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on Financial Markets |
| By: | MINAMI, Koutaroh |
| Abstract: | This study explores the potential of machine learning, Long Short-Term Memory (LSTM), to detect asset price bubbles by analyzing prediction errors. Using monthly data of the Nikkei225 Index, I evaluate the performance of LSTM model in forecasting prices and compare with the GSADF test. I find that LSTM’s prediction accuracy significantly deteriorates during periods associated with asset bubbles, suggesting the presence of structural changes. In particular, the LSTM approach of this paper captures both the emergence and collapse of Japan’s late 1980s bubble separately. In addition, it can also capture structural changes related to policy changes in the 2010s Japan, which are not identified by the GSADF test. These findings suggest that machine learning can be used for not only identifying bubbles but also policy evaluations. |
| Keywords: | Bubbles, Generalized Supremum Augmented Dickey-Fuller test (GSADF), Machine learning, Long Short Term Memory (LSTM) |
| JEL: | G10 G17 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:hit:hcfrwp:g-1-30 |
| By: | Lin Li |
| Abstract: | Using an intangible intensity factor that is orthogonal to the Fama--French factors, we compare the role of intangible investment in predicting stock returns over the periods 1963--1992 and 1993--2022. For 1963--1992, intangible investment is weak in predicting stock returns, but for 1993--2022, the predictive power of intangible investment becomes very strong. Intangible investment has a significant impact not only on the MTB ratio (Fama--French high minus low [HML] factor) but also on operating profitability (OP) (Fama--French robust minus weak [RMW] factor) when forecasting stock returns from 1993 to 2022. For intangible asset-intensive firms, intangible investment is the main predictor of stock returns, rather than MTB ratio and profitability. Our evidence suggests that intangible investment has become an important factor in explaining stock returns over time, independent of other factors such as profitability and MTB ratio. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.16336 |
| By: | Abdullah Karasan; Ozge Sezgin Alp; Gerhard-Wilhelm Weber |
| Abstract: | In this study, we propose a novel machine-learning-based measure for stock price crash risk, utilizing the minimum covariance determinant methodology. Employing this newly introduced dependent variable, we predict stock price crash risk through cross-sectional regression analysis. The findings confirm that the proposed method effectively captures stock price crash risk, with the model demonstrating strong performance in terms of both statistical significance and economic relevance. Furthermore, leveraging a newly developed firm-specific investor sentiment index, the analysis identifies a positive correlation between stock price crash risk and firm-specific investor sentiment. Specifically, higher levels of sentiment are associated with an increased likelihood of stock price crash risk. This relationship remains robust across different firm sizes and when using the detoned version of the firm-specific investor sentiment index, further validating the reliability of the proposed approach. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.16287 |
| By: | Rhys Bidder; Timothy Jackson; Matthias Rottner |
| Abstract: | We examine the impact of a retail central bank digital currency, combining survey evidence from German households with a macroeconomic model featuring endogenous systemic bank runs. The survey reveals non-trivial demand for retail CBDC as a substitute for bank deposits in normal times ("slow disintermediation") and increased withdrawal risks during financial distress ("fast disintermediation"). Informed by the survey, the model indicates that introducing a retail CBDC might reduce financial stability because CBDC offers storage-at-scale - making it attractive to run to. We estimate an optimal holding limit which chokes off fast disintermediation and enhances financial stability by shrinking a fragile banking system. |
| Keywords: | Central bank digital currencies, financial crises, disintermediation, bank runs, banking system, money |
| JEL: | E42 E44 E51 E52 G21 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1280 |
| By: | Broadstock, David C.; Fouquet, Roger; Kim, Jeong Won |
| Abstract: | This paper assesses the relationship between carbon prices and the financial value of United Kingdom companies. It shows that the financial market co-moves with the UK-ETS at least as much as it does with other major energy commodities (i.e., oil, gas and electricity prices), and carbon prices are becoming the single most important energy or environmental variable to consider in determining corporate value. The results indicate that 14.1 % of total market capitalisation is exposed to carbon pricing ‘risk’, 20 % or more of the time. The Energy sector has the largest exposure with £251bn (41.51 % of this sector) exposed at least 20 % of the time. This is equivalent to one-twelfth of the economy’s GDP. Within the Energy sector, 13.5 % of all observations indicate net-positive relationship between carbon pricing and stock returns - these are likely to be associated with low carbon energy sources and technologies. The Financial sector is the second most affected sector with £117bn exposed to carbon pricing at least 20 % of the time. Finally, it is shown that information on ‘carbon sensitivity’ can be utilised to construct investment portfolios wherein carbon sensitive stocks under-perform against the market, while carbon insensitive (‘immune’) stocks closely track market benchmarks, depending on investment weighting strategy. |
| Keywords: | empirical asset pricing; emissions trading scheme; carbon prices; energy prices; dynamic model averaging |
| JEL: | J1 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128928 |
