nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒04‒24
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. The Impact of Regulatory Change on Hedge Fund Performance By Fan Yang
  2. A Unified Framework for Fast Large-Scale Portfolio Optimization By Weichuan Deng; Ronakdilip Shah; Pawel Polak; Abolfazl Safikhani
  3. Stock Price Prediction Using Temporal Graph Model with Value Chain Data By Chang Liu; Sandra Paterlini
  4. Taureau: A Stock Market Movement Inference Framework Based on Twitter Sentiment Analysis By Nicholas Milikich; Joshua Johnson
  5. Preferred habitat investors in the green bond market By Martijn Boermans
  6. Asset allocation and risk taking under different interest rate regimes By Hermans, Lieven; Kostka, Thomas; Vassallo, Danilo
  7. Market Manipulation in NFT Markets By Oh, Sebeom
  8. Pricing Transition Risk with a Jump-Diffusion Credit Risk Model: Evidences from the CDS market By Giulia Livieri; Davide Radi; Elia Smaniotto
  9. US Municipal Green Bonds and Financial Integration By Guglielmo Maria Caporale; Nicola Spagnolo
  10. Bankruptcy regime change and credit risk premium on corporate bonds: Evidence from the Indian economy By Rajeswari Sengupta; Harsh Vardhan

  1. By: Fan Yang (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic)
    Abstract: The paper investigates the effect of recent EU regulations on hedge fund performance. The expansion of hedge funds attracts the attention from authorities who are responsible for monitoring the market risks but the influence of the oversight has been argued for a long time. Prior studies usually focus on the US regulations and they provide controversial findings about the relationship of hedge fund performance and regulations. However, to our knowledge, no studies discuss the impact of the EU regulations-the Alternative Investment Fund Manager Directive (AIFMD). The analysis of the AIFMD and the comparison of the rules from the US and the EU show that the EU Directive has more extensive requirements, compliance cost, and wider scope of disclosure to the public. Considering the additional compliance cost and higher possibility to reveal managers´ strategies, we expect the hedge fund performance is negatively influenced by the EU regulation. Based upon the common difference-in-difference method (DID), we utilize the characteristic that the scope of the AIFMD exempts some EU hedge funds and add the third factor to formulate the triple difference method to test the triple interaction relationship. This method allows us to mitigate the different influence from potential various sensitivities or development speed from control group and treatment group. Our results show that hedge funds domiciled or marketed in the EU had a drop of 0.2% in the alpha. The result has been further enhanced by comparing the matched control group and treatment group by using the propensity matching score, which ensures our research compares the changes of individual authorized hedge funds based on the specified authorization date.
    Keywords: hedge fund, regulation, stock returns
    JEL: G23 G28
    Date: 2023–04
  2. By: Weichuan Deng; Ronakdilip Shah; Pawel Polak; Abolfazl Safikhani
    Abstract: We develop a unified framework for fast large-scale portfolio optimization with shrinkage and regularization for different objectives such as minimum variance, mean-variance, and maximum Sharpe ratio with various constraints on the portfolio weights. For all of the optimization problems, we derive the corresponding quadratic programming problems and implement them in an open-source Python library. We use the proposed framework to evaluate the out-of-sample portfolio performance of popular covariance matrix estimators such as sample covariance matrix, linear and nonlinear shrinkage estimators, and the covariance matrix from the instrumented principal component analysis (IPCA). We use 65 years of monthly returns from (on average) 585 largest companies in the US market, and 94 monthly firm-specific characteristics for the IPCA model. We show that the regularization of the portfolio norms greatly benefits the performance of the IPCA model in portfolio optimization, resulting in outperformance linear and nonlinear shrinkage estimators even under realistic constraints on the portfolio weights.
    Date: 2023–03
  3. By: Chang Liu; Sandra Paterlini
    Abstract: Stock price prediction is a crucial element in financial trading as it allows traders to make informed decisions about buying, selling, and holding stocks. Accurate predictions of future stock prices can help traders optimize their trading strategies and maximize their profits. In this paper, we introduce a neural network-based stock return prediction method, the Long Short-Term Memory Graph Convolutional Neural Network (LSTM-GCN) model, which combines the Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) Cells. Specifically, the GCN is used to capture complex topological structures and spatial dependence from value chain data, while the LSTM captures temporal dependence and dynamic changes in stock returns data. We evaluated the LSTM-GCN model on two datasets consisting of constituents of Eurostoxx 600 and S&P 500. Our experiments demonstrate that the LSTM-GCN model can capture additional information from value chain data that are not fully reflected in price data, and the predictions outperform baseline models on both datasets.
