Prior to artificial intelligence, financial forecasting was mostly based on conventional statistical models like vector autoregression (Sims, 1987), generalised autoregressive conditional heteroskedasticity (Bollerslev, 1986), and autoregressive integrated moving average (Bollerslev, 1987, Bollerslev and Mikkelsen, 1996). The simplicity and analytical clarity of these models made them popular, and they offered organised and interpretable frameworks for time-series forecasting. They were unable to adequately simulate the dynamic and nonlinear character of financial markets, especially during times of high volatility or unanticipated black swan occurrences, since they were predicated on the fundamental assumptions of linearity and stationarity.

Because machine learning approaches performed better at managing high-dimensional financial data and capturing non-linear trends, they were adopted as a result of the constraints of traditional statistical models. As evidenced by works such as Illa et al. (2022), notable contributions include the use of random forests (a machine learning model combining many decision trees) and support vector machines for stock trend classification and price prediction. As noted by Alobaidy and Saeed, (2023), neural networks garnered popularity due to their capacity to represent sequential data, especially multi-layer perceptrons and recurrent neural networks. In a number of benchmark tests, Oruh et al. (2022) demonstrated that long short-term memory networks outperformed conventional machine learning techniques, making them particularly useful for understanding temporal connections in financial time series. Nevertheless, despite their advantages, machine learning and deep learning models sometimes needed a lot of hyperparameter adjustment, had trouble being easily interpreted, and had trouble integrating many types of data, including technical indicators, market emotion, and financial news (Vargas et al., 2017).

To overcome the drawbacks of using only one model, researchers started creating hybrid strategies that improved prediction accuracy by combining many methods. Convolutional neural networks were used to extract features from technical indicators (Chandar, 2022), while long short-term memory networks were combined with sentiment analysis generated from news headlines (Sharaff et al., 2023) in hybrid models. Additionally, Zhou et al. (2023) transformed sequence modelling with the integration of attention processes and the transformer architecture. Because of this breakthrough, sophisticated models like bidirectional encoder representations from transformers. and generative pre-trained transformer (GPT) were created. These models employ attention-based processes to highlight the most important aspects in lengthy sequences, which is very useful for examining complicated time series data that is impacted by a variety of factors. Despite these advancements, most hybrid deep learning models still adopted a discriminative approach, predicting outcomes directly from input data without modelling the underlying generative process that produces such data.

The two neural networks that make up generative adversarial networks are a discriminator and a generator. The discriminator seeks to discern between real and synthetic data, while the generator seeks to produce realistic data in a zero-sum game. These networks have been successfully modified for time-series forecasting in finance, despite being originally created for picture generating. To enhance model generalization and supplement sparse datasets, timeseries generative adversarial networks are utilised to create synthetic financial data (Zhang et al., 2022). Stock-GAN, a generative adversarial networks-based model is helpful for Monte Carlo simulations and stress testing since it produces realistic stock price trajectories (Vullam et al., 2023). Furthermore, generative adversarial networks have been used to predict latent market volatility, simulate macroeconomic shocks, and build synthetic portfolios for trading strategy back-testing. However, despite its potential, problems including mode collapse, training instability, and a lack of defined assessment measures make them harder to implement practically in finance than in computer vision.

By encoding input data into a probabilistic latent space, variational autoencoders enable the generation and sampling of new data, and have been useful in financial applications. Hosseini et al. (2024) demonstrated how they may be used to identify market regimes by grouping market circumstances according to latent traits, which facilitates the creation of regime-switching models. Through the generation of several believable future market trajectories under various assumptions, these autoencoders also assist with scenario analysis and stress testing. They have higher training stability and interpretable latent representations than generative adversarial networks, but when it comes to capturing complicated data distributions, they are often regarded as less expressive and frequently generate blurrier data samples.

Large language models, such as GPT-3 and GPT-4, have greatly increased the capacity to interpret unstructured financial data. Among the many applications of these models are the creation of financial insights from unprocessed textual data (Choi et al, 2025), sentiment analysis of news articles and social media material (Thapa et al., 2025), and the summarizing of earnings calls and financial reports (Lee et al., 2025). Compared to generic NLP models, domain-specific models like FinBERT (Huang et al., 2023), have shown to be much more accurate. These models have been particularly tailored for financial sentiment analysis. More recently, BloombergGPT, a 50-billion parameter model that was only trained on financial data and was published in 2023, has demonstrated state-of-the-art performance in a variety of financial natural language processing tasks. These models can produce content and serve as sophisticated information retrieval agents. They can also synthesise both structured and unstructured data sources to provide forecasts.

