scholarly journals Research on the Improvement of Big Data Feature Investment Analysis Algorithm for Abnormal Trading in the Financial Securities Market

2022 ◽  
Vol 16 ◽  
pp. 406-412
Author(s):  
Jie Zou ◽  
Wenkai Gong ◽  
Guilin Huang ◽  
Gebiao Hu ◽  
Wenbin Gong
Keyword(s):  
Big Data ◽  
Trading Volume ◽  
Financial Time Series ◽  
Investment Strategy ◽  
Investment Analysis ◽  
Securities Market ◽  
Application Performance ◽  
Analysis Algorithm ◽  
Average Accuracy ◽  
Financial Securities

Traditional investment analysis algorithms usually only analyze the similarity between financial time series and financial data, which leads to inaccurate and inefficient analysis of investment characteristics. In addition, the trading volume of financial securities market is huge, the amount of investment data is also very large, and the detection of abnormal transactions is difficult. The aim of feature extraction is to obtain mathematical features that can be recognized by machine. Different from the traditional methods, this paper studies and improves the big data investment analysis algorithm of abnormal transactions in financial securities market. After processing the captured trading data of financial securities market, the big data feature of abnormal trading is extracted. Combined with the abnormal trading and the financial securities market, the investment strategy is determined. The optimization objective function is set and the genetic algorithm is used to improve the investment analysis algorithm. The simulation experiment verifies the improved investment analysis algorithm, and the average Accuracy of investment analysis is increased by at least 11.24%, the ROI is significantly improved, and the efficiency is higher, which indicates that the proposed algorithm has ideal application performance.

Defect analysis of the same batch of substation equipment based on big data analysis algorithm

2021 ◽  
Vol 651 (2) ◽  
pp. 022093
Author(s):  
Qiang Gao ◽  
Chuan Zhong ◽  
Yong Wang ◽  
Peng Wang ◽  
Zaiming Yu ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Defect Analysis ◽  
Analysis Algorithm ◽  
Substation Equipment

Bucket Shops and Outside Brokers

2021 ◽  
pp. 20-58
Author(s):  
Kieran Heinemann
Keyword(s):  
World War I ◽  
Stock Market ◽  
Interwar Period ◽  
Stock Exchange ◽  
Securities Market ◽  
New Perspective ◽  
Great Crash ◽  
Retail Activity ◽  
City Of London ◽  
Financial Securities

In order to finance World War I, the British government sold war bonds to millions of investors and savers, thereby prompting a wider interest in financial securities including stocks and shares during the interwar period. Faced with a large intake of investment newcomers, the City of London was anxious of ‘amateur’ involvement in the market. The largest securities market, the London Stock Exchange, restricted access to small investors where possible, which pushed much of the new retail activity to the market fringes. Here, ‘outside brokers’ and ‘bucket shops’ catered for investment newcomers, the more gullible of which fell prey to fraudulent share pushers. Scholars have entirely overlooked this vibrant grey market for financial securities. But it was here—and not just at the organized exchanges—that ever more people made their first experiences with the ups and the downs of the stock market, most prominently in the great crash of 1929. This new perspective brings a sharper contour on some fundamental challenges that Britain’s financial landscape was facing in the interwar period: a large intake of new investors, a resurgence of financial fraud, and a new struggle over the distinction between speculation and gambling. The City’s response to these challenges can be described as financial paternalism. After a surge in political democratization, there was very little appetite to enfranchise ordinary people in the stock market. Instead, institutions like the Stock Exchange deliberately took a conservative stance on the ‘democratisation of investment’.

Ten Lessons that Internet Auction Markets Can Learn from Securities Market Automation

2011 ◽  
pp. 57-87
Author(s):  
J. Christopher Westland
Keyword(s):  
Stock Exchange ◽  
Emerging Market ◽  
Securities Market ◽  
Internet Auction ◽  
Auction Markets ◽  
Auction Market ◽  
Central Stock ◽  
Product Models ◽  
Communications Technologies ◽  
Financial Securities

Internet auction markets offer customers a compelling new model for price discovery. This model places much more power in the hands of the consumer than a retail model that assumes price taking, while giving consumers choice of vendor and product. Models of auction market automation has been evolving for some time. Securities markets in most countries over the past decade have invested significantly in automating various components with database and communications technologies. This paper explores the automation of three emerging market exchanges ( The Commercial Exchange of Santiago, The Moscow Central Stock Exchange, and Shanghai’s Stock Exchange ( with the intention of drawing parallels between new Internet models of retailing and the older proprietary networked markets for financial securities.

scholarly journals Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage

2020 ◽  
Vol 10 (23) ◽  
pp. 8524
Author(s):  
Cornelia A. Győrödi ◽  
Diana V. Dumşe-Burescu ◽  
Doina R. Zmaranda ◽  
Robert Ş. Győrödi ◽  
Gianina A. Gabor ◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Relational Databases ◽  
Database Systems ◽  
Application Performance ◽  
Big Data Applications ◽  
Database Technology ◽  
Important Challenge ◽  
Big Data Application ◽  
The Impact

In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application.

scholarly journals Individual and institutional trading volume around firm-specific announcements

2016 ◽  
Vol 12 (4) ◽  
pp. 422-444 ◽  
Author(s):  
Priyantha Mudalige ◽  
Petko S Kalev ◽  
Huu Nhan Duong
Keyword(s):  
Institutional Investors ◽  
Trading Volume ◽  
Securities Market ◽  
Individual Investors ◽  
Order Book ◽  
Volume Data ◽  
Content Type ◽  
Before And After ◽  
Abnormal Trading Volume ◽  
Trading Behaviour

Purpose – The purpose of this paper is to investigate the immediate impact of firm-specific announcements on the trading volume of individual and institutional investors on the Australian Securities Exchange (ASX), during a period when the market becomes fragmented. Design/methodology/approach – This study uses intraday trading volume data in five-minute intervals prior to and after firm-specific announcements to measure individual and institutional abnormal volume. There are 70 such intervals per trading day and 254 trading days in the sample period. The first 10 minutes of trading (from 10.00 to 10.10 a.m.) is excluded to avoid the effect of opening auction and to ensure consistency in the “starting time” for all stocks. The volume transacted during five-minute intervals is aggregated and attributed to individual or institutional investors using Broker IDs. Findings – Institutional investors exhibit abnormal trading volume before and after announcements. However, individual investors indicate abnormal trading volume only after announcements. Consistent with outcomes expected from a dividend washing strategy, abnormal trading volume around dividend announcements is statistically insignificant. Both individual and institutional investors’ buy volumes are higher than sell volumes before and after scheduled and unscheduled announcements. Research limitations/implications – The study is Australian focused, but the results are applicable to other limit order book markets of similar design. Practical implications – The results add to the understanding of individual and institutional investors’ trading behaviour around firm-specific announcements in a securities market with continuous disclosure. Social implications – The results add to the understanding of individual and institutional investors’ trading behaviour around firm-specific announcements in a securities market with continuous disclosure. Originality/value – These results will help regulators to design markets that are less predatory on individual investors.

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