    Date: 2023–03
  4. By: Nicholas Milikich; Joshua Johnson
    Abstract: With the advent of fast-paced information dissemination and retrieval, it has become inherently important to resort to automated means of predicting stock market prices. In this paper, we propose Taureau, a framework that leverages Twitter sentiment analysis for predicting stock market movement. The aim of our research is to determine whether Twitter, which is assumed to be representative of the general public, can give insight into the public perception of a particular company and has any correlation to that company's stock price movement. We intend to utilize this correlation to predict stock price movement. We first utilize Tweepy and getOldTweets to obtain historical tweets indicating public opinions for a set of top companies during periods of major events. We filter and label the tweets using standard programming libraries. We then vectorize and generate word embedding from the obtained tweets. Afterward, we leverage TextBlob, a state-of-the-art sentiment analytics engine, to assess and quantify the users' moods based on the tweets. Next, we correlate the temporal dimensions of the obtained sentiment scores with monthly stock price movement data. Finally, we design and evaluate a predictive model to forecast stock price movement from lagged sentiment scores. We evaluate our framework using actual stock price movement data to assess its ability to predict movement direction.
    Date: 2023–03
  5. By: Martijn Boermans
    Abstract: In recent years, the green bond market has seen significant growth as a means of financing environmentally-friendly projects. However, while much research has focused on pricing, little attention has been given to the investors who hold these bonds. This paper uses a preferred habitat framework to analyze the preferences of European investors for green bonds. By analyzing a confidential dataset of portfolio holdings from 2016-Q4 to 2022-Q4, the study finds that European investors, particularly mutual funds and pension funds, show a high demand for green bonds. In contrast, insurance corporations and households tend to avoid green bonds. The research also suggests that the demand for green bonds among mutual funds and pension funds is price inelastic, while banks and insurance corporations display an elastic demand. The findings highlight the presence of a preferred habitat for green bonds among European mutual funds and pension funds. These findings are robust for potential endogeneity concerns when we apply matching techniques, are stronger for domestic green bonds, and also apply to sustainability-linked bonds.
    Keywords: green bonds; preferred habitat; institutional investors; securities holdings statistics; greenium; climate change; environmental impact; sustainability-linked bonds; portfolio holdings
    JEL: G11 G15 G23 Q54 Q56
    Date: 2023–04
  6. By: Hermans, Lieven; Kostka, Thomas; Vassallo, Danilo
    Abstract: We study the effects of low short-term interest rates on the optimal portfolio allocation in Markowitz portfolios and Risk parity portfolios. We propose a measure of Portfolio Instabil-ity, gauging the amount of optimal portfolio shifts needed to respond to exogenous shocks to the expected risk and return of the risky portfolio assets. Portfolio Instability, i.e. the selling pressure on riskier asset holdings, is found to be stronger the lower the risk-free interest rate. Heightened portfolio instability in the presence of low rates is found to emerge through two channels both of which incentivise the build-up of large and leveraged risky asset shares during calm periods which need to be unwound in the event of higher market volatility: first, low rates (mechanically) augment the excess return to be gained by investing in riskier assets and second, they are found to dampen volatility of riskier assets in the portfolio. The inverse relationship between portfolio instability and the risk-free rates is found to increase the closer the risk-free rate approaches the effective lower bound. Counterfactual analyses of the behaviour of optimal multi-asset portfolios demonstrate that the sell-off in riskier asset classes during the Covid crisis in March 2020 was more severe than would have been in the presence of higher short-term interest rates. JEL Classification: C58, E52, G11, G12
    Keywords: CAPM, Counterfactual analysis, portfolio optimization
    Date: 2023–03
  7. By: Oh, Sebeom
    Abstract: Non-Fungible Tokens (NFTs) are a new form of digital asset used for fundraising purposes, similar to equity crowdfunding, but within an unregulated environment. The NFT market has been described as an unregulated and prone to misconduct, but there is a lack of detailed analysis on such behaviors. This paper examines the use of manipulative trading, specifically unrevealed insider trading and wash trading, within the NFT market using publicly available transaction data on the Ethereum blockchain. The results show that insiders buying behavior strongly predicts higher future price returns. Even if the circulated USD amount in wash trades is more than 422 million, wash trades fails to impact meaningful market outcomes. I find that some investors engage in wash trading to earn rewards from NFT marketplaces or promote emerging marketplaces in competition with the dominant platform.
