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on Financial Markets |
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Issue of 2022‒11‒21
ten papers chosen by |
| By: | Callum Jones; Mariano Kulish; James Morley |
| Abstract: | We propose a shadow policy interest rate based on an estimated structural model that accounts for the zero lower bound. The lower bound constraint, if expected to bind, is contractionary and increases the shadow rate compared to an unconstrained systematic policy response. By contrast, forward guidance and other unconventional policies that extend the expected duration of zero-interest-rate policy are expansionary and decrease the shadow rate. By quantifying these distinct effects, our ‘structural’ shadow federal funds rate better captures the stance of monetary policy for given economic conditions than a shadow rate based only on the term structure of interest rates. |
| Keywords: | zero lower bound, forward guidance, shadow rate, monetary policy |
| JEL: | E52 E58 |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2022-61&r=fmk |
| By: | Olivier Ledoit; Michael Wolf |
| Abstract: | Markowitz portfolio selection is a cornerstone in finance, both in academia and in the industry. Most academic studies either ignore transaction costs or account for them in a way that is both unrealistic and suboptimal by (i) assuming transaction costs to be constant across stocks and (ii) ignoring them at the portfolio-selection state and simply paying them 'after the fact'. Our paper proposes a method to fix both shortcomings.. As we show, if transaction costs are accounted for (properly) at the portfolio-selection stage, net performance in terms of the Sharpe ratio increases, particularly so for high-turnover strategies. |
| Keywords: | Covariance matrix estimation, mean-variance efficiency, multivariate GARCH, portfolio selection, transaction costs |
| JEL: | C13 G11 |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:zur:econwp:420&r=fmk |
| By: | Renjie, Rex Wang; Verwijmeren, Patrick; Xia, Shuo |
| Abstract: | Mutual fund families increasingly hold bonds and stocks from the same firm. We study the implications of such dual holdings for corporate governance and firm decision-making. We present evidence that dual ownership allows financially distressed firms to increase investments and to refinance by issuing bonds with lower yields and fewer restrictive covenants. As such, dual ownership reduces shareholder-creditor conflicts, especially when families encourage cooperation among their managers. Overall, our results suggest that mutual fund families internalize the shareholder-creditor agency conflicts of their portfolio companies, highlighting the positive governance externalities of intra-family cooperation. |
| Keywords: | corporate governance,debt overhang,investment,mutual funds |
| JEL: | G23 G32 G34 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwhdps:212022&r=fmk |
| By: | Javier Gil-Bazo; Juan F. Imbet |
| Abstract: | We investigate whether asset management firms use social media to persuade investors. Combining a database of almost 1.6 million Twitter posts by U.S. mutual fund families with textual analysis, we find that flows of money to mutual funds respond positively to tweets with a positive tone. Consistently with the persuasion hypothesis, positive tweets work best when they convey advice or views on the market and when investor sentiment is higher. Using a high-frequency approach, we are able to identify a short-lived impact of families' tweets on ETF share prices. Finally, we reject the alternative hypothesis that asset management companies use social media to alleviate information asymmetries by either lowering search costs or disclosing privately observed information. |
| Keywords: | social media, Twitter, persuasion, mutual funds, mutual fund, flows, machine learning, textual analysis |
| JEL: | G11 G23 D83 |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:bge:wpaper:1366&r=fmk |
| By: | Mittal, Varun; Schaposnik, Laura |
| Abstract: | Reliable forecasting of the housing market can provide salient insights into housing investments. Through the reinterpretation of housing data as candlesticks, we are able to utilize some of the most prominent technical indicators from the stock market to estimate future changes in the housing market. By providing an analysis of MACD, RSI, and Candlestick indicators (Bullish Engulfing, Bearish Engulfing, Hanging Man, and Hammer), we exhibit their statistical significance in making predictions for USA data sets (using Zillow Housing data), as well as for a stable housing market, a volatile housing market, and a saturated market by considering the data-sets of Germany, Japan, and Canada. Moreover, we show that bearish indicators have a much higher statistical significance then bullish indicators, and we further illustrate how in less stable or more populated countries, bearish trends are only slightly more statistically present compared to bullish trends. Finally, we show how the insights gained from our trend study can help consumers save significant amounts of money. |
| Keywords: | Housing Market, Trend Study, Stock Market Indicators, Candlestick Analysis, Heikin-Ashi Candlestick Analysis, RSI, MACD, Bearish Engulfing, Bullish Engulfing, Hammer, Hanging Man, Dark Cloud Over |
| JEL: | C10 G0 G20 |
| Date: | 2022–10–16 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:115009&r=fmk |
| By: | Monika Grzegorczyk; Guntram B. Wolff |
| Abstract: | In this paper, we analyse whether green sovereign bonds are systematically priced differently to conventional sovereign bonds in the secondary markets |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:bre:wpaper:node_8334&r=fmk |
