The objective of this paper is to examine the short-run and long-run dynamic relationship between foreign portfolio investments (FPI) and stock prices of Pakistan. This study first ever attempts in the context of Pakistan economy to investigate the foreign portfolio investment and its impact on the domestic stock market. The surge in FPI in the developing countries is relatively due to growth of stock markets in these countries, liberalisation of their economies, high returns on investment, trade openness, improved communication and global technology. This study used annual time series data starting from 1984 to 2016. The autoregressive distributed lag (ARDL) method used for both the long-run and short-run relationship between FPI and various policy variables. All the results are statistically significant except the exchange rate and the model is best fitted which shows the diagnostic and stability test.

The importance of sentiment analysis in the rapidly evolving financial markets is widely recognized for its ability to interpret market trends and inform investment decisions. This study delves into the target-level financial sentiment analysis (TLFSA) of news headlines related to stock. The study compares the performance in the TLFSA task of various sentiment analysis techniques, including rule-based models (VADER), fine-tuned transformer-based models (DistilFinRoBERTa and Deberta-v3-base-absa-v1.1) as well as zero-shot large language models (ChatGPT and Gemini). The dataset utilized for this analysis, a novel contribution of this research, comprises 1476 manually annotated Bloomberg headlines and is made publicly available (due to copyright restrictions, only the URLs of Bloomberg headlines with the manual annotations are provided; however, these URLs can be used with a Bloomberg terminal to reconstruct the complete dataset) to encourage future research on this subject. The results indicate that the fine-tuned Deberta-v3-base-absa-v1.1 model performs better across all evaluation metrics than other evaluated models in TLFSA. However, LLMs such as ChatGPT-4, ChatGPT-4o, and Gemini 1.5 Pro provide similar performance levels without the need for task-specific fine-tuning or additional training. The study contributes to assessing the performance of LLMs for financial sentiment analysis, providing useful insights into their possible application in the financial domain.