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on Financial Markets |
| By: | Szymon Lis; Robert \'Slepaczuk; Pawe{\l} Sakowski |
| Abstract: | This paper investigates whether short-term market overreactions can be systematically predicted and monetized as momentum signals using high-frequency emotional information and modern machine learning methods. Focusing on Apple Inc. (AAPL), we construct a comprehensive intraday dataset that combines volatility normalized returns with transformer-based emotion features extracted from Twitter messages. Overreactions are defined as extreme return realizations relative to contemporaneous volatility and transaction costs and are modeled as a three-class prediction problem. We evaluate the performance of several nonlinear classifiers, including XGBoost, Random Forests, Deep Neural Networks, and Bidirectional LSTMs, across multiple intraday frequencies (1, 5, 10, and 15 minute data). Model outputs are translated into trading strategies and assessed using risk-adjusted performance measures and formal statistical tests. The results show that machine learning models significantly outperform benchmark overreaction rules at ultra short horizons, while classical behavioral momentum effects dominate at intermediate frequencies, particularly around 10 minutes. Explainability analysis based on SHAP reveals that volatility and negative emotions, especially fear and sadness, play a central role in driving predicted overreactions. Overall, the findings demonstrate that emotion-driven overreactions contain a predictable structure that can be exploited by machine learning models, offering new insights into the behavioral origins of intraday momentum and the interaction between sentiment, volatility, and algorithmic trading. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.18912 |
| By: | Fausch, Jürg; Frigg, Moreno; Ruenzi, Stefan; Weigert, Florian |
| Abstract: | We present improved out-of-sample predictability of future fund flows using state-of-the-art machine learning methods. Nonlinear machine learning models significantly outperform linear models in terms of out-of-sample R-squared. Using interpretable ML methods, we identify past flows and the Morningstar rating as the most important predictors for net- flows, while other past performance variables are of minor importance. We find that the importance of Morningstar ratings and expenses has increased over time. In addition, the interaction effect of past flows with the Morningstar rating has a substantial impact on future flows. Furthermore, our results demonstrate that machine learning-based fund flow predictions can be used to ex-ante differentiate between high and low-performing mutual funds. Finally, funds whose flow predictions can be improved the most using ML reveal the worst performance, consistent with the idea that liquidity management is particularly challenging for these funds. |
| Keywords: | Machine learning, fund flow prediction, big data, interpretable machine learning |
| JEL: | C45 C52 C53 C55 G10 G11 G12 G17 G23 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:cfrwps:337467 |
| By: | Weibels, Sebastian |
| Abstract: | Theories of limited attention predict that investors rely on typical patterns to navigate high-dimensional firm characteristics, making atypical firms hard to process. To quantify this difficulty, we propose a data-driven measure of firm atypicality using an autoencoder (ATYP). The model learns typical patterns that describe most firms, and our measure aggregates the deviations those patterns cannot explain. Unlike proxies based on disclosure or organizational complexity, this approach captures the processing difficulty of the characteristics themselves. Empirically, we document that atypicality strongly predicts future returns. A decile portfolio that sells high-ATYP firms and buys low-ATYP firms earns 1.47% per month (equal-weighted) and 0.82% (value-weighted). The effect strengthens where investor attention is low and arbi- trage is limited, suggesting mispricing as the explanation. |
| Keywords: | atypical firms, processing difficulty, return predictability, mispricing, machine learning |
| JEL: | G10 G11 G12 G14 C45 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:cfrwps:337469 |
| By: | Anna Amirdjanova; David Lynch; Anni Zheng |
| Abstract: | We examine prospective classification of crypto currencies risks within the ISDA Standardized Initial Margin Model (SIMM) framework for calculation of initial margin on trades sensitive to cryptocurrencies’ risk factors in the uncleared market. Consistent with the view that cryptocurrencies are digital assets that fundamentally rely on distributed ledger technology (DLT) and induce financial risks that are significantly different from those in traditional risk classes like commodities or FX, we find that cryptocurrencies are best classified into a distinct risk class within SIMM that is split into two buckets – pegged and floating (unpegged) crypto currencies as risk factors - and suggest risk weights’ calibration methodology within the cryptocurrencies risk class that is consistent with the existing approaches adopted in SIMM. |
| Keywords: | Risk management; Cryptocurrencies; Credit risk; Derivatives |
| JEL: | G12 G13 G18 G28 |
| Date: | 2026–02–12 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:102799 |
| By: | Guglielmo Maria Caporale; Luis Alberiko Gil-Alana; Oluwadare O. Ojo; Modupe I. Omotosho |
| Abstract: | This paper investigates persistence in the MINT (Mexico, Indonesia, Nigeria, Turkey) stock markets applying fractional integration methods to daily data from 1 January 2022 to 31 October 2025. Different model specifications are estimated for prices, log prices and log returns under the assumption of white noise and autocorrelated errors respectively. Mean reversion is found in most cases for prices with autocorrelated errors, which implies that shocks have only temporary effects in this case. Turkey displays the lowest degrees of integration, while Nigeria has the highest for both prices and log-prices. Structural breaks are found in the case of stock prices in all countries, with market inefficiencies appearing to be present in the most recent period characterized by geopolitical uncertainty resulting from the Russia-Ukraine conflict. |
| Keywords: | persistence, fractional integration, MINT, stock markets |
| JEL: | C22 G12 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12406 |
| By: | Murad Farzulla |
| Abstract: | Using the Crypto Fear & Greed Index and Bitcoin daily data, we document that sentiment extremity predicts excess uncertainty beyond realized volatility. Extreme fear and extreme greed regimes exhibit significantly higher spreads than neutral periods -- a phenomenon we term the "extremity premium." Extended validation on the full Fear & Greed history (February 2018--January 2026, N = 2, 896) confirms the finding: within-volatility-quintile comparisons show a significant premium (p |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.07018 |
| By: | Tomas Jankauskas |
| Abstract: | Understanding how short- and long-term assets are priced is one of the fundamental questions in finance. The term structure of risk premia allows us to perform net present value calculations, test asset pricing models, and potentially explain the sources of many cross-sectional asset pricing anomalies. In this post, I construct a forward-looking estimate of the term structure of risk premia in the corporate bond market following Jankauskas (2024). The U.S. corporate bond market is an ideal laboratory for studying the relationship between risk premia and maturity because of its large size (standing at roughly $16 trillion as of the end of 2024) and because the maturities are well defined (in contrast to equities). |
| Keywords: | risk premia term structure; corporate bonds |
| JEL: | G10 G12 |
| Date: | 2026–02–24 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:102808 |