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Open research questions in Stock Market Forecasting Methods

283 unresolved questions extracted from the limitations and future-work sections of 847 Stock Market Forecasting Methods papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Further evaluation of the proposed method using other datasets and markets. Comparison with other state-of-the-art models and techniques. Investigation of the application of the proposed method in other financial forecasting tasks.

    LSTM-RF Stock Prediction Algorithm via Short-Term Directional Probability-Based Model Selection · 2026 · DOI

  • Traditional econometric methods have proven inadequate for capturing complex interactions in stock markets. Prior machine learning models have limitations in predicting stock price trends.

    LSTM-RF Stock Prediction Algorithm via Short-Term Directional Probability-Based Model Selection · 2026 · DOI

  • Future research should focus on online updating, uncertainty quantification, cross-market robustness, and economically meaningful interpretation.

    Unsupervised Learning for Financial Market Regime Detection Using Volatility Features · 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 paper mentions the challenge of ensuring the robustness and generalization of behavioral AI. 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

  • 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

  • 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

  • Conventional approaches often rely on delayed structured data and ignore real-time public sentiment. Traditional models struggle to capture complex nonlinear and multiscale temporal relationships between macroeconomic indicators and social sentiment.

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

  • Future work will focus on validating the proposed framework on larger, multi-plat- form, and cross-country datasets to assess scalability and external validity, while also incorporating drift-detection and robustness mechanisms to address sentiment instabil- ity, platform-specific bias, and evolving user behavior, incorporating rolling-window val- idation 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

  • Technical indicators selected through correlation analysis and domain knowledge may not include all relevant elements, thereby limiting model efficacy [4, 9]. While the models focus on maximizing cumulative profit, more advanced risk management measures are not employed, which may be required for real-world applications. RL models in a real-time trading environment face latency and execution speed problems. Ensuring that the models make timely judgments is critical for practical deployment. 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. In this regard, it is an intriguing avenue for future research to incorporate more dynamic reward functions and additional risk metrics such as VaR or CVaR in risk measures into the agent's decision-making process regarding profitability and risk management. Hybrid models that combine RL with additional methodologies such as time series analysis and hierarchical RL will aid in improving trading performance when applied to difficult scenarios. Low latency and scalability model optimization will enable effective real-time trading. Finally, expanding the technique to portfolio management and subjecting it to stress tests would increase the agent's robustness, as will its inherent risk management.

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

  • Further investigation of the relationship between AI-driven financial technologies and income/gain from investment. Examination of the implications of AI for portfolio optimization and market forecasts in different market conditions.

    The Role of Artificial Intelligence in Portfolio Management and Investment Decision-Making · 2026 · DOI

  • The lack of understanding of the role of AI in portfolio management and investment decision-making. The need for further research on the implications of AI for portfolio optimization and market forecasts.

    The Role of Artificial Intelligence in Portfolio Management and Investment Decision-Making · 2026 · DOI

  • The complexity involved in executing algorithmic trading algorithms. Limited access to advanced trading tools for individual investors. The need for more options available in the market for algorithmic trading platforms.

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

  • The paper identifies the need for applying machine learning tools to analyze economic and financial information. The gap in the current literature is the lack of effective methods for predicting time series values using natural language.

    Building a model for the transition from text to numeric data and predicting its values using machine learning methods · 2026 · DOI

  • Traditional statistical models have limitations in capturing complex, nonlinear relationships inherent in financial time series. The need for a machine learning-based system that can analyze patterns and predict next-day prices with high accuracy.

    UNIVERSAL STOCK MARKET AND CRYPTOCURRENCY PRICE PREDICTOR USING XG BOOST · 2026 · DOI

  • The study identifies a gap in the literature on the application of hybrid modeling approaches to capture the historical dynamics of the consumer price index. The study highlights the need for a systematic evaluation of econometric and hybrid models for the consumer price index.

    HYBRID FORECASTING FOR THE CONSUMER PRICE INDEX USING SARIMAX, SARIMAX-MACHINE LEARNING AND SARIMAX-DEEP LEARNING MODELS: THE CASE OF CÔTE D’IVOIRE · 2026 · DOI

  • The evaluation employs only five standard accuracy metrics (ME, MPE, RMSE, MAE, MAPE); domain-specific evaluation criteria for CPI forecasting accuracy (e.g., directional accuracy, inflation threshold breaches) or statistical significance testing between competing models are not implemented.

    HYBRID FORECASTING FOR THE CONSUMER PRICE INDEX USING SARIMAX, SARIMAX-MACHINE LEARNING AND SARIMAX-DEEP LEARNING MODELS: THE CASE OF CÔTE D’IVOIRE · 2026 · DOI

  • Traditional trading systems suffer from limitations such as rigid execution logic and lack of capital awareness. The need for a continuous, adaptive, and capital-sensitive trading framework.

    Wolf AI: An Artificial Intelligence Framework for Automated Trading in Derivatives Markets Across BankNifty and Crude Oil · 2026 · DOI

  • The next stage of research should not treat forecasting, allocation, and ESG-related corporate finance as separate literatures. The field should explore the use of explainable AI in finance. The field should investigate the interaction between machine learning and portfolio optimization.

    Artificial Intelligence in Financial Decision-Making · 2026 · DOI

  • The literature on AI in financial decision-making is fragmented across multiple domains. There is a lack of a unifying interpretation of AI as a decision infrastructure rather than a collection of isolated algorithms.

    Artificial Intelligence in Financial Decision-Making · 2026 · DOI

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283 open questions have been extracted from the limitations and future-work passages of 847 Stock Market Forecasting Methods papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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