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Open research questions in Stock Market Forecasting Methods

326 gap statements mined from Stock Market Forecasting Methods papers in our 7.9M-paper local library, which holds 1,772 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • Traditional correlation measures are inadequate for capturing complex relationships among assets. Traditional investment portfolio optimization methods based on static relationships struggle to fully adapt to changes in market conditions. The need for a framework that can effectively capture nonlinear dependencies among stocks and adapt to evolving market structures.

    Integrating Dynamic Graph Representation and Reinforcement Learning for Portfolio Optimization · 2026 · DOI

  • The limited historical data of new issues or spin-offs makes it challenging to forecast their volatility. Traditional models may not perform well with limited data.

    Realized volatility forecasting for new issues and spin-offs using multi-source transfer learning · 2026 · DOI

  • The gap in the literature is the lack of evidence on whether geopolitical risk improves the out-of-sample forecasting of emerging-market stock returns. The study addresses the gap by examining the predictive value of geopolitical risk jointly with global commodity and exchange-rate information for the Thai stock market.

    Can Geopolitical Risk Improve the Forecasting of Thai Stock-Market Returns? Evidence from Econometrics and Machine-Learning Models · 2026 · DOI

  • Widely adopted initialization schemes are data-agnostic and may not perform well in non-stationary or class-imbalanced settings. Data-driven initialization methods, such as Laor Initialization, may not incorporate gradient information. There is a need for a framework that combines data-driven initialization with gradient-informed evaluation.

    An Extension of Laor Weight Initialization for Deep Time-Series Forecasting: Evidence from Thai Equity Risk Prediction · 2025 · DOI

  • Traditional visibility graphs do not capture directional price movements. The limitation of traditional visibility graphs is that they do not distinguish between rising and falling trends. The gap is that there is a need for a method that can effectively capture and predict recurrently emerging structural patterns in stock trends.

    Identifying and Forecasting Recurrently Emerging Stock Trend Structures via Rising Visibility Graphs · 2025 · DOI

  • The paper identifies significant research gaps and under-explored areas in the domain of cryptocurrency price prediction. It notes that determining the best selection of parameters and applying the most appropriate methodologies should be given careful attention in this area of research.

    Cryptocurrency Price Prediction Algorithms: A Survey and Future Directions · 2024 · DOI

  • While the influence of news on agricultural prices is widely recognised, how news information shapes price expectations remains poorly understood.

    Decoding agricultural supply chain trends with a news-driven temporal modelling framework · 2026 · DOI

  • The paper identifies the challenge of capturing the bid-ask spread in the limit order book. It highlights the need to handle real-time control and reward collection in high-frequency trading. The paper mentions the challenge of extending the context span of transformers.

    Boost High Frequency Trading with Deep Reinforcement Learning and Transformer · 2026 · DOI

  • The paper acknowledges that there are still downsides to overcome, including the need for further progress in training cost and risk management. It mentions that the proposed model still shows 51.7% on the competition-level MATH benchmark.

    Boost High Frequency Trading with Deep Reinforcement Learning and Transformer · 2026 · DOI

  • The phenomenon of sentiment drift is a persistent challenge in modeling investor behavior. The paper also mentions the risk of algorithmic herding and the need for model diversity.

    Artificial Intelligence for Modeling Investor Behavior and Market Sentiment Dynamics · 2026 · DOI

  • The paper suggests that future research should focus on developing more robust and generalizable behavioral AI models. The paper also highlights the need for further research on the ethical implications of AI-driven sentiment analysis.

    Artificial Intelligence for Modeling Investor Behavior and Market Sentiment Dynamics · 2026 · DOI

  • The lack of a robust and scalable infrastructure for DRL-based portfolio management. The need for ensuring the robustness of DRL agents. The risk of 'model convergence' and crowded trades introduced by the widespread adoption of DRL.

    Deep Reinforcement Learning for Dynamic Portfolio Optimization in Financial Markets · 2026 · DOI

  • The conventional approach of investment has been found to be inefficient and time-consuming. The lack of personalized investment advice is a significant challenge for investors.

    Ai Supported Investment Portfolio Management System · 2026 · DOI

  • Future work will focus on validating the proposed framework on larger, multi-platform, and cross-country datasets to assess scalability and external validity, while also incorporating drift-detection and robustness mechanisms to address sentiment instability, platform-specific bias, and evolving user behavior, incorporating rolling-window validation and walk-forward analysis on extended datasets.

