nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒01‒16
twelve papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period By Weilin Fu; Zhuoran Li; Yupeng Zhang; Xingyou Zhou
  2. The Disappearing Index Effect By Robin Greenwood; Marco C. Sammon
  3. Nostradamus: Weathering Worth By Alapan Chaudhuri; Zeeshan Ahmed; Ashwin Rao; Shivansh Subramanian; Shreyas Pradhan; Abhishek Mittal
  4. Cryptocurrencies and Decentralised Finance By Igor Makarov; Antoinette Schoar
  5. The performance of corporate bond mutual funds and the allocation of underpriced new issues By Cici, Gjergji; Gibson, Scott; Qin, Nan; Zhang, Alex
  6. The Strategic Allocation to Style-Integrated Portfolios of Commodity Futures By Hossein Rad; Rand Kwong Yew Low; Joelle Miffre; Robert Faff
  7. CDO calibration via Magnus Expansion and Deep Learning By Marco Di Francesco; Kevin Kamm
  8. Benchmarking Machine Learning Models to Predict Corporate Bankruptcy By Emmanuel Alanis; Sudheer Chava; Agam Shah
  9. Open banking and customer data sharing: Implications for FinTech borrowers By Nam, Rachel J.
  10. Design interpretable experience of dynamical feed forward machine learning model for forecasting NASDAQ By Pouriya Khalilian; Sara Azizi; Mohammad Hossein Amiri; Javad T. Firouzjaee
  11. Physical and transition risk premiums in euro area corporate bond markets By Joost Bats; Giovanna Bua; Daniel Kapp
  12. Drivers and effects of stock market fragmentation - Insights on SME stocks By Lausen, Jens; Clapham, Benjamin; Gomber, Peter; Bender, Micha

  1. By: Weilin Fu; Zhuoran Li; Yupeng Zhang; Xingyou Zhou
    Abstract: Every financial crisis has caused a dual shock to the global economy. The shortage of market liquidity, such as default in debt and bonds, has led to the spread of bankruptcies, such as Lehman Brothers in 2008. Using the data for the ETFs of the S&P 500, Nasdaq 100, and Dow Jones Industrial Average collected from Yahoo Finance, this study implemented Deep Learning, Neuro Network, and Time-series to analyze the trend of the American Stock Market in the post-COVID-19 period. LSTM model in Neuro Network to predict the future trend, which suggests the US stock market keeps falling for the post-COVID-19 period. This study reveals a reasonable allocation method of Long Short-Term Memory for which there is strong evidence.
    Date: 2022–12
  2. By: Robin Greenwood; Marco C. Sammon
    Abstract: The abnormal return associated with a stock being added to the S&P 500 has fallen from an average of 3.4% in the 1980s and 7.6% in the 1990s to 0.8% over the past decade. This has occurred despite a significant increase in the percentage of stock market assets linked to the index. A similar pattern has occurred for index deletions, with large negative abnormal returns on average during the 1980s and 1990s, but only -0.6% between 2010 and 2020. We investigate potential drivers of this surprising phenomenon and discuss the implications for market efficiency.
    JEL: G1 G10 G14 G4
    Date: 2022–12
  3. By: Alapan Chaudhuri; Zeeshan Ahmed; Ashwin Rao; Shivansh Subramanian; Shreyas Pradhan; Abhishek Mittal
    Abstract: Nostradamus, inspired by the French astrologer and reputed seer, is a detailed study exploring relations between environmental factors and changes in the stock market. In this paper, we analyze associative correlation and causation between environmental elements and stock prices based on the US financial market, global climate trends, and daily weather records to demonstrate significant relationships between climate and stock price fluctuation. Our analysis covers short and long-term rises and dips in company stock performances. Lastly, we take four natural disasters as a case study to observe their effect on the emotional state of people and their influence on the stock market.
    Date: 2022–12
  4. By: Igor Makarov; Antoinette Schoar
    Abstract: The paper provides an overview of cryptocurrencies and decentralized finance. The discussion lays out potential benefits and challenges of the new system and presents a comparison to the traditional system of financial intermediation. Our analysis highlights that while the DeFi architecture might have the potential to reduce transaction costs, similar to the traditional financial system, there are several layers where rents can accumulate due to endogenous constraints to competition. We show that the permissionless and pseudonymous design of DeFi generates challenges for enforcing tax compliance, anti-money laundering laws, and preventing financial malfeasance. We highlight ways to regulate the DeFi system which would preserve a majority of benefits of the underlying blockchain architecture but support accountability and regulatory compliance.
    Keywords: Decentralized finance, blockchain technology, financial intermediation, system risk
    JEL: G12 G15 F38
    Date: 2022–12
  5. By: Cici, Gjergji; Gibson, Scott; Qin, Nan; Zhang, Alex
    Abstract: Using a novel return-based method to detect allocations of corporate bond offerings, which are underpriced on average, we find that mutual funds most active in the primary market generate significant alpha and outperform those that are less active. Our evidence suggests that underwriters direct underpriced allocations repeatedly to fund families with which they have stronger underwriting relationships. Consistent with the concave performance-flow relationship that describes bond fund investors' behavior, families maximize profitability by strategically distributing allocations to member funds that underperformed their style benchmark over the last year at the expense of those that outperformed.
    Date: 2022
  6. By: Hossein Rad (University of Queensland [Brisbane]); Rand Kwong Yew Low (Bond University [Gold Coast]); Joelle Miffre (Audencia Business School); Robert Faff (Bond University [Gold Coast])
    Abstract: Our study lies at the intersection of the literature on the diversification benefits of commodity futures and the literature on style integration. It augments the traditional asset mix of investors with a long-short portfolio that integrates the styles that matter to the pricing of commodity futures. Treating the style-integrated portfolio of commodities as part of the strategic mix of investors is found to enhance out-of-sample performance and reduce crash risk compared to the alternatives considered thus far. The conclusion holds across traditional asset mix, portfolio allocation methods, integration strategies, and sub-periods. The diversification benefits of style integration also persist, albeit lower, in a long-only setting.
