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on Financial Markets |
| By: | Gita Gopinath; Josefin Meyer; Carmen Reinhart; Christoph Trebesch |
| Abstract: | Theory suggests that corporate and sovereign bonds are fundamentally different, also because sovereign debt has no bankruptcy mechanism and is hard to enforce. We show empirically that the two assets are more similar than you think, at least when it comes to high-yield bonds over the past 20 years. We use rich new data to compare high-yield US corporate (“junk”) bonds to high-yield emerging market sovereign bonds 2002-2021. Investor experiences in these two asset classes were surprisingly aligned, with (i) similar average excess returns, (ii) similar average risk-return patterns (Sharpe ratios), (iii) similar default frequency, and (iv) comparable haircuts. A notable difference is that the average default duration is higher for sovereigns. Moreover, the two markets co-move differently with domestic and global factors. US “junk” bond yields are more closely linked to US market conditions such as US stock returns, US stock price volatility (VIX), or US monetary policy. |
| Keywords: | sovereign debt and default, default risk, corporate bonds, corporate default, junk bonds, chapter 11, crisis resolution. |
| JEL: | F30 G10 F40 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11799 |
| By: | Isabella Loaiza; Roberto Rigobon |
| Abstract: | AI is transforming industries, raising concerns about job displacement and decision making reliability. AI, as a universal approximation function, excels in data driven tasks but struggles with small datasets, subjective probabilities, and contexts requiring human judgment, relationships, and ethics.The EPOCH framework highlights five irreplaceable human capabilities: Empathy, Presence, Opinion, Creativity, and Hope. These attributes are vital in financial services for trust, inclusion, innovation, and consumer experience. Although AI improves efficiency in risk management and compliance, it will not eliminate jobs but redefine them, similar to how ATMs reshaped bank tellers' roles. The challenge is ensuring professionals adapt, leveraging AI's strengths while preserving essential human capabilities. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.22035 |
| By: | Antonio Ciccone; Felix Rusche |
| Abstract: | Between 2017 and 2024, the main national stock market indices rose in the US and the five largest European economies. However, the average daily performance of all six indices turns from positive to negative when weighted by daily media coverage. A case in point is the average daily performance of Germany’s DAX index on days it was reported on the country’s most-watched nightly news. While the DAX increased by more than 4 index points per day over the period, the index dropped by more than 10 points on days it was reported -- news was bad news. On days the DAX wasn’t covered on the nightly news, the index rose by around 10 points -- no news was good news. About half of the worse daily performance when the DAX was covered is accounted for by a greater focus on negative news. The other half stems from a novel big news bias: a greater focus on large index changes, whether positive or negative, combined with a negative skew in the daily performance of the index. We show that the big news bias extends to other national stock market indices. |
| Keywords: | media bias, financial markets. |
| JEL: | L82 G10 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11793 |
| By: | Nie, George Y. (Concordia University) |
| Abstract: | We argue that a payment’s risk approaches zero as maturity approaches zero, and that the central bank’s short-term rate best captures the risk-free rate of various assets. We employ two factors to model the expected risk-free rate that the market expects the current monetary policy to move towards the neutral rate over a certain period. Expecting that the T-bill risk (i.e., the macrorisk) largely reflects a country’s inflation risk, we measure the risk as a 5-year payment’s risk to be comparable across assets. To solve the model factors, we use repeated trials to minimize the prediction errors. Our models thus split US and Canada T-bill yields into the risk and risk-free rate, on average explaining 98.7% of the returns. The models assuming independence of the two returns show similar power in predicting T-bill returns, which can significantly simplify the formulas. We also find that the inclusion of a risk constant over maturity, which has a small value of several basis points, significantly reduces the prediction errors. The risk and the risk-free rate is the gateway to corporate the risk of various assets in the country. |
| Date: | 2025–03–01 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:2dazg_v2 |
| By: | Darren Shannon; Jin Gong; Barry Sheehan |
| Abstract: | Public announcement dates are used in the green bond literature to measure equity market reactions to upcoming green bond issues. We find a sizeable number of green bond announcements were pre-dated by anonymous information leakages on the Bloomberg Terminal. From a candidate set of 2, 036 'Bloomberg News' and 'Bloomberg First Word' headlines gathered between 2016 and 2022, we identify 259 instances of green bond-related information being released before being publicly announced by the issuing firm. These pre-announcement leaks significantly alter the equity trading dynamics of the issuing firms over intraday and daily event windows. Significant negative abnormal returns and increased trading volumes are observed following news leaks about upcoming green bond issues. These negative investor reactions are concentrated amongst financial firms, and