IMPACT OF DATA ANALYTICS IN FINANCIAL MARKETS

2021 ◽  
pp. 53-54
Author(s):  
Pranav Kalkotwar
Keyword(s):  
Financial Markets ◽  
Data Analytics ◽  
Adverse Impact ◽  
Fake News ◽  
Learning Models ◽  
Trade Data ◽  
Market Participants ◽  
Depth Data ◽  
Market Framework ◽  
Immense Potential

Analysing the data in a specic goal oriented manner can yield great insights which be used to ensure smooth running and regulation of the markets while avoiding manipulations which can have adverse impact on other market participants and damage the integrity of nancial market framework. Large institutional players often use unethical methods to move the markets in there favour, one of the the example is freak trades. This issue can be countered by making it mandatory for brokers to share live trade data which can be thrown into manipulation detecting models specially for freak trades. Machine Learning and deep learning models can be made for detection of manipulative occurences which includes fake news detection, manipulation of bid ask depth. Data analysis shows immense potential for the process of regulation of nancial markets

scholarly journals Is It Possible to Forecast the Price of Bitcoin?

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 377-420
Author(s):  
Julien Chevallier ◽  
Dominique Guégan ◽  
Stéphane Goutte
Keyword(s):  
Data Analytics ◽  
Daily Variation ◽  
A Priori ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Market Growth ◽  
Network Support ◽  
Market Participants ◽  
Nearest Neighbours ◽  
Stationary Behavior

This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.

The Capital Market Authority Effectiveness in the Regulation of Financial Markets perspectives from the financial sector actors

2012 ◽  
Vol 02 (11) ◽  
pp. 15-24
Author(s):  
Charles Kombo Okioga
Keyword(s):  
Financial Markets ◽  
Financial Market ◽  
Capital Markets ◽  
Capital Market ◽  
Financial Regulation ◽  
International Standards ◽  
Market Regulation ◽  
Financial Market Regulation ◽  
Market Participants ◽  
New Business

Capital Market Authority in Kenya is in a development phase in order to be effective in the regulation of the financial markets. The market participants and the regulators are increasingly adopting international standards in order to make the capital markets in sync with those of developed markets. New products are being introduced and new business lines are being established. The Capital Markets Authority (Regulator) is constantly reviewing existing regulations and recommending changes to regulate the market properly. Business lines and activities are being harmonized by market participants to provide a one stop solution in order to meet the financial and securities services needs of the investors. The convergence of business lines and activities of market intermediaries gives rise to the diversity of a firm’s business operations to meet multiplicity of regulations that its activities are subject to. The methodology used in this study was designed to examine the relationship between capital markets Authority effective regulation and the performance of the financial markets. The study used correlation design, the study population consisted of 30 employees in financial institutions regulated by Capital Markets Authority and 80 investors. The study found out that effective financial market regulation has a significant relationship with the financial market performance indicated by (r=0.571, p<0.01) and (r=0.716, p≤0.01, the study recommended a further research on the factors that hinder effective financial regulation by the Capital Markets Authority.

scholarly journals What is a 'Real' Transaction? Infrastructures, relations and Spoofing in High-Frequency Trading

2016 ◽  
Author(s):  
Juan Pablo Pardo Guerra
Keyword(s):  
Financial Markets ◽  
High Frequency ◽  
High Frequency Trading ◽  
Market Behavior ◽  
Digital Platforms ◽  
Market Participants

Although an old and rare practice, spoofing has re-emerged as a subject ofintense debate within modern financial markets. An activity entailing thefraudulent creation of orders to buy and sell securities with the purposeof manipulating the market, spoofing highlights the multiple and complexmoral valences of contemporary, automated, finance. In this paper, I studyspoofing as an opportunity to understand markets and their relations ofexchange. In particular, by extending Weberian metaphors of markets asmoral and organizational communities, I examine how the courts and marketparticipants distinguish the ‘false’ transactions of spoofing from the‘real’ exchanges of 'normal' market behavior. Combining Marilyn Strathern’stheoretical discussion of the anthropological relation with recentliteratures on infrastructures and markets, I argue that the perceivedreality of transactions is a product of how novel forms of economicknowledge are able to make sense of ‘taken for granted’ behavioral patternswithin digital platforms of market action. The intent that constitutes‘real’ trades is therefore a product of how market participants, economicexperts and the courts interpret the operational underbelly of markets andthe relations that they produce.

