Problems and Solutions in US Stock Market

This research explores critical challenges facing the U.S. stock market, specifically focusing on the impacts of algorithmic trading-induced market volatility and cybersecurity threats. Algorithmic trading, which utilizes high-speed computer systems to make trades, has increased market efficiency but also introduced risks of extreme price fluctuations and flash crashes. In parallel, the digitalization of financial markets has heightened exposure to cybersecurity threats, posing risks to trading integrity and data security. To address these issues, this study proposes two targeted solutions. For the issue of market volatility driven by algorithmic trading, the research recommends implementing circuit breakers and trading halts. These mechanisms can prevent severe market disruptions by temporarily pausing trading during extreme price movements, allowing the market to stabilize and preventing panic-driven selloffs. For the cybersecurity challenges, the study suggests enhancing information sharing and fostering collaborative defense strategies among financial institutions and regulatory bodies. By encouraging greater collaboration, the financial sector can more effectively identify and counteract cybersecurity threats, reducing vulnerabilities across the market.

Authors

Institutions

Publication Details

Journal
Advances in Economics Management and Political Sciences
Published
2024-12-26
DOI
https://doi.org/10.54254/2754-1169/2024.ga18969
Primary Topic
Complex Systems and Time Series Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Problems and Solutions in US Stock Market

Zhenzhen Liu
Advances in Economics Management and Political Sciences
Complex Systems and Time Series Analysis
article

Problems and Solutions in US Stock Market

Zhenzhen Liu
article en

Abstract

This research explores critical challenges facing the U.S. stock market, specifically focusing on the impacts of algorithmic trading-induced market volatility and cybersecurity threats. Algorithmic trading, which utilizes high-speed computer systems to make trades, has increased market efficiency but also introduced risks of extreme price fluctuations and flash crashes. In parallel, the digitalization of financial markets has heightened exposure to cybersecurity threats, posing risks to trading integrity and data security. To address these issues, this study proposes two targeted solutions. For the issue of market volatility driven by algorithmic trading, the research recommends implementing circuit breakers and trading halts. These mechanisms can prevent severe market disruptions by temporarily pausing trading during extreme price movements, allowing the market to stabilize and preventing panic-driven selloffs. For the cybersecurity challenges, the study suggests enhancing information sharing and fostering collaborative defense strategies among financial institutions and regulatory bodies. By encouraging greater collaboration, the financial sector can more effectively identify and counteract cybersecurity threats, reducing vulnerabilities across the market.

Advances in Economics Management and Political SciencesVol. 143(1)
University of Miami (US)
Partnerships for the goals
Openalex Percentile: Top 70%
Complex Systems and Time Series Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.