nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒08‒28
thirteen papers chosen by
Kwang Soo Cheong, Johns Hopkins University


  1. Financial Machine Learning By Bryan T. Kelly; Dacheng Xiu
  2. Are Acquirer Shareholders Happier when Their Industries Are Unhappy? By Jana P. Fidrmuc; Tereza Tykvova
  3. Green Stocks and the 2023 Banking Crisis By Francesco D'Ercole; Alexander F. Wagner
  4. Modelling Sustainable Investing in the CAPM By Thorsten Hens; Ester Trutwin
  5. VolTS: A Volatility-based Trading System to forecast Stock Markets Trend using Statistics and Machine Learning By Ivan Letteri
  6. Selection-Neglect in the NFT Bubble By Dong Huang; William N. Goetzmann
  7. Optimal Markowitz Portfolio Using Returns Forecasted with Time Series and Machine Learning Models By Damian Ślusarczyk; Robert Ślepaczuk
  8. Reinforcement Learning for Credit Index Option Hedging By Francesco Mandelli; Marco Pinciroli; Michele Trapletti; Edoardo Vittori
  9. Dealer Capacity and U.S. Treasury Market Functionality By Darrell Duffie; Michael J. Fleming; Frank M. Keane; Claire Nelson; Or Shachar; Peter Van Tassel
  10. Contrasting the efficiency of stock price prediction models using various types of LSTM models aided with sentiment analysis By Varun Sangwan; Vishesh Kumar Singh; Bibin Christopher V
  11. The Impact of Monetary Policy on the U.S. Stock Market since the Pandemic By Willem THORBECKE
  12. Do hedge funds support liquidity in the Government of Canada bond market? By Jabir Sandhu; Rishi Vala
  13. The pricing of climate transition risk in Europe’s equity market By Philippe Loyson; Rianne Luijendijk; Sweder van Wijnbergen

  1. By: Bryan T. Kelly; Dacheng Xiu
    Abstract: We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.
    JEL: C33 C4 C45 C55 C58 G1 G10 G11 G12 G17
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:31502&r=fmk
  2. By: Jana P. Fidrmuc (University of Warwick); Tereza Tykvova (University of St. Gallen; Swiss Finance Institute)
    Abstract: Many mergers destroy shareholder value because managers intentionally waste corporate resources to pursue private benefits. Using textual analysis, we link industry conditions as reflected in acquirer peers' 10-K statements to acquirer announcement abnormal returns. We find that more negative industry conditions are associated with higher acquirer abnormal returns. Our results suggest that difficult times impose discipline on managers who then tend to focus on deals that create value for acquirer shareholders.
    Keywords: mergers and acquisitions, corporate investment decisions, industry situation, acquirer abnormal returns
    JEL: G34 G41
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp2352&r=fmk
  3. By: Francesco D'Ercole (LUM University); Alexander F. Wagner (University of Zurich ; Swiss Finance Institute; CEPR; and ECGI)
    Abstract: In prior financial and economic crises such as the Global Financial Crisis and COVID-19, environmentally responsible stocks performed well or at least neutrally. Were they also resilient as another banking crisis began unfolding with the collapse of Silicon Valley Bank (SVB) and Signature Bank? Or did they suffer because of the important role that these and other regional banks play for the clean tech sector? We find that stocks with more opportunities in the transition to a low-carbon economy performed worse in the 2023 crisis. Investors favored firms with low debt. Overall, the market appears to anticipate that the (regional) banking sector stress will curtail climate tech development.
    Keywords: Bank failure, Clean tech, ESG, Event study, Financial crisis, Silicon Valley Bank
    JEL: G12 G30 Q57
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp2358&r=fmk
  4. By: Thorsten Hens (University of Zurich; University of Lucerne; Norwegian School of Economics; and Swiss Finance Institute); Ester Trutwin (University of Zurich)
    Abstract: Empirical studies investigate various causes and effects of sustainable investments. While some attempts have been made to describe the results found by theoretical models, these are relatively complex and idiosyncratic. We relate to existing studies and use a parsimonious CAPM in which we model various aspects of sustainable investing. Our results find evidence that ESG-harmful investments require higher returns and ESG rating heterogeneity increases returns. Moreover, sustainable investing changes a firm’s production decision through two channels - the growth and the reform channel.
