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on Financial Markets |
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Issue of 2023‒11‒13
ten papers chosen by |
| By: | Phoebe Koundouri; Conrad Landis |
| Abstract: | Our paper explores the presence of significant sources of priced risk related to ESG and SDG performance. we document, strong ESG momentum time series and cross-sectional effects in international stock returns during the years 2002 to 2023. An out of sample monthly rebalancing ESG momentum Factor mimicking portfolio (double sorted on market capitalization and ESG momentum) yields an annualized Sharpe ratio equal to 0.7 for the sample period. Moreover, we underline the importance that both ESG related performance, as well as ESG controversies are important determinants of financial performance. Last by not least, by transposing the ESG framework into a more holistic framework integrating the SDGs, we describe how our models can be used to trivially calculate the SDG footprint of financial portfolios, which is expected to be very relevant the years following the introduction of the CSRD. |
| Date: | 2023–11–03 |
| URL: | http://d.repec.org/n?u=RePEc:aue:wpaper:2318&r=fmk |
| By: | Yujie Ding; Shuai Jia; Tianyi Ma; Bingcheng Mao; Xiuze Zhou; Liuliu Li; Dongming Han |
| Abstract: | The remarkable achievements and rapid advancements of Large Language Models (LLMs) such as ChatGPT and GPT-4 have showcased their immense potential in quantitative investment. Traders can effectively leverage these LLMs to analyze financial news and predict stock returns accurately. However, integrating LLMs into existing quantitative models presents two primary challenges: the insufficient utilization of semantic information embedded within LLMs and the difficulties in aligning the latent information within LLMs with pre-existing quantitative stock features. We propose a novel framework consisting of two components to surmount these challenges. The first component, the Local-Global (LG) model, introduces three distinct strategies for modeling global information. These approaches are grounded respectively on stock features, the capabilities of LLMs, and a hybrid method combining the two paradigms. The second component, Self-Correlated Reinforcement Learning (SCRL), focuses on aligning the embeddings of financial news generated by LLMs with stock features within the same semantic space. By implementing our framework, we have demonstrated superior performance in Rank Information Coefficient and returns, particularly compared to models relying only on stock features in the China A-share market. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.05627&r=fmk |
| By: | Ye Li; Chen Wang |
| Abstract: | At the peak of the tech bubble, only 0.57% of market valuation comes from dividends in the next year. Taking the ratio of total market value to the value of one-year dividends, we obtain a valuation-based duration of 175 years. In contrast, at the height of the global financial crisis, more than 2.2% of market value is from dividends in the next year, implying a duration of 46 years. What drives valuation duration? We find that market participants have limited information about cash flow beyond one year. Therefore, an increase in valuation duration is due to a decrease in the discount rate rather than good news about long-term growth. Accordingly, valuation duration negatively predicts annual market return with an out-of-sample R2 of 15%, robustly outperforming other predictors in the literature. While the price-dividend ratio reflects the overall valuation level, our valuation-based measure of duration captures the slope of the valuation term structure. We show that valuation duration, as a discount rate proxy, is a critical state variable that augments the price-dividend ratio in spanning the (latent) state space for stock-market dynamics. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.07110&r=fmk |
| By: | Esther Cáceres (Banco de España); Matías Lamas (Banco de España) |
| Abstract: | We measure the reaction of search for income in mutual funds to supervisory-induced dividend restrictions on euro area banks during the COVID-19 pandemic, which operated as an exogenous shock to payouts in this sector. Using granular data on euro area-based mutual funds’ holdings, we show that demand for dividends motivated portfolio decisions in this period and that these decisions had implications for stock returns. Specifically, we document that there were more sales of bank stocks by income-oriented funds after payout restrictions were set in place. These funds were however less inclined to dispose of bank CoCos, an alternative high income-generating asset issued by credit institutions and not subject to supervisory distribution limits. Lastly, we analyze the price impact of these portfolio adjustments, documenting negative abnormal returns in bank stocks more exposed to income-oriented funds after the policy announcement. Our research evidences that search for income is relevant in asset allocation decisions and price formation, and quantifies some of the side effects of dividend restriction policies. |
| Keywords: | search for income, dividends, asset allocation, abnormal returns, mutual funds |
| JEL: | G12 G14 G21 G35 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:2332&r=fmk |
| By: | Fatemeh Moodi; Amir Jahangard-Rafsanjani |
| Abstract: | Due to the influence of many factors, including technical indicators on stock market prediction, feature selection is important to choose the best indicators. One of the feature selection methods that consider the performance of models during feature selection is the wrapper feature selection method. The aim of this research is to identify a combination of the best stock market indicators through feature selection to predict the stock market price with the least error. In order to evaluate the impact of wrapper feature selection techniques on stock market prediction, in this paper SFS and SBS with 10 estimators and 123 technical indicators have been examined on the last 13 years of Apple Company. Also, by the proposed method, the data created by the 3-day time window were converted to the appropriate input for regression methods. Based on the results observed: (1) Each wrapper feature selection method has different results with different machine learning methods, and each method is more correlated with a specific set of technical indicators of the stock market. (2) Ridge and LR estimates alone, and with two methods of the wrapper feature selection, namely SFS and SBS; They had the best results with all assessment criteria for market forecast. (3)The Ridge and LR method with all the R2, MSE, RMSE, MAE and MAPE have the best stock market prediction results. Also, the MLP Regression Method, along with the Sequential Forwards Selection and the MSE, had the best performance. SVR regression, along with the SFS and the MSE, has improved greatly compared to the SVR regression with all indicators. (4) It was also observed that different features are selected by different ML methods with different evaluation parameters. (5) Most ML methods have used the Squeeze_pro, Percentage Price Oscillator, Thermo, Decay, Archer On-Balance Volume, Bollinger Bands, Squeeze and Ichimoku indicator. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.09903&r=fmk |