| By: | Boris Hofmann; Xiaorui Tang; Feng Zhu |
| Abstract: | This paper examines the sentiments of central banks and the media regarding central bank digital currencies across 15 major global economies. Leveraging large language models, we develop jurisdiction-level central bank digital currency sentiment indices derived from central bank publications and news articles on a daily basis. Our findings reveal significant divergences between central bank and media sentiments, with notable variations over time and across jurisdictions. Analyzing the interplay between these sentiments, we observe that central bank sentiment tends to exert a stronger influence on media sentiment than the reverse. Additionally, we identify substantial cross-border sentiment spillovers, where sentiment in leading economies shapes sentiment in other regions. Through an event study approach, we demonstrate that cryptocurrency and equity markets primarily respond to shifts in central bank sentiments. Specifically, more positive central bank sentiments on central bank digital currency are associated with negative impacts on cryptocurrency market returns and the stock performance of banking and payment-related firms. |
| Keywords: | Central bank digital currency (CBDC), central bank communication, media sentiment, large language model (LLM), financial market |
| JEL: | E58 G12 G18 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1279 |
| By: | Ariston Karagiorgis (Athens University of Economics and Business); Dimitrios Anastasiou (Athens University of Economics and Business - Department of Business Administration); Konstantinos Drakos (Athens University of Economics and Business - Department of Accounting and Finance); Steven Ongena (University of Zurich - Department Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR)) |
| Abstract: | Using an extensive matched hedge fund-prime broker panel dataset for the period 2001-2021, we document a strong positive relationship between hedge fund leverage and prime broker's stock price crash risk after controlling for other crash risk drivers. Our results are not only statistically, but also economically significant, showing that a one-standard-deviation increase in hedge fund leverage is associated on average with an increase of around 5% of a standard deviation in the negative skewness or the down-to-up-volatility of bank stock returns. Moreover, they remain robust when accounting for endogeneity and conducting many robustness checks. We also document that some investment strategies, such as one focusing on fixed income, appear to decrease the slope of the risk metrics of prime brokers, and ultimately leading to lower stock price crash risk. |
| Keywords: | Hedge Funds, Leverage, Prime Broker, Price Crash Risk |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2557 |
| By: | Jorge Braga Ferreira |
| Abstract: | This study evaluates the impact of the ECB’s Corporate Sector Purchase Programme (CSPP) on corporate bond spreads at issuance, as measured by Option-Adjusted Spreads (OAS), and on subsequent changes in firms' capital structures, as proxied by year-on-year changes in the debt ratio. Using a sample of 1, 275 Eurozone corporate bonds issued between 2015:Q1 and 2018:Q4, we estimate a two-stage empirical model to evaluate. In the first stage, we find that the initial association between CSPP eligibility and lower spreads disappears once firm- and bond-level characteristics are controlled for, suggesting that observed differences reflect issuer and instrument features rather than programme eligibility. While the CSPP’s effect does not vary systematically by firm or bond characteristics, the results indicate broader market effects, likely driven by the programme’s signaling power and perceived credibility, which extended beyond the impact of direct bond purchases. In the second stage, we assess changes in leverage following the issuance of bonds. CSPP eligibility did not seem to affect the debt ratio in the issuance year. However, longer-maturity eligible bonds are associated with delayed increases in leverage, as firms expanded their debt ratios in the year following issuance. This pattern suggests that improved financing conditions under the programme may have encouraged firms to raise additional debt at a later stage. |
| Keywords: | ECB, CSPP, unconventional monetary policy, bond yields, corporate capital structure, corporate financing. |
| JEL: | C23 E52 E58 G12 G32 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp03902025 |
| By: | Al Mamoon, Abdullah |
| Abstract: | In this work, we systematically analyse the differences and similarities between CeFi (Centralised Finance) and DeFi (Decentralised Finance). Financial technology is rapidly expanding, and large technology firms are making advances in credit markets. The Internet of Value (IOV), with its distributed ledger technology (DLT) as a basis, has developed new types of loan marketplaces. In this paper, we enumerate the prospects & challenges of Centralised Finance (CeFi) lending markets driven by banks and other lending institutes, as well as the benefits of DeFi lending protocols that may support resolving long-standing concerns in the conventional lending landscape. Overall, fintech and big tech credit appear to complement rather than substitute conventional forms of lending. This study provides a comprehensive analysis of the distinctions between CeFi (Centralised Finance) and DeFi (Decentralised Finance) lending. It analyses several aspects including legal considerations, economic factors, security measures, privacy concerns, and market structure. We conclude our study that convergence between centralised finance (CeFi) and decentralised finance (DeFi) can facilitate synergies in the lending market. |
| Keywords: | Blockchain, Decentralized finance, Centralised Finance, Smart contract |
| JEL: | E5 E51 F30 G23 G32 O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:323253 |