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on Financial Markets |
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Issue of 2024‒01‒01
nine papers chosen by |
| By: | Winkler, Julian |
| Abstract: | What can granular data on investor holdings tell us about stock price variation? I model the growth rate of a portfolio manager's holdings based on evolving asset fundamentals by including demand for asset-specific characteristics in a portfolio optimisation function. Alongside changes in asset characteristics, the manager re-allocates wealth according to evolving preferences. This introduces memory into the portfolio management problem, as past investments inform the choice for new allocations. Using the model, I decompose the growth rate of mutual fund holdings by the effect of i) changing stock characteristics, ii) new preferences, and iii) mean reversion in latent demand. I nest these estimated components, by aggregating holding growth rates by the fund's total net assets, into an expression for stock price growth. I find that changing preferences explain at least as much variation in stock prices as changes in fundamentals. This demonstrates the importance of studying heterogeneity in investor preferences, and their evolution, in furthering our understanding of stock market phenomena. |
| Keywords: | Asset demand, stock price volatility, portfolio management, robustness |
| JEL: | G02 G11 G12 |
| Date: | 2023–11–12 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119149&r=fmk |
| By: | Md Sabbirul Haque; Md Shahedul Amin; Jonayet Miah; Duc Minh Cao; Ashiqul Haque Ahmed |
| Abstract: | Prediction of stock prices plays a significant role in aiding the decision-making of investors. Considering its importance, a growing literature has emerged trying to forecast stock prices with improved accuracy. In this study, we introduce an innovative approach for forecasting stock prices with greater accuracy. We incorporate external economic environment-related information along with stock prices. In our novel approach, we improve the performance of stock price prediction by taking into account variations due to future expected macroeconomic policy changes as investors adjust their current behavior ahead of time based on expected future macroeconomic policy changes. Furthermore, we incorporate macroeconomic variables along with historical stock prices to make predictions. Results from this strongly support the inclusion of future economic policy changes along with current macroeconomic information. We confirm the supremacy of our method over the conventional approach using several tree-based machine-learning algorithms. Results are strongly conclusive across various machine learning models. Our preferred model outperforms the conventional approach with an RMSE value of 1.61 compared to an RMSE value of 1.75 from the conventional approach. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06278&r=fmk |
| By: | Nicola Cetorelli; Sarah Zebar |
| Abstract: | March 2023 will rightfully be remembered as a period of major turmoil for the U.S. banking industry. In this post, we go beyond banks to analyze how fixed-income, open-end funds (bond funds) fared in the days after the start of the banking crisis. We find that bond funds experienced net outflows each day for almost three weeks after the run on Silicon Valley Bank (SVB), and that these outflows were experienced diffusely across the entire segment. Our preliminary evidence suggests that the outflows from bond funds may have been an unintended consequence of the exceptional measures taken to strengthen the balance sheet of banks during this time. |
| Keywords: | Silicon Valley Bank (SVB); Bank Term Funding Program (BTFP) |
| JEL: | G2 |
| Date: | 2023–11–28 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednls:97381&r=fmk |
| By: | Anusha Chari; Karlye Dilts Stedman; Christian Lundblad |
| Abstract: | This paper defines risk-on risk-off (RORO), an elusive terminology in pervasive use, as the variation in global investor risk aversion. Our high-frequency RORO index captures time-varying investor risk appetite across multiple dimensions: advanced economy credit risk, equity market volatility, funding conditions, and currency dynamics. The index exhibits risk-off skewness and pronounced fat tails, suggesting its amplifying potential for extreme, destabilizing events. Compared with the conventional VIX measure, the RORO index reflects the multifaceted nature of risk, underscoring the diverse provenance of investor risk sentiment. Practical applications of the RORO index highlight its significance for international portfolio reallocation and return predictability. |
| JEL: | F21 F31 F36 G11 G15 G17 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31907&r=fmk |
| By: | Jiaer He; Roberto Rivera |
| Abstract: | Popular investment structured products in Puerto Rico are stock market tied Individual Retirement Accounts (IRA), which offer some stock market growth while protecting the principal. The performance of these retirement strategies has not been studied. This work examines the expected return and risk of Puerto Rico stock market IRA (PRIRAs) and compares their statistical properties with other investment instruments before and after tax. We propose a parametric modeling approach for structured products and apply it to PRIRAs. Our method first estimates the conditional expected return (and variance) of PRIRA assets from which we extract marginal moments through the Law of Iterated Expectation. Our results indicate that PRIRAs underperform against investing directly in the stock market while still carrying substantial risk. The expected return of the stock market IRA from Popular Bank (PRIRA1) after tax is slightly greater than that of investing in U.S. bonds, while PRIRA1 has almost two times the risk. The stock market IRA from Universal (PRIRA2) performs similarly to PRIRA1, while PRIRA2 has a lower risk than PRIRA1. PRIRAs may be reasonable for some risk-averse investors due to their principal protection and tax deferral. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06282&r=fmk |
