Automated Trading System for Stock Index Using LSTM Neural Networks and Risk Management

Silva, Thalita R.; Li, Audeliano W.; Pamplona, Edson de Oliveira

doi:10.1109/ijcnn48605.2020.9207278

Cited by 8 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Later, works like Security Analysis (Hoffman, 1935) popularized long-term investing education. Fundamental analysis considers financial status, board of directors, annual report, balance sheets, profit/loss accounts, topography, climate, and politics to predict future stock prices (Silva et al, 2020;Jing et al, 2021;Agrawal et al, 2022;. Fundamental analysis must be flexible since factors are unstructured.…”

Section: Fundamental Analysismentioning

confidence: 99%

Stock market and securities index prediction using artificial intelligence: A systematic review

Singh,

Malhotra

2024

Multidiscip. Rev.

View full text Add to dashboard Cite

The recognition of the value and importance of recognizing patterns in the stock market is widely accepted. As a result, using innovative decision-making strategies is expected to lead to significant returns in stock prices. Predicting the stock market proves challenging because of the limited volatility and inherent disorderliness observed within the data. Hence, investors need help optimizing their profits through informed predictions in the stock market. Stock market projections are made by employing mathematical approaches and machine learning methods. The stock market is inherently vulnerable to unforeseen changes due to its intricate nature and absence of a linear progression. Since the advent of personal computers and the proliferation of technological advancements in the 1990s, scholars have investigated the application of artificial intelligence in the investment sector. Many solutions have been created to tackle the matter of volatility in stock market prices. This study examined a total of 146 research articles that were published in academic journals over twelve years (2011-2023). These papers focused on applying artificial intelligence in predicting stock market trends. The listed works encompass several methodologies, including technical analysis, fundamental analysis, sentiment analysis, and time series analysis. Every academic field is comprehensively explored, encompassing its initial findings and most recent advancements. Moreover, the existing body of literature suggests a growing focus on this particular sector, with a heightened level of specialization and an expanded range of topics being explored.

show abstract