This report outlines a transformative initiative in the financial investment industry, where the conventional decision-making process, laden with labor-intensive tasks such as sifting through voluminous documents, is being reimagined. Leveraging language models, our experiments aim to automate information summarization and investment idea generation. We seek to evaluate the effectiveness of fine-tuning methods on a base model (Llama2) to achieve specific application-level goals, including providing insights into the impact of events on companies and sectors, understanding market condition relationships, generating investor-aligned investment ideas, and formatting results with stock recommendations and detailed explanations. Through state-of-the-art generative modeling techniques, the ultimate objective is to develop an AI agent prototype, liberating human investors from repetitive tasks and allowing a focus on high-level strategic thinking. The project encompasses a diverse corpus dataset, including research reports, investment memos, market news, and extensive time-series market data. We conducted three experiments applying unsupervised and supervised LoRA fine-tuning on the llama2_7b_hf_chat as the base model, as well as instruction fine-tuning on the GPT3.5 model. Statistical and human evaluations both show that the fine-tuned versions perform better in solving text modeling, summarization, reasoning, and finance domain questions, demonstrating a pivotal step towards enhancing decision-making processes in the financial domain. Code implementation for the project can be found on GitHub: https://github.com/Firenze11/finance_lm.
{"title":"Multimodal Gen-AI for Fundamental Investment Research","authors":"Lezhi Li, Ting-Yu Chang, Hai Wang","doi":"arxiv-2401.06164","DOIUrl":"https://doi.org/arxiv-2401.06164","url":null,"abstract":"This report outlines a transformative initiative in the financial investment\u0000industry, where the conventional decision-making process, laden with\u0000labor-intensive tasks such as sifting through voluminous documents, is being\u0000reimagined. Leveraging language models, our experiments aim to automate\u0000information summarization and investment idea generation. We seek to evaluate\u0000the effectiveness of fine-tuning methods on a base model (Llama2) to achieve\u0000specific application-level goals, including providing insights into the impact\u0000of events on companies and sectors, understanding market condition\u0000relationships, generating investor-aligned investment ideas, and formatting\u0000results with stock recommendations and detailed explanations. Through\u0000state-of-the-art generative modeling techniques, the ultimate objective is to\u0000develop an AI agent prototype, liberating human investors from repetitive tasks\u0000and allowing a focus on high-level strategic thinking. The project encompasses\u0000a diverse corpus dataset, including research reports, investment memos, market\u0000news, and extensive time-series market data. We conducted three experiments\u0000applying unsupervised and supervised LoRA fine-tuning on the llama2_7b_hf_chat\u0000as the base model, as well as instruction fine-tuning on the GPT3.5 model.\u0000Statistical and human evaluations both show that the fine-tuned versions\u0000perform better in solving text modeling, summarization, reasoning, and finance\u0000domain questions, demonstrating a pivotal step towards enhancing\u0000decision-making processes in the financial domain. Code implementation for the\u0000project can be found on GitHub: https://github.com/Firenze11/finance_lm.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139469391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We analyze the influence of religious social norms on corporate greenwashing behavior. Specifically, we focus on a specific form of greenwashing: selective disclosure. Using a large sample of US firms between 2005 and 2019, we show that firms located in counties where religious adherence is high are less likely to engage in greenwashing. We also find that a stronger religious adherence within the county in which a company is located reduces the magnitude of greenwashing, when observed. We further analyze the mechanism underlying this relationship and show that religious adherence impacts greenwashing behaviors through the channel of risk aversion. A comprehensive set of robustness tests aimed at addressing potential endogeneity concerns confirms that religion is a relevant driver of corporate greenwashing behavior.
