1. Introduction

Financial forecasting is crucial, primarily for investors. It allows them to make the best possible investment decisions by assessing future market trends and minimizing risk while maximizing rewards. Second, financial forecasting helps businesses and financial institutions anticipate risk levels and evaluate risk management. Banks can use projections to evaluate loans and lower potential hazards. Financial forecasting is a tool the government can use to create fiscal and monetary policies that will stabilize the market order and promote greater economic growth. Precise financial forecasting has the potential to enhance market transparency and encourage the steadiness and sound growth of the financial industry. Financial forecasting is crucial in many ways, supporting various economic organizations in achieving greater development and progress while coping with turbulence. Primarily, financial forecasting involves employing diverse statistical, mathematical, and economic techniques to anticipate future performance of the financial markets. In order to predict price movements, dangers, and opportunities in financial markets, it assists firms, governments, and investors in making more informed decisions by examining historical data, market patterns, economic indicators, and other pertinent information. According to Abu-Mostafa and Ayiya, historical and current data and events are used to forecast the market. Based on information on trading volumes and open positions, price trends, historical supply and demand, and statistics on supply and demand, certain forecasts can be made regarding commodities futures. As a result, for a variety of forecasts and applications, they employ more quantitative technical analysis approaches, such as regression analysis and moving average techniques [1]. Different economic agents will make judgments based on their analysis of the facts, combining different techniques to foresee and make choices. Various economic agents decide on the projections by examining, assessing, and contrasting the facts by fusing and integrating diverse modeling methodologies. Elliott and Timmermann note that as financial forecasting is at its core a problem of decision-making, economics need to be a major consideration in this process. A second important point is that diverse forecasting techniques can often be coupled to yield improved forecasts, which is brought to light by taking model misspecification and parameter estimation error into consideration. The third crucial idea is that model comparison and prediction review are crucial steps in the forecasting process, and they may be carried out with greater rigor now than they were in the past. Forecasters construct forecasts that result in financial projections using economic forecasting formulas, risk function formulas, linear regression techniques, Monte Carlo simulations, loss functions, and economic theory rather than traditional forecasting methods [2].

Economic theory-based, machine learning-based, and statistical models are among the techniques used in financial forecasting today. These techniques have a number of significant shortcomings. Firstly, models based on statistical and economic theory frequently have rigid assumptions and preconceived notions, which may make them inflexible when dealing with intricate nonlinear relationships and market fluctuations. Secondly, these conventional techniques lack computational efficiency and processing power when dealing with large amounts of data, which makes it challenging to react swiftly to economic developments. All of these factors contribute to an increase in the bias and uncertainty of the prediction results. The quantity of historical data information available to predict future returns is restricted, as Timmermann notes that data limits lower the accuracy of financial forecasts and that predictable return patterns tend to be self-defeating when faced with competitive pressures [3].

On the other hand, GPT is a deep learning model that has been pre-trained on a sizable corpus of text data, according to Yenduri et al. This means that it may be applied to certain tasks including text categorization, sentiment analysis, language modeling, machine translation, and language synthesis [4]. The GPT offers the following noteworthy benefits in order to overcome the shortcomings of the conventional prediction methods: First of all, enormous volumes of unstructured text may be processed and analyzed by GPT. In order to make predictions that are more accurate, GPT can first process and analyze vast volumes of text data from which it may identify and extract important information as well as sentiment shifts. Second, GPT's deep learning framework makes it possible for it to capture intricate nonlinear relationships and market dynamics, thereby enhancing the prediction's accuracy and comprehensiveness. Additionally, by continuously learning from and updating the model, the model can better predict the future in real time and with greater flexibility.

Once the current state and trajectory of financial forecasting and GPT have been described, the question of whether GPT can contribute to financial forecasting will be examined from two angles: first, from the explanation of GPT's function and the technical means and tools for its application; second, from an understanding of how GPT can be applied to the financial market; and finally, from an examination of the price of electricity, the state of businesses, stocks, and so forth, in order to assess GPT's impact on the financial market. After that, we'll examine the difficulties and restrictions associated with GPT in financial market forecasting and suggest some fixes. Lastly, we will provide an overview of the GPT's financial forecasting applications and analysis and go into further detail about its future development path.

