nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒10‒02
eleven papers chosen by

  1. Integrated Intermediation and Fintech Market Power By Greg Buchak; Vera Chau; Adam Jørring
  2. Recent Developments in Hedge Funds’ Treasury Futures and Repo Positions: is the Basis Trade “Back"? By Daniel Barth; R. Jay Kahn; Robert Mann
  3. Insurers’ investment behaviour and the coronavirus (COVID-19) pandemic By Ghiselli, Angelica; Fay, Constanze
  4. An Empirical Analysis on Financial Market: Insights from the Application of Statistical Physics By Haochen Li; Yi Cao; Maria Polukarov; Carmine Ventre
  5. Do investors overvalue startups? Evidence from the junior stakes of mutual funds By Agarwal, Vikas; Barber, Brad M.; Cheng, Si; Hameed, Allaudeen; Shanker, Harshini; Yasuda, Ayako
  6. Do Mutual Funds Greenwash? Evidence from Fund Name Changes By Alexander Cochardt; Stephan Heller; Vitaly Orlov
  7. Default Clustering Risk Premium and its Cross-Market Asset Pricing Implications By Kiwoong Byun; Baeho Kim; Dong Hwan Oh
  8. Green risk in Europe By Nuno Cassola; Claudio Morana; Elisa Ossola
  9. Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing By Ali Asgarov
  10. Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance By Lefteris Loukas; Ilias Stogiannidis; Prodromos Malakasiotis; Stavros Vassos
  11. Analysis of Optimal Portfolio Management Using Hierarchical Clustering By Kapil Panda

  1. By: Greg Buchak (Stanford University); Vera Chau (University of Geneva; Swiss Finance Institute); Adam Jørring (Boston College)
    Abstract: We document that in the US residential mortgage market, the share of integrated intermediaries acting as both originator and servicer has declined dramatically. Exploiting a regulatory change, we show that borrowers with integrated servicers are more likely to refinance, and conditional on refinance, are more likely to be recaptured by their own servicer. Recaptured borrowers pay lower fees relative to other refinancers. This trend is partially offset by a rise in integrated fintech originator-servicers, who recapture at higher frequency but at worse terms. We build and calibrate a dynamic structural model to interpret these facts and quantify their impact on equilibrium outcomes. Our model suggests that integreated intermediaries enjoy a marginal cost advantage when refinancing recaptured borrowers, and fully disintegrating them would reduce refinancing frequencies and increase fees. Fintechs use technology to reacquire customers and reduce borrower inertia against refinancing. This endogenously creates market power, which fintechs exploit through higher fees. Despite worse terms ex-post, fintechs increase consumer welfare ex-ante by increasing refinancing frequencies. Taken together, our results highlight the importance of intermediaries’ scope in consumer financial outcomes and highlight a novel, quantitatively important application of fintech: customer acquisition.
    Keywords: Financial intermediation, disintermediation, mortgage servicing, refinancing, fintech
    JEL: G21 G23 E44 L12 L42 O16 O33
    Date: 2023–08
  2. By: Daniel Barth; R. Jay Kahn; Robert Mann
    Abstract: In short, the answer is "probably", at least to some degree. This note summarizes recent developments in hedge funds' Treasury futures and repo positions derived from the Commodities Futures and Trading Commission's (CFTC's) Traders in Financial Futures data and the Office of Financial Research's ("OFR") Cleared Repo Collection.
    Date: 2023–08–30
  3. By: Ghiselli, Angelica; Fay, Constanze
    Abstract: This research explores two aspects of European insurers’ investment behaviour related to crises. While they are often considered as financial market stabilisers and long-term investors, there is currently a lack of knowledge about insurers’ investment behaviour in crises under the regulatory Solvency II regime implemented in 2016. With assets of nearly €9 trillion and bond holdings of more than €3 trillion in Q2 2022, European insurers are important financial intermediaries and finance European economies. With an empirical study, we investigate their reaction to the asset price shock at the onset of the coronavirus (COVID-19) pandemic in the first quarter of 2020 and explore cyclical investment behaviour by replicating Timmer’s (2018) study with fixed effects panel regressions. We use a large cross-country dataset, with the novelty of exploiting cross-country heterogeneity for European countries with 458, 758 security-level observations from 2017 to 2022. Overall, our findings are very relevant from a policy perspective as they suggest active and heterogeneous cyclical investment behaviour in the European insurance market with differences across issuer and holder countries of domicile.
