The aim of this comparative research is to recognize and assess intra-day seasonality of investors activity on a stock market using high-frequency data. Three indicators of intra-day investors activity based on different market characteristics are utilized: (1) hourly aggregated trading volume for a stock, (2) hourly percentage relative spread based on the highest and lowest prices of a stock, and (3) the modified version of the Roll's estimator for hourly effective spread based on the logarithmic ultra-short rates of return of a stock. The time-stamped data derived at five-minute intervals from the Warsaw Stock Exchange (WSE) is used. The data set covers the recent period from December 1, 2020 to April 30, 2021. The findings of computational experiments for real-data from the WSE show that visible U-shaped, J-shaped or reverse J-shaped hourly patterns dominate for the majority of equities and investigated indicators of intra-day market activity. What is important, the empirical results are homogenous. Moreover, the robustness tests and statistical analyses based on the rolling-window procedure confirm that results are robust to the choice of the analyzed sub-period. The findings are crucial from a practitioner's point of view as an empirical assessment and visualization of intra-day activity patterns can help investors to state how various stock market characteristics vary within a session. Therefore, it may be a useful, both formal and intuitive tool supporting decision-making processes.
{"title":"Recognizing Intra-day Patterns of Stock Market Activity","authors":"J. Olbryś, Gabriela Sawicka, Ewa Nowosada","doi":"10.2139/ssrn.3899820","DOIUrl":"https://doi.org/10.2139/ssrn.3899820","url":null,"abstract":"The aim of this comparative research is to recognize and assess intra-day seasonality of investors activity on a stock market using high-frequency data. Three indicators of intra-day investors activity based on different market characteristics are utilized: (1) hourly aggregated trading volume for a stock, (2) hourly percentage relative spread based on the highest and lowest prices of a stock, and (3) the modified version of the Roll's estimator for hourly effective spread based on the logarithmic ultra-short rates of return of a stock. The time-stamped data derived at five-minute intervals from the Warsaw Stock Exchange (WSE) is used. The data set covers the recent period from December 1, 2020 to April 30, 2021. The findings of computational experiments for real-data from the WSE show that visible U-shaped, J-shaped or reverse J-shaped hourly patterns dominate for the majority of equities and investigated indicators of intra-day market activity. What is important, the empirical results are homogenous. Moreover, the robustness tests and statistical analyses based on the rolling-window procedure confirm that results are robust to the choice of the analyzed sub-period. The findings are crucial from a practitioner's point of view as an empirical assessment and visualization of intra-day activity patterns can help investors to state how various stock market characteristics vary within a session. Therefore, it may be a useful, both formal and intuitive tool supporting decision-making processes.","PeriodicalId":108284,"journal":{"name":"Econometric Modeling: International Financial Markets - Emerging Markets eJournal","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130981743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fast-growing Emerging Market (EM) economies and their improved transparency and liquidity have attracted international investors. However, the external price shocks can result in a higher level of volatility as well as domestic policy instability. Therefore, an efficient risk measure and hedging strategies are needed to help investors protect their investments against this risk. In this paper, a daily systemic risk measure, called FRM (Financial Risk Meter) is proposed. The FRM-EM is applied to capture systemic risk behavior embedded in the returns of the 25 largest EMs FIs, covering the BRIMST (Brazil, Russia, India, Mexico, South Africa, and Turkey), and thereby reflects the financial linkages between these economies. Concerning the Macro factors, in addition to the Adrian and Brunnermeier (2016) Macro, we include the EM sovereign yield spread over respective US Treasuries and the above-mentioned countries currencies. The results indicated that the FRM of EMs FIs reached its maximum during the US financial crisis following by COVID 19 crisis and the Macro factors explain the BRIMST FIs with various degrees of sensibility. We then study the relationship between those factors and the tail event network behavior to build our policy recommendations to help the investors to choose the suitable market for in-vestment and tail-event optimized portfolios. For that purpose, an overlapping region between portfolio optimization strategies and FRM network centrality is developed. We propose a robust and well-diversified tail-event and cluster risk-sensitive portfolio allocation model and compare it to more classical approaches
