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on Financial Markets |
| By: | Jingchu Zhang |
| Abstract: | This paper mainly utilizes the ARDL model and principal component analysis to investigate the relationship between the volatility of China's Shanghai Composite Index returns and the variables of exchange rate and domestic and foreign bond yields in an internationally integrated stock market. This paper uses a daily data set for the period from July 1, 2010 to April 30, 2024, in which the dependent variable is the Shanghai Composite Index return, and the main independent variables are the spot exchange rate of the RMB against the US dollar, the 10-year treasury bond yields in China and the United States and their lagged variables, with the effect of the time factor added. Firstly, the development of the stock, foreign exchange and bond markets and the basic theories are reviewed, and then each variable is analyzed by descriptive statistics, the correlation between the independent variables and the dependent variable is expanded theoretically, and the corresponding empirical analyses are briefly introduced, and then the empirical analyses and modeling of the relationship between the independent variables and the dependent variable are carried out on the basis of the theoretical foundations mentioned above with the support of the daily data, and the model conclusions are analyzed economically through a large number of tests, then the model conclusions are analyzed economically. economic analysis of the model conclusions, and finally, the author proposes three suggestions to enhance the stability and return of the Chinese stock market, respectively. Key Words: Chinese Stock Market, Volatility, GARCH, ARDL Model |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.08668 |
| By: | Manish Jha |
| Abstract: | This paper demonstrates that hedge funds tend to design their activist campaigns to align with the preferences and ideologies of institutions holding large stakes in the target company. I estimate these preferences by analyzing the institutions' previous proxy voting behavior. The results reveal that activists benefit from this approach. Campaigns with a stronger positive correlation between the preferences of larger institutions and activist communications attract more shareholder attention, receive more votes, and are more likely to succeed. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.16553 |
| By: | Abraham Atsiwo; Andrey Sarantsev |
| Abstract: | The Capital Asset Pricing Model (CAPM) relates a well-diversified stock portfolio to a benchmark portfolio. We insert size effect in CAPM, capturing the observation that small stocks have higher risk and return than large stocks, on average. Dividing stock index returns by the Volatility Index makes them independent and normal. In this article, we combine these ideas to create a new discrete-time model, which includes volatility, relative size, and CAPM. We fit this model using real-world data, prove the long-term stability, and connect this research to Stochastic Portfolio Theory. We fill important gaps in our previous article on CAPM with the size factor. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.19444 |
| By: | Heng-fu Zou |
| Date: | 2025–01–21 |
| URL: | https://d.repec.org/n?u=RePEc:cuf:wpaper:735 |
| By: | Bryan T. Kelly; Boris Kuznetsov; Semyon Malamud; Teng Andrea Xu |
| Abstract: | The core statistical technology in artificial intelligence is the large-scale transformer network. We propose a new asset pricing model that implants a transformer in the stochastic discount factor. This structure leverages conditional pricing information via cross-asset information sharing and nonlinearity. We also develop a linear transformer that serves as a simplified surrogate from which we derive an intuitive decomposition of the transformer's asset pricing mechanisms. We find large reductions in pricing errors from our artificial intelligence pricing model (AIPM) relative to previous machine learning models and dissect the sources of these gains. |
| JEL: | C45 G10 G11 G14 G17 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33351 |
| By: | Youngsuk Yook |
| Abstract: | This paper uses confidential Census data to provide a granular look into the U.S. firms’ cash holding portfolios encompassing nearly four decades. The data provide information on short-term investment securities held in the portfolios, such as time deposits, commercial paper and government securities in addition to cash. The security-level information reveals that portfolios of the same size can have very different levels of liquidity and riskiness as the composition of securities varies considerably across firms and over time. Firms with strong precautionary motives tend to allocate more toward relatively more liquid and less risky securities. Firms actively rebalance their portfolios in response to changing economic conditions or idiosyncratic shocks to securities they hold. Event studies using shocks to Treasury securities and commercial paper shows firms shifting away from affected securities and simultaneously adjusting weights of other securities. |
| Keywords: | Cash management, liquid assets, short-term investment securities |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:25-02 |
| By: | Subhasis Dasgupta; Pratik Satpati; Ishika Choudhary; Jaydip Sen |
| Abstract: | In the recent past, there were several works on the prediction of stock price using different methods. Sentiment analysis of news and tweets and relating them to the movement of stock prices have already been explored. But, when we talk about the news, there can be several topics such as politics, markets, sports etc. It was observed that most of the prior analyses dealt with news or comments associated with particular stock prices only or the researchers dealt with overall sentiment scores only. However, it is quite possible that different topics having different levels of impact on the movement of the stock price or an index. The current study focused on bridging this gap by analysing the movement of Nifty 50 index with respect to the sentiments associated with news items related to various different topic such as sports, politics, markets etc. The study established that sentiment scores of news items of different other topics also have a significant impact on the movement of the index. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.06794 |
| By: | Jerick Shi; Burton Hollifield |
