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on Financial Markets |
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Issue of 2024‒03‒18
eight papers chosen by |
| By: | C. Sarai R. Avila |
| Abstract: | This study investigates the relationship between tweet sentiment across diverse categories: news, company opinions, CEO opinions, competitor opinions, and stock market behavior in the biotechnology sector, with a focus on understanding the impact of social media discourse on investor sentiment and decision-making processes. We analyzed historical stock market data for ten of the largest and most influential pharmaceutical companies alongside Twitter data related to COVID-19, vaccines, the companies, and their respective CEOs. Using VADER sentiment analysis, we examined the sentiment scores of tweets and assessed their relationships with stock market performance. We employed ARIMA (AutoRegressive Integrated Moving Average) and VAR (Vector AutoRegression) models to forecast stock market performance, incorporating sentiment covariates to improve predictions. Our findings revealed a complex interplay between tweet sentiment, news, biotech companies, their CEOs, and stock market performance, emphasizing the importance of considering diverse factors when modeling and predicting stock prices. This study provides valuable insights into the influence of social media on the financial sector and lays a foundation for future research aimed at refining stock price prediction models. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.03353&r=fmk |
| By: | Manish Jha; Jialin Qian; Michael Weber; Baozhong Yang |
| Abstract: | We create a firm-level ChatGPT investment score, based on conference calls, that measures managers' anticipated changes in capital expenditures. We validate the score with interpretable textual content and its strong correlation with CFO survey responses. The investment score predicts future capital expenditure for up to nine quarters, controlling for Tobin's q and other determinants, implying the investment score provides incremental information about firms' future investment opportunities. The investment score also separately forecasts future total, intangible, and R&D investments. High-investment-score firms experience significant negative future abnormal returns. We demonstrate ChatGPT's applicability to measure other policies, such as dividends and employment. |
| JEL: | C81 E22 G14 G31 G32 O33 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32161&r=fmk |
| By: | Zihan Dong; Xinyu Fan; Zhiyuan Peng |
| Abstract: | Financial market predictions utilize historical data to anticipate future stock prices and market trends. Traditionally, these predictions have focused on the statistical analysis of quantitative factors, such as stock prices, trading volumes, inflation rates, and changes in industrial production. Recent advancements in large language models motivate the integrated financial analysis of both sentiment data, particularly market news, and numerical factors. Nonetheless, this methodology frequently encounters constraints due to the paucity of extensive datasets that amalgamate both quantitative and qualitative sentiment analyses. To address this challenge, we introduce a large-scale financial dataset, namely, Financial News and Stock Price Integration Dataset (FNSPID). It comprises 29.7 million stock prices and 15.7 million time-aligned financial news records for 4, 775 S&P500 companies, covering the period from 1999 to 2023, sourced from 4 stock market news websites. We demonstrate that FNSPID excels existing stock market datasets in scale and diversity while uniquely incorporating sentiment information. Through financial analysis experiments on FNSPID, we propose: (1) the dataset's size and quality significantly boost market prediction accuracy; (2) adding sentiment scores modestly enhances performance on the transformer-based model; (3) a reproducible procedure that can update the dataset. Completed work, code, documentation, and examples are available at github.com/Zdong104/FNSPID. FNSPID offers unprecedented opportunities for the financial research community to advance predictive modeling and analysis. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.06698&r=fmk |
| By: | Mark S. Carey; Christopher Healy |
| Abstract: | Some observers have argued that the short selling of bank stock contributes to bank runs and bank failures. Previously, no evidence has been available. We find no evidence that more short selling of bank stock is associated with materially larger outflows of bank deposits. We believe this means that proposals to restrict the short selling of bank stock should be supported by other arguments. |
| Keywords: | short-selling; bank runs; bank deposits |
| JEL: | G21 G12 G01 G18 |
| Date: | 2024–02–08 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwq:97870&r=fmk |
| By: | Himanshu Gupta; Aditya Jaiswal |