| By: | Afees A. Salisu (Centre for Econometrics & Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Ahamuefula E. Ogbonna (Centre for Econometrics & Applied Research, Ibadan, Nigeria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (School of Business, Lebanese American University, Lebanon) |
| Abstract: | The aim of this paper is to predict the daily return volatility of 28 developed and developing stock markets based on the monthly metrics of corresponding country and global energy-related uncertainty indexes (EUIs) recently proposed in the literature. Using the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework, the results show that country-specific and global EUIs have predictive powers for stock returns volatility for the in-sample periods, with increased levels of EUIs exhibiting the tendency to heighten volatility. This predictability also withstands various out-of-sample forecast horizons, implying that EUI is a statistically relevant predictor of stock returns volatility in the out-of-sample analysis. Moreover, the forecast precision of the GARCH-MIDAS model is improved by incorporating global EUIs relatively more than country-specific EUIs. Our findings are robust to the choice of EUI proxies and sample definition. They have important implications for investors and policymakers concerned with stability in the global financial system and economy. |
| Keywords: | Monthly energy-related uncertainty index, daily stock returns volatility, developed and developing economies, GARCH-MIDAS; predictions |
| JEL: | C32 C53 G15 G17 Q43 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202336&r=fmk |
| By: | Charles, Constantin; Frydman, Cary; Kilic, Mete |
| Abstract: | We experimentally study the transmission of subjective expectations into actions. Subjects in our experiment report valuations that are far too insensitive to their expectations, relative to the prediction from a frictionless model. We propose that the insensitivity is driven by a noisy cognitive process that prevents subjects from precisely computing asset valuations. The empirical link between subjective expectations and actions becomes stronger as subjective expectations approach rational expectations. Our results highlight the importance of incorporating weak transmission into belief-based asset pricing models. Finally, we discuss how cognitive noise can provide a microfoundation for inelastic demand in the stock market. |
| Keywords: | 1749824 |
| JEL: | F3 G3 |
| Date: | 2023–10–02 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:120788&r=fmk |
| By: | Matteo Foglia (Department of Economics and Finance, University of Bari ``Aldo Moro", Italy); Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, Komotini, Greece); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (School of Business, Lebanese American University, Lebanon) |
| Abstract: | Measuring risk lies at the core of the decision-making process of every financial market participant and monetary authority. However, the bulk of literature treats risk as a function of the second moment (volatility) of the return distribution, based on the implicit unrealistic assumption that asset return are normally distributed. In this paper, we depart from centred moments of distribution by examining risk spillovers involving robust estimates of second and third moments of model-implied distributions of stock returns derived from the quantile autoregressive distributed lag mixed-frequency data sampling (QADL-MIDAS) method. Using a century of data on the stock indices of the G7 and Switzerland over the period May 1917 to February 2023 and applying the multilayer approach to spillovers, we show the following. Firstly, the risk spillover among stock markets is significant within each layer (i.e. volatility and skewness) and across the two layers. Secondly, geopolitical risks have the power to shape both risk layer values, based on an out-of-sample forecasting exercise involving machine-learning methods. Interestingly, the multi-layer approach offers a comprehensive and nuanced view of how risk information is transmitted across major stock markets, while global measures of geopolitical risk affect risk spillovers at shorter horizons up to 6 months, while, at longer horizons, the forecasting exercise is dominated by market-specific characteristics. |
| Keywords: | Risk spillover, advanced stock markets, multi-layer spillover approach, machine learning, geopolitical risks, forecasting |
| JEL: | C22 C32 C53 G15 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202337&r=fmk |
| By: | Conrad, Christian; Schoelkopf, Julius Theodor; Tushteva, Nikoleta |
| Abstract: | We show that the S&P 500’s instantaneous response to surprises in U.S. macroeconomic announcements depends on the level of long-term stock market volatility. When long-term volatility is high, stock returns are more sensitive to news, and there is a pronounced asymmetry in the response to good and bad news. We explain this by combining the Campbell-Shiller log-linear present value framework with a two-component volatility model for the conditional variance of cash flow news and allowing for volatility feedback. In our model, innovations to the long-term volatility component are the most important driver of discount rate news. Large announcement surprises lead to upward revisions in future required returns, which dampens/amplifies the effect of good/bad news. |
| Keywords: | event study; long- and short-term volatility; macroeconomic announcements; stock market response; time-varying risk premia; volatility feedback effect |
| Date: | 2023–12–05 |
| URL: | http://d.repec.org/n?u=RePEc:awi:wpaper:0739&r=fmk |