{"title":"FRM Financial Risk Meter for Emerging Markets","authors":"Souhir Ben Amor, Michael Althof, W. Härdle","doi":"10.2139/ssrn.3785488","DOIUrl":"https://doi.org/10.2139/ssrn.3785488","url":null,"abstract":"The fast-growing Emerging Market (EM) economies and their improved transparency and liquidity have attracted international investors. However, the external price shocks can result in a higher level of volatility as well as domestic policy instability. Therefore, an efficient risk measure and hedging strategies are needed to help investors protect their investments against this risk. In this paper, a daily systemic risk measure, called FRM (Financial Risk Meter) is proposed. The FRM-EM is applied to capture systemic risk behavior embedded in the returns of the 25 largest EMs FIs, covering the BRIMST (Brazil, Russia, India, Mexico, South Africa, and Turkey), and thereby reflects the financial linkages between these economies. Concerning the Macro factors, in addition to the Adrian and Brunnermeier (2016) Macro, we include the EM sovereign yield spread over respective US Treasuries and the above-mentioned countries currencies. The results indicated that the FRM of EMs FIs reached its maximum during the US financial crisis following by COVID 19 crisis and the Macro factors explain the BRIMST FIs with various degrees of sensibility. We then study the relationship between those factors and the tail event network behavior to build our policy recommendations to help the investors to choose the suitable market for in-vestment and tail-event optimized portfolios. For that purpose, an overlapping region between portfolio optimization strategies and FRM network centrality is developed. We propose a robust and well-diversified tail-event and cluster risk-sensitive portfolio allocation model and compare it to more classical approaches","PeriodicalId":108284,"journal":{"name":"Econometric Modeling: International Financial Markets - Emerging Markets eJournal","volume":"346 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132682371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Blanco, Bernardo Díaz de Astarloa, Andrés Drenik, C. Moser, Danilo R. Trupkin
This paper studies earnings inequality and dynamics in Argentina between 1996 and 2015. Following the 2001–2002 crisis, the Argentine economy transitioned from a low‐ to a high‐inflation regime, while collective bargaining and the minimum wage gained influence. This transition was associated with a persistent decrease in earnings dispersion and cyclical movements in higher‐order moments of the distribution of earnings changes. To shed light on the changing nature of wage rigidity during this period, we develop a new method to estimate regular‐wage processes. As the Argentine economy transitioned from low to high inflation, the monthly frequency of regular‐wage changes almost doubled, while the distribution of regular‐wage changes morphed from having a mode around zero and positive skewness to having a positive mode and more symmetric tails.
{"title":"The Evolution of the Earnings Distribution in a Volatile Economy: Evidence from Argentina","authors":"J. Blanco, Bernardo Díaz de Astarloa, Andrés Drenik, C. Moser, Danilo R. Trupkin","doi":"10.2139/ssrn.3779632","DOIUrl":"https://doi.org/10.2139/ssrn.3779632","url":null,"abstract":"This paper studies earnings inequality and dynamics in Argentina between 1996 and 2015. Following the 2001–2002 crisis, the Argentine economy transitioned from a low‐ to a high‐inflation regime, while collective bargaining and the minimum wage gained influence. This transition was associated with a persistent decrease in earnings dispersion and cyclical movements in higher‐order moments of the distribution of earnings changes. To shed light on the changing nature of wage rigidity during this period, we develop a new method to estimate regular‐wage processes. As the Argentine economy transitioned from low to high inflation, the monthly frequency of regular‐wage changes almost doubled, while the distribution of regular‐wage changes morphed from having a mode around zero and positive skewness to having a positive mode and more symmetric tails.","PeriodicalId":108284,"journal":{"name":"Econometric Modeling: International Financial Markets - Emerging Markets eJournal","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128683459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper attempts to investigate the effects of Coronavirus spread on stock markets using panel data analysis, on daily basis over the period from March 1, 2020 until September 30, 2020. Coronavirus spread has been measured by daily cases and daily deaths per million of population, while stock return is measured by Δ in sectoral indices. This has been conducted after dividing the research period into 6 months from March to September and has been applied on 17 sectors in the Egyptian Exchange.
