nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒01‒09
eighteen papers chosen by

  1. Do gamblers invest in lottery stocks? By Kormanyos, Emily; Hanspal, Tobin; Hackethal, Andreas
  2. Why do investors buy shares of actively managed equity mutual funds? Considering the Correct Reference Portfolio from an Uninformed Investor's Perspective 1, 2 By Radu Burlacu; Patrice Fontaine; Sonia Jimenez-Garc\`es
  3. The Unicorn Puzzle By Davydova, Daria; Fahlenbrach, Rudiger; Sanz, Leandro; Stulz, Rene M.
  4. Stock Return Predictability: comparing Macro- and Micro-Approaches By Arthur Stalla-Bourdillon
  5. Understanding stock market instability via graph auto-encoders By Dragos Gorduza; Xiaowen Dong; Stefan Zohren
  6. Stock Market Response to Firms’ Misconduct By Elisa Navarra
  7. A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks By Jie Zou; Jiashu Lou; Baohua Wang; Sixue Liu
  8. Text Representation Enrichment Utilizing Graph based Approaches: Stock Market Technical Analysis Case Study By Sara Salamat; Nima Tavassoli; Behnam Sabeti; Reza Fahmi
  9. The efficiency of various types of input layers of LSTM model in investment strategies on S&P500 index By Thi Thu Giang Nguyen; Robert Ślepaczuk
  10. Environmental and Social Preferences and Investments in Crypto-Assets By Pavel Ciaian; Andrej Cupak; Pirmin Fessler; d’Artis Kancs
  11. Gone with the fire: Market reaction to cryptocurrency exchange shutdown By Lee, Hanol; Wie, Dainn
  12. Can Decentralized Finance Provide More Protection for Crypto Investors? By Agostino Capponi; Nathan Kaplan; Asani Sarkar
  13. Tax-Loss Harvesting with Cryptocurrencies By Lin William Cong; Wayne Landsman; Edward Maydew; Daniel Rabetti
  14. Corporate Decision-Making under Uncertainty: Review and Future Research Directions By Murillo Campello; Gaurav Kankanhalli
  15. How Do Investors Value ESG? By Malcolm Baker; Mark L. Egan; Suproteem K. Sarkar
  16. ESG Factors and Firms’ Credit Risk By Laura Bonacorsi; Vittoria Cerasi; Paola Galfrascoli; Matteo Manera
  17. A jumping index of jumping stocks? An MCMC analysis of continuous-time models for individual stocks By Pollastri, Alessandro; Rodrigues, Paulo Jorge Maurício; Schlag, Christian; Seeger, Norman
  18. Risk management, expectations and global finance: The case of Deutsche Bank 1970-1990 By Nützenadel, Alexander

  1. By: Kormanyos, Emily; Hanspal, Tobin; Hackethal, Andreas
    Abstract: Previous studies document a relationship between gambling activity at the aggregate level and investments in securities with lottery-like features. We combine data on individual gambling consumption with portfolio holdings and trading records to examine whether gambling and trading act as substitutes or complements. We find that gamblers are more likely than the average investor to hold lottery stocks, but significantly less likely than active traders who do not gamble. Our results suggest that gambling behavior across domains is less relevant compared to other portfolio characteristics that predict investing in high-risk and high-skew securities, and that gambling on and off the stock market act as substitutes to satisfy the same need, e.g., sensation seeking.
    Keywords: Gambling, Retail investors, Lottery stocks
    JEL: G50 G40 D14 G11 G15
    Date: 2022
  2. By: Radu Burlacu (CERAG); Patrice Fontaine (EUROFIDAI); Sonia Jimenez-Garc\`es
    Abstract: We use the Grossman \& Stiglitz (1980) framework to build a reference portfolio for uninformed investors and employ this portfolio to assess the performance of actively managed equity mutual funds. We propose an empirical methodology to construct this reference portfolio using the information on prices and supply. We show that mutual funds provide, on average, an insignificant alpha of 23 basis points per year when considering this portfolio as a reference. With the stock market index as a proxy for the market portfolio, the average fund alpha is negative and highly significant, --128 basis points per year. The results are robust when considering various subsets of funds based on their characteristics and their degree of selectivity. In line with rational expectations equilibrium models considering asymmetrically informed investors and partially revealing equilibrium prices, our study supports that active management adds value for uniformed investors.