| By: | Jules Sadefo Kamdem (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier); Danielle Selambi (African Institute for Mathematical Sciences (AIMS-Cameroon)) |
| Abstract: | In this paper, we estimate the cost of a data breach using the number of compromised records. The number of such records is predicted by means of a machine learning model, particularly the Random Forest. We further analyse the fat tail phenomena which capture the underlying dynamics in the number of affected records. The objective is to calculate the maximum loss in order to answer the question of the insurability of cyber risk. Our results show that the total number of affected records follow a Frechet distribution, and we then estimate the Generalized Extreme Value (GEV) parameters to calculate the value at risk (VaR). This analysis is critical because it gives an idea of the maximum loss that can be generated by an enterprise data breach. These results are usable in anticipating the premiums for cyber risk coverage in the insurance markets. |
| Keywords: | Cyber insurance,Cyber risk,Machine Learning,Regression Trees,Random Forest,Generalized Extreme Value |
| Date: | 2022–10–13 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03814979&r=fmk |
| By: | Marek, Philipp; Stein, Ingrid |
| Abstract: | This paper examines how Basel III capital reforms affected bank lending in Ger- many. We focus on the increase of minimum risk-based capital requirements and the introduction of the leverage ratio. The announcement of stricter risk-based capital regulation significantly affected low capitalized banks. The impact depends on a bank's credit risk model, i.e. whether a bank applies the standardized approach (SA) or an internal ratings-based approach (IRBA) to determine risk weights. Low capitalized SA banks significantly cut lending whereas IRBA banks did not ad- just lending volumes. By contrast, low capitalized IRBA banks significantly in- creased collateralization while low capitalized SA banks adjusted collateralization only marginally. Moreover, the impact on SMEs and large companies also differs. In terms of lending, SMEs were affected more strongly, whilst in terms of collateralization the impact on large companies was bigger. The announcement of the leverage ratio had, however, a rather limited impact. We find some evidence that low capitalized banks reduced lending. Furthermore, low capitalized banks somewhat tightened collateral requirements, especially for large companies. |
| Keywords: | Basel III,bank lending,nancial regulation,small and medium-sizedenterprises (SMEs) |
| JEL: | D22 E58 G21 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:372022&r=fmk |
| By: | Zikai Wei; Bo Dai; Dahua Lin |
| Abstract: | Modeling and characterizing multiple factors is perhaps the most important step in achieving excess returns over market benchmarks. Both academia and industry are striving to find new factors that have good explanatory power for future stock returns and good stability of their predictive power. In practice, factor investing is still largely based on linear multi-factor models, although many deep learning methods show promising results compared to traditional methods in stock trend prediction and portfolio risk management. However, the existing non-linear methods have two drawbacks: 1) there is a lack of interpretation of the newly discovered factors, 2) the financial insights behind the mining process are unclear, making practitioners reluctant to apply the existing methods to factor investing. To address these two shortcomings, we develop a novel deep multi-factor model that adopts industry neutralization and market neutralization modules with clear financial insights, which help us easily build a dynamic and multi-relational stock graph in a hierarchical structure to learn the graph representation of stock relationships at different levels, e.g., industry level and universal level. Subsequently, graph attention modules are adopted to estimate a series of deep factors that maximize the cumulative factor returns. And a factor-attention module is developed to approximately compose the estimated deep factors from the input factors, as a way to interpret the deep factors explicitly. Extensive experiments on real-world stock market data demonstrate the effectiveness of our deep multi-factor model in the task of factor investing. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.12462&r=fmk |
| By: | Jeremi Assael (BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab, MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay); Laurent Carlier (BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab); Damien Challet (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay) |
| Abstract: | We systematically investigate the links between price returns and ESG features in the European market. We propose a cross-validation scheme with random company-wise validation to mitigate the relative initial lack of quantity and quality of ESG data, which allows us to use most of the latest and best data to both train and validate our models. Boosted trees successfully explain a part of 1 annual price returns not accounted by the market factor. We check with benchmark features that ESG features do contain significantly more information than basic fundamental features alone. The most relevant sub-ESG feature encodes controversies. Finally, we find opposite effects of better ESG scores on the price returns of small and large capitalization companies: better ESG scores are generally associated with larger price returns for the latter, and reversely for the former. |
| Keywords: | ESG features,sustainable investing,interpretable machine learning,model selection,asset management,equity returns |
| Date: | 2022–09–29 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03791538&r=fmk |