Using panel data analysis, results indicate significant negative industry effects for each of banking sector (BANK), Food, Beverages and Tobacco sector (FOBT) and Health Care & Pharmaceuticals sector (HLTH). Besides, findings show significant positive industry effects for each of Contracting & Construction Engineering sector (COCE), Energy & Support Services sector (ENGY), IT, Media & Communication Services sector (IMCS), Shipping & Transportation Services sector (SHTS) and Trade & Distributors sector (TRDB).
The robustness check supports the significant negative industry effects for each of Food, Beverages and Tobacco (FOBT) and Health Care & Pharmaceuticals (HLTH) (as losers) and the significant positive industry effects for each of Energy & Support Services (ENGY), Shipping & Transportation Services (SHTS) and Trade & Distributors (TRDB) (as winners).
{"title":"The Effects of COVID-19 Spread on the Egyptian Exchange Sectors: Winners and Losers across Time","authors":"N. Alber, Abanob Refaat","doi":"10.2139/ssrn.3741179","DOIUrl":"https://doi.org/10.2139/ssrn.3741179","url":null,"abstract":"This paper attempts to investigate the effects of Coronavirus spread on stock markets using panel data analysis, on daily basis over the period from March 1, 2020 until September 30, 2020. Coronavirus spread has been measured by daily cases and daily deaths per million of population, while stock return is measured by Δ in sectoral indices. This has been conducted after dividing the research period into 6 months from March to September and has been applied on 17 sectors in the Egyptian Exchange. <br><br>Using panel data analysis, results indicate significant negative industry effects for each of banking sector (BANK), Food, Beverages and Tobacco sector (FOBT) and Health Care & Pharmaceuticals sector (HLTH). Besides, findings show significant positive industry effects for each of Contracting & Construction Engineering sector (COCE), Energy & Support Services sector (ENGY), IT, Media & Communication Services sector (IMCS), Shipping & Transportation Services sector (SHTS) and Trade & Distributors sector (TRDB). <br><br>The robustness check supports the significant negative industry effects for each of Food, Beverages and Tobacco (FOBT) and Health Care & Pharmaceuticals (HLTH) (as losers) and the significant positive industry effects for each of Energy & Support Services (ENGY), Shipping & Transportation Services (SHTS) and Trade & Distributors (TRDB) (as winners).<br>","PeriodicalId":108284,"journal":{"name":"Econometric Modeling: International Financial Markets - Emerging Markets eJournal","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130921271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modeling cross-sectional correlations between thousands of stocks, across countries and industries, can be challenging. In this paper, we demonstrate the advantages of using Hierarchical Principal Component Analysis (HPCA) over the classic PCA. We also introduce a statistical clustering algorithm for identifying of homogeneous clusters of stocks, or "synthetic sectors". We apply these methods to study cross-sectional correlations in the US, Europe, China, and Emerging Markets.
