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on Financial Markets |
| By: | Zhaobo Zhu (Audencia Business School); Licheng Sun (Old Dominion University, Strome College of Business) |
| Abstract: | Motivated by the implication of return extrapolation models that a joint consideration of past price changes and firm fundamentals could efficiently identify stock mispricing, we propose an integrated approach that combines fundamental and technical information.This integrated approach generates substantial economic gains, which are comparable to those of strategies double-sorted on characteristics related to high turnover and trading costs and state-of-the-art machine learning strategies in existing studies. The performance net of transaction costs is still attractive. Simple transaction cost mitigation approaches could further enhance the performance of the integrated approach by reducing portfolio turnover. Consistent with behavioral models, limits to arbitrage and information asymmetry play a significant role in explaining the super performance of this integrated approach. |
| Keywords: | Fundamental Analysis, Technical Analysis, Arbitrage Risk, Informed Trading |
| Date: | 2024–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04703041 |
| By: | John Phan; Hung-Fu Chang |
| Abstract: | This paper investigates the application of machine learning models, Long Short-Term Memory (LSTM), one-dimensional Convolutional Neural Networks (1D CNN), and Logistic Regression (LR), for predicting stock trends based on fundamental analysis. Unlike most existing studies that predominantly utilize technical or sentiment analysis, we emphasize the use of a company's financial statements and intrinsic value for trend forecasting. Using a dataset of 269 data points from publicly traded companies across various sectors from 2019 to 2023, we employ key financial ratios and the Discounted Cash Flow (DCF) model to formulate two prediction tasks: Annual Stock Price Difference (ASPD) and Difference between Current Stock Price and Intrinsic Value (DCSPIV). These tasks assess the likelihood of annual profit and current profitability, respectively. Our results demonstrate that LR models outperform CNN and LSTM models, achieving an average test accuracy of 74.66% for ASPD and 72.85% for DCSPIV. This study contributes to the limited literature on integrating fundamental analysis into machine learning for stock prediction, offering valuable insights for both academic research and practical investment strategies. By leveraging fundamental data, our approach highlights the potential for long-term stock trend prediction, supporting portfolio managers in their decision-making processes. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.03913 |
| By: | Sahar Arshad; Nikhar Azhar; Sana Sajid; Seemab Latif; Rabia Latif |
| Abstract: | In the modern economic landscape, integrating financial services with Financial Technology (FinTech) has become essential, particularly in stock trend analysis. This study addresses the gap in comprehending financial dynamics across diverse global economies by creating a structured financial dataset and proposing a cross-lingual Natural Language-based Financial Forecasting (NLFF) pipeline for comprehensive financial analysis. Utilizing sentiment analysis, Named Entity Recognition (NER), and semantic textual similarity, we conducted an analytical examination of news articles to extract, map, and visualize financial event timelines, uncovering the correlation between news events and stock market trends. Our method demonstrated a meaningful correlation between stock price movements and cross-linguistic news sentiments, validated by processing two-year cross-lingual news data on two prominent sectors of the Pakistan Stock Exchange. This study offers significant insights into key events, ensuring a substantial decision margin for investors through effective visualization and providing optimal investment opportunities. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.00024 |
| By: | Shuaiyu Chen; T. Clifton Green; Huseyin Gulen; Dexin Zhou |
| Abstract: | We examine how large language models (LLMs) interpret historical stock returns and compare their forecasts with estimates from a crowd-sourced platform for ranking stocks. While stock returns exhibit short-term reversals, LLM forecasts over-extrapolate, placing excessive weight on recent performance similar to humans. LLM forecasts appear optimistic relative to historical and future realized returns. When prompted for 80% confidence interval predictions, LLM responses are better calibrated than survey evidence but are pessimistic about outliers, leading to skewed forecast distributions. The findings suggest LLMs manifest common behavioral biases when forecasting expected returns but are better at gauging risks than humans. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.11540 |
| By: | Chen, Honghui; Qu, Yuanyu; Shen, Tao; Wang, Qinghai |
| Abstract: | Using detailed information on mutual fund manager company visits in China, we identify fund managers who engage in soft information acquisition to study how soft information is used in portfolio management. We find a clear divergence in fund managers’ preference for soft information. “Soft-information” managers hold fewer stocks and tend to invest in companies with high growth potential and significant idiosyncratic risk. Trades driven by soft-information acquisition are profitable, resulting in superior performance by these managers, especially in their holdings of stocks rich with soft information. Fund managers’ distinct preferences for soft vs. hard information create segmentation in both information acquisition and investments. |
| Date: | 2024–09–27 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:84tfm |
| By: | Diana Barro (Dept. of Economics, University of Venice); Antonella Basso (Dept. of Economics, University of Venice); Stefania Funari (Dept. of Management, University of Venice); Guglielmo Alessandro Visentin (Dept. of Management, University of Venice) |