The Explore of Using Deep Learning Models for Fake News Classification

2020 ◽  
pp. 389-393
Author(s):  
Ting-Hao Chang ◽  
Wei-Hung Tu ◽  
Jia-Wei Chang ◽  
Tien-Chi Huang ◽  
Yi-Xiang Luo
Keyword(s):  
Deep Learning ◽  
Fake News ◽  
Learning Models

Telugu News Data Classification Using Machine Learning Approach

2022 ◽  
pp. 181-194
Author(s):  
Bala Krishna Priya G. ◽  
Jabeen Sultana ◽  
Usha Rani M.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
Learning Approach ◽  
Fake News ◽  
Learning Models ◽  
Machine Learning Classifiers ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Machine Learning Models

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

Fake news detection using deep learning models: A novel approach

2019 ◽  
Vol 31 (2) ◽  
Author(s):  
Sachin Kumar ◽  
Rohan Asthana ◽  
Shashwat Upadhyay ◽  
Nidhi Upreti ◽  
Mohammad Akbar
Keyword(s):  
Deep Learning ◽  
Fake News ◽  
Learning Models ◽  
Novel Approach

China's Emerging Credit Rating Industry: The Official Foundations of Private Authority

2008 ◽  
Vol 193 ◽  
pp. 65-83 ◽  
Author(s):  
Scott Kennedy
Keyword(s):  
Financial Markets ◽  
Stock Markets ◽  
Credit Rating ◽  
Credit Rating Agencies ◽  
Bond Ratings ◽  
Trade Associations ◽  
Rating Agencies ◽  
Market Participants ◽  
Private Authority ◽  
Private Actors

AbstractAlthough China has had difficulty improving the performance of its banks and stock markets, it has struggled even more to develop a credit rating industry. Credit rating agencies (CRA), which provide bond ratings, are vital to financial markets in advanced capitalist countries, but China's credit rating companies are weak and have had little influence over the behaviour of those who issue or invest in bonds. Some argue that CRAs gain authority through their strong reputation in the eyes of market participants, but the experience of rating agencies in China supports evidence from elsewhere that their private authority is largely dependent on government mandate, a benefit China's CRAs have only recently begun to enjoy. Many private actors, from trade associations to charity groups, are struggling to gain public influence in China, but credit rating agencies may be the best barometer to measure the Chinese government's general stance towards private authority.

High-Frequency Trading and Conflict in the Financial Markets

2017 ◽  
Vol 32 (3) ◽  
pp. 270-282 ◽  
Author(s):  
Ricky Cooper ◽  
Jonathan Seddon ◽  
Ben Van Vliet
Keyword(s):  
Financial Markets ◽  
High Frequency ◽  
High Frequency Trading ◽  
Market Makers ◽  
Market Participants ◽  
Software Vendor ◽  
Traditional Market ◽  
Recent Developments ◽  
The Way

The last few decades has seen an ever-increasing growth in the way activities are productized and associated with a financial cost. This phenomenon, termed financialization, spans all areas including government, finance, health and manufacturing. Recent developments within finance over that past decade have radically altered the way trading occurs. This paper analyses high-frequency trading (HFT) as a necessary component of the infrastructure that makes financialization possible. Through interviews with HFT firms, a software vendor, regulators and banks, the effects of HFT on market efficiency, and its impact on costs to long-term investors are explored. This paper contributes to the literature by exploring the conflict that exists between HFT and traditional market makers in today's fragmented markets. This paper argues that society should be unconcerned with this conflict and should instead focus on the effects these participants have on the long-term investors, for whom the markets ultimately exist. In order to facilitate the best outcomes, regulation should be simple, aimed at keeping participants’ behavior stable, and the interactions among them transparent and straightforward. Financialization and HFT are inextricably linked, and society is best served by ensuring that the creative energy of these market participants is directed on providing liquidity and removing inefficiencies.

Sign in / Sign up

Export Citation Format

Share Document