    Application of deep learning-based social media data analysis in macroeconomic forecasting · 2026 · DOI

  • The study identifies a gap in the application of RL techniques for intraday trading strategy optimization in the Indian stock market.- The study highlights the need for high-quality data and appropriate reward functions in RL-based trading strategies.

    Empirical analysis of Q learning, DQN and DDQN for intraday trading · 2026 · DOI

  • Further research will improve the trading agent by combining sentiment analysis from news and social media with macroeconomic variables to produce a more accurate understanding of market performance.

    Empirical analysis of Q learning, DQN and DDQN for intraday trading · 2026 · DOI

  • Feature selection is often used in stock price prediction, but its results remain mixed.

    Comparing Random Forest and SHAP Feature Selection for LSTM-Based Stock Price Prediction · 2026 · DOI

  • As this is a relatively new field in market prediction, few studies have been conducted, and this study aims to advance this developing topic.

    Candlestick Type Prediction in Financial Charts · 2026 · DOI

  • Institutional dominance has concentrated resources and innovation within large corporations, limiting access to advanced trading tools for individual investors. Retail traders may need more exposure to algorithmic trading approaches.

    Designing a Cutting-Edge User-Centric Algorithmic Trading Platform with QuantConnect and Deep Learning · 2026 · DOI

  • Linear and parametric models are not very useful to complex and nonlinear financial systems, - Simulation methods rely on a set of predetermined stochastic assumptions, - The study used a limited number of firms and observation…

    Do Machine Learning Models Beat a Constant Benchmark When Forecasting Next-Month Hurst Exponents? Evidence from Saudi-Listed Firms · 2026 · DOI

  • Further evaluation of the proposed strategy on other real-world forecasting tasks, - Investigation of the method’s performance on other types of data, - Exploration of other data-driven initialization…

    An Extension of Laor Weight Initialization for Deep Time-Series Forecasting: Evidence from Thai Equity Risk Prediction · 2025 · DOI

  • Further research could explore the application of RVGWL to other financial markets, - Investigating the use of other graph kernels or machine learning models to improve predictive…

    Identifying and Forecasting Recurrently Emerging Stock Trend Structures via Rising Visibility Graphs · 2025 · DOI

  • We introduce each contribution, situate it within the broader literature, and close by outlining open challenges and directions for future research.

    Machine learning and big data in finance* · 2026 · DOI

  • By addressing a critical gap in the literature, this research highlights the potential of advanced machine learning techniques in financial forecasting.

    Assessing the Impact of Microeconomic Factors on Indian Stock Prices: An LSTM-Based Deep Learning Approach to Price Prediction · 2025 · DOI

  • Exploring the use of 'Green AI' to improve the efficiency of training algorithms. Investigating the potential for DRL to transform the global socio-technical infrastructure of capital markets. Developing a roadmap toward sustainable and interpretable financial AI.

    Deep Reinforcement Learning for Dynamic Portfolio Optimization in Financial Markets · 2026 · DOI

Most-cited papers in Stock Market Forecasting Methods

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Open questions raised by papers listed on this page include: (1) The gap in the literature is the lack of evidence on whether geopolitical risk improves the out-of-sample forecasting of emerging-market stock returns (“Can Geopolitical Risk Improve the Forecasting of Thai Stock-Market Returns?”, 2026); (2) While the influence of news on agricultural prices is widely recognised, how news information shapes price expectations remains poorly understood (“Decoding agricultural supply chain trends with a news-driven temporal modelling…”, 2026); (3) The paper suggests that future research should focus on developing more robust and generalizable behavioral AI models (“Artificial Intelligence for Modeling Investor Behavior and Market Sentiment…”, 2026); (4) The lack of a robust and scalable infrastructure for DRL-based portfolio management (“Deep Reinforcement Learning for Dynamic Portfolio Optimization in Financial…”, 2026); (5) The lack of personalized investment advice is a significant challenge for investors (“Ai Supported Investment Portfolio Management System”, 2026). 326 gap statements have been mined from Stock Market Forecasting Methods papers in our 7.9M-paper local library, which holds 1,772 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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