Analyses on W&R and BIAS Expert System of Stock-Market Software

2013 ◽  
Vol 433-435 ◽  
pp. 2391-2394 ◽  
Author(s):  
Hai Ping Huang ◽  
Pin Wang
Keyword(s):  
Expert System ◽  
Stock Market ◽  
Simulation Experiment ◽  
Return Rate ◽  
Profit Rate ◽  
Net Profit ◽  
Experiment Method ◽  
Stock Market Trading ◽  
Extensive Quantity ◽  
Factual Data

According to extensive quantity of public and factual data, this essay does tests on BIAS and W&R expert system of stock-market trading software in simulation experiment method, and compares their strength and weakness in the respect of mathematical statistics theory and Management objects, winning rate, annual return rate and net profit rate. The tests show that BIAS expert system is 2.58 times as good as W&R expert system in the items of annual return rate and net profit rate, 0.26 times in the item of the total number of trading. As a whole, BIAS expert system is superior to W&R expert system.

Discussions on Securities Software Expert System MA and RSI

2013 ◽  
Vol 798-799 ◽  
pp. 757-760 ◽  
Author(s):  
Hai Ping Huang ◽  
Pin Wang
Keyword(s):  
Expert System ◽  
Simulation Experiment ◽  
Securities Market ◽  
Massive Data ◽  
The Public ◽  
Management Objectives ◽  
Winning Percentage ◽  
Net Profit ◽  
Experiment Method ◽  
Securities Trading

Based on the public and actual massive data of securities market, through testing the MA and RSI expert system of securities trading software by simulation experiment method, with the winning percentage, the annual rate of return, and the net profit margin as the management objectives, and with the theory of mathematical statistics as the research basis, the article carried out the comparative analysis of strength and weakness of these two systems. Except the annual transaction number, RSI expert system is comprehensively superior to MA expert system.

An Empirical Analysis on BOLL Expert System Based on Maximum Income

2013 ◽  
Vol 850-851 ◽  
pp. 1040-1043 ◽  
Author(s):  
Pin Wang ◽  
Hai Ping Huang
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Empirical Analysis ◽  
Annual Rate ◽  
Rate Of Return ◽  
Security Market ◽  
Profit Rate ◽  
Management Objectives ◽  
Statistical Experiments ◽  
Net Profit

This paper presents tests security transaction BOLL expert systems, through statistical experiments, with the wining rate, annual rate of return and net profit rate as the management objectives, on basis of the mass data generated by the security market; The results show that win rate value of BOLL expert system as high as 88.95%, and annual rate of return and net profit rate are 33.37% and 13.9% respectively. Compared with RSI expert system, the transaction times are only 52.09% of RSI expert system, which should be the true reason why the rate of turn of BOLL expert system is not high.

scholarly journals Artificial Intelligence Applied to Stock Market Trading: A Review

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 30898-30917 ◽  
Author(s):  
Fernando G. D. C. Ferreira ◽  
Amir H. Gandomi ◽  
Rodrigo T. N. Cardoso
Keyword(s):  
Artificial Intelligence ◽  
Stock Market ◽  
Stock Market Trading

scholarly journals Ownership And Firm Performance: The Experience Of Saudi Arabias Emerging Economy

2011 ◽  
Vol 8 (8) ◽  
Author(s):  
Abuzar M. A. Eljelly
Keyword(s):  
Saudi Arabia ◽  
Firm Performance ◽  
Stock Market ◽  
Market Performance ◽  
Return On Assets ◽  
Private Companies ◽  
Return On Equity ◽  
Net Profit ◽  
The Government ◽  
The Relationship

This study examines the relationship between firm ownership and corporate performance in Saudi Arabia, using a sample of Listed Private Companies (LPCs) and Listed Government Related Companies (LGRCs). The study compares the operating and market performance of the LPCs and LGRCs during the period 2000-2003 and found that, in general, LGRCs outperform or match the performance of LPCs. More specifically, the study finds that LGRCs tend to mostly outperform LPCs in terms of profitability, as measured by Return on equity (ROE) and Net Profit Margin (NPM), operating efficiently, as measured in terms of Return on assets (ROA), and match them in their stock market risk adjusted performance. The study concludes that these results may have implications for the issue of privatization programs which the government has recently started.

Single step estimation of ARMA roots for non-fundamental nonstationary fractional models

2022 ◽  
Author(s):  
Ignacio N Lobato ◽  
Carlos Velasco
Keyword(s):  
Stock Market ◽  
Strong Evidence ◽  
Long Memory ◽  
Trading Volume ◽  
Moving Average ◽  
Single Step ◽  
Arma Process ◽  
Dow Jones Industrial Average ◽  
Fractional Models ◽  
Stock Market Trading

Abstract We propose a single step estimator for the autoregressive and moving-average roots (without imposing causality or invertibility restrictions) of a nonstationary Fractional ARMA process. These estimators employ an efficient tapering procedure, which allows for a long memory component in the process, but avoid estimating the nonstationarity component, which can be stochastic and/or deterministic. After selecting automatically the order of the model, we robustly estimate the AR and MA roots for trading volume for the thirty stocks in the Dow Jones Industrial Average Index in the last decade. Two empirical results are found. First, there is strong evidence that stock market trading volume exhibits non-fundamentalness. Second, non-causality is more common than non-invertibility.

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