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on Financial Markets |
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Issue of 2024‒04‒22
eight papers chosen by |
| By: | Charles W. Calomiris; Matthew S. Jaremski |
| Abstract: | The high social costs of financial crises imply that economists, policymakers, businesses, and households have a tremendous incentive to understand, and try to prevent them. And yet, so far we have failed to learn how to avoid them. In this article, we take a novel approach to studying financial crises. We first build ten case studies of financial crises that stretch over two millennia, and then consider their salient points of differences and commonalities. We see this as the beginning of developing a useful taxonomy of crises – an understanding of the most important factors that reappear across the many examples, which also allows (as in any taxonomy) some examples to be more similar to each other than others. From the perspective of our review of the ten crises, we consider the question of why it has proven so difficult to learn from past crises to avoid future ones. |
| JEL: | E30 G01 N20 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32213&r=fmk |
| By: | Lee, David |
| Abstract: | This paper presents a model to calculate daily returns and corresponding value changes of hedge funds. In the past, the values of hedge funds were typically available on a monthly basis. The model link daily hedge fund performance with the returns on indices selected to provide a comprehensive spectrum of possible market exposures. The model gives an estimate of the daily returns of hedge funds based on the daily values of a list of market indices. The daily return of each hedge fund is estimated as a linear combination of daily market index returns. The coefficients of this linear combination are obtained through linear regression of monthly index returns against monthly hedge fund returns. |
| Keywords: | hedge fund performance, daily return, cash flow, market index, linear regression. |
| JEL: | C1 C13 C51 G11 G12 |
| Date: | 2024–03–02 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120350&r=fmk |
| By: | Maheronnaghsh, Mohammad Javad; Gheidi, Mohammad Mahdi; Fazli, MohammadAmin |
| Abstract: | In the dynamic world of financial markets, accurate price predictions are essential for informed decision-making. This research proposal outlines a comprehensive study aimed at forecasting stock and currency prices using state-of-the-art Machine Learning (ML) techniques. By delving into the intricacies of models such as Transformers, LSTM, Simple RNN, NHits, and NBeats, we seek to contribute to the realm of financial forecasting, offering valuable insights for investors, financial analysts, and researchers. This article provides an in-depth overview of our methodology, data collection process, model implementations, evaluation metrics, and potential applications of our research findings. The research indicates that NBeats and NHits models exhibit superior performance in financial forecasting tasks, especially with limited data, while Transformers require more data to reach full potential. Our findings offer insights into the strengths of different ML techniques for financial prediction, highlighting specialized models like NBeats and NHits as top performers - thus informing model selection for real-world applications. To enhance readability, all acronyms used in the paper are defined below: ML: Machine Learning LSTM: Long Short-Term Memory RNN: Recurrent Neural Network NHits: Neural Hierarchical Interpolation for Time Series Forecasting NBeats: Neural Basis Expansion Analysis for Time Series ARIMA: Autoregressive Integrated Moving Average GARCH: Generalized Autoregressive Conditional Heteroskedasticity SVMs: Support Vector Machines CNNs: Convolutional Neural Networks MSE: Mean Squared Error MAE: Mean Absolute Error RMSE: Recurrent Mean Squared Error API: Application Programming Interface F1-score: F1 Score GRU: Gated Recurrent Unit yfinance: Yahoo Finance (a Python library for fetching financial data) |
| Date: | 2023–09–30 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:dzp26&r=fmk |
| By: | Bruno de Melo |
| Abstract: | Performance attribution analysis, defined as the process of explaining the drivers of the excess performance of an investment portfolio against a benchmark, stands as a significant aspect of portfolio management and plays a crucial role in the investment decision-making process, particularly within the fund management industry. Rooted in a solid financial and mathematical framework, the importance and methodologies of this analytical technique are extensively documented across numerous academic research papers and books. The integration of large language models (LLMs) and AI agents marks a groundbreaking development in this field. These agents are designed to automate and enhance the performance attribution analysis by accurately calculating and analyzing portfolio performances against benchmarks. In this study, we introduce the application of an AI Agent for a variety of essential performance attribution tasks, including the analysis of performance drivers and utilizing LLMs as calculation engine for multi-level attribution analysis and question-answer (QA) exercises. Leveraging advanced prompt engineering techniques such as Chain-of-Thought (CoT) and Plan and Solve (PS), and employing a standard agent framework from LangChain, the research achieves promising results: it achieves accuracy rates exceeding 93% in analyzing performance drivers, attains 100% in multi-level attribution calculations, and surpasses 84% accuracy in QA exercises that simulate official examination standards. These findings affirm the impactful role of AI agents, prompt engineering and evaluation in advancing portfolio management processes, highlighting a significant advancement in the practical application and evaluation of AI technologies within the domain. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.10482&r=fmk |
| By: | Hafner, C. M.; Linton, O. B.; Wang, L. |
