nep-fmk New Economics Papers
on Financial Markets
Issue of 2023‒02‒20
ten papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. A Survey of Private Debt Funds By Joern Block; Young Soo Jang; Steven N. Kaplan; Anna Schulze
  2. The Technology of Decentralized Finance (DeFi) By Raphael Auer; Bernhard Haslhofer; Stefan Kitzler; Pietro Saggese; Friedhelm Victor
  3. Bond supply, price drifts and liquidity provision before central bank announcements By Lou, Dong; Pinter, Gabor; Uslu, Semih
  4. Equity issuance methods and dilution By Burkart, Mike; Zhong, Hongda
  5. News Diffusion in Social Networks and Stock Market Reactions By David Hirshleifer; Lin Peng; Qiguang Wang
  6. Leveraging Vision-Language Models for Granular Market Change Prediction By Christopher Wimmer; Navid Rekabsaz
  7. An Optimal Control Strategy for Execution of Large Stock Orders Using LSTMs By A. Papanicolaou; H. Fu; P. Krishnamurthy; B. Healy; F. Khorrami
  8. Who Pays For Your Rewards? Redistribution in the Credit Card Market By Sumit Agarwal; Andrea F. Presbitero; André F. Silva; Carlo Wix
  9. The Market for CEOs: Evidence from Private Equity By Paul A. Gompers; Steven N. Kaplan; Vladimir Mukharlyamov
  10. Using machine learning to measure financial risk in China By Al-Haschimi, Alexander; Apostolou, Apostolos; Azqueta-Gavaldon, Andres; Ricci, Martino

  1. By: Joern Block; Young Soo Jang; Steven N. Kaplan; Anna Schulze
    Abstract: Despite its large and increasing size in the U.S. and Europe, there is relatively little research on the private debt (PD) market, particularly compared to the bank and syndicated loan markets. Accordingly, in this paper, we survey U.S. and European investors with private debt assets under management (AuM) of over $300 billion. These investors are primarily direct lending funds. We ask the general partners (GPs) how they source, select, and evaluate deals, how they think of private debt relative to bank and syndicated loan financing, how they monitor their investments, how they interact with private equity (PE) sponsors and how they view the future of the market. The respondents provide primarily cash flow-based loans and believe that they finance companies and leverage levels that banks would not fund. The direct lending funds target unlevered returns that appear high relative to their risk. They use leverage in their funds, but appreciably less than banks and collateralized loan obligation funds (CLOs). They use and negotiate for both financial and incurrence covenants to monitor their investments. The presence of PE sponsors helps them lend more and craft more effective covenants. U.S. and European funds are similar on many dimensions, but the European funds rely less on PE sponsors and compete more with banks. Overall, the private debt market is both different from, but shares characteristics with the bank loan and syndicated loan markets.
    JEL: G24 G32 G34
    Date: 2023–01
  2. By: Raphael Auer; Bernhard Haslhofer; Stefan Kitzler; Pietro Saggese; Friedhelm Victor
    Abstract: Decentralized Finance (DeFi) is a new financial paradigm that leverages distributed ledger technologies to offer services such as lending, investing, or exchanging cryptoassets without relying on a traditional centralized intermediary. A range of DeFi protocols implements these services as a suite of smart contracts, ie software programs that encode the logic of conventional financial operations. Instead of transacting with a counterparty, DeFi users thus interact with software programs that pool the resources of other DeFi users to maintain control over their funds. This paper provides a deep dive into the overall architecture, the technical primitives, and the financial functionalities of DeFi protocols. We analyse and explain the individual components and how they interact through the lens of a DeFi stack reference (DSR) model featuring three layers: settlement, applications and interfaces. We discuss the technical aspects of each layer of the DSR model. Then, we describe the financial services for the most relevant DeFi categories, ie decentralized exchanges, lending protocols, derivatives protocols and aggregators. The latter exploit the property that smart contracts can be "composed", ie utilize the functionalities of other protocols to provide novel financial services. We discuss how composability allows complex financial products to be assembled, which could have applications in the traditional financial industry. We discuss potential sources of systemic risk and conclude by mapping out an agenda for research in this area.