    Date: 2022–12
  3. By: Davydova, Daria (Ecole Polytechnique Federale de Lausanne); Fahlenbrach, Rudiger (Ecole Polytechnique Federale de Lausanne and Swiss Finance Institute); Sanz, Leandro (Ohio State University); Stulz, Rene M. (Ohio State University and ECGI, Brussels)
    Abstract: From 2010 to 2021, 639 US VC-funded firms achieved unicorn status. We investigate why there are so many unicorns and why controlling shareholders give investors privileges to obtain unicorn status. We show that unicorns rely more than other VC-funded firms on organizational capital as well as network effects and the internet. Unicorn status enables startups to access new sources of capital. With this capital, they can invest more in organizational intangible assets with less expropriation risk than if they were public. As a result, they are more likely to capture the economies of scale that make their business model valuable.
    JEL: G24 G32 G34
    Date: 2022–11
  4. By: Arthur Stalla-Bourdillon
    Abstract: Economic theory identifies two potential sources of return predictability: time variation in expected returns (beta-predictability) or market inefficiencies (alpha-predictability). For the latter, Samuelson argued that macro-returns exhibit more inefficiencies than micro-returns, as individual stories are averaged out, leaving only harder-to-eliminate macro-mispricing at the index-level. To evaluate this claim, we compare macro- and micro-predictability on US data to gauge if the former turns out higher than the latter. Additionally, we extend over time the methodology of Rapach et al. (2011) to disentangle the two sources of predictability. We first find that Samuelson's view appears incorrect, as micro-predictability is not structurally lower than macro-predictability. Second, we find that our estimated alpha- and betapredictability indices are coherent with their corresponding theoretical implications (the alpha-predictability being high in times of bullish markets, and the beta-predictability in recessive periods), thus suggesting that the two mechanisms are at play in our dataset.
    Keywords: Out-of-Sample Return Predictability; Efficient Market Hypothesis; Conditional Beta Pricing Model; Alpha Predictability
    JEL: C22 C53 G12 G14 G17
    Date: 2022
  5. By: Dragos Gorduza; Xiaowen Dong; Stefan Zohren
    Abstract: Understanding stock market instability is a key question in financial management as practitioners seek to forecast breakdowns in asset co-movements which expose portfolios to rapid and devastating collapses in value. The structure of these co-movements can be described as a graph where companies are represented by nodes and edges capture correlations between their price movements. Learning a timely indicator of co-movement breakdowns (manifested as modifications in the graph structure) is central in understanding both financial stability and volatility forecasting. We propose to use the edge reconstruction accuracy of a graph auto-encoder (GAE) as an indicator for how spatially homogeneous connections between assets are, which, based on financial network literature, we use as a proxy to infer market volatility. Our experiments on the S&P 500 over the 2015-2022 period show that higher GAE reconstruction error values are correlated with higher volatility. We also show that out-of-sample autoregressive modeling of volatility is improved by the addition of the proposed measure. Our paper contributes to the literature of machine learning in finance particularly in the context of understanding stock market instability.
    Date: 2022–12
  6. By: Elisa Navarra
    Abstract: Labor rights violations and environmental challenges have caused companies to comeunder increasing society’s pressure to achieve higher sustainability standards. Using a noveldataset of worldwide industrial disasters and companies allegedly involved in them, I examinewhether large companies suffer systematic stock market losses after disasters. I estimate anaverage drop in price returns of 1.47 percentage points on the day after the disaster and3.21 over one week. Accordingly, volatility soars. I then discuss the possible mechanismsbehind this negative market response. I focus on harm to the reputation for sustainabilityand I examine the media’s attention to environmental and labor topics through a sentimentanalysis of disaster-related news. I find that a more negative tone of the news is associatedwith larger stock market losses.
