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on Financial Markets |
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Issue of 2024‒06‒24
thirteen papers chosen by |
| By: | Lukas Menkhoff; Jannis Westermann |
| Abstract: | The low degree of stock market participation (SMP) is one of the big puzzles in finance. Numerous determinants have been proposed. We put these determinants into a structure that is derived from a standard static portfolio model. Then we discuss arguments put forward regarding specific SMP determinants and the empirical evidence that has been provided. The focus of our survey is on the identification of a causal impact of determinants on SMP via shocks. We summarize the evidence by suggesting established and likely SMP determinants and providing an outlook for future research and policy. |
| Keywords: | Stock market participation, transaction costs, information, return volatility, risk tolerance |
| JEL: | G11 G51 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwwpp:dp2078&r= |
| By: | Gang Hu; Ming Gu |
| Abstract: | Investment portfolios, central to finance, balance potential returns and risks. This paper introduces a hybrid approach combining Markowitz's portfolio theory with reinforcement learning, utilizing knowledge distillation for training agents. In particular, our proposed method, called KDD (Knowledge Distillation DDPG), consist of two training stages: supervised and reinforcement learning stages. The trained agents optimize portfolio assembly. A comparative analysis against standard financial models and AI frameworks, using metrics like returns, the Sharpe ratio, and nine evaluation indices, reveals our model's superiority. It notably achieves the highest yield and Sharpe ratio of 2.03, ensuring top profitability with the lowest risk in comparable return scenarios. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.05449&r= |
| By: | Philippe van der Beck; Jean-Philippe Bouchaud; Dario Villamaina |
| Abstract: | Many active funds hold concentrated portfolios. Flow-driven trading in these securities causes price pressure, which pushes up the funds' existing positions resulting in realized returns. We decompose fund returns into a price pressure (self-inflated) and a fundamental component and show that when allocating capital across funds, investors are unable to identify whether realized returns are self-inflated or fundamental. Because investors chase self-inflated fund returns at a high frequency, even short-lived impact meaningfully affects fund flows at longer time scales. The combination of price impact and return chasing causes an endogenous feedback loop and a reallocation of wealth to early fund investors, which unravels once the price pressure reverts. We find that flows chasing self-inflated returns predict bubbles in ETFs and their subsequent crashes, and lead to a daily wealth reallocation of 500 Million from ETFs alone. We provide a simple regulatory reporting measure -- fund illiquidity -- which captures a fund's potential for self-inflated returns. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.12768&r= |
| By: | Andrew Atkeson; Jonathan Heathcote; Fabrizio Perri |
| Abstract: | We present two valuation models that we use to account for the annual data on price per share and dividends per share for the CRSP Value-Weighted Index from 1929-2023. We show that it is a simple matter to account for these data based purely on a model of variation in the expected ratio of dividends per share to aggregate consumption over time under two conditions. First, investors must receive news shocks regarding the expected ratio of dividends per share to aggregate consumption in the long run. Second, the discount rate used to evaluate the impact of this news on the current price per share must be low. We argue that both of these conditions are likely satisfied in the data. Because our valuation model reproduces the data on price per share and dividends per share exactly over this long time period, it also reproduces realized values of returns, dividend growth, the dividend-price ratio, and all Campbell-Shiller-style regression results involving these variables. Thus, we conclude that the answer to Shiller (1981)’s question “Do stock prices move too much to be justified by subsequent movements in dividends?” is No. |
| JEL: | G0 G12 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32481&r= |
| By: | Shuxin Guo; Qiang Liu |
| Abstract: | We study the data-generating processes for factors expressed in return differences, which the literature on time-series asset pricing seems to have overlooked. For the factors' data-generating processes or long-short zero-cost portfolios, a meaningful definition of returns is impossible; further, the compounded market factor (MF) significantly underestimates the return difference between the market and the risk-free rate compounded separately. Surprisingly, if MF were treated coercively as periodic-rebalancing long-short (i.e., the same as size and value), Fama-French three-factor (FF3) would be economically unattractive for lacking compounding and irrelevant for suffering from the small "size of an effect." Otherwise, FF3 might be misspecified if MF were buy-and-hold long-short. Finally, we show that OLS with net returns for single-index models leads to inflated alphas, exaggerated t-values, and overestimated Sharpe ratios (SR); worse, net returns may lead to pathological alphas and SRs. We propose defining factors (and SRs) with non-difference compound returns. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.10920&r= |