    Keywords: financial engineering, decentralized finance, DeFi, blockchain, ethereum, DLT, cryptocurrencies, stablecoins, cryptoassets
    JEL: E42 E58 F31 G19 G23 L50 O33 G12
    Date: 2023–01
  3. By: Lou, Dong (London School of Economics); Pinter, Gabor (Bank of England); Uslu, Semih (John Hopkins Carey)
    Abstract: We document that UK government bond yields systematically rise in a two-day window before Monetary Policy Committee (MPC) meetings, which we refer to as pre-MPC windows. The effect concentrates on pre-MPC windows that coincide with new issuance of government bonds. Decomposing the effect into an expected short-rate and a risk premium component, we find that the majority of the yield drift is attributed to increases in risk premia. These effects are present in the US as well. Using UK transaction-level data and analysing trading activity after primary issuances, we find that the dealer sector sells significantly more to the client sector during pre-MPC windows, consistent with dealers’ limited risk-bearing capacity. Importantly, we find significant changes in the composition of liquidity providers: hedge funds buy a large share of the issue outside pre-MPC windows, but they shy away from liquidity provision in pre-MPC windows, being replaced by less speculative investors such as foreign government entities and pension funds. We propose a theoretical model to rationalise the change in the composition of liquidity providers before high-informational events, which can also explain the price drift observed in the data.
    Keywords: monetary policy announcements; price drift; bond supply
    JEL: E52 E63 G10 G20
    Date: 2022–10–21
  4. By: Burkart, Mike; Zhong, Hongda
    Abstract: We analyze rights and public offerings when informed shareholders strategically choose to subscribe. Absent wealth constraints, rights offerings achieve the full information outcome and dominate public offerings. When some shareholders are wealth constrained, rights offerings lead to more dilution of their stakes and lower payoffs, despite the income from selling these rights. In both rights and public offerings, there is a trade-off between investment efficiency and wealth transfers among shareholders. When firms can choose the flotation method, either all firms choose the same offer method or high and low types opt for rights offerings, while intermediate types select public offerings.
    Keywords: ES/S016686/1; ES/T003758/1; OUP deal
    JEL: G32
    Date: 2023–02–01
  5. By: David Hirshleifer; Lin Peng; Qiguang Wang
    Abstract: We study how the social transmission of public news influences investors' beliefs and securities markets. Using an extensive dataset to measure investor social networks, we find that earnings announcements from firms in higher-centrality locations generate stronger immediate price and trading volume reactions. Post announcement, such firms experience weaker price drifts but higher and more persistent volume. This evidence suggests that while greater social connectedness facilitates timely incorporation of news into prices, it also triggers opinion divergence and excessive trading. We provide a model of these effects and present further supporting evidence with granular data based on StockTwits messages and household trading records.
    JEL: G11 G12 G14 G4 G41
    Date: 2023–01
  6. By: Christopher Wimmer; Navid Rekabsaz
    Abstract: Predicting future direction of stock markets using the historical data has been a fundamental component in financial forecasting. This historical data contains the information of a stock in each specific time span, such as the opening, closing, lowest, and highest price. Leveraging this data, the future direction of the market is commonly predicted using various time-series models such as Long-Short Term Memory networks. This work proposes modeling and predicting market movements with a fundamentally new approach, namely by utilizing image and byte-based number representation of the stock data processed with the recently introduced Vision-Language models. We conduct a large set of experiments on the hourly stock data of the German share index and evaluate various architectures on stock price prediction using historical stock data. We conduct a comprehensive evaluation of the results with various metrics to accurately depict the actual performance of various approaches. Our evaluation results show that our novel approach based on representation of stock data as text (bytes) and image significantly outperforms strong deep learning-based baselines.
