the predicted price will increase as compare to previous price but if the sum is negative the value should decrease base on these facts evaluation is done between predicted and occurred value and so the algorithms are used to generate the prediction and hence used to calculate the accuracy provided by the algorithm .Using news as the factor may help us in the more chance of increase in the detecting the fluctuation in the values as the news is one of the greatest factor effecting the change in stock prize as news contain the brief every possible event happened in the previous day and also contain about the company that is their release of product , status , bonds , funds , investments.

Pricing models of shares of Russian companies and their practical application

2019 ◽  
pp. 48-76 ◽  
Author(s):  
Alexander E. Abramov ◽  
Alexander D. Radygin ◽  
Maria I. Chernova
Keyword(s):  
Stock Market ◽  
Institutional Investors ◽  
Factor Model ◽  
Investment Strategies ◽  
Pricing Models ◽  
Foreign Portfolio Investment ◽  
Market Portfolio ◽  
Stock Market Performance ◽  
Long Time ◽  
Russian Stock Market

The article analyzes the problems of applying stock pricing models in the Russian stock market. The novelty of the study lies in the peculiarities of the methodology used and the substantive conclusions on the specifics of the influence of fundamental factors on the pricing of shares of Russian companies. The study was conducted using its own 5-factor basic pricing model based on a sample of the most complete number of issues of shares of Russian issuers and a long time horizon, from 1997 to 2017. The market portfolio was the widest for a set of issuers. We consider the factor model as a kind of universal indicator of the efficiency of the stock market performance of its functions. The article confirms the significance of factors of a broad market portfolio, size, liquidity and, in part, momentum (inertia). However, starting from 2011, the significance of factors began to decrease as the qualitative characteristics of the stock market deteriorated due to the outflow of foreign portfolio investment, combined with the low level of development of domestic institutional investors. Also identified is the cyclical nature of the actions of company size and liquidity factors. Their ability to generate additional income on shares rises mainly at the stage of the fall of the stock market. The results of the study suggest that as domestic institutional investors develop on the Russian stock market, factor investment strategies can be used as a tool to increase the return on investor portfolios.

METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

2019 ◽  
pp. 71-76
Author(s):  
A.E. Semenov
Keyword(s):  
Random Forest ◽  
Develop Model ◽  
Random Forest Algorithm ◽  
Saint Petersburg ◽  
Pedestrian Navigation ◽  
Factors Influencing ◽  
The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

Classification and photometric redshift estimation of quasars in photometric surveys

2020 ◽  
Vol 15 (S359) ◽  
pp. 40-41
Author(s):  
L. M. Izuti Nakazono ◽  
C. Mendes de Oliveira ◽  
N. S. T. Hirata ◽  
S. Jeram ◽  
A. Gonzalez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbour ◽  
Random Forest Algorithm ◽  
Photometric Redshift ◽  
Using Data

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

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