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on Financial Markets |
| By: | Graham L. Giller |
| Abstract: | This brief note discusses some of the aspects of a model for the covariance of equity returns based on a simple "isotropic" structure in which all pairwise correlations are taken to be the same value. The effect of the structure on feasible values for the common correlation of returns and on the "effective degrees of freedom" within the equity cross-section are discussed, as well as the impact of this constraint on the asymptotic Normality of portfolio returns is examined. An eigendecomposition of the covariance matrix is presented and used to decompose returns into a common market factor and "non-diversifiable" idiosyncratic risk. A empirical analysis of the recent history of the returns of S&P 500 Index members is presented and compared to the expectations from both this model and linear factor models. This analysis supports the isotropic covariance model and does not seem to provide evidence in support of linear factor models. The fact that idiosyncratic risk may not be removed in a model that that data supports undermines the basic premises of structures such as the C.A.P.M. and A.P.T. If the cross-section of equity returns is more accurately described by this structure then an inevitable consequence is that picking stocks is not a "pointless" activity, as the returns to residual risk would be non-zero. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08864 |
| By: | Asef Yelghi; Aref Yelghi; Shirmohammad Tavangari |
| Abstract: | The development of artificial intelligence has made significant contributions to the financial sector. One of the main interests of investors is price predictions. Technical and fundamental analyses, as well as econometric analyses, are conducted for price predictions; recently, the use of AI-based methods has become more prevalent. This study examines daily Dollar/TL exchange rates from January 1, 2020, to October 4, 2024. It has been observed that among artificial intelligence models, random forest, support vector machines, k-nearest neighbors, decision trees, and gradient boosting models were not suitable; however, multilayer perceptron and linear regression models showed appropriate suitability and despite the sharp increase in Dollar/TL rates in Turkey as of 2019, the suitability of valid models has been maintained. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.04259 |
| By: | Yeguang Chi (Ruihua); Qionghua (Ruihua); Chu; Wenyan Hao |
| Abstract: | We investigate the return-forecasting and volatility-forecasting power of intraday on-chain flow data for BTC, ETH, and USDT, and the associated option strategies. First, we find that USDT net inflow into cryptocurrency exchanges positively forecasts future returns of both BTC and ETH, with the strongest effect at the 1-hour frequency. Second, we find that ETH net inflow into cryptocurrency exchanges negatively forecasts future returns of ETH. Third, we find that BTC net inflow into cryptocurrency exchanges does not significantly forecast future returns of BTC. Finally, we confirm that selling 0DTE ETH call options is a profitable trading strategy when the net inflow into cryptocurrency exchanges is high. Our study lends new insights into the emerging literature that studies the on-chain activities and their asset-pricing impact in the cryptocurrency market. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.06327 |
| By: | Lyuhong Wang; Jiawei Jiang; Yang Zhao |
| Abstract: | We introduce an innovative framework that leverages advanced big data techniques to analyze dynamic co-movement between stocks and their underlying fundamentals using high-frequency stock market data. Our method identifies leading co-movement stocks through four distinct regression models: Forecast Error Variance Decomposition, transaction volume-normalized FEVD, Granger causality test frequency, and Granger causality test days. Validated using Chinese banking sector stocks, our framework uncovers complex relationships between stock price co-movements and fundamental characteristics, demonstrating its robustness and wide applicability across various sectors and markets. This approach not only enhances our understanding of market dynamics but also provides actionable insights for investors and policymakers, helping to mitigate broader market volatilities and improve financial stability. Our model indicates that banks' influence on their peers is significantly affected by their wealth management business, interbank activities, equity multiplier, non-performing loans, regulatory requirements, and reserve requirement ratios. This aids in mitigating the impact of broader market volatilities and provides deep insights into the unique influence of banks within the financial ecosystem. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.03922 |
| By: | Sayyed Faraz Mohseni; Hamid R. Arian; Jean-Fran\c{c}ois B\'egin |
| Abstract: | Portfolio diversification, traditionally measured through asset correlations and volatilitybased metrics, is fundamental to managing financial risk. However, existing diversification metrics often overlook non-numerical relationships between assets that can impact portfolio stability, particularly during market stresses. This paper introduces the lexical ratio (LR), a novel metric that leverages textual data to capture diversification dimensions absent in standard approaches. By treating each asset as a unique document composed of sectorspecific and financial keywords, the LR evaluates portfolio diversification by distributing these terms across assets, incorporating entropy-based insights from information theory. We thoroughly analyze LR's properties, including scale invariance, concavity, and maximality, demonstrating its theoretical robustness and ability to enhance risk-adjusted portfolio returns. Using empirical tests on S&P 500 portfolios, we compare LR's performance to established metrics such as Markowitz's volatility-based measures and diversification ratios. Our tests reveal LR's superiority in optimizing portfolio returns, especially under varied market conditions. Our findings show that LR aligns with conventional metrics and captures unique diversification aspects, suggesting it is a viable tool for portfolio managers. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.06080 |
| By: | Rui Liu; Jiayou Liang; Haolong Chen; Yujia Hu |
| Abstract: | This article applies natural language processing (NLP) to extract and quantify textual information to predict stock performance. Using an extensive dataset of Chinese analyst reports and employing a customized BERT deep learning model for Chinese text, this study categorizes the sentiment of the reports as positive, neutral, or negative. The findings underscore the predictive capacity of this sentiment indicator for stock volatility, excess returns, and trading volume. Specifically, analyst reports with strong positive sentiment will increase excess return and intraday volatility, and vice versa, reports with strong negative sentiment also increase volatility and trading volume, but decrease future excess return. The magnitude of this effect is greater for positive sentiment reports than for negative sentiment reports. This article contributes to the empirical literature on sentiment analysis and the response of the stock market to news in the Chinese stock market. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.08726 |