    Date: 2023–01
  7. By: A. Papanicolaou; H. Fu; P. Krishnamurthy; B. Healy; F. Khorrami
    Abstract: In this paper, we simulate the execution of a large stock order with real data and general power law in the Almgren and Chriss model. The example that we consider is the liquidation of a large position executed over the course of a single trading day in a limit order book. Transaction costs are incurred because large orders walk the order book, that is, they consume order-book liquidity beyond the best bid/ask. We model these transaction costs with a power law that is inversely proportional to trading volume. We obtain a policy approximation by training a long short term memory (LSTM) neural network to minimize transaction costs accumulated when execution is carried out as a sequence of smaller sub orders. Using historical S&P100 price and volume data, we evaluate our LSTM strategy relative to strategies based on time-weighted average price (TWAP) and volume-weighted average price (VWAP). For execution of a single stock, the input to the LSTM includes the entire cross section of data on all 100 stocks, including prices, volume, TWAPs and VWAPs. By using the entire data cross section, the LSTM should be able to exploit any inter-stock co-dependence in volume and price movements, thereby reducing overall transaction costs. Our tests on the S&P100 data demonstrate that in fact this is so, as our LSTM strategy consistently outperforms TWAP and VWAP-based strategies.
    Date: 2023–01
  8. By: Sumit Agarwal; Andrea F. Presbitero; André F. Silva; Carlo Wix
    Abstract: We study credit card rewards as an ideal laboratory to quantify redistribution between consumers in retail financial markets. Comparing cards with and without rewards, we find that, regardless of income, sophisticated individuals profit from reward credit cards at the expense of naive consumers. To probe the underlying mechanisms, we exploit bank-initiated account limit increases at the card level and show that reward cards induce more spending, leaving naive consumers with higher unpaid balances. Naive consumers also follow a sub-optimal balance-matching heuristic when repaying their credit cards, incurring higher costs. Banks incentivize the use of reward cards by offering lower interest rates than on comparable cards without rewards. We estimate an aggregate annual redistribution of $15 billion from less to more educated, poorer to richer, and high to low minority areas, widening existing disparities.
    Keywords: Household finance; Credit cards; Financial sophistication; Rewards
    JEL: G21 G40 G51 G53
    Date: 2023–01–20
  9. By: Paul A. Gompers; Steven N. Kaplan; Vladimir Mukharlyamov
    Abstract: Most research on the CEO labor market studies public company CEOs while largely ignoring CEOs in private equity (PE) funded companies. We fill this gap by studying the market for CEOs among U.S. companies purchased by PE firms in large leveraged buyout transactions. 71% of those companies hired new CEOs under PE ownership. More than 75% of the new CEOs are external hires with 67% being complete outsiders. These results are strikingly different from studies of public companies, particularly, Cziraki and Jenter (2022) who find that 72% of new CEOs in S&P 500 companies are internal promotions. The most recent experience of 67% of the outside CEOs was at a public company with almost 50% of external hires having some previous experience at an S&P 500 firm. We estimate the total compensation of buyout CEOs and find that it is much higher than that of CEOs of similarly sized public companies and slightly lower than that of S&P 500 CEOs. Overall, our results suggest that the broader market for CEOs is active and that, at least for PE funded portfolio companies, firm-specific human capital is relatively unimportant.
    JEL: G24 G3 G32 J30
    Date: 2023–01
  10. By: Al-Haschimi, Alexander; Apostolou, Apostolos; Azqueta-Gavaldon, Andres; Ricci, Martino
    Abstract: We develop a measure of overall financial risk in China by applying machine learning techniques to textual data. A pre-defined set of relevant newspaper articles is first selected using a specific constellation of risk-related keywords. Then, we employ topical modelling based on an unsupervised machine learning algorithm to decompose financial risk into its thematic drivers. The resulting aggregated indicator can identify major episodes of overall heightened financial risks in China, which cannot be consistently captured using financial data. Finally, a structural VAR framework is employed to show that shocks to the financial risk measure have a significant impact on macroeconomic and financial variables in China and abroad. JEL Classification: C32, C65, E32, F44, G15
    Keywords: China, financial risk, LDA, machine learning, textual analysis, topic modelling
    Date: 2023–01

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