| By: | Tut, DANIEL |
| Abstract: | What factors drive the valuation of Bitcoin and other crypto-assets? We use a novel measure and show that [1] Sentiments in Bitcoin drive the price action and have a material effect on returns [2] Sentiments in Bitcoin drive the valuation of other cryptocurrency assets [3] Sentiments in Bitcoin drive returns in other cryptocurrency assets. Our results show that optimistic sentiments in Bitcoin drive overvaluation in Bitcoin itself and other cryptocurrency assets. Our results support the notion that liquidity measures are salient factors in price discovery. |
| Keywords: | Valuation, Cryptocurrencies, Bitcoin, Digital Assets, sentiments, speculation |
| JEL: | D8 D84 G21 G24 G3 G32 G39 |
| Date: | 2024–03–18 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120866&r= |
| By: | Thomas Drechsel |
| Abstract: | This paper combines new data and a narrative approach to identify shocks to political pressure on the Federal Reserve. From archival records, I build a data set of personal interactions between U.S. Presidents and Fed officials between 1933 and 2016. Since personal interactions do not necessarily reflect political pressure, I develop a narrative identification strategy based on President Nixon's pressure on Fed Chair Burns. I exploit this narrative through restrictions on a structural vector autoregression that includes the personal interaction data. I find that political pressure shocks (i) increase inflation strongly and persistently, (ii) lead to statistically weak negative effects on activity, (iii) contributed to inflationary episodes outside of the Nixon era, and (iv) transmit differently from standard expansionary monetary policy shocks, by having a stronger effect on inflation expectations. Quantitatively, increasing political pressure by half as much as Nixon, for six months, raises the price level more than 8%. |
| JEL: | C32 D72 E31 E40 E50 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32461&r= |
| By: | Huiyu Li; Junhua Hu |
| Abstract: | Stock price prediction has always been a difficult task for forecasters. Using cutting-edge deep learning techniques, stock price prediction based on investor sentiment extracted from online forums has become feasible. We propose a novel hybrid deep learning framework for predicting stock prices. The framework leverages the XLNET model to analyze the sentiment conveyed in user posts on online forums, combines these sentiments with the post popularity factor to compute daily group sentiments, and integrates this information with stock technical indicators into an improved BiLSTM-highway model for stock price prediction. Through a series of comparative experiments involving four stocks on the Chinese stock market, it is demonstrated that the hybrid framework effectively predicts stock prices. This study reveals the necessity of analyzing investors' textual views for stock price prediction. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.10584&r= |
| By: | Jingyang Wu; Xinyi Zhang; Fangyixuan Huang; Haochen Zhou; Rohtiash Chandra |
| Abstract: | There has been much interest in accurate cryptocurrency price forecast models by investors and researchers. Deep Learning models are prominent machine learning techniques that have transformed various fields and have shown potential for finance and economics. Although various deep learning models have been explored for cryptocurrency price forecasting, it is not clear which models are suitable due to high market volatility. In this study, we review the literature about deep learning for cryptocurrency price forecasting and evaluate novel deep learning models for cryptocurrency stock price prediction. Our deep learning models include variants of long short-term memory (LSTM) recurrent neural networks, variants of convolutional neural networks (CNNs), and the Transformer model. We evaluate univariate and multivariate approaches for multi-step ahead predicting of cryptocurrencies close-price. Our results show that the univariate LSTM model variants perform best for cryptocurrency predictions. We also carry out volatility analysis on the four cryptocurrencies which reveals significant fluctuations in their prices throughout the COVID-19 pandemic. Additionally, we investigate the prediction accuracy of two scenarios identified by different training sets for the models. First, we use the pre-COVID-19 datasets to model cryptocurrency close-price forecasting during the early period of COVID-19. Secondly, we utilise data from the COVID-19 period to predict prices for 2023 to 2024. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.11431&r= |
