Can ChatGPT improve investment decisions? From a portfolio management perspective

Ko, Hyungjin; Lee, Jaewook

doi:10.1016/j.frl.2024.105433

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…On the one hand, since these models are general purpose language models and are not explicitly trained for prediction, they do not seem to be useful for predicting the evolution of a numerical time series such as price. Thus, Xie, Han, Lai, et al [21] find that ChatGPT is no better than simple linear regression when using numerical data in prediction tasks, and Ko and Lee [22] attempt to use ChatGPT to help with a stock portfolio selection problem, but find no positive performance.…”

Section: Research Gap Analysismentioning

confidence: 99%

Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market

Menéndez Medina,

Heredia Álvaro

2024

Energies

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The electricity market in Spain holds significant importance in the nation’s economy and sustainability efforts due to its diverse energy mix that encompasses renewables, fossil fuels, and nuclear power. Accurate energy price prediction is crucial in Spain, influencing the country’s ability to meet its climate goals and ensure energy security and affecting economic stakeholders. We have explored how leveraging advanced GPT tools like OpenAI’s ChatGPT to analyze energy news and expert reports can extract valuable insights and generate additional variables for electricity price trend prediction in the Spanish market. Our research proposes two different training and modelling approaches of generative pre-trained transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. We aim to shed light on the capabilities of GPT solutions and demonstrate how they can augment prediction models by introducing additional variables. Our findings suggest that insights derived from GPT analysis of electricity news and specialized reports align closely with price fluctuations post-publication, indicating their potential to improve predictions and offer deeper insights into market dynamics. This endeavor can support informed decision-making for stakeholders in the Spanish electricity market and companies reliant on electricity costs and price volatility for their margins.

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Section: Research Gap Analysismentioning

confidence: 99%

Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market

Menéndez Medina,

Heredia Álvaro

2024

Energies

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“…They find that ChatGPT has the ability to predict stock returns. Ko and Lee (2024) show that ChatGPT can help with setting up diversified portfolios. Ma et al (2024) demonstrate that ChatGPT also has the ability to forecast equity risk premiums.…”

Section: Introductionmentioning

confidence: 99%

ChatGPT, Help! I Am in Financial Trouble

Schlosky,

Karadas,

Raskie

2024

JRFM

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This study examines the capability of ChatGPT to provide financial advice based on personal finance cases. We first write our own cases and feed them to ChatGPT to get its advice (recommendations) on them. Next, we assess the quality and the validity of ChatGPT’s recommendations on these cases. We find that ChatGPT serves as a suitable starting point, but its recommendations tend to be generic, and they often overlook alternative solutions and viewpoints and priority of recommendations. Overall, our analysis demonstrates the strengths and weaknesses of using ChatGPT in personal finance matters. Further, it serves as a helpful guide to financial advisors, households, and instructors of personal finance who are already using or considering using ChatGPT and want to develop a suitable understanding of the benefits and limitations of this new technology in addressing their professional and personal needs.

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Modeling Asset Price Process: An Approach for Imaging Price Chart with Generative Diffusion Models

Park,

Ko,

Lee

2024

Comput Econ

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Artificial Intelligence (AI) models have been recently studied to discover data patterns for prediction and forecasting tasks in finance. However, the use of deep generative models in finance remains relatively unexplored. In this paper, we investigate the potential of deep generative diffusion models to estimate unknown dynamics using multiple simulations based on stock chart images. We first demonstrate a novel pre-processing framework and synthetic image generation using opening, high, low, and closing stock chart images to train neural networks. Without assuming the specific process as the underlying asset price process, we can generate synthetic data without predetermined assumptions of the underlying movements of stock prices by trained generative diffusion models. The experimental results demonstrate that the proposed method successfully replicates well-known asset price processes. With various simulation paths, we can also accurately estimate option pricing on the S &P 500. We conclude that financial simulation with AI can be a novel approach to financial decision-making.

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Can ChatGPT improve investment decisions? From a portfolio management perspective

Cited by 5 publications

References 29 publications

Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market

Using Generative Pre-Trained Transformers (GPT) for Electricity Price Trend Forecasting in the Spanish Market

ChatGPT, Help! I Am in Financial Trouble

Modeling Asset Price Process: An Approach for Imaging Price Chart with Generative Diffusion Models

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