    Keywords: Industrial Disasters; Reputation; Stock Market Returns
    JEL: F23 F63 G14 Q53
  7. By: Jie Zou; Jiashu Lou; Baohua Wang; Sixue Liu
    Abstract: More and more stock trading strategies are constructed using deep reinforcement learning (DRL) algorithms, but DRL methods originally widely used in the gaming community are not directly adaptable to financial data with low signal-to-noise ratios and unevenness, and thus suffer from performance shortcomings. In this paper, to capture the hidden information, we propose a DRL based stock trading system using cascaded LSTM, which first uses LSTM to extract the time-series features from stock daily data, and then the features extracted are fed to the agent for training, while the strategy functions in reinforcement learning also use another LSTM for training. Experiments in DJI in the US market and SSE50 in the Chinese stock market show that our model outperforms previous baseline models in terms of cumulative returns and Sharp ratio, and this advantage is more significant in the Chinese stock market, a merging market. It indicates that our proposed method is a promising way to build a automated stock trading system.
    Date: 2022–12
  8. By: Sara Salamat; Nima Tavassoli; Behnam Sabeti; Reza Fahmi
    Abstract: Graph neural networks (GNNs) have been utilized for various natural language processing (NLP) tasks lately. The ability to encode corpus-wide features in graph representation made GNN models popular in various tasks such as document classification. One major shortcoming of such models is that they mainly work on homogeneous graphs, while representing text datasets as graphs requires several node types which leads to a heterogeneous schema. In this paper, we propose a transductive hybrid approach composed of an unsupervised node representation learning model followed by a node classification/edge prediction model. The proposed model is capable of processing heterogeneous graphs to produce unified node embeddings which are then utilized for node classification or link prediction as the downstream task. The proposed model is developed to classify stock market technical analysis reports, which to our knowledge is the first work in this domain. Experiments, which are carried away using a constructed dataset, demonstrate the ability of the model in embedding extraction and the downstream tasks.
    Date: 2022–11
  9. By: Thi Thu Giang Nguyen (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance)
    Abstract: The study compares the use of various Long Short-Term Memory (LSTM) variants to conventional technical indicators for trading the S&P 500 index between 2011 and 2022. Two methods were used to test each strategy: a fixed training data set from 2001–2010 and a rolling train–test window. Due to the input sensitivity of LSTM models, we concentrated on data processing and hyperparameter tuning to find the best model. Instead of using the traditional MSE function, we used the Mean Absolute Directional Loss (MADL) function based on recent research to enhance model performance. The models were assessed using the Information Ratio and the Modified Information Ratio, which considers the maximum drawdown and the sign of the annualized return compounded (ARC). LSTM models' performance was compared to benchmark strategies using the SMA, MACD, RSI, and Buy&Hold strategies. We rejected the hypothesis that algorithmic investment strategy using signals from LSTM model consisting only from daily returns in its input layer is more efficient. However, we could not reject the hypothesis that signals generated by LSTM model combining daily returns and technical indicators in its input layer are more efficient. The LSTM Extended model that combined daily returns with MACD and RSI in the input layer generated a better result than Buy&Hold and other strategies using a single technical indicator. The results of the sensitivity analysis show how sensitive this model is to inputs like sequence length, batch size, technical indicators, and the length of the rolling train - test window.
    Keywords: algorithmic investment strategies, machine learning, testing architecture, deep learning, recurrent neural networks, LSTM, technical indicators, forecasting financial-time series, technical indicators, hyperparameter tuning S&P 500 Index
    JEL: C15 C45 C52 C53 C58 C61 G14 G17
    Date: 2022
  10. By: Pavel Ciaian (European Commission - JRC); Andrej Cupak (National Bank of Slovakia and University of Economics in Bratislava); Pirmin Fessler (Oesterreichische Nationalbank, Economic Microdata Lab); d’Artis Kancs (European Commission - JRC)
    Abstract: Individuals invest in Environmental-Social-Governance (ESG)-assets not only because of (higher) expected returns but also driven by ethical and social considerations. Less is known about ESG-conscious investor subjective beliefs about crypto-assets and how these compare to traditional assets. Controversies surrounding the ESG footprint of certain crypto-asset classes – mainly on grounds of their energy-intensive crypto mining – offer a potentially informative object of inquiry. Leveraging a unique representative household finance survey for the Austrian population, we examine whether investors’ environmental and social preferences can explain cross- sectional differences in individual portfolio exposure to crypto-assets. We find a strong association between investors’ environmental and social preferences and the crypto-investment exposure but no significant relationship for the benchmarks of traditional asset classes such as bonds and shares.