leaks that arrive pre-market or early in market trading. We find equity price movements following news leaks can be explained to a greater degree than following public announcements. Sectoral differences are also observed in the key drivers behind investor reactions to green bond leaks by non-financials (Tobin's Q and free cash flow) and financials (ROA). Our results suggest that information leakages have a strong impact on market behaviour, and should be accounted for in green bond literature. Our findings also have broader ramifications for financial literature going forward. Privileged access to financially material information, courtesy of the ubiquitous use of Bloomberg Terminals by professional investors, highlights the need for event studies to consider wider sets of communication channels to confirm the date at which information first becomes available. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.03311 |
| By: | Victor Olkhov |
| Abstract: | We show that Markowitz's (1952) decomposition of a portfolio variance as a quadratic form in the variables of the relative amounts invested into the securities, which has been the core of classical portfolio theory for more than 70 years, is valid only in the approximation when all trade volumes with all securities of the portfolio are assumed constant. We derive the market-based portfolio variance and its decomposition by its securities, which accounts for the impact of random trade volumes and is a polynomial of the 4th degree in the variables of the relative amounts invested into the securities. To do that, we transform the time series of market trades with the securities of the portfolio and obtain the time series of trades with the portfolio as a single market security. The time series of market trades determine the market-based means and variances of prices and returns of the portfolio in the same form as the means and variances of any market security. The decomposition of the market-based variance of returns of the portfolio by its securities follows from the structure of the time series of market trades of the portfolio as a single security. The market-based decompositions of the portfolio's variances of prices and returns could help the managers of multi-billion portfolios and the developers of large market and macroeconomic models like BlackRock's Aladdin, JP Morgan, and the U.S. Fed adjust their models and forecasts to the reality of random markets. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.07929 |
| By: | Dzemski, Andreas (Department of Economics, School of Business, Economics and Law, Göteborg University); Farago, Adam (Department of Economics, School of Business, Economics and Law, Göteborg University); Hjalmarsson, Erik (Department of Economics, School of Business, Economics and Law, Göteborg University); Kiss, Tamas (The School of Business, Örebro University, Sweden) |
| Abstract: | We analyze empirical estimation of the distribution of total payoffs for stock investments over very long horizons, such as 30 years. Formal results for recently proposed bootstrap estimators are derived and alternative parametric methods are proposed. All estimators should be viewed as inconsistent for longer investment horizons. Valid confidence bands are derived and should be the focus when performing inference. Empirically, confidence bands around long-run distributions are very wide and point estimates must be interpreted with great caution. Consequently, it is difficult to distinguish long-run aggregate return distributions across countries; long-run U.S. returns are not significantly different from global returns. |
| Keywords: | Estimation uncertainty; Long-run stock returns; Quantile estimation |
| JEL: | C58 G10 |
| Date: | 2025–04–28 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:gunwpe:0853 |
| By: | Niklas Kroner |
| Abstract: | I provide evidence that investors' attention allocation plays a critical role in how financial markets incorporate macroeconomic news. Using intraday data, I document a sharp increase in the market reaction to Consumer Price Index (CPI) releases during the 2021-2023 inflation surge. Bond yields, market-implied inflation expectations, and other asset prices exhibit significantly stronger responses to CPI surprises, while reactions to other macroeconomic announcements remain largely unchanged. The joint reactions of these asset prices point to an attention-based explanation–an interpretation I corroborate throughout the rest of the paper. Specifically, I construct a measure of CPI investor attention and find that: (1) attention was exceptionally elevated around CPI announcements during the inflation surge, and (2) higher pre-announcement attention robustly leads to stronger market reactions. Studying investor attention in the context of Employment Report releases and Federal Reserve announcements, I document a similar importance of attention allocation for market reactions. Lastly, I find that markets tend to overreact to announcements that attract high levels of attention. |
| Keywords: | Macroeconomic News Announcements; Investor Attention; Financial Markets; Inflation; Federal Reserve; High-frequency event study |
| JEL: | E44 E71 G12 G14 G41 |
| Date: | 2025–03–26 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-22 |
| By: | Anindya Sarkar; G. Vadivu |