    Keywords: Sustainable Investing, ESG rating, CAPM, Growth Channel, Reform Channel
    JEL: G11 G12 G30
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp2356&r=fmk
  5. By: Ivan Letteri
    Abstract: Volatility-based trading strategies have attracted a lot of attention in financial markets due to their ability to capture opportunities for profit from market dynamics. In this article, we propose a new volatility-based trading strategy that combines statistical analysis with machine learning techniques to forecast stock markets trend. The method consists of several steps including, data exploration, correlation and autocorrelation analysis, technical indicator use, application of hypothesis tests and statistical models, and use of variable selection algorithms. In particular, we use the k-means++ clustering algorithm to group the mean volatility of the nine largest stocks in the NYSE and NasdaqGS markets. The resulting clusters are the basis for identifying relationships between stocks based on their volatility behaviour. Next, we use the Granger Causality Test on the clustered dataset with mid-volatility to determine the predictive power of a stock over another stock. By identifying stocks with strong predictive relationships, we establish a trading strategy in which the stock acting as a reliable predictor becomes a trend indicator to determine the buy, sell, and hold of target stock trades. Through extensive backtesting and performance evaluation, we find the reliability and robustness of our volatility-based trading strategy. The results suggest that our approach effectively captures profitable trading opportunities by leveraging the predictive power of volatility clusters, and Granger causality relationships between stocks. The proposed strategy offers valuable insights and practical implications to investors and market participants who seek to improve their trading decisions and capitalize on market trends. It provides valuable insights and practical implications for market participants looking to.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2307.13422&r=fmk
  6. By: Dong Huang; William N. Goetzmann
    Abstract: Using transaction data from a large non-fungible token (NFT) trading platform, this paper examines how the behavioral bias of selection-neglect interacts with extrapolative beliefs, accelerating the boom and delaying the crash in the recent NFT bubble. We show that the price-volume relationship is consistent with extrapolative beliefs about increasing prices which were plausibly triggered by a macroeconomic shock. We test the hypothesis that agents prone to selection-neglect formed even more optimistic beliefs and traded more aggressively than their counterparts during the boom. When liquidity for NFTs declined, observed NFT prices were subject to severe selection bias due in part to seller loss aversion delaying the onset of the crash. Finally, we show that market participants with sophisticated bidding behavior were less subject to selection bias and performed better.
    JEL: G1 G12 G14 G4 G40 G41
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:31498&r=fmk
  7. By: Damian Ślusarczyk (University of Warsaw, Faculty of Economic Sciences); Robert Ślepaczuk (University of Warsaw, Quantitative Finance Research Group, Department of Quantitative Finance, Faculty of Economic Sciences)
    Abstract: We aim to answer the question of whether using forecasted stock returns based on machine learning and time series models in a mean-variance portfolio framework yields better results than relying on historical returns. Nevertheless, the problem of the efficient stock selection has been tested for more than 50 years, the issue of adequate construction of mean-variance portfolio framework and incorporating forecasts of returns in it has not been solved yet. Stock returns portfolios were created using ’raw’ historical returns and forecasted return based on ARIMA-GARCH and the XGBoost models. Two optimization problems were concerned: global maximum information ratio and global mini-mum variance. Then strategies were compared with two benchmarks – an equally weighted portfolio and buy and hold on the DJIA index. Strategies were tested on Dow Jones Industrial Average stocks in the period from 2007-01-01 to 2022-12-31 and daily data was used. The main portfolio performance metrics were information ratio* and information ratio**. The results showed that using forecasted returns we can enhance our portfolio selection based on Markowitz framework, but it is not a universal solution, and we have to control all the parameters and hyperparameters of selected models.