| Abstract: | Predicting the movement of the stock market and other assets has been valuable over the past few decades. Knowing how the value of a certain sector market may move in the future provides much information for investors, as they use that information to develop strategies to maximize profit or minimize risk. However, market data are quite noisy, and it is challenging to choose the right data or the right model to create such predictions. With the rise of large language models, there are ways to analyze certain data much more efficiently than before. Our goal is to determine whether the GPT model provides more useful information compared to other traditional transformer models, such as the BERT model. We shall use data from the Federal Reserve Beige Book, which provides summaries of economic conditions in different districts in the US. Using such data, we then employ the LLM's to make predictions on the correlations. Using these correlations, we then compare the results with well-known strategies and determine whether knowing the economic conditions improves investment decisions. We conclude that the Beige Book does contain information regarding correlations amongst different assets, yet the GPT model has too much look-ahead bias and that traditional models still triumph. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.16569 |
| By: | Mr. Bas B. Bakker |
| Abstract: | The economic literature has long attributed non-zero expected excess returns in currency markets to time-varying risk premiums demanded by risk-averse investors. This paper, building on Bacchetta and van Wincoop's (2021) portfolio balance framework, shows that such returns can also arise when investors are risk-neutral but face portfolio adjustment costs. Models with adjustment costs but no risk aversion predict a negative correlation between exchange rate levels and expected excess returns, while models with risk aversion but no adjustment costs predict a positive one. Using data from nine inflation targeting economies with floating exchange rates (2000–2024), we find strong empirical support for the adjustment costs framework. The negative correlation persists even during periods of low market stress, further evidence that portfolio adjustment costs, not risk premium shocks, drive the link between exchange rates and excess returns. Our model predicts that one-year expected excess returns should have predictive power for multi-year returns, with longer-term expected returns as increasing multiples of short-term expectations, and the predictive power strengthening with the horizon. We confirm these findings empirically. We also examine scenarios combining risk aversion and adjustment costs, showing that sufficiently high adjustment costs are essential to generate the observed negative relationship.These findings provide a simpler, testable alternative to literature relying on assumptions about unobservable factors like time-varying risk premiums, intermediary constraints, or noise trader activity. |
| Keywords: | Exchange Rates; Portfolio Balance; Uncovered Interest Parity; Portfolio Adjustment Costs; Risk Premium; Currency Markets; Expected Returns |
| Date: | 2025–01–17 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/011 |
| By: | Jiahao Zhu; Hengzhi Wu |
| Abstract: | This study explores the use of Transformer-based models to predict both covariance and semi-covariance matrices for ETF portfolio optimization. Traditional portfolio optimization techniques often rely on static covariance estimates or impose strict model assumptions, which may fail to capture the dynamic and non-linear nature of market fluctuations. Our approach leverages the power of Transformer models to generate adaptive, real-time predictions of asset covariances, with a focus on the semi-covariance matrix to account for downside risk. The semi-covariance matrix emphasizes negative correlations between assets, offering a more nuanced approach to risk management compared to traditional methods that treat all volatility equally. Through a series of experiments, we demonstrate that Transformer-based predictions of both covariance and semi-covariance significantly enhance portfolio performance. Our results show that portfolios optimized using the semi-covariance matrix outperform those optimized with the standard covariance matrix, particularly in volatile market conditions. Moreover, the use of the Sortino ratio, a risk-adjusted performance metric that focuses on downside risk, further validates the effectiveness of our approach in managing risk while maximizing returns. These findings have important implications for asset managers and investors, offering a dynamic, data-driven framework for portfolio construction that adapts more effectively to shifting market conditions. By integrating Transformer-based models with the semi-covariance matrix for improved risk management, this research contributes to the growing field of machine learning in finance and provides valuable insights for optimizing ETF portfolios. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.19649 |
| By: | Antonello Cirulli; Gianluca De Nard; Joshua Traut; Patrick Walker |
| Abstract: | The low-risk anomaly challenges traditional financial theory by stating that less volatile stocks generate higher risk-adjusted returns. This paper explores how various portfolio construction choices influence the performance of low-risk portfolios. We show that methodological decisions critically influence portfolio outcomes, causing substantial dispersion in performance metrics across weighting schemes and risk estimators. This can only be marginally mitigated by incorporating constraints such as short-sale restrictions and size or price filters. Our analysis reveals that volatility-based estimators yield the most favorable performance distribution, outperforming beta-based approaches. Transaction costs are found to significantly affect performance and are vitally important in identifying the most attractive portfolios, highlighting the importance of realistic implementation constraints. Through rigorous empirical analysis, this study bridges the gap between theoretical insights and practical applications, offering actionable guidance to investors. The findings advocate for a cautious approach to nonstandard errors in portfolio modeling and emphasize the necessity of robust strategies in low-risk investing. |
| Keywords: | Low-risk investing, methodology, nonstandard errors, portfolio construction |
| JEL: | C52 G11 G12 G15 G17 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:zur:econwp:463 |