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on Financial Markets |
| By: | Pauline Lam; Jeffrey Wurgler |
| Abstract: | Green finance emphasizes “additionality, ” meaning funded projects should offer distinct environmental benefits beyond standard practice. Analysis of U.S. corporate and municipal green bonds, however, indicates that the vast majority of green bond proceeds is used for refinancing ordinary debt, continuing ongoing projects, or initiating projects without green aspects that are novel for the issuer. Only 2% of corporate and municipal green bond proceeds initiate projects with clearly novel green features. Investors and market participants also do not distinguish among levels of additionality: Offering yields, announcement effects, green bond index inclusion, and green bond fund holdings are uncorrelated with additionality. |
| JEL: | G10 G32 Q50 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32960 |
| By: | Kristy Jansen; Hyun Song Shin; Goetz von Peter |
| Abstract: | How do exchange rates affect the asset allocation of bond portfolio investors? Using detailed security-level holdings, we find that euro area-based investors systematically shed sovereign bonds as the dollar strengthens, confirming the role of the dollar as a global risk factor even for euro-based investors. More distinctively, they also shed local currency bonds when the euro strengthens, due to currency mismatches on their own balance sheets. There is no such effect for foreign currency bonds of the same sovereign issuers. These findings are consistent with a Value-at-Risk portfolio choice model that brings out separate roles for local, foreign and reference currencies. |
| Keywords: | Currency mismatch, balance sheet effects, emerging markets, exchange rates, institutional investors, sovereign bonds |
| JEL: | F31 G11 G15 G23 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1210 |
| By: | Itzhak Ben-David; Alex Chinco |
| Abstract: | All of asset-pricing theory currently stems from one key assumption: price equals expected discounted payoff. And much of what we think we know about discount rates comes from studying a particular kind of expected payoff: the earnings forecasts in analyst reports. Researchers typically access these numbers through an easy-to-use database and never read the underlying documents. This is unfortunate because the text of each report contains an explicit description of how the analyst priced their own earnings forecast. We study a sample of 513 reports and find that most analysts use a trailing P/E (price-to-earnings) ratio not a discount rate. Instead of computing the present value of a company’s future earnings, they ask: “How would a firm with similar earnings have been priced last year?” Even if other investors do things differently, it does not make sense to put discount rates at the center of every asset-pricing model if market participants do not always use one. There are other options. Trailing twelve-month P/E ratios account for 91% of the variation in analysts’ price targets.We construct a new kind of asset-pricing model around this fact and show that it explains the market response to earnings surprises. |
| JEL: | G12 G14 G32 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32942 |
| By: | Di Wu |
| Abstract: | The year 2024 witnessed a major development in the cryptocurrency industry with the long-awaited approval of spot Bitcoin exchange-traded funds (ETFs). This innovation provides investors with a new, regulated path to gain exposure to Bitcoin through a familiar investment vehicle (Kumar et al., 2024). However, unlike traditional ETFs that directly hold underlying assets, Bitcoin ETFs rely on a creation and redemption process managed by authorized participants (APs). This unique structure introduces distinct characteristics in terms of premium/discount behavior compared to traditional ETFs. This paper investigates the premium and discount patterns observed in Bitcoin ETFs during first four-month period (January 11th, 2024, to May 17th, 2024). Our analysis reveals that these patterns differ significantly from those observed in traditional index ETFs, potentially exposing investors to additional risk factors. By identifying and analyzing these risk factors associated with Bitcoin ETF premiums/discounts, this paper aims to achieve two key objectives: Enhance market understanding: Equip and market and investors with a deeper comprehension of the unique liquidity risks inherent in Bitcoin ETFs. Provide a clearer risk management frameworks: Offer a clearer perspective on the risk-return profile of digital asset ETFs, specifically focusing on Bitcoin ETFs. Through a thorough analysis of premium/discount behavior and the underlying factors contributing to it, this paper strives to contribute valuable insights for investors navigating the evolving landscape of digital asset investments |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.00270 |
| By: | Pablo D. Azar; Adrian Casillas; Maryam Farboodi |
| Abstract: | This paper considers the “DeFi intermediation chain”—the market structure that underlies the creation and distribution of ETH, the native cryptocurrency of Ethereum—to examine how information asymmetry shapes intermediation rents. We argue that using proof-of-stake blockchain technology in DeFi leads to a novel limit to arbitrage, arising from the tension between arbitrageurs' privacy needs and blockchain transparency. Using a new dataset which distinguishes private and public transactions in Ethereum, we find that a 1% increase in private information advantage leads to a 1.4% increase in intermediaries' profit share. We develop a dynamic bargaining model that predicts information market power stems exclusively from participants' private information advantage. Our analysis illustrates how blockchain technology can sustain arbitrage opportunities despite low entry barriers. |
| JEL: | C83 D82 D86 G23 G29 L86 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32949 |
| By: | Kubitza, Christian |