    Keywords: Algorithmic Investment Strategies, Markowitz framework, portfolio optimization, forecasting, ARIMA, GARCH, XGBoost, minimum variance
    JEL: C4 C14 C45 C53 C58 G13
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:war:wpaper:2023-17&r=fmk
  8. By: Francesco Mandelli; Marco Pinciroli; Michele Trapletti; Edoardo Vittori
    Abstract: In this paper, we focus on finding the optimal hedging strategy of a credit index option using reinforcement learning. We take a practical approach, where the focus is on realism i.e. discrete time, transaction costs; even testing our policy on real market data. We apply a state of the art algorithm, the Trust Region Volatility Optimization (TRVO) algorithm and show that the derived hedging strategy outperforms the practitioner's Black & Scholes delta hedge.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2307.09844&r=fmk
  9. By: Darrell Duffie; Michael J. Fleming; Frank M. Keane; Claire Nelson; Or Shachar; Peter Van Tassel
    Abstract: We show a significant loss in U.S. Treasury market functionality when intensive use of dealer balance sheets is needed to intermediate bond markets, as in March 2020. Although yield volatility explains most of the variation in Treasury market liquidity over time, when dealer balance sheet utilization reaches sufficiently high levels, liquidity is much worse than predicted by yield volatility alone. This is consistent with the existence of occasionally binding constraints on the intermediation capacity of bond markets.
    Keywords: Treasury market; liquidity; volatility; dealer intermediation; Value-at-Risk
    JEL: G01 G1 G12 G18 E58
    Date: 2023–08–01
    URL: http://d.repec.org/n?u=RePEc:fip:fednsr:96553&r=fmk
  10. By: Varun Sangwan; Vishesh Kumar Singh; Bibin Christopher V
    Abstract: Our research aims to find the best model that uses companies projections and sector performances and how the given company fares accordingly to correctly predict equity share prices for both short and long term goals.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2307.07868&r=fmk
  11. By: Willem THORBECKE
    Abstract: Inflation in 2021 and 2022 grew much faster than the Federal Reserve expected. The Fed downplayed inflation in 2021 and then increased the federal funds rate by 500 basis points between March 2022 and May 2023. This paper investigates how this unprecedented tightening impacted the stock market. To do so it estimates a fully specified multi-factor model that measures the exposure of 53 assets to Bauer and Swanson (2022) monetary policy surprises over the 1988 to 2019 period. It then uses the monetary policy betas to gauge investors’ beliefs about monetary policy between 2020 and 2023. The results indicate that changing perceptions about monetary policy multiplied uncertainty and stock market volatility.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:eti:dpaper:23054&r=fmk
  12. By: Jabir Sandhu; Rishi Vala
    Abstract: In March 2020, shutdowns associated with the spread of COVID‑19 led to turmoil in financial markets globally. Asset managers sold large volumes of fixed-income securities within a short time period, while dealers did not have sufficient capacity to purchase securities using their own balance sheets (Fontaine et al. 2021).
    Keywords: Coronavirus disease (COVID-19); Financial markets; Financial stability; Market structure and pricing
    JEL: D D47 D5 D53 G G14 G2 G23
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:bca:bocsan:23-11&r=fmk
  13. By: Philippe Loyson (VU Amsterdam); Rianne Luijendijk (DNB); Sweder van Wijnbergen (University of Amsterdam)
    Abstract: We assess whether climate transition risk is priced in Europe’s equity market by analysing relative equity returns of high versus low CO2-emitting firms. We use a panel data set covering firm-specific carbon emissions of 1, 555 European companies over the period 2005-2019. We add to the existing literature by addressing problems in carbon data and by using various econometric methods ranging from panel data analysis to the SCM. Fama-French style panel regressions at both the individual firm level as well as portfolio level suggest that carbon intensity is negatively related to stock returns. Treatment effect models, however, provide some evidence for increased pricing of climate transition risk after the Paris Agreement.
    Keywords: Climate Change, Carbon Emissions Intensity, Paris Agreement, Transition Risk Premia.
    JEL: G12 Q54
    Date: 2023–07–24
    URL: http://d.repec.org/n?u=RePEc:tin:wpaper:20230042&r=fmk

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