{"title":"Does religiosity influence corporate greenwashing behavior?","authors":"Mathieu GomesCleRMa, UCA, IAE - UCA, Sylvain MarsatCleRMa, UCA, IAE - UCA, Jonathan PeillexCleRMa, UCA, IAE - UCA, Guillaume PijourletCleRMa, UCA, IAE - UCA","doi":"arxiv-2312.14515","DOIUrl":"https://doi.org/arxiv-2312.14515","url":null,"abstract":"We analyze the influence of religious social norms on corporate greenwashing\u0000behavior. Specifically, we focus on a specific form of greenwashing: selective\u0000disclosure. Using a large sample of US firms between 2005 and 2019, we show\u0000that firms located in counties where religious adherence is high are less\u0000likely to engage in greenwashing. We also find that a stronger religious\u0000adherence within the county in which a company is located reduces the magnitude\u0000of greenwashing, when observed. We further analyze the mechanism underlying\u0000this relationship and show that religious adherence impacts greenwashing\u0000behaviors through the channel of risk aversion. A comprehensive set of\u0000robustness tests aimed at addressing potential endogeneity concerns confirms\u0000that religion is a relevant driver of corporate greenwashing behavior.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139035310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roberto Rivera, Guido Rocco, Massimiliano Marzo, Enrico Talin
This whitepaper introduces RIVCoin, a cryptocurrency built on Cosmos, fully stabilized by a diversified portfolio of both CeFi and DeFi assets, available in a digital, non-custodial wallet called RIV Wallet, that aims to provide Users an easy way to access the cryptocurrency markets, compliant to the strictest AML laws and regulations up to date. The token is a cryptocurrency at any time stabilized by a basket of assets: reserves are invested in a portfolio composed long term by 50% of CeFi assets, comprised of Fixed Income, Equity, Mutual and Hedge Funds and 50% of diversified strategies focused on digital assets, mainly staking and LP farming on the major, battle tested DeFi protocols. The cryptocurrency, as well as the dollar before Bretton Woods, is always fully stabilized by vaulted proof of assets: it is born and managed as a decentralized token, minted by a Decentralized Autonomous Organization, and entirely stabilized by assets evaluated by professional independent third parties. Users will trade, pool, and exchange the token without any intermediary, being able to merge them into a Liquidity Pool whose rewards will be composed by both the trading fees and the liquidity rewards derived from the reserve's seigniorage. Users who wish and decide to pool RIVCoin in the Liquidity Pool will receive additional RIVCoin for themselves, and new RIVCoin are minted when the reserves increase in value or in case of purchase of new RIVCoin. The proposed model allows for alignment of incentives: decreasing the risk exposure by wealthier Users, but implicitly increasing that of smaller ones to a level perceived by them as still sustainable. Users indirectly benefit from the access to the rewards of sophisticated cryptocurrency portfolios hitherto precluded to them, without this turning into a disadvantage for the wealthy User.
{"title":"RIVCoin: an alternative, integrated, CeFi/DeFi-Vaulted Cryptocurrency","authors":"Roberto Rivera, Guido Rocco, Massimiliano Marzo, Enrico Talin","doi":"arxiv-2401.05393","DOIUrl":"https://doi.org/arxiv-2401.05393","url":null,"abstract":"This whitepaper introduces RIVCoin, a cryptocurrency built on Cosmos, fully\u0000stabilized by a diversified portfolio of both CeFi and DeFi assets, available\u0000in a digital, non-custodial wallet called RIV Wallet, that aims to provide\u0000Users an easy way to access the cryptocurrency markets, compliant to the\u0000strictest AML laws and regulations up to date. The token is a cryptocurrency at\u0000any time stabilized by a basket of assets: reserves are invested in a portfolio\u0000composed long term by 50% of CeFi assets, comprised of Fixed Income, Equity,\u0000Mutual and Hedge Funds and 50% of diversified strategies focused on digital\u0000assets, mainly staking and LP farming on the major, battle tested DeFi\u0000protocols. The cryptocurrency, as well as the dollar before Bretton Woods, is\u0000always fully stabilized by vaulted proof of assets: it is born and managed as a\u0000decentralized token, minted by a Decentralized Autonomous Organization, and\u0000entirely stabilized by assets evaluated by professional independent third\u0000parties. Users will trade, pool, and exchange the token without any\u0000intermediary, being able to merge them into a Liquidity Pool whose rewards will\u0000be composed by both the trading fees and the liquidity rewards derived from the\u0000reserve's seigniorage. Users who wish and decide to pool RIVCoin in the Liquidity Pool will receive\u0000additional RIVCoin for themselves, and new RIVCoin are minted when the reserves\u0000increase in value or in case of purchase of new RIVCoin. The proposed model\u0000allows for alignment of incentives: decreasing the risk exposure by wealthier\u0000Users, but implicitly increasing that of smaller ones to a level perceived by\u0000them as still sustainable. Users indirectly benefit from the access to the\u0000rewards of sophisticated cryptocurrency portfolios hitherto precluded to them,\u0000without this turning into a disadvantage for the wealthy User.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139464280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study backward stochastic difference equations (BS{Delta}E) driven by a d-dimensional stochastic process on a lattice whose increments have only d + 1 possible values that generates the lattice. Regarding the driving process as a d dimensional asset price process, we give applications to an optimal investment problem and a market equilibrium analysis, where utility functionals are defined through BS{Delta}E.
