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on Financial Markets |
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Issue of 2024‒01‒15
eight papers chosen by |
| By: | Bo Li |
| Abstract: | Observations indicate that the distributions of stock returns in financial markets usually do not conform to normal distributions, but rather exhibit characteristics of high peaks, fat tails and biases. In this work, we assume that the effects of events or information on prices obey normal distribution, while financial markets often overreact or underreact to events or information, resulting in non normal distributions of stock returns. Based on the above assumptions, we propose a reaction function for a financial market reacting to events or information, and a model based on it to describe the distribution of real stock returns. Our analysis of the returns of China Securities Index 300 (CSI 300), the Standard & Poor's 500 Index (SPX or S&P 500) and the Nikkei 225 Index (N225) at different time scales shows that financial markets often underreact to events or information with minor impacts, overreact to events or information with relatively significant impacts, and react slightly stronger to positive events or information than to negative ones. In addition, differences in financial markets and time scales of returns can also affect the shapes of the reaction functions. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.02472&r=fmk |
| By: | Tom\'as de la Rosa |
| Abstract: | Once there is a decision of rebalancing or updating a portfolio of funds, the process of changing the current portfolio to the target one, involves a set of transactions that are susceptible of being optimized. This is particularly relevant when managers have to handle the implications of different types of instruments. In this work we present linear programming and heuristic search approaches that produce plans for executing the update. The evaluation of our proposals shows cost improvements over the compared based strategy. The models can be easily extended to other realistic scenarios in which a holistic portfolio management is required |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.16204&r=fmk |
| By: | Xiao Cen; Winston Wei Dou; Leonid Kogan; Wei Wu |
| Abstract: | Investment fund managers make asset allocation decisions on behalf of a significant segment of US households. To elucidate the incentives they operate under, as well as the income and career risks they face, we construct a unique and novel dataset, which encompasses detailed information on the compensation and career trajectories of managers within US active equity mutual funds. The dataset is the first-ever to contain such information, having been compiled based on the US Census Bureau's LEHD program and leveraging various “big” textual data sources. Our causal evidence indicates that, contrary to fund disclosures, managers' pay is primarily driven by Assets Under Management (AUM), with performance influencing compensation only via AUM. Fund flows, although they do not align with client interests, have a significant 6% positive impact on compensation for every one-standard-deviation increase. Systematic flow components impact base salaries, while idiosyncratic elements alter bonuses. Crucially, fund flows, as opposed to fund performance, exert a strong impact on the career outcomes of fund managers, especially concerning their downside career risk. Specifically, large fund outflows elevate a manager's likelihood of job turnover (with a substantial decline in income) by 4 percentage points. |
| JEL: | G11 G23 J24 J31 J33 J44 J63 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31986&r=fmk |
| By: | Agostino Capponi; Garud Iyengar; Jay Sethuraman |
| Abstract: | Financial markets are undergoing an unprecedented transformation. Technological advances have brought major improvements to the operations of financial services. While these advances promote improved accessibility and convenience, traditional finance shortcomings like lack of transparency and moral hazard frictions continue to plague centralized platforms, imposing societal costs. In this paper, we argue how these shortcomings and frictions are being mitigated by the decentralized finance (DeFi) ecosystem. We delve into the workings of smart contracts, the backbone of DeFi transactions, with an emphasis on those underpinning token exchange and lending services. We highlight the pros and cons of the novel form of decentralized governance introduced via the ownership of governance tokens. Despite its potential, the current DeFi infrastructure introduces operational risks to users, which we segment into five primary categories: consensus mechanisms, protocol, oracle, frontrunning, and systemic risks. We conclude by emphasizing the need for future research to focus on the scalability of existing blockchains, the improved design and interoperability of DeFi protocols, and the rigorous auditing of smart contracts. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.01018&r=fmk |
| By: | Ravi Kashyap |