    Keywords: Cyclicality, Debt Capital Flows, Financial Stability, Insurance companies, Pandemic, Portfolio Allocation
    Date: 2023–09
  4. By: Haochen Li; Yi Cao; Maria Polukarov; Carmine Ventre
    Abstract: In this study, we introduce a physical model inspired by statistical physics for predicting price volatility and expected returns by leveraging Level 3 order book data. By drawing parallels between orders in the limit order book and particles in a physical system, we establish unique measures for the system's kinetic energy and momentum as a way to comprehend and evaluate the state of limit order book. Our model goes beyond examining merely the top layers of the order book by introducing the concept of 'active depth', a computationally-efficient approach for identifying order book levels that have impact on price dynamics. We empirically demonstrate that our model outperforms the benchmarks of traditional approaches and machine learning algorithm. Our model provides a nuanced comprehension of market microstructure and produces more accurate forecasts on volatility and expected returns. By incorporating principles of statistical physics, this research offers valuable insights on understanding the behaviours of market participants and order book dynamics.
    Date: 2023–08
  5. By: Agarwal, Vikas; Barber, Brad M.; Cheng, Si; Hameed, Allaudeen; Shanker, Harshini; Yasuda, Ayako
    Abstract: We show that mutual funds report their junior stakes in startups at 43% higher valuation than model fair values that consider multi-tier capital structures of startups. The latest-issued and most senior security is worth 48% per share than junior securities held by mutual funds, implying that mutual funds mark junior securities close to par with the senior securities. Our findings are robust to model assumptions. Identical valuations reported for dual holdings of senior and junior securities imply 37% discrepancy in implied values of the firm. Overvaluation is lower for fund families with longer experience in private startup investments, and higher for junior securities purchased in secondary transactions. Overvaluation declines after down rounds (new financing rounds with purchase prices lower than previous rounds) and near IPOs. The results are consistent with mutual funds neglecting the probability of negative outcomes in which junior securities are paid less than senior securities and overweighting successful exits where all securities convert to common equity and are valued equally.
    Keywords: Startup valuation, Mutual funds, Venture capital, Fair value, Private valuation
    JEL: G23 G24 G28 G32
    Date: 2023
  6. By: Alexander Cochardt (University of St. Gallen); Stephan Heller (University of St. Gallen); Vitaly Orlov (University of St. Gallen; Swiss Finance Institute)
    Abstract: This paper investigates whether mutual funds that introduce sustainability-related buzzwords in their names actually shift their focus to sustainable investing following the name change. Relatively less successful funds tend to engage in such rebrandings to regain investor flows. Following the name change, funds improve their portfolio sustainability scores by imposing negative screens on poor-sustainability-performing firms. However, we find no evidence that such funds exert any commitment to improve firms’ sustainability practices through voting on environmental, social or governance proposals. The commitment to sustainability is even less present when their votes are more likely to be pivotal, consistent with greenwashing.
    Keywords: Mutual Funds, ESG, Greenwashing, Voting
    JEL: G11 G41
    Date: 2023–08
  7. By: Kiwoong Byun; Baeho Kim; Dong Hwan Oh
    Abstract: This study examines the market-implied premiums for bearing default clustering risk by analyzing credit derivatives contracts on the CDX North American Investment Grade (CDX.NA.IG) portfolio between September 2005 and March 2021. Our approach involves constructing a time series of reference tranche rates exclusively derived by single-name CDS spreads. The default clustering risk premium (DCRP) is captured by comparing the original and reference tranche spreads, with the former exceeding the latter when investors require greater compensation for correlated defaults at the portfolio level. The fitted DCRP level significantly increased in response to the 2007-9 global financial crisis and remained relatively stable for a period, followed by a gradual decline beginning in 2016. Notably, the COVID-19 shock caused another sharp rise in the DCRP level. Our empirical analysis finds that the estimated DCRP has significant implications for asset pricing, particularly in affecting the investment opportunities available to U.S. stock investors during times of instability in the financial system.