| By: | Hongyang Yang; Boyu Zhang; Neng Wang; Cheng Guo; Xiaoli Zhang; Likun Lin; Junlin Wang; Tianyu Zhou; Mao Guan; Runjia Zhang; Christina Dan Wang |
| Abstract: | As financial institutions and professionals increasingly incorporate Large Language Models (LLMs) into their workflows, substantial barriers, including proprietary data and specialized knowledge, persist between the finance sector and the AI community. These challenges impede the AI community's ability to enhance financial tasks effectively. Acknowledging financial analysis's critical role, we aim to devise financial-specialized LLM-based toolchains and democratize access to them through open-source initiatives, promoting wider AI adoption in financial decision-making. In this paper, we introduce FinRobot, a novel open-source AI agent platform supporting multiple financially specialized AI agents, each powered by LLM. Specifically, the platform consists of four major layers: 1) the Financial AI Agents layer that formulates Financial Chain-of-Thought (CoT) by breaking sophisticated financial problems down into logical sequences; 2) the Financial LLM Algorithms layer dynamically configures appropriate model application strategies for specific tasks; 3) the LLMOps and DataOps layer produces accurate models by applying training/fine-tuning techniques and using task-relevant data; 4) the Multi-source LLM Foundation Models layer that integrates various LLMs and enables the above layers to access them directly. Finally, FinRobot provides hands-on for both professional-grade analysts and laypersons to utilize powerful AI techniques for advanced financial analysis. We open-source FinRobot at \url{https://github.com/AI4Finance-Found ation/FinRobot}. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.14767&r= |
| By: | Altmejd, Adam (Stockholm University); Jansson, Thomas (Sveriges Riksbank); Karabulut, Yigitcan (Frankfurt School of Finance and Management) |
| Abstract: | Using university admission cutoffs that generate exogenous variation in college-major choices, we provide causal evidence that enrollment in a business or economics program leads individuals to invest significantly more in the stock market, earn higher portfolio returns, and ultimately accumulate higher levels of wealth later in life. Underlying these effects, beyond differences in risk-taking, innate ability, labor market outcomes, or scale effects, is the enhanced ability of business educated individuals to acquire and process economic information and make informed investment decisions. Early investments in financial literacy thus play an important role in generating higher returns that significantly alter individuals' life-cycle wealth profiles. |
| Keywords: | portfolio choice, financial literacy, portfolio returns, household wealth, returns to education, distribution of wealth |
| JEL: | G11 G51 G53 I26 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16976&r= |
| By: | Ngoepe, Letlhogonolo Kearabilwe; Bonga-Bonga, Lumengo |
| Abstract: | Various ‘green’ investment channels cater specifically to environmentally conscious investments. In this paper, we investigate the optimal green investment strategy by comparing the risk of three green financial instruments– green bonds, green equity, and a balanced 50/50 bond equity fund. Using the dynamic and frequency connectedness approaches by Diebold and Yilmaz (2012) and Baruník and Křehlík (2018), we analyze how financial risk affects green investment over various time horizons. Our findings show that green equity possesses the highest risk spillovers. Furthermore, green bonds and the ESG equity index provide risk diversification benefits for green investors. The balanced index displays a low risk-return nexus, further indicating that green investors are better off by investing in a diversified portfolio. Lastly, under unfavourable market conditions, the green investment market instruments provide little to no diversification against each other. |
| Keywords: | Green equity, ESG equity index, balanced index, frequency connetedness |
| JEL: | C5 F3 G15 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:121091&r= |
| By: | Aney, Madhav S.; Banerji, Sanjay |
| Abstract: | We analyse the impact of the introduction of a new technology on the National Stock Exchange in India that allowed trading of stocks without the need to transfer paper share certificates (demat trading). We document a decrease in the bid-ask spread and an increase in trading volume following its introduction particularly for those stocks that were previously illiquid. We present evidence that suggests that the primary channel for the increase in liquidity was the elimination of the risk of being sold forged securities as the clearing system took on the risk of reimbursing buyers of forged shares at the introduction of demat trading. |
| Keywords: | Liquidity trading, Bid-ask Spead, Frauds, Market manipulations |
| JEL: | G18 G19 G28 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bofitp:296486&r= |