The integration of structured data like historical prices and technical indicators with unstructured data like news stories and analyst reports, through multimodal generative models is a growing area of interest for emerging research. Multimodal generative adversarial networks, for instance, have been created to improve stock market forecasts by fusing textual news headlines with visual data, including pictures from candlestick charts (Gangwani and Panthi, 2025). By matching attention heads across several data modalities, Transformer-based models also leverage cross-modal attention processes to interpret financial language and numerical pricing data concurrently (Dashtaki et al., 2025). These multimodal methods provide human–readable justifications for their projections in addition to increasing prediction accuracy. In stock market prediction, where a complex interaction between quantitative patterns and qualitative market feelings shapes market behaviour, this type of integration is very important.

Trust in algorithmic and AI systems is not binary; rather, it requires trust calibration, in which users modify their reliance based on perceived accuracy, transparency, and previous performance. Previous research has shown that faulty trust can emerge as automation bias, in which users over-rely on algorithmic outputs even when faults are obvious, thereby compromising human judgment in complex decision contexts (Parasuraman and Riley, 1997; Romeo, G., and Conti, 2025). In contrast, algorithm aversion describes users’ unwillingness to rely on algorithms after seeing even slight failures, despite evidence of improved average performance (Dietvorst et al., 2015). In financial decision-making contexts, where accountability and risk are high, investors often oscillate between appreciation and aversion, resulting in partial or conditional trust rather than blind acceptance (Logg et al., 2019). This delicate trust dynamic is especially important for generative AI, which produces probabilistic results that call into question standard conceptions of trustworthiness and control.

Verification behaviour refers to the deliberate actions taken by users to validate, cross-check, or contextualise AI-generated outputs before acting upon them, particularly in high-stakes domains such as finance. Prior research indicates that users rarely rely on AI systems blindly; instead, they engage in selective verification, comparing AI outputs with trusted external sources, domain knowledge, or alternative analytical tools to manage perceived risk and uncertainty (Dietvorst et al., 2015; Logg et al., 2019). In financial contexts, verification often involves triangulating AI–generated insights with market news platforms, technical indicators, historical data, or expert commentary, reflecting a calibrated trust posture rather than full automation reliance (Shin, 2021). Studies on human-AI collaboration further suggest that verification behaviour functions as a cognitive safeguard against automation bias and AI hallucinations, enabling users to retain decision authority while benefiting from AI-driven efficiency (Bansal et al., 2021). Particularly in generative AI systems, where outputs are probabilistic and occasionally erroneous, verification acts as a critical risk-mitigation mechanism, reinforcing the role of AI as decision support rather than a decision maker (Amershi et al., 2019; Weidinger et al., 2022). These insights position verification behaviour as a key mediating process between AI capabilities and responsible decision outcomes.

The human-in-the-loop paradigm views AI systems as helpful agents that supplement, rather than replace, human decision-making. Recent research highlights the role of AI as an assistant, supporting users by integrating information, providing alternatives, and lowering cognitive load while maintaining human judgment and accountability (Amershi et al., 2019). This is consistent with the dichotomy between choice augmentation, where AI complements human cognition, and full decision replacement, which frequently encounters resistance in high-stakes industries like finance (Bansal et al., 2021). Studies suggest that decision augmentation increases user acceptability, trust calibration, and outcome quality by enabling people to contextualise AI recommendations using subject experience and situational awareness. (Shin, 2021). As a result, HITL frameworks are increasingly seen as critical for responsible AI implementation in investment and risk-sensitive environments.

An increasing body of literature emphasises the risk of hallucinations in large language models, whereby systems produce fluent but factually wrong or misleading results. In financial environments, such errors can distort risk evaluations, spread misinformation, and adversely affect investment decisions if left unchecked (Weidinger et al., 2022). These dangers emphasise the need for verification behaviour in high-stakes domains, where users are urged to crossvalidate AI outputs using trusted data sources, expert judgment, or alternative analytical tools (Amodei et al., 2016; Amershi et al., 2019).

This review of literature highlights several important holes that need be filled in generative AI research for financial forecasting in the future. First, there are not many thorough comparison performance studies that compare generative AI models with deep learning and conventional machine learning models in a range of market scenarios. Secondly, the explainability of generative models is still largely unexplored; relatively little research examines their interpretability or reliability. It is recommended to use strategies like attention heatmaps, LIME ((Local Interpretable Model-agnostic Explanations), or SHAP (SHapley Additive exPlanations) to improve financial transparency. Thirdly, much current research is retrospective in nature, emphasizing the necessity of using generative models in real-time for portfolio optimization and live market prediction. The lack of attention paid to ethical issues including bias prevention, fairness, and responsible AI activities highlights the necessity for specialised research in these areas of financial forecasting.