我们研究了由网格上的一维随机过程驱动的后向随机差分方程（BS{Delta}E），该网格的增量只有 d + 1 个可能值。将驱动过程视为一维资产价格过程，我们给出了最优投资问题和市场均衡分析的应用，其中效用函数是通过 BS{Delta}E 来定义的。
{"title":"Backward stochastic difference equations on lattices with application to market equilibrium analysis","authors":"Masaaki Fukasawa, Takashi Sato, Jun Sekine","doi":"arxiv-2312.10883","DOIUrl":"https://doi.org/arxiv-2312.10883","url":null,"abstract":"We study backward stochastic difference equations (BS{Delta}E) driven by a\u0000d-dimensional stochastic process on a lattice whose increments have only d + 1\u0000possible values that generates the lattice. Regarding the driving process as a\u0000d dimensional asset price process, we give applications to an optimal\u0000investment problem and a market equilibrium analysis, where utility functionals\u0000are defined through BS{Delta}E.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138744394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper provides a general method to translate a standard economic model with a large number of agents into a field-formalism model. This formalism preserves the system's interactions and microeconomic features at the individual level but reveals the emergence of collective states.We apply this method to a simple microeconomic framework of investors and firms. Both macro and micro aspects of the formalism are studied.At the macro-scale, the field formalism shows that, in each sector, three patterns of capital accumulation may emerge. A distribution of patterns across sectors constitute a collective state. Any change in external parameters or expectations in one sector will affect neighbouring sectors, inducing transitions between collective states and generating permanent fluctuations in patterns and flows of capital. Although changes in expectations can cause abrupt changes in collective states, transitions may be slow to occur. Due to its relative inertia, the real economy is bound to be more affected by these constant variations than the financial markets.At the micro-scale we compute the transition functions of individual agents and study their probabilistic dynamics in a given collective state, as a function of their initial state. We show that capital accumulation of an individual agent depends on various factors. The probability associated with each firm's trajectories is the result of several contradictory effects: the firm tends to shift towards sectors with the greatest long-term return, but must take into account the impact of its shift on its attractiveness for investors throughout its trajectory. Since this trajectory depends largely on the average capital of transition sectors, a firm's attractiveness during its relocation depends on the relative level of capital in those sectors. Moreover, the firm must also consider the effects of competition in the intermediate sectors that tends to oust under-capitalized firm towards sectors with lower average capital. For investors, capital allocation depends on their short and long-term returns and investors will tend to reallocate their capital to maximize both. The higher their level of capital, the stronger the re-allocation will be.