{"title":"Hierarchical PCA and Modeling Asset Correlations","authors":"J. A. Serur, M. Avellaneda","doi":"10.2139/ssrn.3903460","DOIUrl":"https://doi.org/10.2139/ssrn.3903460","url":null,"abstract":"Modeling cross-sectional correlations between thousands of stocks, across countries and industries, can be challenging. In this paper, we demonstrate the advantages of using Hierarchical Principal Component Analysis (HPCA) over the classic PCA. We also introduce a statistical clustering algorithm for identifying of homogeneous clusters of stocks, or \"synthetic sectors\". We apply these methods to study cross-sectional correlations in the US, Europe, China, and Emerging Markets.","PeriodicalId":108284,"journal":{"name":"Econometric Modeling: International Financial Markets - Emerging Markets eJournal","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131682668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This study investigates the cross-sectional and time-series properties of momentum returns in 19 emerging market countries. Consistent with previous studies, we find that overall momentum profits are lower in emerging markets. One explanation for this underperformance is the negative relationship between momentum returns and market factor in down market states, which lowers overall momentum returns in emerging market countries. In this regard, we find that risk management of momentum reduces exposure to the market factor, thereby boosting returns, Sharpe ratios, and asset pricing model alphas. Finally, momentum returns are lower in more risk averse emerging market countries, and momentum crashes usually occur when risk aversion is higher.
{"title":"Revisiting Momentum Profits in Emerging Markets","authors":"Hilal Anwar Butt, J. Kolari, Mohsin Sadaqat","doi":"10.2139/ssrn.3704504","DOIUrl":"https://doi.org/10.2139/ssrn.3704504","url":null,"abstract":"Abstract This study investigates the cross-sectional and time-series properties of momentum returns in 19 emerging market countries. Consistent with previous studies, we find that overall momentum profits are lower in emerging markets. One explanation for this underperformance is the negative relationship between momentum returns and market factor in down market states, which lowers overall momentum returns in emerging market countries. In this regard, we find that risk management of momentum reduces exposure to the market factor, thereby boosting returns, Sharpe ratios, and asset pricing model alphas. Finally, momentum returns are lower in more risk averse emerging market countries, and momentum crashes usually occur when risk aversion is higher.","PeriodicalId":108284,"journal":{"name":"Econometric Modeling: International Financial Markets - Emerging Markets eJournal","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121057541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The COVID-19 pandemic and associated policy responses triggered a historically large wave of capital reallocation between markets and asset classes. Using high-frequency country-level data, this paper examines if and how the number of COVID cases, the stringency of the lockdown, and the fiscal and monetary policy response determined the dynamics of portfolio flows. Despite more dominant global factors, we find that these domestic factors played an important role, particularly for emerging markets and bond flows, contributing to a global wave of reallocation to safer asset classes. Our results indicate that rising domestic COVID cases had a strong positive effect on portfolio flows, which responded to an increase in financing needs in affected economies. Lockdown and fiscal policy measures also led to an increase in portfolio flows; however, evidence from the CDS market suggests that the increase in flows was dominated by supply forces, reflecting investors' preference for stronger policy responses. In contrast, we find that interest rate cuts led to a decline in portfolio flows as investors searched for higher yield. Finally, we show that COVID policy responses also affected countries' exposure to the global shock and that pre-COVID macroeconomic conditions, such as lower sovereign risk and higher trade openness, contributed to larger flows during the COVID episode.