Technology adoption theories, such as the technology acceptance model and the unified theory of acceptance and use of technology, give a starting point for understanding how investors interact with AI-based decision-support systems. The technology acceptance model focuses on perceived usefulness and simplicity of use as main adoption factors (Davis, 1989), whereas the unified theory broadens this paradigm by including performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al., 2003). In the case of generative AI, these notions connect with difficulties of trust, verification, and risk perception, making adoption a behavioural and cognitive process rather than a technical one.

This study uses snowball sampling, a non-probability sampling method that is frequently employed in qualitative research when it is hard to find or get in touch with potential participants (Gierczyk et al., 2024). To guarantee the applicability and breadth of insights, research participants were chosen according to inclusion criteria. Individual investors who actively participate in stock market investments more than 5 years’ experience from Delhi, India, were considered eligible participants for the study. They must, among other things, be familiar with generative AI technologies used for stock analysis or investing decision-making, such as ChatGPT, brokerage AI, news summarisers or any other user-facing generative AI platforms. To participate in a semi-structured interview, participants must also be receptive to sharing their thoughts, feelings, and observations about the use of generative AI in stock prediction (Adeoye-Olatunde and Olenik, 2021). The first group of participants was found through academic-industry networks, professional financial forums, and LinkedIn and Facebook groups devoted to investments. By encouraging these first responders to recommend more eligible people, a referral-based strategy was used to increase the number of participants.

A semi-structured interview guide was developed to facilitate in-depth discussions, featuring open-ended questions that align with the study’s research objectives (Kallio et al., 2016). The guide covers important thematic areas such as participants’ experience with AI and investment background, their awareness and use of generative AI tools, their perceptions of the advantages and disadvantages of these tools in stock forecasting, their level of trust in AI-generated outputs, and how these factors affect their investment decisions. Based on the convenience and desire of the participant, the interviews were performed using Zoom (Oliffe et al., 2021). With the participant’s prior agreement, each 30 to 45 minutes interview was audio recorded to guarantee proper data gathering. Interviews were conducted until response saturation was achieved (Hennink and Kaiser, 2022). Response saturation was observed by the 17th interview; however, interviews were continued to pre-arranged 25 participants to respect scheduling, ensuring no emergent or divergent perspective were overlooked to confirm theme stability.

Important ethical practices include informed consent, in which research participants were fully told of the study’s objectives, their voluntary involvement, the guarantee of their anonymity, and their freedom to discontinue participation at any time without facing repercussions (Husband, 2020).

Every interview was manually transcribed verbatim, and the researcher then confirmed the correctness and thoroughness of the transcription. Following verification, the transcripts were entered into the qualitative data analysis program NVivo 12.0, which is utilised for content visualization, theme extraction, and systematic categorization (Dhakal, 2022). Thematic analysis was used to examine the data, adhering to the six-step paradigm (Braun and Clarke, 2006). In order to obtain a preliminary understanding of the material, the researcher first familiarised themselves with the interview transcripts by carefully reading and rereading them. Open codes were created in the second stage, initial coding, to capture important and useful data properties. In the third phase, topic identification, these codes were arranged by combining related codes into more general thematic groups. Reviewing themes, the fourth phase, entailed assessing the themes’ relevance, coherence, and internal consistency to make sure the evidence supports them. The fifth phase, identifying and naming themes, involved refining each topic, giving it a precise definition, and assigning a title that captures its essence. The sixth phase, writing the report, provided a thorough comprehension of the findings by interpreting and presenting the themes in light of the research questions and body of current literature.

A variety of methodological procedures was used to assure the reliability of the qualitative results. An audit trail was created using NVivo 12 to methodically document code decisions, theme development, and analytical iterations, improving transparency and reliability. Peer debriefing with a co-researcher was carried out to assess a selection of coded data and developing themes, allowing for critical debate and refining interpretations, while limiting the possibility of individual researcher bias. Furthermore, reflective note writing was employed throughout the study process to identify and regulate the researcher’s preconceptions and positionality. The inclusion of verbatim participant statements in the findings increased trustworthiness by offering a detailed description and allowing readers to evaluate the relationship between empirical facts and interpretative assertions.

This distribution shows that investors are highly engaged and utilise the product cautiously, with adoption incentives and use cases being the most discussed topics, followed closely by benefits and trust-related issues.

NVivo’s integrated sentiment classifier was used to do sentiment analysis, classifying responses into three categories: positive, neutral/mixed, and negative (Table -2). NVivo’s sentiment classifier assessed entire coded text units and was confirmed by manual review.

The results show that the main drivers of generative AI adoption are accessibility, automation, and speed. The capacity to swiftly and efficiently summarise lengthy financial reports, evaluate news, or spot possible hazards with little manual labour is valued by investors. The findings are consistent with other research (Singh et al., 2023; Boute et al., 2021) that highlighted time savings as a key benefit of AI in finance. Remarkably, peer pressure and curiosity also had a significant impact, illustrating how social proof and online communities accelerated the adoption of AI: a topic that has not received much attention in the literature but merits more research.

