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on Financial Markets |
| By: | Ayaan Qayyum |
| Abstract: | This paper will discuss how headline data can be used to predict stock prices. The stock price in question is the SPDR S&P 500 ETF Trust, also known as SPY that tracks the performance of the largest 500 publicly traded corporations in the United States. A key focus is to use news headlines from the Wall Street Journal (WSJ) to predict the movement of stock prices on a daily timescale with OpenAI-based text embedding models used to create vector encodings of each headline with principal component analysis (PCA) to exact the key features. The challenge of this work is to capture the time-dependent and time-independent, nuanced impacts of news on stock prices while handling potential lag effects and market noise. Financial and economic data were collected to improve model performance; such sources include the U.S. Dollar Index (DXY) and Treasury Interest Yields. Over 390 machine-learning inference models were trained. The preliminary results show that headline data embeddings greatly benefit stock price prediction by at least 40% compared to training and optimizing a machine learning system without headline data embeddings. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.01970 |
| By: | Hongyi Liu |
| Abstract: | We propose a new pseudo-Siamese Network for Asset Pricing (SNAP) model, based on deep learning approaches, for conditional asset pricing. Our model allows for the deep alpha, deep beta and deep factor risk premia conditional on high dimensional observable information of financial characteristics and macroeconomic states, while storing the long-term dependency of the informative features through long short-term memory network. We apply this method to monthly U.S. stock returns from 1970-2019 and find that our pseudo-SNAP model outperforms the benchmark approaches in terms of out-of-sample prediction and out-of-sample Sharpe ratio. In addition, we also apply our method to calculate deep mispricing errors which we use to construct an arbitrage portfolio K-Means clustering. We find that the arbitrage portfolio has significant alphas. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.04812 |
| By: | Badenhoop, Nikolai; Mücke, Christian |
| Abstract: | This paper analyzes the impact of disclosures of sustainable investment targets under the EU Sustainable Finance Disclosure Regulation (SFDR) on mutual fund flows. Using a staggered difference-in-differences setup and focusing on retail-oriented index funds, we find that sustainable investment targets have a temporarily positive impact on fund flows in comparison to funds without sustainable investment targets. Furthermore, we find a negative linear relationship between sustainable investment targets and fund flows. While lower targets attract higher fund inflows, higher targets result in significantly lower or even no inflows. Our results suggest that up to a target level of 20% in sustainable investments, index funds can attract more inflows. This suggests a trade-off between sustainability commitments and performance considerations. |
| Keywords: | Sustainability, ESG, SFDR, Mutual Funds, Index Funds, Fund Flows, Disclosure |
| JEL: | G11 G23 G28 G14 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:safewp:325502 |