    Keywords: Crypto-assets, investment portfolio, financial behaviour, financial literacy, environmental and social preferences
    JEL: D14 G11 G41
    Date: 2022–11
  11. By: Lee, Hanol; Wie, Dainn
    Abstract: Disruption of exchanges frequently happens in the cryptocurrency market, though their potential impacts are relatively under-investigated. This study employs a 20-hour service interruption on October 15th, 2022, at Upbit, the dominant cryptocurrency exchange in Korea, as an exogenous shock of service interruption on the cryptocurrency market. Event study estimation shows that the change in abnormal returns depends on how important the specific exchange is to those cryptocurrencies. Cryptocurrencies predominantly traded on Upbit showed sharp reactions to both service disruption and recovery, while major currencies such as Bitcoin and Ethereum presented limited reactions to service interruption only.
    Keywords: Cryptocurrency,abnormal return,Event study,Network service disruption
    JEL: G12 G14 G15 G23
    Date: 2022
  12. By: Agostino Capponi; Nathan Kaplan; Asani Sarkar
    Abstract: Several centralized crypto entities failed in 2022, resulting in the cascading failure of other crypto firms and raising questions about the protection of crypto investors. While the total amount invested in the crypto sector remains small in the United States, more than 10 percent of all Americans are invested in cryptocurrencies. In this post, we examine whether migrating crypto activities from centralized platforms to decentralized finance (DeFi) protocols might afford investors better protection, especially in the absence of regulatory changes. We argue that while DeFi provides some benefits for investors, it also introduces new risks and so more work is needed to make it a viable option for mainstream investors.
    Keywords: Crypto; cryptocurrencies; decentralized finance; DeFi; regulations; financial intermediation; fire sale
    JEL: G1 G2
    Date: 2022–12–21
  13. By: Lin William Cong; Wayne Landsman; Edward Maydew; Daniel Rabetti
    Abstract: We describe the landscape of taxation in the crypto markets, especially that concerning U.S. taxpayers, and examine how recent increases in tax scrutiny have led to changes in trading behavior by crypto traders. We predict under a simple theoretical framework and then empirically document that increased tax scrutiny leads crypto investors to utilize legal tax planning with tax-loss harvesting as an alternative to non-compliance. In particular, domestic traders increase tax-loss harvesting following the increase in tax scrutiny, and U.S. exchanges exhibit a significantly greater amount of wash trading. Additional findings suggest that broad-based and targeted changes in tax scrutiny can differentially affect crypto traders' preference for U.S.-based exchanges. We also discuss other gray areas for tax regulation related to new crypto assets such as Non-Fungible Tokens and Decentralized Finance protocols that further highlight the importance of coordinating tax policy and other regulations.
    JEL: G15 G18 G29 K29 K42 O16
    Date: 2022–12
  14. By: Murillo Campello; Gaurav Kankanhalli
    Abstract: Uncertainty over future business conditions lies at the heart of firm decision-making. Uncertainty can arise from a myriad of sources and is difficult to measure. We present a simple conceptual framework showing how several key corporate decisions are affected by uncertainty. We also highlight recent advances in the measurement of uncertainty, distinguishing between approaches that gauge aggregate uncertainty and those that capture different dimensions of firm-specific uncertainty. These approaches incorporate information obtained from market prices, big data, machine learning techniques, surveys, and more. We then review the growing body of empirical work that studies the role played by uncertainty in shaping outcomes ranging across corporate investment, asset base composition, innovation, liquidity management, payouts, and mergers. Our review outlines several opportunities for future research.