{"title":"A Statistical Field Perspective on Capital Allocation and Accumulation","authors":"Pierre GosselinIF, Aïleen Lotz","doi":"arxiv-2312.16173","DOIUrl":"https://doi.org/arxiv-2312.16173","url":null,"abstract":"This paper provides a general method to translate a standard economic model\u0000with a large number of agents into a field-formalism model. This formalism\u0000preserves the system's interactions and microeconomic features at the\u0000individual level but reveals the emergence of collective states.We apply this\u0000method to a simple microeconomic framework of investors and firms. Both macro\u0000and micro aspects of the formalism are studied.At the macro-scale, the field\u0000formalism shows that, in each sector, three patterns of capital accumulation\u0000may emerge. A distribution of patterns across sectors constitute a collective\u0000state. Any change in external parameters or expectations in one sector will\u0000affect neighbouring sectors, inducing transitions between collective states and\u0000generating permanent fluctuations in patterns and flows of capital. Although\u0000changes in expectations can cause abrupt changes in collective states,\u0000transitions may be slow to occur. Due to its relative inertia, the real economy\u0000is bound to be more affected by these constant variations than the financial\u0000markets.At the micro-scale we compute the transition functions of individual\u0000agents and study their probabilistic dynamics in a given collective state, as a\u0000function of their initial state. We show that capital accumulation of an\u0000individual agent depends on various factors. The probability associated with\u0000each firm's trajectories is the result of several contradictory effects: the\u0000firm tends to shift towards sectors with the greatest long-term return, but\u0000must take into account the impact of its shift on its attractiveness for\u0000investors throughout its trajectory. Since this trajectory depends largely on\u0000the average capital of transition sectors, a firm's attractiveness during its\u0000relocation depends on the relative level of capital in those sectors. Moreover,\u0000the firm must also consider the effects of competition in the intermediate\u0000sectors that tends to oust under-capitalized firm towards sectors with lower\u0000average capital. For investors, capital allocation depends on their short and\u0000long-term returns and investors will tend to reallocate their capital to\u0000maximize both. The higher their level of capital, the stronger the\u0000re-allocation will be.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139068645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study just-in-time (JIT) liquidity provision within blockchain-based decentralized exchanges (DEXs). In contrast to passive liquidity providers (LPs) who deposit assets into liquidity pools before observing order flows, JIT LPs take a more active approach. They monitor pending orders from public blockchain mempools and swiftly supply liquidity, only to withdraw it in the same block. Our game-theoretical analysis uncovers a paradoxical scenario: the presence of a JIT LP, rather than enhancing liquidity as expected, can inadvertently reduce it. A central reason behind the paradox is the adverse selection problem encountered by passive LPs, stemming from the presence of informed arbitrageurs. Unlike passive LPs, JIT LPs have the advantage of analyzing the order flow prior to providing liquidity and block confirmation. We show that this second-mover advantage mitigates their adverse selection costs and potentially crowds out passive LPs, particularly when order flows are not highly elastic to changes in pool liquidity. These equilibrium effects may lead to an overall reduction of pool liquidity and to an increased execution risk for liquidity demanders. To alleviate the detrimental effects of JIT liquidity, we propose a two-tiered fee structure for passive and JIT LPs. We show that this structure may prevent crowding out and improve welfare.