{"title":"Capital Markets, COVID-19 and Policy Measures","authors":"Khalid ElFayoumi, Martina. Hengge","doi":"10.2139/ssrn.3674260","DOIUrl":"https://doi.org/10.2139/ssrn.3674260","url":null,"abstract":"The COVID-19 pandemic and associated policy responses triggered a historically large wave of capital reallocation between markets and asset classes. Using high-frequency country-level data, this paper examines if and how the number of COVID cases, the stringency of the lockdown, and the fiscal and monetary policy response determined the dynamics of portfolio flows. Despite more dominant global factors, we find that these domestic factors played an important role, particularly for emerging markets and bond flows, contributing to a global wave of reallocation to safer asset classes. Our results indicate that rising domestic COVID cases had a strong positive effect on portfolio flows, which responded to an increase in financing needs in affected economies. Lockdown and fiscal policy measures also led to an increase in portfolio flows; however, evidence from the CDS market suggests that the increase in flows was dominated by supply forces, reflecting investors' preference for stronger policy responses. In contrast, we find that interest rate cuts led to a decline in portfolio flows as investors searched for higher yield. Finally, we show that COVID policy responses also affected countries' exposure to the global shock and that pre-COVID macroeconomic conditions, such as lower sovereign risk and higher trade openness, contributed to larger flows during the COVID episode.","PeriodicalId":108284,"journal":{"name":"Econometric Modeling: International Financial Markets - Emerging Markets eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130928989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Korean Abstract: 본 연구는 한국 주식 시장에서 위험과 수익률 간의 관계를 분석하는 것이 목적이다. 또한 본 연구는 위험에 대한 측정 지표로 CAPM에서 제안하고 있는 시장 베타를 활용한다. 베타를 추정한 후 베타의 크기로 그룹화한 결과 한국 주식 시장에서 베타가 클수록 수익률이 하락하는 현상이 발견되었다. 이를 좀 더 정밀하게 분석하기 위해 수익률을 시장초과수익률과 관련 없는 수익률과 시장초과수익률과 관련 있는 수익률로 구분하여 분석했다. 그 결과 시장초과수익률과 관련 없는 수익률의 경우, 낮은 베타를 갖는 종목이 높은 베타를 갖는 종목보다 수익률이 높은 현상이 발견되었다. 반대로 시장초과수익률과 관련 있는 수익률의 경우, 낮은 베타를 갖는 종목이 높은 베타를 갖는 종목보다 수익률이 낮았다. 또한 수익률을 상승장과 하락장으로 구분하여 분석한 결과, 시장초과수익률과 관련 없는 수익률의 경우에는 하락장에서 베타가 증가할수록 수익률은 하락하는 것으로 나타났다. 반면, 시장초과수익률과 관련 있는 수익률은 상승장과 하락장에 관계없이 베타가 증가할수록 수익률은 상승하는 것으로 나타났다.
English Abstract: The purpose of this study is to analyze the relationship between risk and return in the Korean stock market. And it uses market beta proposed by the CAPM (Capital Asset Pricing Model) as measure of risk.
After estimating the beta for the stocks and grouping the stocks by the size of the beta, we found that the larger the market beta in the Korean stock market, the lower the return. In order to analyze this more precisely, this study divides the returns into returns that is not related to market excess returns and those that are related to market excess returns. As a result, in the case of a return that is not related to market excess return, it is found that the stocks with a low market beta have a higher return than stocks with a high market beta. In contrast, in the case of returns related to market excess return, the returns on the stocks with low beta are lower than those on the stocks with high beta.
In addition, as a result dividing return into a bull market and a bear market, in case of returns that is not related to market excess return, as the beta increased, return declined. On the other hand, return associated with the market excess return increases as the market beta increases in both rising and falling markets.
Korean Abstract:本研究的目的是分析韩国股票市场中风险和收益率之间的关系。另外，本研究还利用CAPM提出的市场beta作为风险测定指标。对beta进行推测后，按照beta的大小进行分组的结果，在韩国股票市场上发现了beta越大收益率越低的现象。为了更精密地分析这一点，将收益率区分为与市场超额收益率无关的收益率和与市场超额收益率相关的收益率。结果发现，在与市场超额收益率无关的收益率情况下，拥有低beta的项目比拥有高beta的项目收益率更高。相反，从与市场超额收益率相关的收益率情况看，拥有低beta的项目比拥有高beta的项目收益率低。另外，将收益率分为牛市和熊市进行分析的结果显示，与市场超额收益率无关的收益率，在熊市beta越多，收益率就越低。与此相反，与市场超额收益率相关的收益率与涨跌市场无关，随着beta的增加，收益率也会上升。英语Abstract: The purpose of this study is to analyze The relationship between risk and return in The Korean stock market。And it uses market beta proposed by the CAPM (Capital Asset Pricing Model) as measure of risk。After estimating the beta for the stocks and grouping the stocks by the size of the beta, we found that the larger the market beta in the Korean stock market, the lower the return。In order to analyze this more precisely, this study divides the returns into returns that is not related to market excess returns and those that are related to market excess returns。