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on Financial Markets |
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Issue of 2024‒06‒10
ten papers chosen by |
| By: | Abdulnasser Hatemi-J |
| Abstract: | Providing a measure of market risk is an important issue for investors and financial institutions. However, the existing models for this purpose are per definition symmetric. The current paper introduces an asymmetric capital asset pricing model for measurement of the market risk. It explicitly accounts for the fact that falling prices determine the risk for a long position in the risky asset and the rising prices govern the risk for a short position. Thus, a position dependent market risk measure that is provided accords better with reality. The empirical application reveals that Apple stock is more volatile than the market only for the short seller. Surprisingly, the investor that has a long position in this stock is facing a lower volatility than the market. This property is not captured by the standard asset pricing model, which has important implications for the expected returns and hedging designs. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.14137&r= |
| By: | Breckenfelder, Johannes; De Falco, Veronica |
| Abstract: | Large-Scale Asset Purchases can impact the price of securities directly, when securities are targeted by the central bank, or indirectly through portfolio re-balancing of private investors. We quantify both the direct and the portfolio re-balancing impact, emphasizing the role of investor heterogeneity. We use proprietary security-level data on asset holdings of different investors. We measure the direct impact on security level, finding that it is smaller for securities predominantly held by more price-elastic investors, funds and banks. Comparing a security at the 90th percentile of the investor elasticity distribution to a security at the 10th percentile, the price impact is only two-thirds as large. To assess the portfolio re-balancing effects, we construct a novel shift-share instrument to measure investors’ quasi-exogenous exposure to central bank purchases, based on investors’ holdings of eligible securities before the QE program was announced. We show that funds and banks sell eligible securities to the central bank and re-balance their portfolios towards ineligible securities, with investors ex-ante more exposed to central bank purchases re-balancing more. Using detailed holdings data of mutual funds, we estimate that for each euro sold to the central bank, the average fund allocates 88 cents to ineligible assets and 12 cents to other eligible assets that the central bank does not buy in that time period. The price of ineligible securities held by more exposed funds increases compared to those held by less exposed funds, underscoring the portfolio re-balancing channel at work. JEL Classification: E52, E58, G11, G12, G23 |
| Keywords: | asset pricing, central bank, financial intermediaries, mutual funds |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20242938&r= |
| By: | Chen, Andrew Y.; Lopez-Lira, Alejandro; Zimmermann, Tom |
| Abstract: | Mining 29, 000 accounting ratios for t-statistics over 2.0 leads to cross-sectional predictability similar to the peer review process. For both methods, about 50% of predictability remains after the original sample periods. Data mining generates other features of peer review including the rise in returns as original sample periods end, the speed of post-sample decay, and themes like investment, issuance, and accruals. Predictors supported by peer-reviewed risk explanations underperform data mining. Similarly, the relationship between modeling rigor and post-sample returns is negative. Our results suggest peer review systematically mislabels mispricing as risk, though only 18% of predictors are attributed to risk. |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:294837&r= |
| By: | Minwu Kim |
| Abstract: | This work develops a predictive model to identify potential targets of activist investment funds, which strategically acquire significant corporate stakes to drive operational and strategic improvements and enhance shareholder value. Predicting these targets is crucial for companies to mitigate intervention risks, for activists to select optimal targets, and for investors to capitalize on associated stock price gains. Our analysis utilizes data from the Russell 3000 index from 2016 to 2022. We tested 123 variations of models using different data imputation, oversampling, and machine learning methods, achieving a top AUC-ROC of 0.782. This demonstrates the model's effectiveness in identifying likely targets of activist funds. We applied the Shapley value method to determine the most influential factors in a company's susceptibility to activist investment. This interpretative approach provides clear insights into the driving forces behind activist targeting. Our model offers stakeholders a strategic tool for proactive corporate governance and investment strategy, enhancing understanding of the dynamics of activist investing. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.16169&r= |
| By: | O. Bertolami |