| Abstract: | This research proposes a cutting-edge ensemble deep learning framework for stock price prediction by combining three advanced neural network architectures: The particular areas of interest for the research include but are not limited to: Variational Autoencoder (VAE), Transformer, and Long Short-Term Memory (LSTM) networks. The presented framework is aimed to substantially utilize the advantages of each model which would allow for achieving the identification of both linear and non-linear relations in stock price movements. To improve the accuracy of its predictions it uses rich set of technical indicators and it scales its predictors based on the current market situation. By trying out the framework on several stock data sets, and benchmarking the results against single models and conventional forecasting, the ensemble method exhibits consistently high accuracy and reliability. The VAE is able to learn linear representation on high-dimensional data while the Transformer outstandingly perform in recognizing long-term patterns on the stock price data. LSTM, based on its characteristics of being a model that can deal with sequences, brings additional improvements to the given framework, especially regarding temporal dynamics and fluctuations. Combined, these components provide exceptional directional performance and a very small disparity in the predicted results. The present solution has given a probable concept that can handle the inherent problem of stock price prediction with high reliability and scalability. Compared to the performance of individual proposals based on the neural network, as well as classical methods, the proposed ensemble framework demonstrates the advantages of combining different architectures. It has a very important application in algorithmic trading, risk analysis, and control and decision-making for finance professions and scholars. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.22192 |
| By: | Buchetti, Bruno; Bouteska, Ahmed; Harasheh, Murad; Santon, Alessandro |
| Abstract: | The primary objective of this study is to explore the dynamic relationships between equity returns or volatility and sentiment factors in European markets during both the periods preceding the COVID-19 pandemic, the COVID-19 itself, and the Russia-Ukraine war. We achieve this by applying the network methodology initially introduced by Diebold & Yilmaz (2014), along with its extensions based on realized measures and generalized forecast error variance decomposition, as proposed by Baruník & Křehlík (2018) and Chatziantoniou et al. (2023). Additionally, we investigate how the global sentiment factor influences the overall connectedness index by employing a quantile-on-quantile approach, following the methods outlined by Sim & Zhou (2015) and Bouri et al. (2022). To conduct our analysis, we utilize daily-frequency data encompassing the period from January 1, 2011, to December 31, 2023, covering the entirety of the COVID-19 pandemic in 2020 and the Russia-Ukraine conflict in 2022 across six European stock indices. Our primary discovery is the interconnectedness of both returns and sentiment. Furthermore, our resultsindicate that during the COVID-19 and Russia-Ukraine war, there is a notable increase in volatility spillovers among the analyzed stock indices, driven by the heightened interconnectedness between stock market returns. JEL Classification: G11, G12, G14, G40 |
| Keywords: | COVID-19, dynamic spillover and connectedness, European financial markets, investor sentiment, Russia-Ukraine war |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253050 |
| By: | Zhenwei Lin (Graduate School of Economics, University of Tokyo); Masafumi Nakano (GCI Asset Management); Akihiko Takahashi (Graduate School of Economics, The University of Tokyo) |
| Abstract: | This paper presents a novel approach to sentiment analysis in the context of investments in the Japanese stock market. Specifically, we begin by creating an original set of keywords derived from news headlines sourced from a Japanese financial news platform. Subsequently, we develop new polarity scores for these keywords, based on market returns, to construct sentiment lexicons. These lexicons are then utilized to guide investment decisions regarding the stocks of companies included in either the TOPIX 500 or the Nikkei 225, which are Japan’s representative stock indices. Furthermore, empirical studies validate the effectiveness of our proposed method, which significantly outperforms a ChatGPT-based sentiment analysis approach. This provides strong evidence for the advantage of integrating market data into textual sentiment evaluation to enhance financial investment strategies. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:cfi:fseres:cf601 |
| By: | Dhrubajyoti Ghosh |
| Abstract: | In this study, we employ k-means clustering algorithm of polyspectral means to analyze 49 stocks in the Indian stock market. We have used spectral and bispectral information obtained from the data, by using spectral and bispectral means with different weight functions that will give us varying insights into the temporal patterns of the stocks. In particular, the higher order polyspectral means can provide significantly more information than what we can gather from power spectra, and can also unveil nonlinear trends in a time series. Through rigorous analysis, we identify five distinctive clusters, uncovering nuanced market structures. Notably, one cluster emerges as that of a conglomerate powerhouse, featuring ADANI, BIRLA, TATA, and unexpectedly, government-owned bank SBI. Another cluster spotlights the IT sector with WIPRO and TCS, while a third combines private banks, government entities, and RELIANCE. The final cluster comprises publicly traded companies with dispersed ownership. Such clustering of stocks sheds light on intricate financial relationships within the stock market, providing valuable insights for investors and analysts navigating the dynamic landscape of the Indian stock market. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.07021 |