1. Introduction

Stock market forecasting is a long-standing challenge at the intersection of finance and computer science. Traditional financial theory (e.g. the Efficient Market Hypothesis) implies that consistently beating the market is difficult, yet practitioners continue to seek predictive signals. Early approaches relied on linear time-series models like ARIMA and GARCH, often using handcrafted technical indicators (e.g. moving averages, momentum) as inputs . In recent years, advances in data availability and computing have enabled complex machine learning (ML) techniques. Neural network models in particular have become mainstream for stock forecasting. They can capture nonlinear relationships and long-range dependencies in price data (for example, LSTM networks have demonstrated the ability to learn long-term patterns and outperform simpler benchmarks) [1]. Meanwhile, reinforcement learning (RL) – where an agent learns trading rules by maximizing returns – has emerged for decision-making tasks.In recent years, as more historical data has accumulated in the market, coupled with the improvement of computer hardware performance, the use of data to do research methods has become more and more popular. Now there are cloud computing and chips dedicated to computing, such as GPUs and TPUs, which allows researchers to train complex models on much larger data sets than before. So now people who do research can not only use traditional price data, but also use news and social media to prevent new data sources.Study these methods to see how they are designed, how they work, and how they can be used in real finance.

2. Methodologies

2.1. Time-series models

Classical forecasting relies on econometric models. Autoregressive Integrated Moving Average (ARIMA) and its variants model prices under stationarity assumptions. GARCH models capture conditional volatility. Such linear models are interpretable and well-understood, but they often lack the capacity to model complex patterns in market data. Hybrid approaches combine ARIMA with machine learning (e.g. support vector machines or boosting) to capture linear trends and nonlinear residuals. For example, Kumar & Thenmozhi showed that an ARIMA–SVM ensemble could significantly improve forecast accuracy by fitting both linear and nonlinear components. Overall, ARIMA-style models remain useful baselines but have been outperformed by deep learning approaches in recent studies.

Other classical forecasting techniques extend the ARIMA/GARCH framework to cover various scenarios:

SARIMA: Includes seasonal components (e.g., quarterly earnings or macro cycles) into ARIMA models. Seasonal ARIMA can model repeating patterns such as earnings seasons or holiday effects.

VAR (Vector Autoregression): Models multiple related time series (e.g. prices of stocks within an index or economy) simultaneously, capturing interdependencies across assets.

State-space and Kalman filter models: Capture time-varying trends and can handle missing or irregularly sampled data by modeling hidden states.

Markov regime-switching models: Allow model parameters to change across market.These methods still rely on linear relationships and stationarity assumptions, which can limit their performance under regime shifts or turbulent market conditions.

Practitioners often choose models via criteria like AIC/BIC or cross-validated error metrics (RMSE, MAE). In practice, ARIMA models are preferred for their simplicity and transparency. However, they may fail to adapt quickly during market shocks; for instance, ARIMA forecasts can be inaccurate during sudden volatility spikes, since they assume past patterns will persist. Hybrid ARIMA-ML models remain a compromise: by modeling residuals with machine learning techniques, researchers attempt to capture both the smooth trend and complex irregularitiesfile-wbjagx6ym1dyvstjfoon6w. Nonetheless, the core limitations of linear models motivate the shift toward purely data-driven, nonlinear methods.

2.2. Neural networks

Feedforward neural networks and multilayer perceptrons can handle complex data relationships, but people prefer to use sequence models to do this. Recursive networks such as long-term short-term memory networks and gated loop units are particularly useful because they specialize in processing time-to-back relationships. One feature of these models is that they can remember information from a long time ago, so they are particularly suitable for analyzing time series data such as stock.Stock trends are better than returning to neural networks.

Neural network inputs often include a variety of financial features:

Price and volume data: These can be fed as raw values or transformed (e.g., returns, log-prices) to stabilize variance.

Technical indicators: Calculated measures like moving averages, Bollinger Bands, MACD, and RSI are commonly included to capture momentum or mean-reversion signals.

Fundamental data: Company financials (earnings, revenue, debt ratios) or macroeconomic variables (interest rates, GDP growth) can provide longer-term context.

Sentiment features: Quantified news sentiment, analyst ratings, or social media indicators can be incorporated to reflect market mood.Feeding diverse features into neural networks can improve robustness. Some models use multiple input channels (e.g., combining raw price series with indicator sequences).

