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on Financial Markets |
| By: | Kei Nakagawa; Masanori Hirano; Yugo Fujimoto |
| Abstract: | This study aims to evaluate the sentiment of financial texts using large language models~(LLMs) and to empirically determine whether LLMs exhibit company-specific biases in sentiment analysis. Specifically, we examine the impact of general knowledge about firms on the sentiment measurement of texts by LLMs. Firstly, we compare the sentiment scores of financial texts by LLMs when the company name is explicitly included in the prompt versus when it is not. We define and quantify company-specific bias as the difference between these scores. Next, we construct an economic model to theoretically evaluate the impact of sentiment bias on investor behavior. This model helps us understand how biased LLM investments, when widespread, can distort stock prices. This implies the potential impact on stock prices if investments driven by biased LLMs become dominant in the future. Finally, we conduct an empirical analysis using Japanese financial text data to examine the relationship between firm-specific sentiment bias, corporate characteristics, and stock performance. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.00420 |
| By: | Dragos Gorduza; Yaxuan Kong; Xiaowen Dong; Stefan Zohren |
| Abstract: | We investigate the effectiveness of a momentum trading signal based on the coverage network of financial analysts. This signal builds on the key information-brokerage role financial sell-side analysts play in modern stock markets. The baskets of stocks covered by each analyst can be used to construct a network between firms whose edge weights represent the number of analysts jointly covering both firms. Although the link between financial analysts coverage and co-movement of firms' stock prices has been investigated in the literature, little effort has been made to systematically learn the most effective combination of signals from firms covered jointly by analysts in order to benefit from any spillover effect. To fill this gap, we build a trading strategy which leverages the analyst coverage network using a graph attention network. More specifically, our model learns to aggregate information from individual firm features and signals from neighbouring firms in a node-level forecasting task. We develop a portfolio based on those predictions which we demonstrate to exhibit an annualized returns of 29.44% and a Sharpe ratio of 4.06 substantially outperforming market baselines and existing graph machine learning based frameworks. We further investigate the performance and robustness of this strategy through extensive empirical analysis. Our paper represents one of the first attempts in using graph machine learning to extract actionable knowledge from the analyst coverage network for practical financial applications. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.20597 |
| By: | Laura Gardini; Davide Radi; Noemi Schmitt; Iryna Sushko; Frank Westerhoff |
| Abstract: | We utilize a chartist-fundamentalist model to examine the limits of informationally efficient stock markets. In our model, chartists are permanently active in the stock market, while fundamentalists trade only when their mispricing-dependent trading signals are strong. Our findings indicate the possible coexistence of two distinct regimes. Depending on the initial conditions, the stock market may exhibit either constant or oscillatory mispricing. Constant mispricing occurs when chartists remain the sole active speculators, causing the stock price to converge toward a nonfundamental value. Conversely, the stock price oscillates around its fundamental value when fundamentalists repeatedly enter and exit the market. Exogenous shocks result in intricate regime-switching dynamics. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.21198 |
| By: | Antonio Roma; Costanza Consolandi |
| Abstract: | This study explores the size effect in financial markets, focusing on how mergers, acquisitions, and other corporate transactions influence the returns of small versus large stocks. Employing a comprehensive dataset of U.S. listed companies from 1992 to 2021, which includes 51, 780 events, this research improves upon previous methodologies by integrating detailed timing information on deal announcements and completions with stock size and return data. Our analysis shows that small stocks are often the targets of transactions that significantly enhance their returns, not limited to takeovers. We find that pre-announcement returns are consistently larger for small stocks, likely due to less analyst coverage, resulting in largely unanticipated deal news. The study deepens our understanding of the size effect, suggesting that deal-related dynamics are essential for analyzing performance variations across different stock sizes and contributing to discussions on market efficiency and the valuation effects of corporate actions |
| Keywords: | takeovers, size effect, Fama-French, SMB factor |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:usi:wpaper:920 |
| By: | Yanan Niu; Ilja Kantorovitch |
| Abstract: | This research expands the existing literature on Bitcoin (BTC) price misalignments by incorporating transaction-level data from a peer-to-peer (P2P) exchange, LocalBitcoins.com (LB). It examines how broader economic and regulatory factors influence cryptocurrency markets and highlights the role of cryptocurrencies in facilitating international capital movements. By constructing shadow exchange rates (SERs) for national currencies against the US dollar based on BTC prices, we calculate discrepancies between these SERs and their official exchange rates (OERs), referred to as BTC premiums. We analyze various factors driving the BTC premiums on LB, including those sourced from the BTC blockchain, mainstream centralized BTC exchanges, and international capital transfer channels. Unlike in centralized markets, our results indicate that the microstructure of the BTC blockchain does not correlate with BTC premiums in the P2P market. Regarding frictions from international capital transfers, we interpret remittance costs as indicators of inefficiencies in traditional capital transfer systems. For constrained currencies subject to severe capital controls and managed exchange rate regimes, increased transaction costs in conventional currency exchange channels almost entirely translate into higher BTC premiums. Additionally, our analysis suggests that BTC premiums can serve as short-term predictors of future exchange rate depreciation for unconstrained currencies. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.22443 |
| By: | Guilherme Thomaz; Denis Maua |
| Abstract: | Mutual funds aim to generate returns above market averages. While predicting their future portfolio allocations can bring economic advantages, the task remains challenging and largely unexplored. To fill that gap, this work frames mutual fund portfolio prediction as a Next Novel Basket Recommendation (NNBR) task, focusing on predicting novel items in a fund's next portfolio. We create a comprehensive benchmark dataset using publicly available data and evaluate the performance of various recommender system models on the NNBR task. Our findings reveal that predicting novel items in mutual fund portfolios is inherently more challenging than predicting the entire portfolio or only repeated items. While state-of-the-art NBR models are outperformed by simple heuristics when considering both novel and repeated items together, autoencoder-based approaches demonstrate superior performance in predicting only new items. The insights gained from this study highlight the importance of considering domain-specific characteristics when applying recommender systems to mutual fund portfolio prediction. The performance gap between predicting the entire portfolio or repeated items and predicting novel items underscores the complexity of the NNBR task in this domain and the need for continued research to develop more robust and adaptable models for this critical financial application. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.18098 |
| By: | Saber Talazadeh; Dragan Perakovic |
| Abstract: | Stock trend forecasting, a challenging problem in the financial domain, involves ex-tensive data and related indicators. Relying solely on empirical analysis often yields unsustainable and ineffective results. Machine learning researchers have demonstrated that the application of random forest algorithm can enhance predictions in this context, playing a crucial auxiliary role in forecasting stock trends. This study introduces a new approach to stock market prediction by integrating sentiment analysis using FinGPT generative AI model with the traditional Random Forest model. The proposed technique aims to optimize the accuracy of stock price forecasts by leveraging the nuanced understanding of financial sentiments provided by FinGPT. We present a new methodology called "Sentiment-Augmented Random Forest" (SARF), which in-corporates sentiment features into the Random Forest framework. Our experiments demonstrate that SARF outperforms conventional Random Forest and LSTM models with an average accuracy improvement of 9.23% and lower prediction errors in pre-dicting stock market movements. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.07143 |