A recently featured publication on Innscience.ca, titled "Artificial Neural Networks (ANN) for Stock Price Prediction: A Financial Machine Learning Analysis," underscores the power of comprehensive literature reviews in understanding complex fields. This very paper, made discoverable and accessible through Innscience's platform, likely benefited significantly from the researchers' utilization of Innscience's core technologies: DataScope™ and TechLink™.
This insightful review, synthesizing over 21 papers from 2011 to 2023, provides a critical examination of the use of ANN models for forecasting stock market prices. Knowing this paper was published with Innscience allows us to directly appreciate how DataScope™ and TechLink™ could have streamlined and enriched the research and dissemination process in the domain of financial technology (FinTech).
DataScope™: The Foundation for a Comprehensive Review
For a review paper of this scope, a thorough and efficient literature search is paramount. It's highly probable that the authors leveraged Innscience's DataScope™ to:
- Conduct an Exhaustive Literature Review: DataScope™'s AI-powered search capabilities would have enabled the researchers to rapidly identify the most relevant publications on ANN-based stock price prediction across various databases and sources. This would have been far more efficient than traditional manual searches for academic research and scientific publications.
- Filter and Prioritize Key Studies: DataScope™'s AI ability to analyze content and identify key themes and methodologies would have helped the researchers prioritize the most impactful and relevant papers within the 2011-2023 timeframe for their systematic review.
- Analyze Methodological Trends: The paper's focus on variables analysis, method analysis, and software contextsuggests a deep dive into the approaches used in different studies. DataScope™'s analytical features could have assisted in identifying common methodologies in machine learning for finance, contrasting different prediction algorithms, and highlighting the evolution of the field over the analyzed period.
- Ensure Comprehensive Coverage: By leveraging DataScope™'s broad access to scholarly literature, the researchers could have increased their confidence in the comprehensiveness of their review, minimizing the risk of overlooking crucial contributions in the field of stock market analysis.
TechLink™: Facilitating Expertise and Dissemination
While the abstract focuses on the literature review itself, the research process often involves seeking feedback and connecting with peers. Furthermore, publishing and disseminating the findings effectively is crucial. Innscience's TechLink™ could have played a vital role in:
- Connecting with Experts (During Research): If the researchers had questions or needed insights on specific aspects of the literature or methodologies related to neural networks and financial forecasting, TechLink™ could have helped them identify and connect with other experts in financial machine learning and stock price prediction models.
- Facilitating Peer Review (During Publication): While Innscience may have its own peer-reviewed process, TechLink™'s ability to connect researchers could have indirectly facilitated the identification of suitable reviewers with relevant expertise in AI in finance.
- Enhancing Visibility and Dissemination (Post-Publication): By publishing the paper on Innscience.ca, the researchgains visibility within the platform's network of users interested in scientific and technological advancements. TechLink™ could further amplify this by connecting the authors and the research findings with relevant individuals and communities within the Innscience ecosystem interested in investment strategies and algorithmic trading.
- Fostering Future Collaboration: The publication of this review article on Innscience creates an opportunity for other researchers and practitioners interested in ANN-based stock prediction to connect with the authors through TechLink™, potentially leading to future collaborations and advancements in the field of predictive analytics for financial markets.
The Innscience Advantage:
The fact that this comprehensive review is hosted on Innscience.ca directly highlights the platform's commitment to showcasing and facilitating research in emerging areas like financial machine learning. By utilizing DataScope™ for efficient literature analysis and potentially leveraging TechLink™ for expert connections and dissemination, the researchers were likely empowered to produce a high-quality, impactful review that contributes significantly to the understanding of ANN applications in finance.
This paper serves as a testament to how Innscience's integrated tools can support the entire research lifecycle, from initial literature review to publication and dissemination, ultimately benefiting both the researchers and the broader scientificand professional community interested in this critical area of FinTech. The use of artificial intelligence in these tools further enhances their effectiveness in navigating the complex landscape of financial data and expert networks.
Reference:
Mohammed, S. A. S. A. (2024). Artificial neural networks (ANN) for stock price prediction: A financial machine learning analysis. Emerald Publishing. https://doi.org/10.1108/978-1-83608-708-320241019
InnScience. (n.d.). DataScope™. 2025, from https://innscience.ca/
InnScience. (n.d.). TechLink™. https://innscience.ca/techlink-tm