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on Financial Markets |
| By: | Duraj, Kamila; Grunow, Daniela; Chaliasos, Michael; Laudenbach, Christine; Siegel, Stephan |
| Abstract: | We revisit the limited stock market participation puzzle leveraging a qualitative research approach that is commonly used in many social sciences, but much less so in finance or economics. We conduct in-depth interviews of stock market participants and non-participants in Germany, a high-income country with a low stock market participation rate. Differently from a survey using preset questions based on theory, we elicit views in an open-ended discussion, which starts with a general question about "money", is not flagged as regarding stock market participation, and allows for probing and follow-up questions. Many of the factors proposed by the literature are mentioned by interviewees. However, non-investors perceive surprisingly high entry and participation costs due to a fundamental misunderstanding of the potential for selecting "good" stocks and avoiding "bad" ones and for market timing through frequent trading. Surprisingly, the investors we interview often share these views. However, they find a way to overcome these costs with the help of family, friends, or financial advisors they trust. While the insights from our qualitative interviews are based on a small number of interviewees, we find consistent evidence in a population-wide survey of investors and non-investors. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:imfswp:304392 |
| By: | Manuela Pedio; Massimo Guidolin; Giulia Panzeri |
| Abstract: | Machine learning is significantly shaping the advancement of various fields, and among them, notably, finance, where its range of applications and efficiency impacts are seemingly boundless. Contemporary techniques, particularly in reinforcement learning, have prompted both practitioners and academics to contemplate the potential of an artificial intelligence revolution in portfolio management. In this paper, we provide an overview of the primary methods in machine learning currently utilized in portfolio decision-making. We delve into discussions surrounding the existing limitations of machine learning algorithms and explore prevailing hypotheses regarding their future expansions. Specifically, we categorize and analyze the applications of machine learning in systematic trading strategies, portfolio weight optimization, smart beta and passive investment strategies, textual analysis, and trade execution, each separately surveyed for a comprehensive understanding. |
| Keywords: | Machine learning; portfolio choice; artificial intelligence; neural language processing; stock return predictions, market timing, mean-variance asset allocation. |
| JEL: | C45 C61 G10 G11 G17 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp24233 |
| By: | Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Pixiong Chen (Division of Model Risk Management, Wells Fargo Bank, Charlotte, NC 28202, USA) |
| Abstract: | In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio. |
| Keywords: | Asset return; Machine learning; Nonlinearity; Portfolio allocations; Predictability. |
| JEL: | C45 C55 C58 G11 G17 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:kan:wpaper:202411 |
| By: | Diego Vallarino |
| Abstract: | This study evaluates the effectiveness of a Mixture of Experts (MoE) model for stock price prediction by comparing it to a Recurrent Neural Network (RNN) and a linear regression model. The MoE framework combines an RNN for volatile stocks and a linear model for stable stocks, dynamically adjusting the weight of each model through a gating network. Results indicate that the MoE approach significantly improves predictive accuracy across different volatility profiles. The RNN effectively captures non-linear patterns for volatile companies but tends to overfit stable data, whereas the linear model performs well for predictable trends. The MoE model's adaptability allows it to outperform each individual model, reducing errors such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). Future work should focus on enhancing the gating mechanism and validating the model with real-world datasets to optimize its practical applicability. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.07234 |