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on Artificial Intelligence |
| By: | Ennio Bilancini; Leonardo Boncinelli; Eugenio Vicario |
| Abstract: | This paper presents an analysis of two pre-registered experimental studies examining the impact of `Motivational Interviewing' and `Directing Style' on discussions about Sustainable Development Goals. To evaluate the effectiveness of these communication styles in enhancing awareness and motivating action toward the Sustainable Development Goals, we measured the engagement levels of participants, along with their self-reported interest and learning outcomes. Our results indicate that `Motivational Interviewing' is more effective than `Directing Style' for engagement and interest, while no appreciable difference is found on learning. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.01057&r= |
| By: | Magnus Lundgren |
| Abstract: | This study investigates the efficacy of large language models (LLMs) as tools for grading master-level student essays. Utilizing a sample of 60 essays in political science, the study compares the accuracy of grades suggested by the GPT-4 model with those awarded by university teachers. Results indicate that while GPT-4 aligns with human grading standards on mean scores, it exhibits a risk-averse grading pattern and its interrater reliability with human raters is low. Furthermore, modifications in the grading instructions (prompt engineering) do not significantly alter AI performance, suggesting that GPT-4 primarily assesses generic essay characteristics such as language quality rather than adapting to nuanced grading criteria. These findings contribute to the understanding of AI's potential and limitations in higher education, highlighting the need for further development to enhance its adaptability and sensitivity to specific educational assessment requirements. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.16510&r= |
| By: | Yupeng Cao; Zhiyuan Yao; Zhi Chen; Zhiyang Deng |
| Abstract: | The integration of Large Language Models (LLMs) into financial analysis has garnered significant attention in the NLP community. This paper presents our solution to IJCAI-2024 FinLLM challenge, investigating the capabilities of LLMs within three critical areas of financial tasks: financial classification, financial text summarization, and single stock trading. We adopted Llama3-8B and Mistral-7B as base models, fine-tuning them through Parameter Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA) approaches. To enhance model performance, we combine datasets from task 1 and task 2 for data fusion. Our approach aims to tackle these diverse tasks in a comprehensive and integrated manner, showcasing LLMs' capacity to address diverse and complex financial tasks with improved accuracy and decision-making capabilities. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.01953&r= |
| By: | Daiya Mita (Nomura Asset Management Co, ltd., Graduate School of Economics, The University of Tokyo); Akihiko Takahashi (Graduate School of Economics, The University of Tokyo) |
| Abstract: | This study proposes a novel equity investment strategy that effectively integrates artificial intelligence (AI) techniques, multi factor models and financial technical indicators. To be practically useful as an investment fund, the strategy is designed to achieve high investment performance without losing interpretability, which is not always the case especially for complex models based on artificial intelligence. Specifically, as an equity long (buying) strategy, this paper extends a five factor model in Fama & French [1], a well-known finance model for its explainability to predict future returns by using a gradient boosting machine (GBM) and a state space model. In addition, an index futures short (selling) strategy for downside hedging is developed with IF-THEN rules and three technical indicators. Combining individual equity long and index futures short models, the strategy invests in high expected return equities when the expected return of the portfolio is positive and also the market is expected to rise, otherwise it shorts (sells) index futures. To the best of our knowledge, the current study is the first attempt to develop an equity investment strategy based on a new predictable five factor model, which becomes successful with effective use of AI techniques and technical indicators. Finally, empirical analysis shows that the proposed strategy outperforms not only the baseline buy-and-hold strategy, but also typical mutual funds for the Japanese equities. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:cfi:fseres:cf588&r= |
| By: | Anishka Chauhan; Pratham Mayur; Yeshwanth Sai Gokarakonda; Pooriya Jamie; Naman Mehrotra |
| Abstract: | This paper presents price prediction models using Machine Learning algorithms augmented with Superforecasters predictions, aimed at enhancing investment decisions. Five Machine Learning models are built, including Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU, and a model built using LSTM and GRU algorithms. The models are evaluated using the Mean Absolute Error to determine their predictive accuracy. Additionally, the paper suggests incorporating human intelligence by identifying Superforecasters and tracking their predictions to anticipate unpredictable shifts or changes in stock prices . The predictions made by these users can further enhance the accuracy of stock price predictions when combined with Machine Learning and Natural Language Processing techniques. Predicting the price of any commodity can be a significant task but predicting the price of a stock in the stock market deals with much more uncertainty. Recognising the limited knowledge and exposure to stocks among certain investors, this paper proposes price prediction models using Machine Learning algorithms. In this work, five Machine learning models are built using Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU and the last one is built using LSTM and GRU algorithms. Later these models are assessed using MAE scores to find which model is predicting with the highest accuracy. In addition to this, this paper also suggests the use of human intelligence to closely predict the shift in price patterns in the stock market The main goal is to identify Superforecasters and track their predictions to anticipate unpredictable shifts or changes in stock prices. By leveraging the combined power of Machine Learning and the Human Intelligence, predictive accuracy can be significantly increased. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.02236&r= |
| By: | Peng Yifeng; Gao Chen |
| Abstract: | This study proposes an innovative evaluation method based on large language models (LLMs) specifically designed to measure the digital transformation (DT) process of enterprises. By analyzing the annual reports of 4407 companies listed on the New York Stock Exchange and Nasdaq from 2005 to 2022, a comprehensive set of DT indicators was constructed. The findings revealed that DT significantly improves a company's financial performance, however, different digital technologies exhibit varying effects on financial performance. Specifically, blockchain technology has a relatively limited positive impact on financial performance. In addition, this study further discovered that DT can promote the growth of financial performance by enhancing operational efficiency and reducing costs. This study provides a novel DT evaluation tool for the academic community, while also expanding the application scope of generative artificial intelligence technology in economic research. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.18440&r= |