| By: | Daniele Massacci (University of Naples Federico II, King’s Business School, and CSEF) |
| Abstract: | We study the problem of detecting structural instability of factor strength in asset pricing models for financial returns with observable factors. We allow for strong and weaker factors, in which the sum of squared betas grows at a rate equal to and slower than the number of test assets, respectively: this growth rate determines the strength of the corresponding factor. We propose LM and Wald statistics for the null hypothesis of stability and derive their asymptotic distribution when the break fraction is known, as well as when it is unknown and has to be estimated. We corroborate our theoretical results through a comprehensive series of Monte Carlo experiments. An extensive empirical analysis uncovers the dynamics of instability of factor strength in financial returns from equity portfolios. |
| Keywords: | Factor strength, structural break, hypothesis testing, stock portfolios. |
| JEL: | C12 C33 C58 G10 G12 |
| Date: | 2023–10–13 |
| URL: | http://d.repec.org/n?u=RePEc:sef:csefwp:685&r=fmk |
| By: | Ali Namaki; Reza Eyvazloo; Shahin Ramtinnia |
| Abstract: | Early warning systems (EWSs) are critical for forecasting and preventing economic and financial crises. EWSs are designed to provide early warning signs of financial troubles, allowing policymakers and market participants to intervene before a crisis expands. The 2008 financial crisis highlighted the importance of detecting financial distress early and taking preventive measures to mitigate its effects. In this bibliometric review, we look at the research and literature on EWSs in finance. Our methodology included a comprehensive examination of academic databases and a stringent selection procedure, which resulted in the final selection of 616 articles published between 1976 and 2023. Our findings show that more than 90\% of the papers were published after 2006, indicating the growing importance of EWSs in financial research. According to our findings, recent research has shifted toward machine learning techniques, and EWSs are constantly evolving. We discovered that research in this area could be divided into four categories: bankruptcy prediction, banking crisis, currency crisis and emerging markets, and machine learning forecasting. Each cluster offers distinct insights into the approaches and methodologies used for EWSs. To improve predictive accuracy, our review emphasizes the importance of incorporating both macroeconomic and microeconomic data into EWS models. To improve their predictive performance, we recommend more research into incorporating alternative data sources into EWS models, such as social media data, news sentiment analysis, and network analysis. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.00490&r=fmk |
| By: | Abhishek Subramanian; Parthajit Kayal ((Corresponding Author)Assistant Professor, Madras School of Economics) |
| Abstract: | This paper studies the volatility-managed portfolios of Moreira and Muir (2017) and analyses whether the volatility-management trading strategy provides a large utility gain for mean-variance investors for the CBOE Volatility Index (VIX) across multiple equity factors. Upon direct comparison, we document that the volatility-managed scaled factor earns higher returns compared to its original unscaled counterpart. The results from our in-sample spanning regression supports the above findings indicating that volatility-managed factors outperform the original factor by extending the mean-variance frontier even after controlling for additional factors. This result is significant in particular with the volatility-managed momentum factor. The ex-post optimization parameters also suggest a positive Sharpe ratio and CER percent (Certainty Equivalent Return) across equity factors. |
| Keywords: | Volatility-managed portfolios, Volatility-management, Momentum |
| JEL: | G10 G11 G12 |
| URL: | http://d.repec.org/n?u=RePEc:mad:wpaper:2023-242&r=fmk |
| By: | Selva Bahar Baziki (Bloomberg); María J. Nieto (Banco de España); Rima Turk-Ariss (Fondo Monetario Internacional) |
| Abstract: | We extend the literature on the sovereign-bank nexus by examining the composition effects of sovereign portfolios on banks’ risk profile, unlike previous studies which generally analyzed the determinants of banks’ sovereign portfolios or the size effects of these portfolios. We also differ from previous studies with respect to the measures of risk considered and by covering a sample period that goes well beyond the global financial crisis (2009-2018). Drawing on granular data from the European Banking Authority, we find that banks are riskier when their portfolio includes a higher proportion of securities issued by higher-risk sovereigns or when they are themselves domiciled in a country with high sovereign credit risk. Nevertheless, we do not find conclusive evidence that larger holdings of government securities of the country where the bank is incorporated increase bank risk ex-post. However, the risk profile is higher for banks that received government capital injections than for banks that did not receive capital support in the aftermath of the global financial crisis. Banks that received government capital injections are less risky when their portfolio includes a higher proportion of securities issued by higher-risk sovereigns. These results may indicate that regulatory arbitrage motives at these banks are particularly important. |
| Keywords: | banks, sovereign crisis, EU |
| JEL: | G01 G21 G28 G38 |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:2325&r=fmk |
| By: | Raphael Auer; Giulio Cornelli; Christian Zimmermann |
| Abstract: | We present a ranking of journals geared toward measuring the policy relevance of research. We compute simple impact factors that count only citations made in central bank publications, such as their working paper series. Whereas this ranking confirms the policy relevance of the major general interest journals in the field of economics, the major finance journals fare less favourably. Journals specialising in monetary economics, international economics and financial intermediation feature highly, but surprisingly not those specialising in econometrics. The ranking is topped by the Brookings Papers on Economic Activity, followed by the Quarterly Journal of Economics and the Journal of Monetary Economics, the American Economic Journal: Macroeconomics, and the Journal of Political Economy. |
| Keywords: | central banks; citations; academic journals; ranking |
| JEL: | A11 E50 E58 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:97224&r=fmk |