Drawbacks of Artificial Intelligence Might Have on Stock Markets Reduced Benefits Due to Cyber Issues

Drawbacks of Artificial Intelligence Might Have on Stock Markets Reduced Benefits Due to Cyber Issues

Applications powered by AI might change many factions of stock markets as this happens. First, due to the speed of analysis, forecasts, and investment issues, investors may find in the future that medium-term benefits will be below the expectations of the competitive base set by other stock trading firms (Sushma & Tarun, 2020). Therefore, there might be a push for the businesses to return to old ways of getting a financial adviser who will be suggesting the portfolio mix in stock trading due to the uncertainty of machine learning in the stock market. It is important to note that due to cyber issues, AI has been at risk of letting computer technicians with ill-fated intentions hack data, which can lead to loss of resources (Umer et al., 2019). Due to the power of microservices architecture, the stock markets might be overweighed by cybercrime to establish anti-phishing attacks that may yield to the collapse of investment.

The decline in Portfolio Payoff

When using AI, there is a high possibility that portfolio payoff may decline. When excluding microcaps, 62% of the payoff may go down hence disappearing returns (Xie & Akiyama, 2021). That means stocks can be difficult to trade as they might have negligible market capitalizations. When excluding non-rated firms, it might be 68% lower and 80% for distressed firms around credit cap elements (Xie & Akiyama, 2021). According to research, machine learning in stock markets may only be profitable when there is high investor sentiment and low market liquidity, among other factors. Therefore, despite AI’s powerful rise in stock trading, the subject has drawbacks on the same.

Cross-sectional Return receptibility

Artificial Intelligence may not solve everything in the stock market. It can only be valuable for real-time business, risk control, and established firms. The reason is that machine learning specializes in stock picking rather than industry rotation. Strategies that seek to maximize the next-level economic cycles may be unable to move funds equally in the industry (Sharma & Kaushik, 2017). Therefore, it is important to note that machine learning in stock markets may face the challenge of cross-sectional return receptibility hence making stock to be difficult to arbitrage in investment areas.

Conclusion

Artificial Intelligence in the stock market has played a key role in advancing the trading of shares. Through AI, investors can create strategies that can help analyze complex trading data and investment sequences. Stock market companies such as Kavout and Epoque have been boosted after adopting machine learning in their stock trading business. There are challenges of AI in the subject as they may have cross-sectional return forecast made hard to actualize by the investors. Additionally, the rise of cybercrime raises concern over many issues as some financial data might be manipulated.

Recommendations

Due to the continued embracing of AI in the stock market, it is important for modern-day companies to have machine learning power in their business. By 2025, stock markets shall be at a point where the business shall be conducted more efficiently than it is now. The following list shows recommendations for the report. The recommendations can be useful if utilized in the right way.

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1. Industries should embrace the use of AI as it ensures effective transaction and analysis of complex data
2. The use of AI should