Participants employ generative AI technologies for a variety of information filtering, sentiment analysis, and portfolio management activities in addition to price prediction. The adaptability of generative AI, particularly large language models like GPT, which are excellent at deriving meaning from unstructured financial content, is confirmed by this multifaceted usage. These results corroborate prior research showing how generative AI may enhance conventional numerical forecasting with contextual knowledge (Zhou and Sheu, 2025). Furthermore, use cases like risk warnings and scenario modelling point to a change in investors’ perceptions of AI—not just as a forecasting tool, but also as an interactive helper for all-encompassing decision assistance.

Participants listed the elimination of emotional bias in decision-making as one of the main advantages. According to behavioural finance theories, emotions have a significant influence in irrational investor behaviour (Padmavathy, 2024; Sattar et al., 2020). With the use of data-driven summaries and unbiased analysis, generative AI assists customers in taking a more logical, empirically-supported approach to investing. This supports augmented intelligence frameworks and socio-technical systems theory’s view of AI as a cognitive extension of human decisionmaking.

Even with the potential benefits of generative AI, a number of issues remain. The lack of context in AI-generated summaries is a significant problem as it might result in interpretations that are either superficial or lacking. The participants also brought up issues with hallucinations and overly generalised AI outputs in large language models, which can lead to AI generating false or erroneous information. Furthermore, the dependability and credibility of these tools are further restricted in high-stakes investment scenarios by challenges in validating outcomes and a lack of clear explainability. These worries align with concerns about lack of transparency (Almasarwah et al, 2024) and hallucinations (Erdem et al., 2025) in AI literature. Better explainable AI models and openness in algorithmic logic are necessary to close the trust gap brought on by investors’ apprehension about depending on AI outputs without validation. Furthermore, the need of creating responsible AI frameworks suited to financial markets is underscored by ethical and legal considerations, notably those pertaining to data protection, manipulation, and prejudice.

The findings demonstrate that investors often have a limited level of faith in generative AI products. Though they often cross-check with reliable sources like Bloomberg, Reuters, or technical indications, they value AI insights. The “AI-as-co-pilot" approach, which maintains the importance of human judgment, is supported by this behaviour. This is consistent with earlier work by Joshi (2025), which highlights the value of human-in-the-loop systems for accountability and trust in high-stakes industries such as banking. The process of back testing insights produced by AI also demonstrates a high degree of user participation, indicating that modern investors are incorporating AI into a more comprehensive analytical workflow rather than only depending on it.

This study identified an AI adoption framework as shown in figure 1.

The findings of the study extend to current literature by positioning investor use of generative AI within recognised behavioural finance and human-AI interaction theories, rather than regarding AI adoption as a purely technical event. The findings indicate that generative AI functions as a cognitive amplifier, with its influence on decision-making mediated by trust calibration, verification behaviour, and social environment.

This study advances the theoretical knowledge of how generative artificial intelligence (Generative AI) is affecting financial decision-making and investor behaviour. While most of the previous research has concentrated on the technical correctness of predictive algorithms, this study highlights the perspectives, decision-making processes, and human-AI interaction of individual investors who utilise generative AI products. Insights from this qualitative investigation have various significant theoretical ramifications for fields including behavioural finance, financial decision theory, augmented intelligence, and technology adoption. This study adds to and supports several well-established theoretical frameworks. By showing that perceived utility and usability continue to be crucial factors in generative AI adoption, it validates the technology acceptance model. Additionally, it incorporates the unified theory of acceptance and use of technology, which illustrates how hedonic motivation (curiosity), enabling factors (availability of ChatGPT), and social influence (peer recommendations) impact user engagement. The study adds to the notion of augmented intelligence by demonstrating that investors use generative AI as a decision-support tool rather than as a complete replacement.

This study offers practical suggestions to developers, fintech companies, and financial advisers from a practical perspective. This study suggests that models should be created to provide an explanation for the generation of a certain forecast or insight. This research suggests adding generative AI products into existing platforms such as portfolio management software, mobile applications, and brokerage accounts. The long-term adoption of generative AI in financial decision-making as well as user pleasure and trust may be greatly increased by putting these strategies into practice. Generative AI is on the verge of becoming a crucial tool for investors. Its successful use, however, hinges on how effectively stakeholders handle the practical difficulties of responsibility, openness, context, and trust. This research emphasises that although Generative AI has the potential to meaningfully support financial analysis and decision-making, its actual effectiveness hinges on responsible design, user education, and smart deployment. To guarantee that generative AI becomes a helpful, moral, and enabling instrument in contemporary investment, all parties involved, users, developers, advisers, and regulators, must cooperate.