Convolutional neural networks (CNNs) have also been applied by converting price sequences into images or treating time series like 1D signals; CNNs can extract local patterns or technical indicator sequences. Recently, Transformer architectures and attention mechanisms, originally from NLP, are being explored for capturing long-range dependencies in price data. Graph neural networks (GNNs) model relationships among multiple assets. Generative models such as GANs have been used to generate synthetic financial data or improve robustness. Emerging trends include using large language models (LLMs) to incorporate textual news or social media data into forecasting models. In summary, modern stock prediction models span a variety of neural structures – RNNs, CNNs, Transformers, GNNs, GANs, etc. – many of which have shown empirical success in learning nonlinear market dynamics.

2.3. Reinforcement Learning (RL)

RL formulates trading as a sequential decision problem. An RL agent observes market state (e.g. price history, indicators) and chooses actions (buy, sell, hold, or portfolio weights) to maximize a cumulative reward (typically profit or risk-adjusted returns). Unlike supervised models that predict future price directly, RL models learn a trading policy through trial-and-error. Popular deep RL algorithms applied to trading include Deep Q-Networks (DQN), Double DQN, Policy Gradient methods (e.g. DDPG, TD3), and Actor-Critic variants. For example, Théate & Ernst introduced a Trading Deep Q-Network (TDQN) tailored to maximize the Sharpe ratio; they trained the agent on simulated price trajectories and reported promising outperformance relative to benchmarks [2]. Similarly, Kabbani & Dumanused the TD3 (Twin Delayed DDPG) algorithm, modeling the problem as a partially-observed Markov decision process; their agent achieved a Sharpe ratio of 2.68 on test data, indicating strong performance of deep RL over traditional forecasting techniques [3]. RL’s advantage is the ability to learn from future cumulative outcomes and directly optimize trading objectives. However, it requires careful design of reward functions and realistic market simulations to be practical.

Key components of an RL trading framework include:

State representation: The input to the agent often includes recent price returns, technical indicators, current portfolio holdings, and sometimes market-wide variables (volatility index, interest rates). Choosing informative state features is crucial.

Action space: This could be discrete (e.g. buy, sell, hold for each asset) or continuous (e.g. adjusting portfolio weight percentages). Some RL agents decide actions for a single asset at a time, while others output a vector of weights for an entire portfolio.

Reward function: Common rewards include daily profit/loss or risk-adjusted returns (Sharpe ratio, Sortino ratio). Designing the reward to balance return vs risk is an active area of research.

Exploration strategy: To learn effectively, agents must explore different actions. Techniques include adding randomness (epsilon-greedy, Ornstein–Uhlenbeck noise) or entropy bonuses in policy gradient methods.

These methods have a great impact on AI performance. For example, if transaction costs are considered in the reward function, it can avoid frequent AI buying and selling.AI uses this prediction as a reference to trading decisions. This combination of training works better. There is also a new direction for multi-agent reinforcement learning, allowing several AIs to learn to interact together, such as simulating the behavior of different traders or institutions. This approach better reflects the real market situation, because the real market is affected by various traders.

3. Applications

Machine learning models have been applied in various real-world financial contexts. A primary use is algorithmic trading, where models generate buy/sell signals or manage portfolio weights automatically. Deep forecasting models (e.g. LSTM) are used to anticipate short-term price movements, while RL models are used to formulate end-to-end strategies. For example, in an emerging market case study, Tran et al. combined LSTM forecasts with technical indicators (SMA, MACD, RSI) for Vietnamese stock indices; their model achieved 93% accuracy in predicting price direction, demonstrating effective short-term forecasting with deep learning. In another study, Lahboub and Benali compared ARIMA, Transformers, and LSTM on Moroccan credit stocks and found LSTM vastly outperformed the others (with R² >0.99 for LSTM, vs. poor performance for the Transformer model). These applications show that neural models can yield high predictive accuracy on real financial datasets.

Beyond directional forecasting, ML is used for portfolio optimization and asset allocation (e.g. RL agents learning to balance risk and return), and for risk management by forecasting volatility or market downturns. In practice, hedge funds and quant firms increasingly integrate ML: large asset managers and fintech startups use ML-driven signals in automated trading systems. For instance, Deng et al. developed a Deep Direct RL framework that learned trading policies from raw market data, and reported that such RL agents could adapt to complex financial signals. (Numerous proprietary case studies similarly highlight ML’s role in real-world trading engines.) In credit risk and macro-finance, ML models are used for stress-testing and scenario analysis, though these are beyond pure stock forecasting.