    Keywords: Commodity futures, Style integration, Strategic asset allocation, Diversification
    Date: 2022–12
  7. By: Marco Di Francesco; Kevin Kamm
    Abstract: In this paper, we improve the performance of the large basket approximation developed by Reisinger et al. to calibrate Collateralized Debt Obligations (CDO) to iTraxx market data. The iTraxx tranches and index are computed using a basket of size $K= 125$. In the context of the large basket approximation, it is assumed that this is sufficiently large to approximate it by a limit SPDE describing the portfolio loss of a basket with size $K\rightarrow \infty$. For the resulting SPDE, we show four different numerical methods and demonstrate how the Magnus expansion can be applied to efficiently solve the large basket SPDE with high accuracy. Moreover, we will calibrate a structural model to the available market data. For this, it is important to efficiently infer the so-called initial distances to default from the Credit Default Swap (CDS) quotes of the constituents of the iTraxx for the large basket approximation. We will show how Deep Learning techniques can help us to improve the performance of this step significantly. We will see in the end a good fit to the market data and develop a highly parallelizable numerical scheme using GPU and multithreading techniques.
    Date: 2022–12
  8. By: Emmanuel Alanis; Sudheer Chava; Agam Shah
    Abstract: Using a comprehensive sample of 2, 585 bankruptcies from 1990 to 2019, we benchmark the performance of various machine learning models in predicting financial distress of publicly traded U.S. firms. We find that gradient boosted trees outperform other models in one-year-ahead forecasts. Variable permutation tests show that excess stock returns, idiosyncratic risk, and relative size are the more important variables for predictions. Textual features derived from corporate filings do not improve performance materially. In a credit competition model that accounts for the asymmetric cost of default misclassification, the survival random forest is able to capture large dollar profits.
    Date: 2022–12
  9. By: Nam, Rachel J.
    Abstract: With open banking, consumers take greater control over their own financial data and share it at their discretion. Using a rich set of loan application data from the largest German FinTech lender in consumer credit, this paper studies what characterizes borrowers who share data and assesses its impact on loan application outcomes. I show that riskier borrowers share data more readily, which subsequently leads to an increase in the probability of loan approval and a reduction in interest rates. The effects hold across all credit risk profiles but are the most pronounced for borrowers with lower credit scores (a higher increase in loan approval rate) and higher credit scores (a larger reduction in interest rate). I also find that standard variables used in credit scoring explain substantially less variation in loan application outcomes when customers share data. Overall, these findings suggest that open banking improves financial inclusion, and also provide policy implications for regulators engaged in the adoption or extension of open banking policies.
    Keywords: Open banking,FinTech,Marketplace lending,P2P lending,Big data,Customer data sharing,Data access,Data portability,Digital footprints
    Date: 2022
  10. By: Pouriya Khalilian; Sara Azizi; Mohammad Hossein Amiri; Javad T. Firouzjaee
    Abstract: National Association of Securities Dealers Automated Quotations(NASDAQ) is an American stock exchange based. It is one of the most valuable stock economic indices in the world and is located in New York City \cite{pagano2008quality}. The volatility of the stock market and the influence of economic indicators such as crude oil, gold, and the dollar in the stock market, and NASDAQ shares are also affected and have a volatile and chaotic nature \cite{firouzjaee2022lstm}.In this article, we have examined the effect of oil, dollar, gold, and the volatility of the stock market in the economic market, and then we have also examined the effect of these indicators on NASDAQ stocks. Then we started to analyze the impact of the feedback on the past prices of NASDAQ stocks and its impact on the current price. Using PCA and Linear Regression algorithm, we have designed an optimal dynamic learning experience for modeling these stocks. The results obtained from the quantitative analysis are consistent with the results of the qualitative analysis of economic studies, and the modeling done with the optimal dynamic experience of machine learning justifies the current price of NASDAQ shares.
    Date: 2022–12
  11. By: Joost Bats; Giovanna Bua; Daniel Kapp
    Abstract: We study climate risk premiums in euro area corporate bond markets. As gauges of climate risk, we distinguish between physical and transition risks using textual analysis. Our findings show that, since the Paris agreement, physical risk is significantly priced in corporate bonds with longer-term maturities. Physical risk is also priced in bonds with shorter-term maturities, but the premium is smaller and less significant. The estimated physical risk premium reflects investors demanding higher future returns on bonds that underperform during adverse physical risk shocks. Our findings also point to a sizable transition risk premium, although the transition risk estimates are insignificant.
    Keywords: Climate risk; physical risk; transition risk; corporate bonds
    JEL: G12 Q51 Q54
    Date: 2023–01
  12. By: Lausen, Jens; Clapham, Benjamin; Gomber, Peter; Bender, Micha
    Abstract: We analyze how market fragmentation affects market quality of SME and other less actively traded stocks. Compared to large stocks, they are less likely to be traded on multiple venues and show, if at all, low levels of fragmentation. Concerning the impact of fragmentation on market quality, we find evidence for a hockey stick effect: Fragmentation has no effect for infrequently traded stocks, a negative effect on liquidity of slightly more active stocks, and increasing benefits for liquidity of large and actively traded stocks. Consequently, being traded on multiple venues is not necessarily harmful for SME stock market quality.
    Keywords: Market Microstructure,Market Fragmentation,Securities Market Regulation,Market Quality,SME Trading
    JEL: G10 G14
    Date: 2022

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