As a result, in the case of a return that is not related to market excess return, it is found that the stocks with a low market beta have a higher return than stocks with a high market beta。In contrast, In the case of returns related to market excess return, the returns on the stocks with low beta are lower than those on the stocks with high beta。In addition as a result dividing return into a bull market and a bear market, In case of returns that is not related to market excess return, as the beta increased, return declined。the other hand, return associated with the market excess return increases as the market beta increases in both rising and falling markets。
{"title":"한국 주식시장에서 시장 베타와 수익률 간의 관계에 대한 실증 분석 (An Empirical Analysis on the Relationship between Market Beta and Return on the Korean Stock Market)","authors":"Seong Ju Hong, Sang-Youp Lim","doi":"10.2139/ssrn.3511913","DOIUrl":"https://doi.org/10.2139/ssrn.3511913","url":null,"abstract":"<b>Korean Abstract:</b> 본 연구는 한국 주식 시장에서 위험과 수익률 간의 관계를 분석하는 것이 목적이다. 또한 본 연구는 위험에 대한 측정 지표로 CAPM에서 제안하고 있는 시장 베타를 활용한다.<br>베타를 추정한 후 베타의 크기로 그룹화한 결과 한국 주식 시장에서 베타가 클수록 수익률이 하락하는 현상이 발견되었다. 이를 좀 더 정밀하게 분석하기 위해 수익률을 시장초과수익률과 관련 없는 수익률과 시장초과수익률과 관련 있는 수익률로 구분하여 분석했다. 그 결과 시장초과수익률과 관련 없는 수익률의 경우, 낮은 베타를 갖는 종목이 높은 베타를 갖는 종목보다 수익률이 높은 현상이 발견되었다. 반대로 시장초과수익률과 관련 있는 수익률의 경우, 낮은 베타를 갖는 종목이 높은 베타를 갖는 종목보다 수익률이 낮았다.<br>또한 수익률을 상승장과 하락장으로 구분하여 분석한 결과, 시장초과수익률과 관련 없는 수익률의 경우에는 하락장에서 베타가 증가할수록 수익률은 하락하는 것으로 나타났다. 반면, 시장초과수익률과 관련 있는 수익률은 상승장과 하락장에 관계없이 베타가 증가할수록 수익률은 상승하는 것으로 나타났다. <br><br><b>English Abstract:</b> The purpose of this study is to analyze the relationship between risk and return in the Korean stock market. And it uses market beta proposed by the CAPM (Capital Asset Pricing Model) as measure of risk.<br><br>After estimating the beta for the stocks and grouping the stocks by the size of the beta, we found that the larger the market beta in the Korean stock market, the lower the return. In order to analyze this more precisely, this study divides the returns into returns that is not related to market excess returns and those that are related to market excess returns. As a result, in the case of a return that is not related to market excess return, it is found that the stocks with a low market beta have a higher return than stocks with a high market beta. In contrast, in the case of returns related to market excess return, the returns on the stocks with low beta are lower than those on the stocks with high beta.<br><br>In addition, as a result dividing return into a bull market and a bear market, in case of returns that is not related to market excess return, as the beta increased, return declined. On the other hand, return associated with the market excess return increases as the market beta increases in both rising and falling markets.","PeriodicalId":108284,"journal":{"name":"Econometric Modeling: International Financial Markets - Emerging Markets eJournal","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126527323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-28DOI: 10.16980/jitc.15.6.201912.105
Byungjin Yim, Yefei Huang
Purpose - This study aims to analyze the fluctuating impact of monetary policy effect on the bond market and the stock market. Design/methodology/approach - Monthly data from January 2008 to October 2018 were selected. Seasonal treatment was done to eliminate the influence of seasonal factors on the time series, and then heteroscedasticity was eliminated by processing the data logarithmically. Findings - We analyzed the theoretical transmission of monetary policy in the bonds market and found two things. First, the stock bonds market plays an important role in the transmission of monetary policy. Secondly, the bonds market not only plays an important role in the transmission of monetary policy, but its development also affects the relationship between the supply and demand of money in the monetary market, thus affecting the implementation effect of monetary policy. Research implications or Originality – The narrow money supply was found to have a greater impact on bond price, and the other monetary aggregates and interest rate had a less impact on the bond market. This shows that China’s interest rate marketization has a gradual improvement process and with the continuous advancement of interest rate marketization, interest rates may play a greater role in the future.