2. What is GPT and its technical means

2.1. What is GPT

OpenAI's GPT (Generative Pre-trained Transformer) language model creates writing that resembles that of a human by utilizing deep learning methods. The Transformer-based GPT model pre-trains on vast amounts of textual data to identify a language's patterns and structure. Krause cites the Large Language Model as an example of an artificial intelligence model created expressly to comprehend, produce, and forecast human language. LLMs are beneficial for a variety of natural language processing (NLP) applications because they can produce coherent and contextually appropriate sentences by employing statistical patterns and correlations that have been learned from a wide range of textual data. LLMs have been used in sentiment analysis, text production, and machine translation within the finance field. Their capacity to produce and process text creates fresh opportunities for improvement [5]. Khan and Umer considered the LLM's development to be a significant advancement in generative artificial intelligence (AI). They cited OpenAI's brilliant use of the Generative Pre-Training Transformer (GPT) architecture as an example. ChatGPT is a family of GPTs that has grown from the GPT series' initial GPT-1 training of 117 million parameters to its most recent GPT-4 training of 1.76 trillion parameters, demonstrating the technology's evolution and allowing it to produce responses that are human-like and even process image input [6].

2.2. Technical means of GPT

As noted by Achiam et al., the GPT GPT-4 model relies on a Transformer to function [7], Text and other sequential data can be processed using the deep learning model known as the Transformer architecture. According to Yenduri et al., GPT is a deep learning model that has been trained on a sizable corpus of textual data and may be adjusted for a variety of tasks, including text classification, sentiment analysis, language modeling, machine translation, and language synthesis [4]. Language modeling, which entails forecasting the subsequent word based on the previous text, comes next. The model may produce grammatically correct and cohesive content in this fashion. GPT employs autoregressive generation, which produces the text word by word. The coherence and consistency of the created text are ensured by the fact that the words formed at each step are dependent on the content generated earlier. Large amounts of training data and strong processing capabilities are necessary for GPT to be successful. After a significant quantity of data has been processed, GPT uses a range of optimization and regularization approaches to modify the model in order to get results. This improves the model's performance and stability.

3. How GPT applies to financial forecasting

In financial forecasting, GPT begins with data preparation and collecting. Text data and data processing ensure the validity of the data that GPT obtains. The next step is to choose an appropriate pre-trained GPT model, which has been trained on a substantial quantity of generic data and has strong language creation and comprehension skills. Following the process of feature extraction, an appropriate machine learning or deep learning model is chosen. The extracted feature set is then used to train the model, which enhances its prediction capacity by iteratively modifying its parameters and refining its training approach. Evaluation criteria including accuracy, precision, and recall are used to assess the prediction model's performance. The evaluation results are then applied to certain scenarios after model optimization based on the evaluation outcomes is carried out, which involves modifying the feature extraction technique and model parameters.

3.1. GPT for Sentiment Analysis in Financial Forecasting

Financial Market Sentiment Analysis is the method of predicting market sentiment and trends by extracting sentiment information from textual data using natural language processing techniques. As a large-scale language model, GPT excels at deciphering intricate language and identifying the mood of the market to generate financial forecasts that assist various economic actors in decision-making. Fatouros et al. conducted research on the ability of large-scale language models (ChatGPT 3.5 in particular) to analyze multiple ChatGPT threads on a carefully selected dataset of news headlines related to forex using a zero-sample cueing approach. Performance is measured using metrics like precision, recall, f1 scores, and Mean Absolute Error (MAE) for sentiment categories [8]. Mbanyele's research examined the wider effects of generative AI on corporate policy by examining how these insights affected the assessment of business performance and discovered that ChatGPT could explain sentiment in financial reports, conference calls, and analyst reports [9]. Furthermore, by extracting features from collected tweets using a variety of classifier machine learning models, such as gradient boosting, decision trees, and random forests, Amin et al. investigated whether sentiment expressed in tweets of AI progress can predict daily fluctuations in the stock prices of affiliated companies. These models were used to train tweets on sentiment and related features to predict stock price movements of large companies like Microsoft and OpenAI. changes in stock prices [10].