| Abstract: | In the last decades, financial markets have experienced great uncertainty that has led investors to look for alternative assets to further diversify their portfolios. Art and collectibles fall under such category, and there is a lively debate among academics and practitioners regarding the role of art in financial markets and portfolio choices. For such reason, we investigate the financial characteristics of the art market, and build a portfolio diversified with art to asses whether it outperforms, in terms of risk and return, portfolios which do not include art. We show that art performs well compared with standard and other alternative investments with which art is low correlated. In addition, we find that art returns follow an untypical seasonal pattern, and that much of the volatility in the art market can be attributed to the seasonal component of the time series. Finally, the results of the portfolio optimization analysis indicate that art enters efficient portfolios, and when additional constraints are implemented into the classical mean-variance optimization model, to account for investors’ preferences for liquidity, portfolios which include art still perform well. |
| Keywords: | Art investment, Alternative assets, Portfolio diversification, STL decomposition, Mean-variance optimization |
| JEL: | C22 G11 Z11 |
| Date: | 2023–10 |
| URL: | https://d.repec.org/n?u=RePEc:vnm:wpdman:205 |
| By: | Nina Boyarchenko; Richard K. Crump; Anna Kovner; Or Shachar |
| Abstract: | We link bond market functioning to future economic activity through a new measure, the Corporate Bond Market Distress Index (CMDI). The CMDI coalesces metrics from primary and secondary markets in real time, offering a unified measure to capture access to debt capital markets. The index correctly identifies periods of distress and predicts future realizations of commonly used measures of market functioning, while the converse is not the case. We show that disruptions in access to corporate bond markets have an economically material, statistically significant impact on the real economy, even after controlling for standard predictors including credit spreads. |
| Keywords: | credit conditions; primary and secondary corporate bond market; dimension reduction; financial conditions; real activity |
| JEL: | C38 E32 E44 G12 G32 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedrwp:98841 |
| By: | Maysam Khodayari Gharanchaei; Reza Babazadeh |
| Abstract: | Forming quantitative portfolios using statistical risk models presents a significant challenge for hedge funds and portfolio managers. This research investigates three distinct statistical risk models to construct quantitative portfolios of 1, 000 floating stocks in the US market. Utilizing five different investment strategies, these models are tested across four periods, encompassing the last three major financial crises: The Dot Com Bubble, Global Financial Crisis, and Covid-19 market downturn. Backtests leverage the CRSP dataset from January 1990 through December 2023. The results demonstrate that the proposed models consistently outperformed market excess returns across all periods. These findings suggest that the developed risk models can serve as valuable tools for asset managers, aiding in strategic decision-making and risk management in various economic conditions. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.14510 |
| By: | Aldasoro, Iñaki; Ferrari Minesso, Massimo; Gambacorta, Leonardo; Habib, Maurizio Michael; Cornelli, Giulio |
| Abstract: | Using a new series of crypto shocks, we document that money market funds’ (MMF) assets under management, and traditional financial market variables more broadly, do not react to crypto shocks, whereas stablecoin market capitalization does. U.S. monetary policy shocks, in contrast, drive developments in both crypto and traditional markets. Crucially, the reaction of MMF assets and stablecoin market capitalization to monetary policy shocks is different: while prime-MMF assets rise after a monetary policy tightening, stablecoin market capitalization declines. In assessing the state of the stablecoin market, the risk-taking environment as dictated by monetary policy is much more consequential than flight-to-quality dynamics observed within stablecoins and MMFs. JEL Classification: E50, F30 |
| Keywords: | Bitcoin, crypto, monetary policy shocks, money market funds, stablecoins |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20242987 |
| By: | Kyungsub Lee |
| Abstract: | This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.14157 |
| By: | John Armstrong; George Tatlow |
| Abstract: | We train neural networks to learn optimal replication strategies for an option when two replicating instruments are available, namely the underlying and a hedging option. If the price of the hedging option matches that of the Black--Scholes model then we find the network will successfully learn the Black-Scholes gamma hedging strategy, even if the dynamics of the underlying do not match the Black--Scholes model, so long as we choose a loss function that rewards coping with model uncertainty. Our results suggest that the reason gamma hedging is used in practice is to account for model uncertainty rather than to reduce the impact of transaction costs. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.13567 |
| By: | Fabian Billert; Stefan Conrad |
| Abstract: | We extract the sentiment from german and english news articles on companies in the DAX40 stock market index and use it to create a sentiment-powered pendant. Comparing it to existing products which adjust their weights at pre-defined dates once per month, we show that our index is able to react more swiftly to sentiment information mined from online news. Over the nearly 6 years we considered, the sentiment index manages to create an annualized return of 7.51% compared to the 2.13% of the DAX40, while taking transaction costs into account. In this work, we present the framework we employed to develop this sentiment index. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.20397 |