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on Market Microstructure |
| By: | Peter Andre (Leibniz Institute for Financial Research SAFE); Philipp Schirmer (University of Bonn); Johannes Wohlfart (University of Cologne) |
| Abstract: | Investors’ return expectations are pivotal in stock markets, but the reasoning behind these expectations is not well understood. This paper sheds light on economic agents’ mental models – their subjective understanding – of the stock market. We conduct surveys with the general population, retail investors, financial professionals, and academic experts. Respondents forecast and explain how future returns respond to stale news about the future earnings streams of companies. We document four main results. First, while academic experts view stale news as irrelevant, households and professionals often believe that stale good news leads to persistently higher expected future returns. Second, academic experts refer to market efficiency to explain their forecasts, whereas households and many professionals directly equate higher future earnings with higher future returns, neglecting the offsetting effects of endogenous price adjustments. Third, additional experiments with households demonstrate that this neglect of equilibrium pricing does not reflect inattention to trading or price responses or ignorance about how returns are calculated. Instead, it reflects a gap in respondents’ mental models: they are unfamiliar with the concept of equilibrium pricing. Lastly, we illustrate the potential consequences of neglecting equilibrium pricing. We use panel data on household expectations to show that this neglect predicts previously documented belief anomalies such as return extrapolation and pro-cyclicality. |
| Keywords: | Mental models, Return expectations |
| JEL: | D83 D84 G11 G12 G41 G51 G53 |
| Date: | 2024–11–13 |
| URL: | https://d.repec.org/n?u=RePEc:kud:kucebi:2307 |
| By: | Aleksei Pastushkov |
| Abstract: | We examine the dynamics of informational efficiency in a market with asymmetrically informed, boundedly rational traders who adaptively learn optimal strategies using simple multiarmed bandit (MAB) algorithms. The strategies available to the traders have two dimensions: on the one hand, the traders must endogenously choose whether to acquire a costly information signal, on the other, they must determine how aggressively they trade by choosing the share of their wealth to be invested in the risky asset. Our study contributes to two strands of literature: the literature comparing the effects of competitive and strategic behavior on asset price efficiency under costly information as well as the actively growing literature on algorithmic tacit collusion and pseudo-collusion in financial markets. We find that for certain market environments (with low information costs) our model reproduces the results of Kyle [1989] in that the ability of traders to trade strategically leads to worse price efficiency compared to the purely competitive case. For other environments (with high information costs), on the other hand, our results show that a market with strategically acting traders can be more efficient than a purely competitive one. Furthermore, we obtain novel results on the ability of independently learning traders to coordinate on a pseudo-collusive behavior, leading to non-competitive pricing. Contrary to some recent contributions (see e.g. [Cartea et al. 2022]), we find that the pseudo-collusive behavior in our model is robust to a large number of agents, demonstrating that even in the setting of financial markets with a large number of independently learning traders non-competitive pricing and pseudo-collusive behavior can frequently arise. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05032 |
| By: | Ali Elahi; Fatemeh Taghvaei |
| Abstract: | Predicting financial markets and stock price movements requires analyzing a company's performance, historic price movements, industry-specific events alongside the influence of human factors such as social media and press coverage. We assume that financial reports (such as income statements, balance sheets, and cash flow statements), historical price data, and recent news articles can collectively represent aforementioned factors. We combine financial data in tabular format with textual news articles and employ pre-trained Large Language Models (LLMs) to predict market movements. Recent research in LLMs has demonstrated that they are able to perform both tabular and text classification tasks, making them our primary model to classify the multi-modal data. We utilize retrieval augmentation techniques to retrieve and attach relevant chunks of news articles to financial metrics related to a company and prompt the LLMs in zero, two, and four-shot settings. Our dataset contains news articles collected from different sources, historic stock price, and financial report data for 20 companies with the highest trading volume across different industries in the stock market. We utilized recently released language models for our LLM-based classifier, including GPT- 3 and 4, and LLaMA- 2 and 3 models. We introduce an LLM-based classifier capable of performing classification tasks using combination of tabular (structured) and textual (unstructured) data. By using this model, we predicted the movement of a given stock's price in our dataset with a weighted F1-score of 58.5% and 59.1% and Matthews Correlation Coefficient of 0.175 for both 3-month and 6-month periods. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.01368 |