{"title":"The Paradox Of Just-in-Time Liquidity in Decentralized Exchanges: More Providers Can Sometimes Mean Less Liquidity","authors":"Agostino Capponi, Ruizhe Jia, Brian Zhu","doi":"arxiv-2311.18164","DOIUrl":"https://doi.org/arxiv-2311.18164","url":null,"abstract":"We study just-in-time (JIT) liquidity provision within blockchain-based\u0000decentralized exchanges (DEXs). In contrast to passive liquidity providers\u0000(LPs) who deposit assets into liquidity pools before observing order flows, JIT\u0000LPs take a more active approach. They monitor pending orders from public\u0000blockchain mempools and swiftly supply liquidity, only to withdraw it in the\u0000same block. Our game-theoretical analysis uncovers a paradoxical scenario: the\u0000presence of a JIT LP, rather than enhancing liquidity as expected, can\u0000inadvertently reduce it. A central reason behind the paradox is the adverse\u0000selection problem encountered by passive LPs, stemming from the presence of\u0000informed arbitrageurs. Unlike passive LPs, JIT LPs have the advantage of\u0000analyzing the order flow prior to providing liquidity and block confirmation.\u0000We show that this second-mover advantage mitigates their adverse selection\u0000costs and potentially crowds out passive LPs, particularly when order flows are\u0000not highly elastic to changes in pool liquidity. These equilibrium effects may\u0000lead to an overall reduction of pool liquidity and to an increased execution\u0000risk for liquidity demanders. To alleviate the detrimental effects of JIT\u0000liquidity, we propose a two-tiered fee structure for passive and JIT LPs. We\u0000show that this structure may prevent crowding out and improve welfare.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We have shown, in a series of articles, that a classical description of a large number of economic agents can be replaced by a statistical fields formalism. To better understand the accumulation and allocation of capital among different sectors, the present paper applies this statistical fields description to a large number of heterogeneous agents divided into two groups. The first group is composed of a large number of firms in different sectors that collectively own the entire physical capital. The second group, investors, holds the entire financial capital and allocates it between firms across sectors according to investment preferences, expected returns, and stock prices variations on financial markets. In return, firms pay dividends to their investors. Financial capital is thus a function of dividends and stock valuations, whereas physical capital is a function of the total capital allocated by the financial sector. Whereas our previous work focused on the background fields that describe potential long-term equilibria, here we compute the transition functions of individual agents and study their probabilistic dynamics in the background field, as a function of their initial state. We show that capital accumulation depends on various factors. The probability associated with each firm's trajectories is the result of several contradictory effects: the firm tends to shift towards sectors with the greatest long-term return, but must take into account the impact of its shift on its attractiveness for investors throughout its trajectory. Since this trajectory depends largely on the average capital of transition sectors, a firm's attractiveness during its relocation depends on the relative level of capital in those sectors. Thus, an under-capitalized firm reaching a high-capital sector will experience a loss of attractiveness, and subsequently, in investors. Moreover, the firm must also consider the effects of competition in the intermediate sectors. An under-capitalized firm will tend to be ousted out towards sectors with lower average capital, while an over-capitalized firm will tend to shift towards higher averagecapital sectors. For investors, capital allocation depends on their short and long-term returns. These returns are not independent: in the short-term, returns are composed of both the firm's dividends and the increase in its stock prices. In the long-term, returns are based on the firm's growth expectations, but also, indirectly, on expectations of higher stock prices. Investors' capital allocation directly depends on the volatility of stock prices and {ldots}rms'dividends. Investors will tend to reallocate their capital to maximize their short and long-term returns. The higher their level of capital, the stronger the reallocation will be.
{"title":"A Statistical Field Perspective on Capital Allocation and Accumulation: Individual dynamics","authors":"Pierre GosselinIF, Aïleen Lotz","doi":"arxiv-2401.06142","DOIUrl":"https://doi.org/arxiv-2401.06142","url":null,"abstract":"We have shown, in a series of articles, that a classical description of a\u0000large number of economic agents can be replaced by a statistical fields\u0000formalism. To better understand the accumulation and allocation of capital\u0000among different sectors, the present paper applies this statistical fields\u0000description to a large number of heterogeneous agents divided into two groups.