4. Comparative analysis

Overall, deep learning models have largely supplanted classical approaches in recent research. Studies consistently report that RNN-based models, particularly LSTM, outperform linear and shallow methods on predictive benchmarks [4]. For example, Liu et al. found LSTM RNNs significantly outperformed ARIMA and regression neural nets across different time series patterns. Similarly, Lahboub & Benali observed that LSTM achieved far higher R² than ARIMA or even advanced Transformer models. Neural networks thus capture nonlinear dependencies that simple time-series models miss.

However, this performance often comes with higher complexity. Deep networks require large datasets and careful tuning, whereas ARIMA models are simpler and faster to train[5]. Classical models are also more interpretable and impose fewer data requirements. Hybrid approaches can combine the best of both, as evidenced by superior accuracy from ARIMA–ML ensembles. Reinforcement learning methods can potentially outgain supervised predictors by optimizing for long-term returns: the high Sharpe ratios reported in recent RL studiessuggest that RL-based trading agents can exceed static forecasting schemes. But RL also faces pitfalls (e.g. overfitting to simulated environments).

In summary, neural models (especially RNNs) generally provide stronger predictive power at the cost of complexity; RL offers a distinct paradigm focused on decision-making under uncertainty. The best choice depends on the application: for pure price forecasting, LSTM/CNN models dominate, whereas for automated trading, deep RL is promising.

5. Challenges

Despite successes, significant challenges remain. Financial data is noisy and non-stationary, so models risk overfitting to past patterns that may not persist. Market regimes can shift, making static models unreliable. Many studies also omit realistic transaction costs and market impact; as noted by Jiang, simple trading strategies may be “impractical” when real market frictions are included. Furthermore, ML models (especially deep nets) are “black boxes” – their decisions lack transparency, raising concerns for regulatory compliance and risk management. Reinforcement learning in particular can suffer from high variance and instability; an RL agent that performs well in simulation might incur large losses in live trading due to unforeseen market moves. Data limitations are another issue: while big tech datasets are growing, many stock markets still have limited historical depth, which constrains model training [6].

Overall, ML in finance must contend with efficiency of markets (where arbitrage opportunities are fleeting), the evolving nature of financial data, and the difficulty of robustly measuring “success.” These factors complicate both the development and the evaluation of ML models, and they highlight why even strong in-sample performance may not translate to consistent real-world gains.

6. Prospect

The field is rapidly evolving. Recent surveys and studies suggest several future paths. Explainable AI is likely to become important: developing interpretable ML models that reveal why they make certain predictions could foster trust and uncover new financial insights. Hybrid models that integrate domain knowledge (e.g. economic indicators or structural models) with data-driven ML may yield more robust forecasts. The use of alternative data (news, social sentiment, satellite imagery) combined with ML is an active trend, as is leveraging large language models to interpret textual information for market signals [7].

In reinforcement learning, future work may focus on safer exploration (to limit potential losses) and multi-agent RL (modeling interactions among traders) [8]. Deep RL’s success in other domains suggests it will see more adoption in trading, especially with improved simulation environments.Transfer learning – using models trained on one market or asset and adapting to another – has shown early promise in stock prediction and is likely to expand. Finally, the community is also working on robustness: ensuring models can handle adversarial conditions or extreme market events.

7. Conclusion

Machine learning has significantly impacted stock market analysis, providing powerful tools for prediction and decision-making. Time-series methods, while foundational, have largely been augmented or replaced by neural network models that capture nonlinear dynamics and temporal dependencies. Reinforcement learning offers a complementary approach that directly targets trading performance.Empirical studies indicate that deep ML models often achieve superior accuracy, but they require careful design to address financial realities. This review has surveyed the recent landscape of ML in finance, highlighting representative models and their real-world applications. Moving forward, advancing the practical relevance of these techniques – through explainability, integration of diverse data sources, and rigorous out-of-sample testing – will be key to realizing their full potential in industry.

A key takeaway is that ML tools should complement rather than replace traditional financial expertise. Advanced models can uncover patterns, but domain knowledge remains crucial for interpreting predictions and guiding investment decisions. Interdisciplinary collaboration (between data scientists, financial analysts, and regulators) and the development of interpretable ML techniques will be important for the responsible deployment of AI in finance. Ultimately, bridging research advances with practical trading systems will require not only sophisticated algorithms but also robust risk controls and clear understanding of market mechanisms.