| Abstract: | We develop a dynamic framework to detect the occurrence of permanent and transitory breaks in the illiquidity process. We propose various tests that can be applied separately to individual events and can be aggregated across different events over time for a given firm or across different firms. In an empirical study, we use this methodology to study the impact of stock splits on the illiquidity dynamics of the Dow Jones index constituents and the effects of reverse splits using stocks from the S&P 500, S&P 400 and S&P 600 indices. Our empirical results show that stock splits have a positive and significant effect on the permanent component of the illiquidity process while a majority of the stocks engaging in reverse splits experience an improvement in liquidity conditions. |
| Keywords: | Amihud illiquidity, Difference in Difference, Event Study, Nonparametric Estimation, Reverse Split, Structural Change |
| JEL: | C12 C14 G14 G32 |
| Date: | 2024–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camjip:2404&r=fmk |
| By: | Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France.); Oguzhan Cepni (Department of Economics, Copenhagen Business School, Denmark; Ostim Technical University, Ankara, Turkiye); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | We compare the contribution of various popular economic policy uncertainty (EPU) measures with that of widely-studied realized moments (realized leverage, realized skewness, realized kurtosis, realized good and bad volatilities, realized jumps, and realized up and down tail risks) to the performance of out-of-sample forecasts of stock market volatility of the United States (US) over the sample period from 2011 to 2023. To this end, we construct optimal forecasting models by combining the popular heterogeneous autoregressive realized volatility (HAR-RV) model with optimal stepwise predictor selection algorithms and shrinkage estimators (lasso, elastic net, and ridge regression), where we control for macroeconomic factors and sentiment as well. We find that realized moments improve out-of-sample forecasting performance relative to the baseline HAR-RV model. EPU measures do not add to forecasting performance beyond realized moments, and even deteriorate forecasting performance as the length of the forecast horizon increases. The punchline is that realized moments rather than EPU measures matter for forecasting stock market volatility. |
| Keywords: | Stock market, Volatility, Forecasting, Moments, Economic policy uncertainty |
| JEL: | C22 C53 G10 G17 D80 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202408&r=fmk |
| By: | Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Ostim Technical University, Ankara, Turkiye); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | This paper extends the literature on the nexus between political geography and financial markets to the stock market volatility context by examining the interrelation between political geography and the predictive relation between the state- and aggregate-level stock market volatility via recently constructed measures of political alignment. Using monthly data for the period from February 1994 to March 2023 and a machine learning technique called random forests, we show that the importance of the state-level realized stock market volatilities as a driver of aggregate stock market volatility displays considerable cross- sectional dispersion as well as substantial variation over time, with the state of New York playing a prominent role. Further analysis shows that stronger political alignment of a state with the ruling party is associated with a lower contribution of the state's realized volatility to aggregate stock market volatility, highlighting the role of risk effects associated with the political geography of firms. Finally, we show that the negative link between the political alignment of a state and the importance of that state's realized volatility over aggregate stock market volatility is statistically significant during high-sentiment periods, but weak and statistically insignificant during low-sentiment periods, underscoring the role of investor sentiment for the nexus between political geography and financial markets. Our findings presents new insight to the risk-based arguments that associate political geography with stock market dynamics. |
| Keywords: | Stock market volatility, Random forests, Political alignment, Investor sentiment |
| JEL: | C22 C23 C51 C53 G10 D81 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202414&r=fmk |
| By: | Yuvana Jaichand (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Renee van Eyden (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | This paper examines the time-varying causality between presidential approval ratings and stock market performance, as measured by stock returns and realised volatility, in Latin America over the monthly period 1990M01 to 2016M05. Our study focuses on four prominent Latin American countries, Brazil, Chile, Colombia, and Mexico. While the standard constant parameter causality test does not reveal significant evidence of causality, the time-varying analysis uncovers bidirectional causal relationships persisting throughout the sample period. Moreover, our results remain robust when controlling for macroeconomic conditions and presidential approval ratings in other Latin American countries, using principal component analysis to construct these control variables. Furthermore, we explore the impact of US presidential approval ratings on Latin American stock market performance and presidential approval ratings. Our analysis reveals a significant causal impact of US presidential approval ratings on both Latin American presidential approval ratings and stock market performance. Our findings underscore the significant role of US presidential approval ratings in understanding global stock market dynamics and contagion effects. |
| Keywords: | Presidential approval ratings, stock returns, stock market volatility, time-varing causality |
| JEL: | C32 G10 G17 |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202411&r=fmk |