    Keywords: Blockchain; NFT; Manipulative Trading; Insider Trading; Wash Trading
    JEL: G14 G28
    Date: 2023–03–22
  8. By: Giulia Livieri; Davide Radi; Elia Smaniotto
    Abstract: Transition risk can be defined as the business-risk related to the enactment of green policies, aimed at driving the society towards a sustainable and low-carbon economy. In particular, the value of certain firms' assets can be lower because they need to transition to a less carbon-intensive business model. In this paper we derive formulas for the pricing of defaultable coupon bonds and Credit Default Swaps to empirically demonstrate that a jump-diffusion credit risk model in which the downward jumps in the firm value are due to tighter green laws can capture, at least partially, the transition risk. The empirical investigation consists in the model calibration on the CDS term-structure, performing a quantile regression to assess the relationship between implied prices and a proxy of the transition risk. Additionally, we show that a model without jumps lacks this property, confirming the jump-like nature of the transition risk.
    Date: 2023–03
  9. By: Guglielmo Maria Caporale; Nicola Spagnolo
    Abstract: This paper examines mean and volatility spillovers between four green municipal bonds issued by the US states of California, Colorado, Columbia and Ohio, and the role played by the recent Covid-19 pandemic and the COP policy announcements respectively. Specifically, four-variate VAR-GARCH-BEKK models are estimated which include suitably defined dummies corresponding to those events. Significant dynamic linkages (interdependence) between the four municipal bonds under investigation are found in some cases. Moreover, there is evidence of shifts in the second moment parameters coinciding with the Covid-19 pandemic (contagion), whilst the COP policy announcements do not appear to affect the transmission mechanism between municipal green bond returns and volatilities. On the whole, the evidence suggests weaker linkages, and thus a lower degree of financial integration (and greater portfolio diversification opportunities), during the Covid-19 period, though this is likely to be only a temporary phenomenon.
    Keywords: municipal bonds, financial integration, spillovers, multivariate GARCH-BEKK, volatility
    JEL: C32 G12 G32
    Date: 2023
  10. By: Rajeswari Sengupta (Indira Gandhi Institute of Development Research); Harsh Vardhan
    Abstract: Enactment of the Insolvency and Bankruptcy Code (IBC) in 2016 marked a watershed event in the commercial credit landscape in India, and represented a major enhancement in the rights of creditors. In this paper we hypothesise that in the new regime, creditors would demand a lower price for credit now that the IBC has strengthened their rights in the event of a borrower defaulting. We focus on one class of creditors--investors in the bond market. We consider IBC as a quasi-natural experiment and empirically investigate its impact on credit spreads in the corporate bond market in India. We find that post IBC, credit spreads declined for the non-financial firms in the private corporate sector. However, even for these firms, bond investors seem to assign greater importance to firm-specific characteristics such as firm size and firm financial health compared to the impact of the new bankruptcy regime. It is plausible that a few years after IBC was implemented, the general discontentment in the financial markets regarding the effectiveness of the bankruptcy law may have dampened the effect on credit spreads. Ours is the first study to analyse the influence of the IBC on the cost of credit in the bond market. Currently, the bond market in India is skewed towards high rated bonds which account for the bulk of all issuances. In order to develop a deep and liquid market for lower rated bonds, investor confidence in effective bankruptcy resolution will be crucial. This study provides us with valuable insights about the reaction of the bond investors to the IBC.
    Keywords: Bond pricing, Credit spreads, Bankruptcy law, Creditor rights, Credit rating, Maturity, Liquidity, Risk perception
    JEL: G12 G32 G34
    Date: 2023–02

This nep-fmk issue is ©2023 by Kwang Soo Cheong. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.