{"title":"An Empirical Study on the Impact of Monetary Policy on the Bond Market in China","authors":"Byungjin Yim, Yefei Huang","doi":"10.16980/jitc.15.6.201912.105","DOIUrl":"https://doi.org/10.16980/jitc.15.6.201912.105","url":null,"abstract":"Purpose - This study aims to analyze the fluctuating impact of monetary policy effect on the bond market and the stock market. \u0000 \u0000Design/methodology/approach - Monthly data from January 2008 to October 2018 were selected. Seasonal treatment was done to eliminate the influence of seasonal factors on the time series, and then heteroscedasticity was eliminated by processing the data logarithmically. \u0000 \u0000Findings - We analyzed the theoretical transmission of monetary policy in the bonds market and found two things. First, the stock bonds market plays an important role in the transmission of monetary policy. Secondly, the bonds market not only plays an important role in the transmission of monetary policy, but its development also affects the relationship between the supply and demand of money in the monetary market, thus affecting the implementation effect of monetary policy. \u0000 \u0000Research implications or Originality – The narrow money supply was found to have a greater impact on bond price, and the other monetary aggregates and interest rate had a less impact on the bond market. This shows that China’s interest rate marketization has a gradual improvement process and with the continuous advancement of interest rate marketization, interest rates may play a greater role in the future.","PeriodicalId":108284,"journal":{"name":"Econometric Modeling: International Financial Markets - Emerging Markets eJournal","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117139445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We use machine learning methods to forecast individual stock returns in the Brazilian stock market, using a unique data set including technical and fundamental predictors. We find that portfolios formed on the highest quintile of predicted returns significantly outperform market benchmarks. However, portfolios formed on the lowest quintile of predicted returns earn positive returns and have high volatilities, making traditional long-short strategies unnatractive. To resolve this problem, we propose an equal risk contribution (ERC) ensemble approach to build a portfolio combining long-short portfolios obtained with various machine learning methods such that (i) the risk contributions of all individual long-short portfolios are equal, and (ii) the aggregate risk contribution of all long positions equals that of all short positions. The ERC ensemble portfolio outperforms, on an after cost, risk-adjusted basis, all individual machine learning long-short portfolios, as well as equally-weighted ensembles of these portfolios.
{"title":"Machine learning portfolios with equal risk contributions: evidence from the Brazilian market","authors":"Alexandre Rubesam","doi":"10.2139/ssrn.3432760","DOIUrl":"https://doi.org/10.2139/ssrn.3432760","url":null,"abstract":"We use machine learning methods to forecast individual stock returns in the Brazilian stock market, using a unique data set including technical and fundamental predictors. We find that portfolios formed on the highest quintile of predicted returns significantly outperform market benchmarks. However, portfolios formed on the lowest quintile of predicted returns earn positive returns and have high volatilities, making traditional long-short strategies unnatractive. To resolve this problem, we propose an equal risk contribution (ERC) ensemble approach to build a portfolio combining long-short portfolios obtained with various machine learning methods such that (i) the risk contributions of all individual long-short portfolios are equal, and (ii) the aggregate risk contribution of all long positions equals that of all short positions. The ERC ensemble portfolio outperforms, on an after cost, risk-adjusted basis, all individual machine learning long-short portfolios, as well as equally-weighted ensembles of these portfolios.","PeriodicalId":108284,"journal":{"name":"Econometric Modeling: International Financial Markets - Emerging Markets eJournal","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116703765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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