| Abstract: | The unfolding climate crisis is a physical manifestation of the damage that market economy, driven by the high intensity consumption of fossil fuels, has inflicted on the Earth System and on the stability conditions that were established by a complex conjugation of natural factors during the Holoecene. The magnitude of the human activities and its predatory nature is such that it is no longer possible to consider the Earth System and the services it provides for the habitability of the planet, the so-called natural capital, as an economical externality. Thus one is left with two main choices in what concerns the sustaintability of the planet's habitability: radical economic degrowth or highly efficient solutions to internalise the maintenance and the restoration of ecosystems and the services of the Earth System. It is proposed that an interesting strategy for the latter is to consider the natural capital as a stock option. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.14041&r= |
| By: | Hulusi Mehmet Tanrikulu; Hakan Pabuccu |
| Abstract: | Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A small improvement in forecasting performance can lead to increased profitability; therefore, obtaining a realistic forecast is very important for investors. Successful forecasting provides traders with effective buy-or-hold strategies, allowing them to make more profits. The most important thing in this process is to produce accurate forecasts suitable for real-life applications. Bitcoin, frequently mentioned recently due to its volatility and chaotic behavior, has begun to pay great attention and has become an investment tool, especially during and after the COVID-19 pandemic. This study provided a comprehensive methodology, including constructing continuous and trend data using one and seven years periods of data as inputs and applying machine learning (ML) algorithms to forecast Bitcoin price movement. A binarization procedure was applied using continuous data to construct the trend data representing each input feature trend. Following the related literature, the input features are determined as technical indicators, google trends, and the number of tweets. Random forest (RF), K-Nearest neighbor (KNN), Extreme Gradient Boosting (XGBoost-XGB), Support vector machine (SVM) Naive Bayes (NB), Artificial Neural Networks (ANN), and Long-Short-Term Memory (LSTM) networks were applied on the selected features for prediction purposes. This work investigates two main research questions: i. How does the sample size affect the prediction performance of ML algorithms? ii. How does the data type affect the prediction performance of ML algorithms? Accuracy and area under the ROC curve (AUC) values were used to compare the model performance. A t-test was performed to test the statistical significance of the prediction results. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.19324&r= |
| By: | David Bort Mondragon (CGA Consultores Castellón SL); Mª Carmen Saorín Iborra (Dpto. Dirección de Empresas JUAN J. RENAU PIQUERAS); Vicente Safón Cano (Universitat de València) |
| Abstract: | When it comes to managing the relationship with stakeholders in order to create value, there are many questions that remain unanswered. Previous studies have concluded that a company which invests more resources in its stakeholders to meet their legitimate demands and needs than strictly necessary may not always be rewarded with greater value creation when compared to other companies that do not invest to the same extent. Nonetheless, the instrumental stakeholder theory shows that overinvesting in stakeholders will increase business results since stakeholders will return what they have received from the company by adopting a positive attitude towards it. Given the inconclusive evidence, our paper attempts to provide an answer. In this sense, we argue and propose that only stakeholders with whom there is values congruence will disclose valuable knowledge to the company, thus enabling it to create value and obtain a competitive advantage. We test our proposal empirically by analyzing an express courier transport company and its relationship with stakeholders. The results obtained show that the affective commitment of stakeholders towards a company is essential for the latter to obtain private knowledge from them and hence create value. Cuando se trata de gestionar la relación con los grupos de interés para crear valor, hay muchas preguntas que quedan sin respuesta. Estudios anteriores han concluido que una empresa que invierte más recursos en sus grupos de interés para satisfacer sus demandas y necesidades legítimas de lo estrictamente necesario no siempre será recompensada con una mayor creación de valor en comparación con otras empresas que no invierten en la misma medida. No obstante, la teoría instrumental de los grupos de interés muestra que sobreinvertir en los grupos de interés aumentará los resultados de la empresa, ya que los grupos de interés devolverán lo que han recibido de la empresa adoptando una actitud positiva hacia ella. Dado que la evidencia es inconclusa, nuestro trabajo intenta proporcionar una respuesta. En este sentido, argumentamos y proponemos que solo los grupos de interés con los que hay congruencia de valores revelarán conocimientos valiosos a la empresa, permitiéndole así crear valor y obtener una ventaja competitiva. Probamos nuestra propuesta empíricamente analizando una empresa de transporte de mensajería exprés y su relación con los grupos de interés. Los resultados obtenidos muestran que el compromiso afectivo de los grupos de interés hacia una empresa es esencial para que esta obtenga conocimiento privado de ellos y, por lo tanto, cree valor. |