3.2. How GPT Helps Decision Makers with Financial Forecasting

Financial forecasting using GPT can offer decision makers thorough, accurate, and up-to-date market analysis and forecasting support. Large-scale financial data processing and analysis enable GPT to evaluate financial reports, optimize investment portfolios, and offer individualized investment advice in addition to forecasting market trends and hazards. GPT's capacity to process and make sense of vast amounts of data and deliver insights and explanations in an understandable and succinct manner is one of its main advantages. According to Fu et al., ChatGPT can identify and evaluate financial risks, provide financial forecasting forecasts and recommendations for decision-making based on the facts given, and help accountants with astute financial analysis [11]. Jiang also contends that ChatGPT has a thorough comprehension of financial concepts, technologies, and market data because it was trained on a wide range of texts from a variety of sources. This makes it possible for it to support financial forecasting and analysis by offering data and insights on a variety of financial subjects. Furthermore, ChatGPT can comprehend and respond to complicated queries thanks to its natural language processing skills, which facilitates financial professionals' access to the data they require to make judgments [12].

3.3. How GPT can be improved in financial forecasting

Although GPT offers a lot of potential for financial forecasting, there is still room for improvement. By fusing Hidden Markov Models (HMMs) with the XGBoost method and augmenting it with a mixture of Shapley values from the Explainable AI (XAI) domain, Yue and Au provide a novel approach to financial proximity forecasting. Furthermore, we convert sophisticated machine learning predictions with over 1,000 characteristics into human-readable proximity reports by fusing Shapley values with GPT-4. These reports are customized based on the data that has to be examined [13]. Contrarily, Arnone has discovered that ChatGPT is a sophisticated language model for both financial fraud detection and stock price prediction. He has cleverly integrated ChatGPT into the stock price prediction domain, leveraging its capacity to comprehend and contextualize textual content to enhance fraud detection systems, particularly when it comes to recognizing instances of financial forgery [14]. As a result, GPT becomes more precise and offers greater reference value.

3.4. Predictive effectiveness of GPT in specific areas of financial markets

In financial forecasting, GPT can appear in a number of particular circumstances. Li and Zhou investigate the ways in which cutting-edge large-scale language models, such as GPT-4, interpret and apply data from company filings to forecast future earnings in the context of corporate earnings. The findings indicate that when there is a clear information environment, disclosure specificity, and readability to forecast production earnings, GPT's prediction accuracy is greater [15]. Menéndez and Heredia looked into the integration of GPT-based analysis of expert news and reports into an energy price forecasting model in relation to the price of electricity in Spain. This integration enables a more thorough and current comprehension of the dynamics of the market through two paradigms: contextual learning and fine-tuning, which are based on the outcomes of short-term and medium/long-term analyses in comparison to cutting-edge sentiment analysis techniques that employ GPT. Higher-level language models, like GPT, yield superior outcomes, particularly when customized for particular activities and domains [16]. Wang and Zhou used answers from OpenAI's GPT-3.5 and GPT-4 APIs to study GPT's prediction of China's overnight stock market index. They carefully crafted hints to supply the required context and descriptions. In order for the GPT model to adjust to a particular situation and produce replies that meet the demands of the user, hints are essential. by determining if the Shanghai Composite Index opened at a high or low level. In the end, whether it will rise or fall is determined using a thorough explanation. This well-crafted suggestion not only assesses the change of the overnight index, but also offers a thorough explanation based on the information given [17].

4. Challenges of GPT in Financial Forecasting and the Solutions

4.1. Challenges and difficulties of GPT in financial forecasting

Unstable contextual consistency, the potential to produce erroneous or misleading outputs, and unfair information due to the influence of training data bias are some of the drawbacks of GPT. First, Krause points out that these AI models can occasionally find it difficult to keep consistent contextual dialog or documents over time, which can lead to erratic answers or subtle phrasing differences in inputs that are responsive to little changes. Apart from the continual difficulty of ensuring precise contextual awareness and consistency throughout the interaction, generative AI models also run the risk of creating factually wrong or deceptive output, even though they can produce smooth and contextually relevant language. Large-scale text corpora used for model training may unintentionally yield information that looks correct but is actually false, necessitating rigorous validation and fact-checking procedures. AI is vulnerable to data bias when generating data when it is used to incorporate biased or training data. This can result in unjust scenarios in the financial industry and imprecise analysis [5].