    JEL: G31 G32
    Date: 2022–12
  15. By: Malcolm Baker; Mark L. Egan; Suproteem K. Sarkar
    Abstract: Environmental, social, and governance (ESG) objectives have risen to near the top of the agenda for corporate executives and boards, driven in large part by their perceptions of shareholder interest. We quantify the value that shareholders place on ESG using a revealed preference approach, where shareholders pay higher fees for ESG-oriented index funds in exchange for their financial and non-financial benefits. We find that investors are willing, on average, to pay 20 basis points more per annum for an investment in a fund with an ESG mandate as compared to an otherwise identical mutual fund without an ESG mandate, suggesting that investors as a group expect commensurately higher pre-fee, gross returns, either financial or non-financial, from an ESG mandate. Our point estimate has risen from 9 basis points in 2019 when our sample begins to as much as 28 basis points in 2022. When we incorporate the possibility that investors are willing to accept lower financial returns in exchange for the psychic and societal benefits of ESG, when we consider that the holdings of ESG and non-ESG index funds overlap, when we measure the ESG ratings of these holdings, and when we focus on 401(k) participants who report being concerned about climate change or who work in industries with lower levels of emissions, we find that the implicit value that investors place on ESG stocks is higher still. A simple model of supply suggests that the large majority of these benefits accrue to investors and firms, with intermediaries capturing 5.9 basis points in fees, half of which reflect higher markups.
    JEL: G0 G11 G5 Q50
    Date: 2022–12
  16. By: Laura Bonacorsi; Vittoria Cerasi; Paola Galfrascoli; Matteo Manera
    Abstract: We study the relationship between the risk of default and Environmental, Social and Governance (ESG) factors using Machine Learning (ML) techniques on a cross-section of European listed companies. Our proxy for credit risk is the z-score originally proposed by Altman (1968).We consider an extensive number of ESG raw factors sourced from the rating provider MSCI as potential explanatory variables. In a first stage we show, using different SML methods such as LASSO and Random Forest, that a selection of ESG factors, in addition to the usual accounting ratios, helps explaining a firm’s probability of default. In a second stage, we measure the impact of the selected variables on the risk of default. Our approach provides a novel perspective to understand which environmental, social responsibility and governance characteristics may reinforce the credit score of individual companies.
    Keywords: credit risk, z-scores, ESG factors, Machine learning.
    JEL: C5 D4 G3
    Date: 2022–12
  17. By: Pollastri, Alessandro; Rodrigues, Paulo Jorge Maurício; Schlag, Christian; Seeger, Norman
    Abstract: This paper examines continuous-time models for the S&P 100 index and its constituents. We find that the jump process of the typical stock looks significantly different than that of the index. Most importantly, the average size of a jumps in the returns of the typical stock is positive, while it is negative for the index. Furthermore, the estimates of the parameters for the stochastic processes exhibit pronounced heterogeneity in the crosssection of stocks. For example, we find that the jump size in returns decrease for larger companies. Finally, we find that a jump in the index is not necessarily accompanied by a large number of contemporaneous jumps in its constituents stocks. Indeed, we find index jump days on which only one index constituent also jumps. As a consequence, we show that index jumps can be classified as induced by either synchronous price movements of individual stocks or macroeconomic events.
    Keywords: Jump-diffusion models, individual stocks, Markov Chain Monte Carlo
    JEL: G11 G12
    Date: 2022
  18. By: Nützenadel, Alexander
    Abstract: What impact do past experiences have on the expectation formation of banks? This article analyses the risk management of Germany's largest bank during the 1970 and 1980s. In this period, financial deregulation and globalization increased the likelihood of credit defaults and forced banks to implement new strategies of risk assessment. The Herstatt failure of 1974 triggered a series of new regulations, partly based on initiatives of the banks themselves. After the sovereign debt crisis of the 1980s, banks introduced a comprehensive strategy of country-risk assessment. They systematically professionalized their information resources and integrated risk and liability management. Economic forecasting was often based on historical data used for the classification and diversification of risks. However, learning from past experiences had limitations, as recent events were often overrated. This had the effect that the banks' country risk assessment focused mainly on developing countries while the industrial world was not included in the schemes. This might explain why many banks have continually underestimated the financial risks present in developed countries since the 1990s.
    Keywords: Risk management,financial markets,banks,expectations,historical experience
    JEL: F65 G15 G17 G32 N2
    Date: 2022

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