| Keywords: | teoría de los grupos de interés, congruencia de valores, compromiso afectivo, conocimiento priva-do, estudio empírico stakeholder theory, values congruence, affective commitment, private knowledge, empirical study |
| JEL: | M14 L25 D83 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:ivi:wpivie:2024-03&r= |
| By: | Sid Bhatia; Samuel Gedal; Himaya Jeyakumar Grace Lee; Ravinder Chopra; Daniel Roman; Shrijani Chakroborty |
| Abstract: | This paper examines the dynamics of the cryptocurrency market and proposes a novel blockchain-based protocol for real estate transactions. Our analysis includes a detailed review of price trends, volatility, and correlations within the cryptocurrency market, focusing on major assets like Bitcoin, Ethereum, and Tether. We provide a critical assessment of the impact of significant market events, such as the FTX bankruptcy, highlighting the vulnerabilities and resilience of the crypto market. The study also explores the potential of blockchain technology to innovate real estate transactions by enabling the secure and transparent handling of property deeds without traditional intermediaries. We introduce a blockchain protocol that reduces transaction costs, enhances security, and increases transparency, making real estate transactions more accessible and efficient. Our proposal aims to leverage the inherent benefits of blockchain to address real-world challenges in real estate transactions, providing a scalable and secure platform for property sales in a global market. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.02547&r= |
| By: | Valère Fourel; Alice Schwenninger |
| Abstract: | The Covid-19 crisis triggered a “dash for cash” phenomenon that revealed vulnerabilities on short-term debt markets. To foster monetary policy transmission and indirectly to ensure firms’ short-term financing needs, the Eurosystem effectively bought for the first time corporate commercial paper (CP) market in March 2020, as part of the Pandemic Emergency Purchase Programme (PEPP).Using a difference-in-differences approach that exploits the PEPP eligibility criteria, our findings suggest that the program triggered a shift in the debt composition of eligible firms. Maturity at issuance increased on average by 42 days for eligible issuers, which contributed to a reduction in rollover risk. This asset purchase program was effective in easing financing conditions, which translated into a compression of yields between 8 and 11 basis points for eligible firms. Eligible issuances increased but we do not find that the PEPP fostered issuance at the aggregate level. For issuers whose debt was mainly held by money market funds prior to the crisis, we found that the effect on maturity is more contained, indicating that firms’ investor sector matters. |
| Keywords: | Commercial Paper, Pandemic Emergency Purchase Programme, Eurosystem, Debt Structure, Money Market Funds |
| JEL: | E52 E58 G01 G12 G20 G23 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:946&r= |
| By: | Yupeng Cao; Zhi Chen; Qingyun Pei; Prashant Kumar; K. P. Subbalakshmi; Papa Momar Ndiaye |
| Abstract: | In the realm of financial analytics, leveraging unstructured data, such as earnings conference calls (ECCs), to forecast stock performance is a critical challenge that has attracted both academics and investors. While previous studies have used deep learning-based models to obtain a general view of ECCs, they often fail to capture detailed, complex information. Our study introduces a novel framework: \textbf{ECC Analyzer}, combining Large Language Models (LLMs) and multi-modal techniques to extract richer, more predictive insights. The model begins by summarizing the transcript's structure and analyzing the speakers' mode and confidence level by detecting variations in tone and pitch for audio. This analysis helps investors form an overview perception of the ECCs. Moreover, this model uses the Retrieval-Augmented Generation (RAG) based methods to meticulously extract the focuses that have a significant impact on stock performance from an expert's perspective, providing a more targeted analysis. The model goes a step further by enriching these extracted focuses with additional layers of analysis, such as sentiment and audio segment features. By integrating these insights, the ECC Analyzer performs multi-task predictions of stock performance, including volatility, value-at-risk (VaR), and return for different intervals. The results show that our model outperforms traditional analytic benchmarks, confirming the effectiveness of using advanced LLM techniques in financial analytics. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.18470&r= |