Financial data comprises a vast and complex amount of both organized and unstructured data in terms of quality and quantity, and GPT is prone to errors due to the heavy processing of the data. Dahal makes the point that the quality of the data has a significant impact on how accurate AI predictions are, and that any bias in the data might result in incorrect analysis. Another issue is relying too much on AI to make decisions, particularly if the AI analysis is flawed. Concerns with transparency are raised by the "black box" aspect of AI decision-making processes, which frequently makes it difficult to grasp their results [18].

GPT has problems with its proprietary model's limitations and sensitivity to leads. Using technology like GPT also raises key ethical and regulatory questions. A study by Niszczota and Abbas discovered a number of GPT work constraints. Initially, we make use of an exclusive language model from OpenAI. Several firms have created alternative models (like Bard or LLaMA) since the launch of ChatGPT. Second, since LLM responses are sensitive to cues, it might be possible to get more accurate results by utilizing distinct cues. Third, it is obvious that the GPT will continue to be financially savvy despite having significantly increased its financial literacy. According to certain research, the GPT-4 may lose some of its capabilities. Similar to this, the GPT will encounter some moral and legal dilemmas, which will make financial forecasting difficult for the GPT. There are several ethical and regulatory issues that arise with the growing application of artificial intelligence in the financial markets. The ethical and responsible application of AI is at the center of these worries, especially when it comes to privacy, openness, and regulatory compliance. For GPT, this is also true [19].

4.2. How to address these difficulties and challenges

Krause pointed out that there are a number of approaches that may be taken to leverage AI models to improve financial analysis. First, using prompts that are precise and comprehensive can result in more pertinent and accurate data. Defining the necessary information and metrics of interest in place of general prompts can result in a more focused study [5].

Dahal makes the point that a thorough process of data gathering and purification, which includes data validation and verification, can improve data quality in order to address the issue of bias in data. Developers of artificial intelligence must also make an effort to choose current and pertinent data sources. Conducting bias audits on training data, recognizing and addressing bias when it is found, and regularly checking AI systems for inadvertent bias in their forecasts are all crucial steps in reducing bias. When faced with ethical and regulatory challenges related to AI, financial firms that use it prioritize regulatory compliance. A number of legal frameworks have been put in place to control the application of AI in finance. It is imperative to adhere to these standards in order to prevent legal and financial ramifications. To make sure that AI systems meet legal standards and criteria, financial institutions need to traverse a complex regulatory framework. Financial institutions and regulators are proactively addressing these issues. Artificial intelligence (AI)-driven tools that can track trading activity and spot odd trends or anomalies are being added to market surveillance systems. In the era of artificial intelligence, cooperation between financial institutions, regulators, and AI specialists is essential to creating strong defenses against market manipulation [18].

Professional financial advice considers the advisee's attributes, including age, income, and risk tolerance, according to Niszczota and Abbas. Second, in order to better govern the functioning of GPT, financial advice should consider national rules [19].

5. Conclusion

This study examines the GPT in large generative language model (LLP) artificial intelligence models to conduct financial market prediction analysis. Different financial market forecasting techniques are covered, and the use of GPT as a language model in financial forecasting—along with its unique application situations, difficulties, and solutions—is thoroughly examined.

The investigation reveals that GPT is a large-scale language generation model that excels in natural language processing and comprehension. It can also extract information from unstructured textual material, including news articles, social media posts, analytical reports, and more. For sentiment research and event-driven market predictions, this is quite helpful. In a similar vein, both the quantity and quality of training data will affect GPT. The model can provide insightful predictions and useful predictions if it is trained on a substantial amount of high-quality financial data. Sentiment analysis and market sentiment can help event-driven market forecasting by helping to recognize and comprehend significant news events and their possible effects. This covers stock indexes, firm production earnings, economic data releases, etc.

However, GPT's risk and unpredictability persist, as do the financial markets' extreme volatility and unpredictability. Since financial projections are inherently speculative, it is best to combine them with other analytical techniques when using GPT-generated estimates. Again, the accuracy of GPT's forecasting scenarios is not as great as it may be when the data is too huge or not adequately connected. Additionally, ethical and compliance concerns, such as market regulation, information disclosure, and data privacy, must be taken into account when employing AI for financial forecasting. All things considered, GPT has a lot of promise for financial forecasting, particularly when it comes to processing unstructured data and sentiment analysis. But given the complexity and unpredictability of the market, a mix of approaches and strategies is required to produce more accurate and trustworthy forecasts.