| Abstract: | Financial losses can have persistent effects on the financial system. This paper proposes an empirical measure for the duration of these effects, Spillover Persistence. I document that Spillover Persistence is strongly correlated with financial conditions; during banking crises, Spillover Persistence is higher, whereas in the run-up phase of stock market bubbles it is lower. Lower Spillover Persistence also associates with a more fragile system, e.g., a higher probability of future crises, consistent with the volatility paradox. The results emphasize the dynamics of loss spillovers as an important dimension of systemic risk and financial constraints as a key determinant of persistence. JEL Classification: E44, G01, G12, G20, G32 |
| Keywords: | asset price bubbles, financial crises, fire sales, fragility, systemic risk |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20242981 |
| By: | Peng Zhu; Yuante Li; Yifan Hu; Qinyuan Liu; Dawei Cheng; Yuqi Liang |
| Abstract: | Stock price prediction is a challenging problem in the field of finance and receives widespread attention. In recent years, with the rapid development of technologies such as deep learning and graph neural networks, more research methods have begun to focus on exploring the interrelationships between stocks. However, existing methods mostly focus on the short-term dynamic relationships of stocks and directly integrating relationship information with temporal information. They often overlook the complex nonlinear dynamic characteristics and potential higher-order interaction relationships among stocks in the stock market. Therefore, we propose a stock price trend prediction model named LSR-IGRU in this paper, which is based on long short-term stock relationships and an improved GRU input. Firstly, we construct a long short-term relationship matrix between stocks, where secondary industry information is employed for the first time to capture long-term relationships of stocks, and overnight price information is utilized to establish short-term relationships. Next, we improve the inputs of the GRU model at each step, enabling the model to more effectively integrate temporal information and long short-term relationship information, thereby significantly improving the accuracy of predicting stock trend changes. Finally, through extensive experiments on multiple datasets from stock markets in China and the United States, we validate the superiority of the proposed LSR-IGRU model over the current state-of-the-art baseline models. We also apply the proposed model to the algorithmic trading system of a financial company, achieving significantly higher cumulative portfolio returns compared to other baseline methods. Our sources are released at https://github.com/ZP1481616577/Baseline s\_LSR-IGRU. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08282 |
| By: | Shengkun Wang; Taoran Ji; Linhan Wang; Yanshen Sun; Shang-Ching Liu; Amit Kumar; Chang-Tien Lu |
| Abstract: | The stock price prediction task holds a significant role in the financial domain and has been studied for a long time. Recently, large language models (LLMs) have brought new ways to improve these predictions. While recent financial large language models (FinLLMs) have shown considerable progress in financial NLP tasks compared to smaller pre-trained language models (PLMs), challenges persist in stock price forecasting. Firstly, effectively integrating the modalities of time series data and natural language to fully leverage these capabilities remains complex. Secondly, FinLLMs focus more on analysis and interpretability, which can overlook the essential features of time series data. Moreover, due to the abundance of false and redundant information in financial markets, models often produce less accurate predictions when faced with such input data. In this paper, we introduce StockTime, a novel LLM-based architecture designed specifically for stock price data. Unlike recent FinLLMs, StockTime is specifically designed for stock price time series data. It leverages the natural ability of LLMs to predict the next token by treating stock prices as consecutive tokens, extracting textual information such as stock correlations, statistical trends and timestamps directly from these stock prices. StockTime then integrates both textual and time series data into the embedding space. By fusing this multimodal data, StockTime effectively predicts stock prices across arbitrary look-back periods. Our experiments demonstrate that StockTime outperforms recent LLMs, as it gives more accurate predictions while reducing memory usage and runtime costs. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08281 |
| By: | Robert Taylor |
| Abstract: | This paper presents the experimental process and results of SVM, Gradient Boosting, and an Attention-GRU Hybrid model in predicting the Implied Volatility of rolled-over five-year spread contracts of credit default swaps (CDS) on European corporate debt during the quarter following mid-May '24, as represented by the iTraxx/Cboe Europe Main 1-Month Volatility Index (BP Volatility). The analysis employs a feature matrix inspired by Merton's determinants of default probability. Our comparative assessment aims to identify strengths in SOTA and classical machine learning methods for financial risk prediction |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.15404 |
| By: | OECD |
| Abstract: | This paper examines the recent evolution of competition in the financial services sector in Latin America and the Caribbean (LAC), focusing on the rise of Fintechs and the emergence of a pro-competitive regulatory framework. This evolution results from a symbiosis of positive feedback between technology and regulation, which reinforce and balance each other in shaping a new era for the sector in the LAC region. Over the last decade, the financial sector in LAC has undergone profound changes, including the entry of new players, the emergence of new products and the reconfiguration of market boundaries. These developments have led to competitive gains and the provision of better, more accessible, customised and inclusive financial services. Regulatory advances have played a crucial role at various stages of this process, sometimes laying the groundwork, sometimes welcoming and protecting new technologies and models driven by financial digitalisation, and more recently, even leading and fostering disruptive innovations. Open Banking currently stands as a key element of this shared agenda, both regionally and globally, aimed at deepening market transformation towards greater competition, innovation and inclusion. |
| Date: | 2024–09–27 |
| URL: | https://d.repec.org/n?u=RePEc:oec:dafaac:313-en |