| Abstract: | Predicting a fast and accurate model for stock price forecasting is been a challenging task and this is an active area of research where it is yet to be found which is the best way to forecast the stock price. Machine learning, deep learning and statistical analysis techniques are used here to get the accurate result so the investors can see the future trend and maximize the return of investment in stock trading. This paper will review many deep learning algorithms for stock price forecasting. We use a record of s&p 500 index data for training and testing. The survey motive is to check various deep learning and statistical model techniques for stock price forecasting that are Moving Averages, ARIMA which are statistical techniques and LSTM, RNN, CNN, and FULL CNN which are deep learning models. It will discuss various models, including the Auto regression integration moving average model, the Recurrent neural network model, the long short-term model which is the type of RNN used for long dependency for data, the convolutional neural network model, and the full convolutional neural network model, in terms of error calculation or percentage of accuracy that how much it is accurate which measures by the function like Root mean square error, mean absolute error, mean squared error. The model can be used to predict the stock price by checking the low MAE value as lower the MAE value the difference between the predicting and the actual value will be less and this model will predict the price more accurately than other models. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.06689&r=fmk |
| By: | Yuan Gao; Haokun Chen; Xiang Wang; Zhicai Wang; Xue Wang; Jinyang Gao; Bolin Ding |
| Abstract: | Machine learning models have demonstrated remarkable efficacy and efficiency in a wide range of stock forecasting tasks. However, the inherent challenges of data scarcity, including low signal-to-noise ratio (SNR) and data homogeneity, pose significant obstacles to accurate forecasting. To address this issue, we propose a novel approach that utilizes artificial intelligence-generated samples (AIGS) to enhance the training procedures. In our work, we introduce the Diffusion Model to generate stock factors with Transformer architecture (DiffsFormer). DiffsFormer is initially trained on a large-scale source domain, incorporating conditional guidance so as to capture global joint distribution. When presented with a specific downstream task, we employ DiffsFormer to augment the training procedure by editing existing samples. This editing step allows us to control the strength of the editing process, determining the extent to which the generated data deviates from the target domain. To evaluate the effectiveness of DiffsFormer augmented training, we conduct experiments on the CSI300 and CSI800 datasets, employing eight commonly used machine learning models. The proposed method achieves relative improvements of 7.2% and 27.8% in annualized return ratio for the respective datasets. Furthermore, we perform extensive experiments to gain insights into the functionality of DiffsFormer and its constituent components, elucidating how they address the challenges of data scarcity and enhance the overall model performance. Our research demonstrates the efficacy of leveraging AIGS and the DiffsFormer architecture to mitigate data scarcity in stock forecasting tasks. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.06656&r=fmk |
| By: | Couts, Spencer J. (U of Southern California); Goncalves, Andrei S. (Ohio State U) |
| Abstract: | Private Commercial Real Estate (CRE) funds provide institutional investors an opportunity to access the CRE market, but most of them are inaccessible to typical individual (retail) investors. In this paper, we study the early performance (2016 to 2023) of a special and emerging class of non-listed CRE funds that are accessible to individual investors. These funds, referred to as Net Asset Value (NAV) Real Estate Investment Trusts (REITs), have grown in importance over the last decade. They now represent a major alternative to publicly traded REITs in providing individual investors a way to access CRE investments without direct ownership. We find that the observed returns from these NAV REITs suffer from smoothness due to lagged pricing updates, and thus unsmoothing returns is important for studying their risk-adjusted performance. We then show that NAV REITs have delivered large alphas relative to public indices over our sample period. Finally, we highlight that traditional alpha analysis may not be adequate for a short sample like ours and provide an alternative alpha analysis that indicates the alphas of NAV REITs over our sample period were economically meaningful, albeit substantially lower than traditional alpha analysis suggests. |
| JEL: | G11 G23 |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:ohidic:2024-01&r=fmk |
| By: | Lodge, David; Manu, Ana-Simona; Van Robays, Ine |
| Abstract: | Using daily data since 2017, we disentangle China-specific structural shocks driving Chinese financial markets and examine spillovers across global markets. The novelty of this paper consists of simultaneously identifying China shocks with shocks emanating from the United States and shocks to global risk sentiment - two major forces driving global financial markets - to ensure that China spillover estimates do not reflect common factors. Our results show that shocks originating in China have material impacts on global equity markets, although spillovers are much smaller than those following shocks in the United States, or those triggered by shifts in global risk sentiment. By contrast, shocks from China account for a significant proportion of variation in global commodity prices, more on a par with those of the United States. Nevertheless, spillovers from China can be significantly amplified in an environment of heightened global volatility, or when the shocks are large. |
| Keywords: | China shocks, spillovers, global financial markets, commodities |
| JEL: | E44 E52 G15 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bofitp:283609&r=fmk |