    Keywords: Credit Default Swap (CDS); CDS Index (CDX); Reference Tranche Rate; Default Clustering Risk Premium
    JEL: G10 C60 C40
    Date: 2023–08–18
  8. By: Nuno Cassola; Claudio Morana; Elisa Ossola
    Abstract: Climate change poses serious economic, financial, and social challenges to humanity, and green transition policies are now actively implemented in many industrialized countries. Whether financial markets price climate risks is critical to ensuring that the necessary funding flows into environmentally sound projects and that stranded assets risk is adequately managed. In this paper, we assess climate risks for the European stock market within the context of Alessi et al. (2023) greenness and transparency factor. We show that measures of returns spreads of green vs. brown investment might reflect climate risks and assets' exposition to systematic macro-financial risk factors. These latter factors should be filtered out to measure climate risks accurately. We show that climate risks are priced in the European stock market by focusing on aggregate, industry, and company-level data. We propose a market-based green rating procedure, which might be of particular interest to evaluate non-transparent and non-disclosing companies for which ESG information is unavailable. We illustrate its implementation using a sample of over 800 non-transparent firms.
    Keywords: Climate risk, environmental disclosure, macro-finance interface, unconditional factor models, asset pricing, European Union.
    JEL: G01 G11 G12 Q54
    Date: 2023–09
  9. By: Ali Asgarov
    Abstract: Forecasting financial market trends through time series analysis and natural language processing poses a complex and demanding undertaking, owing to the numerous variables that can influence stock prices. These variables encompass a spectrum of economic and political occurrences, as well as prevailing public attitudes. Recent research has indicated that the expression of public sentiments on social media platforms such as Twitter may have a noteworthy impact on the determination of stock prices. The objective of this study was to assess the viability of Twitter sentiments as a tool for predicting stock prices of major corporations such as Tesla, Apple. Our study has revealed a robust association between the emotions conveyed in tweets and fluctuations in stock prices. Our findings indicate that positivity, negativity, and subjectivity are the primary determinants of fluctuations in stock prices. The data was analyzed utilizing the Long-Short Term Memory neural network (LSTM) model, which is currently recognized as the leading methodology for predicting stock prices by incorporating Twitter sentiments and historical stock prices data. The models utilized in our study demonstrated a high degree of reliability and yielded precise outcomes for the designated corporations. In summary, this research emphasizes the significance of incorporating public opinions into the prediction of stock prices. The application of Time Series Analysis and Natural Language Processing methodologies can yield significant scientific findings regarding financial market patterns, thereby facilitating informed decision-making among investors. The results of our study indicate that the utilization of Twitter sentiments can serve as a potent instrument for forecasting stock prices, and ought to be factored in when formulating investment strategies.
    Date: 2023–08
  10. By: Lefteris Loukas; Ilias Stogiannidis; Prodromos Malakasiotis; Stavros Vassos
    Abstract: We propose the use of conversational GPT models for easy and quick few-shot text classification in the financial domain using the Banking77 dataset. Our approach involves in-context learning with GPT-3.5 and GPT-4, which minimizes the technical expertise required and eliminates the need for expensive GPU computing while yielding quick and accurate results. Additionally, we fine-tune other pre-trained, masked language models with SetFit, a recent contrastive learning technique, to achieve state-of-the-art results both in full-data and few-shot settings. Our findings show that querying GPT-3.5 and GPT-4 can outperform fine-tuned, non-generative models even with fewer examples. However, subscription fees associated with these solutions may be considered costly for small organizations. Lastly, we find that generative models perform better on the given task when shown representative samples selected by a human expert rather than when shown random ones. We conclude that a) our proposed methods offer a practical solution for few-shot tasks in datasets with limited label availability, and b) our state-of-the-art results can inspire future work in the area.
    Date: 2023–08
  11. By: Kapil Panda
    Abstract: Portfolio optimization is a task that investors use to determine the best allocations for their investments, and fund managers implement computational models to help guide their decisions. While one of the most common portfolio optimization models in the industry is the Markowitz Model, practitioners recognize limitations in its framework that lead to suboptimal out-of-sample performance and unrealistic allocations. In this study, I refine the Markowitz Model by incorporating machine learning to improve portfolio performance. By using a hierarchical clustering-based approach, I am able to enhance portfolio performance on a risk-adjusted basis compared to the Markowitz Model, across various market factors.
    Date: 2023–08

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