\u0000The first group is composed of a large number of firms in different sectors\u0000that collectively own the entire physical capital. The second group, investors,\u0000holds the entire financial capital and allocates it between firms across\u0000sectors according to investment preferences, expected returns, and stock prices\u0000variations on financial markets. In return, firms pay dividends to their\u0000investors. Financial capital is thus a function of dividends and stock\u0000valuations, whereas physical capital is a function of the total capital\u0000allocated by the financial sector. Whereas our previous work focused on the\u0000background fields that describe potential long-term equilibria, here we compute\u0000the transition functions of individual agents and study their probabilistic\u0000dynamics in the background field, as a function of their initial state. We show\u0000that capital accumulation depends on various factors. The probability\u0000associated with each firm's trajectories is the result of several contradictory\u0000effects: the firm tends to shift towards sectors with the greatest long-term\u0000return, but must take into account the impact of its shift on its\u0000attractiveness for investors throughout its trajectory. Since this trajectory\u0000depends largely on the average capital of transition sectors, a firm's\u0000attractiveness during its relocation depends on the relative level of capital\u0000in those sectors. Thus, an under-capitalized firm reaching a high-capital\u0000sector will experience a loss of attractiveness, and subsequently, in\u0000investors. Moreover, the firm must also consider the effects of competition in\u0000the intermediate sectors. An under-capitalized firm will tend to be ousted out\u0000towards sectors with lower average capital, while an over-capitalized firm will\u0000tend to shift towards higher averagecapital sectors. For investors, capital\u0000allocation depends on their short and long-term returns. These returns are not\u0000independent: in the short-term, returns are composed of both the firm's\u0000dividends and the increase in its stock prices. In the long-term, returns are\u0000based on the firm's growth expectations, but also, indirectly, on expectations\u0000of higher stock prices. Investors' capital allocation directly depends on the\u0000volatility of stock prices and {ldots}rms'dividends. Investors will tend to\u0000reallocate their capital to maximize their short and long-term returns. The\u0000higher their level of capital, the stronger the reallocation will be.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139469284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The clarion call for causal reduction in the study of capital markets is intensifying. However, in self-referencing and open systems such as capital markets, the idea of unidirectional causation (if applicable) may be limiting at best, and unstable or fallacious at worst. In this research, we critically assess the use of scientific deduction and causal inference within the study of empirical finance and econometrics. We then demonstrate the idea of competing causal chains using a toy model adapted from ecological predator/prey relationships. From this, we develop the alternative view that the study of empirical finance, and the risks contained therein, may be better appreciated once we admit that our current arsenal of quantitative finance tools may be limited to ex post causal inference under popular assumptions. Where these assumptions are challenged, for example in a recognizable reflexive context, the prescription of unidirectional causation proves deeply problematic.
{"title":"Epistemic Limits of Empirical Finance: Causal Reductionism and Self-Reference","authors":"Daniel Polakow, Tim Gebbie, Emlyn Flint","doi":"arxiv-2311.16570","DOIUrl":"https://doi.org/arxiv-2311.16570","url":null,"abstract":"The clarion call for causal reduction in the study of capital markets is\u0000intensifying. However, in self-referencing and open systems such as capital\u0000markets, the idea of unidirectional causation (if applicable) may be limiting\u0000at best, and unstable or fallacious at worst. In this research, we critically\u0000assess the use of scientific deduction and causal inference within the study of\u0000empirical finance and econometrics. We then demonstrate the idea of competing\u0000causal chains using a toy model adapted from ecological predator/prey\u0000relationships. From this, we develop the alternative view that the study of\u0000empirical finance, and the risks contained therein, may be better appreciated\u0000once we admit that our current arsenal of quantitative finance tools may be\u0000limited to ex post causal inference under popular assumptions. Where these\u0000assumptions are challenged, for example in a recognizable reflexive context,\u0000the prescription of unidirectional causation proves deeply problematic.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138522694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hong Kong's anti-ELAB movement had a significant impact on the stock market the stock price of listed companies. Using the number of protestors as the measurement of daily protesting intensity from 2019/6/6 to 2020/1/17, this paper documents that the stock price of listed companies associated with the pan-democratic parties were more negatively affected by protesting than other companies. Furthermore, this paper finds that after the implementation of the anti-mask law, protesting had a positive impact on red chips but a negative impact on companies related to pan-democracy parties. Therefore, this paper believes that after the central government and the HKSAR government adopted strict measures to stop violence and chaos, the value of the political connection of red chips became positive while the value of the connection with pan-democracy parties became negative.