| Abstract: | The primary innovation we pioneer -- focused on blockchain information security -- is called the Safe-House. The Safe-House is badly needed since there are many ongoing hacks and security concerns in the DeFi space right now. The Safe-House is a piece of engineering sophistication that utilizes existing blockchain principles to bring about greater security when customer assets are moved around. The Safe-House logic is easily implemented as smart contracts on any decentralized system. The amount of funds at risk from both internal and external parties -- and hence the maximum one time loss -- is guaranteed to stay within the specified limits based on cryptographic fundamentals. To improve the safety of the Safe-House even further, we adapt the one time password (OPT) concept to operate using blockchain technology. Well suited to blockchain cryptographic nuances, our secondary advancement can be termed the one time next time password (OTNTP) mechanism. The OTNTP is designed to complement the Safe-House making it even more safe. We provide a detailed threat assessment model -- discussing the risks faced by DeFi protocols and the specific risks that apply to blockchain fund management -- and give technical arguments regarding how these threats can be overcome in a robust manner. We discuss how the Safe-House can participate with other external yield generation protocols in a secure way. We provide reasons for why the Safe-House increases safety without sacrificing the efficiency of operation. We start with a high level intuitive description of the landscape, the corresponding problems and our solutions. We then supplement this overview with detailed discussions including the corresponding mathematical formulations and pointers for technological implementation. This approach ensures that the article is accessible to a broad audience. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.00033&r=fmk |
| By: | Turan G. Bali (Georgetown University); Heiner Beckmeyer (University of Münster); Amit Goyal (University of Lausanne; Swiss Finance Institute) |
| Abstract: | Motivated by structural credit risk models, we propose a parsimonious reduced-form joint factor model for bonds, options, and stocks. By extending the instrumented principal component analysis to accommodate heterogeneity in how firm characteristics instrument the sensitivity of bonds, options, and stocks, we find that our model is able to jointly explain the risk-return tradeoff for the three asset classes. Just six factors are sufficient to explain 31% of the total variation of bond, option, and stock returns; these six factors leave the returns of only 7 out of 169 characteristic-managed portfolios unexplained. Finally, we investigate the patterns of commonality in return predictability. |
| Keywords: | factor model, IPCA, corporate bond, option returns |
| JEL: | G10 G11 G12 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp23106&r=fmk |
| By: | Marco Di Maggio (Harvard Business School; NBER); Francesco A. Franzoni (Universita della Svizzera italiana; Swiss Finance Institute; CEPR); Shimon Kogan (Reichman University; University of Pennsylvania); Ran Xing (Stockholm University; Aarhus University; Swedish House of Finance) |
| Abstract: | Despite positive and significant earnings announcement premia, we find that institutional investors reduce their exposure to stocks before earnings announcements. A novel result on the sensitivity of flows to individual stock returns provides a potential explanation. We show that extreme announcement returns for an individual holding lead to substantial outflows, controlling for overall performance, and they increase the probability of managers leaving the fund. Reducing the exposure to these stocks before the announcement mitigates the outflows. We build a model to describe and quantify this tradeoff. Overall, the paper identifies a new dimension of limits to arbitrage for institutions. |
| Keywords: | News trading, mutual fund performance, fund flows, limits of arbitrage, financial constraints, earnings announcements |
| JEL: | G12 G23 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp23108&r=fmk |
| By: | Maochun Xu; Zixun Lan; Zheng Tao; Jiawei Du; Zongao Ye |
| Abstract: | Artificial Intelligence (AI) and Machine Learning (ML) are transforming the domain of Quantitative Trading (QT) through the deployment of advanced algorithms capable of sifting through extensive financial datasets to pinpoint lucrative investment openings. AI-driven models, particularly those employing ML techniques such as deep learning and reinforcement learning, have shown great prowess in predicting market trends and executing trades at a speed and accuracy that far surpass human capabilities. Its capacity to automate critical tasks, such as discerning market conditions and executing trading strategies, has been pivotal. However, persistent challenges exist in current QT methods, especially in effectively handling noisy and high-frequency financial data. Striking a balance between exploration and exploitation poses another challenge for AI-driven trading agents. To surmount these hurdles, our proposed solution, QTNet, introduces an adaptive trading model that autonomously formulates QT strategies through an intelligent trading agent. Incorporating deep reinforcement learning (DRL) with imitative learning methodologies, we bolster the proficiency of our model. To tackle the challenges posed by volatile financial datasets, we conceptualize the QT mechanism within the framework of a Partially Observable Markov Decision Process (POMDP). Moreover, by embedding imitative learning, the model can capitalize on traditional trading tactics, nurturing a balanced synergy between discovery and utilization. For a more realistic simulation, our trading agent undergoes training using minute-frequency data sourced from the live financial market. Experimental findings underscore the model's proficiency in extracting robust market features and its adaptability to diverse market conditions. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.15730&r=fmk |