{"title":"The impact of Hong Kong's anti-ELAB movement on political related firms","authors":"Ziqi Wang","doi":"arxiv-2401.13676","DOIUrl":"https://doi.org/arxiv-2401.13676","url":null,"abstract":"Hong Kong's anti-ELAB movement had a significant impact on the stock market\u0000the stock price of listed companies. Using the number of protestors as the\u0000measurement of daily protesting intensity from 2019/6/6 to 2020/1/17, this\u0000paper documents that the stock price of listed companies associated with the\u0000pan-democratic parties were more negatively affected by protesting than other\u0000companies. Furthermore, this paper finds that after the implementation of the\u0000anti-mask law, protesting had a positive impact on red chips but a negative\u0000impact on companies related to pan-democracy parties. Therefore, this paper\u0000believes that after the central government and the HKSAR government adopted\u0000strict measures to stop violence and chaos, the value of the political\u0000connection of red chips became positive while the value of the connection with\u0000pan-democracy parties became negative.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139579524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xihan Xiong, Zhipeng Wang, Xi Chen, William Knottenbelt, Michael Huth
Lido, the leading Liquid Staking Derivative (LSD) provider on Ethereum, allows users to stake an arbitrary amount of ETH to receive stETH, which can be integrated with Decentralized Finance (DeFi) protocols such as Aave. The composability between Lido and Aave enables a novel strategy called "leverage staking", where users stake ETH on Lido to acquire stETH, utilize stETH as collateral on Aave to borrow ETH, and then restake the borrowed ETH on Lido. Users can iteratively execute this process to optimize potential returns based on their risk profile. This paper systematically studies the opportunities and risks associated with leverage staking. We are the first to formalize the leverage staking strategy within the Lido-Aave ecosystem. Our empirical study identifies 262 leverage staking positions on Ethereum, with an aggregated staking amount of 295,243 ETH (482M USD). We discover that 90.13% of leverage staking positions have achieved higher returns than conventional staking. Furthermore, we perform stress tests to evaluate the risk introduced by leverage staking under extreme conditions. We find that leverage staking significantly amplifies the risk of cascading liquidations. We hope this paper can inform and encourage the development of robust risk management approaches to protect the Lido-Aave LSD ecosystem.
{"title":"Leverage Staking with Liquid Staking Derivatives (LSDs): Opportunities and Risks","authors":"Xihan Xiong, Zhipeng Wang, Xi Chen, William Knottenbelt, Michael Huth","doi":"arxiv-2401.08610","DOIUrl":"https://doi.org/arxiv-2401.08610","url":null,"abstract":"Lido, the leading Liquid Staking Derivative (LSD) provider on Ethereum,\u0000allows users to stake an arbitrary amount of ETH to receive stETH, which can be\u0000integrated with Decentralized Finance (DeFi) protocols such as Aave. The\u0000composability between Lido and Aave enables a novel strategy called \"leverage\u0000staking\", where users stake ETH on Lido to acquire stETH, utilize stETH as\u0000collateral on Aave to borrow ETH, and then restake the borrowed ETH on Lido.\u0000Users can iteratively execute this process to optimize potential returns based\u0000on their risk profile. This paper systematically studies the opportunities and risks associated with\u0000leverage staking. We are the first to formalize the leverage staking strategy\u0000within the Lido-Aave ecosystem. Our empirical study identifies 262 leverage\u0000staking positions on Ethereum, with an aggregated staking amount of 295,243 ETH\u0000(482M USD). We discover that 90.13% of leverage staking positions have achieved\u0000higher returns than conventional staking. Furthermore, we perform stress tests\u0000to evaluate the risk introduced by leverage staking under extreme conditions.\u0000We find that leverage staking significantly amplifies the risk of cascading\u0000liquidations. We hope this paper can inform and encourage the development of\u0000robust risk management approaches to protect the Lido-Aave LSD ecosystem.","PeriodicalId":501372,"journal":{"name":"arXiv - QuantFin - General Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139496116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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