The Significance of Market Capitalization in Portfolio Management over Time

CFA Digest ◽  
2000 ◽  
Vol 30 (1) ◽  
pp. 41-42
Author(s):  
William H. Sackley
Keyword(s):  
Portfolio Management ◽  
Market Capitalization ◽  
Over Time

Empirical analysis of dynamic spillovers between exchange rate return, return volatility and investor sentiment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tihana Škrinjarić ◽  
Zrinka Lovretin Golubić ◽  
Zrinka Orlović
Keyword(s):  
Exchange Rates ◽  
Dynamic Analysis ◽  
Empirical Analysis ◽  
Portfolio Management ◽  
Investor Sentiment ◽  
Daily Basis ◽  
Investment Strategies ◽  
Monthly Data ◽  
Content Type ◽  
Over Time

Purpose This paper aims to analyze the effects of investors’ sentiment, return and risk series on one to another of selected exchange rates. The empirical analysis consists of a time-varying inter-dependence between the observed variables, with the focus on spillovers between the variables. Design/methodology/approach Monthly data on the index Sentix, exchange rates EUR–USD, EUR–CHF and EUR–JPY are analyzed from February 2003 to December 2019. The applied methodology consists of vector autoregression models (VAR) with Diebold and Yilmaz (2009, 2011) spillover indices. Findings The results of the empirical research indicate that using static analysis could result in misleading conclusions, with dynamic analysis indicating that the financial of 2007-2008 and specific negative events increase the spillovers of shock between the observed variables for all three exchange rates. The sources of shocks in the model change over time because of variables changing their positions being net emitters and net receivers of shocks. Research limitations/implications The shortfalls of this study include using the monthly data frequency, as this was available for the authors, namely, investors are interested to obtain new information on a weekly and daily basis, not only monthly. However, at the time of writing this research, we could obtain only monthly data. Practical implications As the obtained results are in line with previous literature and were found to be robust, there exists the potential to use such analysis in the future when forecasting risk and return series for portfolio management purposes. Thus, a basic comparison was made regarding the investment strategies, which were based on the results from the estimation. It was shown that using information about shock spillovers could result in strategies that can obtain better portfolio value over time compared to basic benchmark strategies. Originality/value First, this paper allows for the spillovers of shocks in variables within the VAR models in all directions. Second, a dynamic analysis is included in the study. Third, the mentioned spillover indices are included in the study as well.

scholarly journals What is the True Effect of Rebalancing – A Higher Return or a Lower Risk?

2018 ◽  
Vol 66 (6) ◽  
pp. 1417-1430 ◽  
Author(s):  
Martin Boďa ◽  
Mária Kanderová
Keyword(s):  
Portfolio Management ◽  
Lower Risk ◽  
Portfolio Risk ◽  
Market Capitalization ◽  
True Effect ◽  
Time Periods ◽  
Quadratic Tracking ◽  
Investment Styles

The paper is motivated by the fact that rebalancing in portfolio management has an effect recognisable with both return and risk, although its purported ambition is to control (or decrease) portfolio risk. Focusing upon rebalancing strategies in quadratic tracking, the paper investigates whether rebalancing contributes to higher returns or lower risks. The investigation is conducted as a case study of tracking the S&P 500 Index by means of its constituents in four different time periods spanning from 2011 to 2017. Different approaches to stock pre‑selection (according to investment styles induced by market capitalization and the P/B ratio), portfolio nominal sizes (ranging between 10 and 30 stocks) and rebalancing (including periodic, deviation or no rebalancing at all) are considered. The results suggest that the effect of rebalancing is generally more apparent with return and less with risk, and that risk may in times of turbulent markets be aggravated by rebalancing interventions.

What’s in Your Benchmark? A Factor Analysis of Major Market Indexes

2018 ◽  
Keyword(s):  
Factor Analysis ◽  
Financial Markets ◽  
Top Down ◽  
Market Capitalization ◽  
Major Market ◽  
Market Indexes ◽  
Over Time

The authors examine the factor exposures of several popular market-capitalization indexes and how they vary over time. The authors find that most market-capitalization-weight indexes are effectively exposed to only two or three factors, with value and momentum being increasingly dominant. They find that the proportion of index movements explained by factors has materially increased in recent years, which is consistent with a more top-down, macro-driven environment or the increasing importance of economy-wide risks for financial markets.

scholarly journals A TIME SERIES ANALYSIS OF FOUR MAJOR CRYPTOCURRENCIES

2018 ◽  
pp. 271
Author(s):  
Boris Radovanov ◽  
Aleksandra Marcikić ◽  
Nebojša Gvozdenović
Keyword(s):  
Time Series ◽  
Portfolio Management ◽  
Investment Decision ◽  
Market Price ◽  
Data Series ◽  
Crucial Point ◽  
Investment Decision Making ◽  
Volatility Models ◽  
Daily Returns ◽  
Over Time

Because of increasing interest in cryptocurrency investments, there is a need to quantify their variation over time. Therefore, in this paper we try to answer a few important questions related to a time series of cryptocurrencies. According to our goals and due to market capitalization, here we discuss the daily market price data of four major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) and Litecoin (LTC). In the first phase, we characterize the daily returns of exchange rates versus the U.S. Dollar by assessing the main statistical properties of them. In many ways, the interpretation of these results could be a crucial point in the investment decision making process. In the following phase, we apply an autocorrelation function in order to find repeating patterns or a random walk of daily returns. Also, the lack of literature on the comparison of cryptocurrency price movements refers to the correlation analysis between the aforementioned data series. These findings are an appropriate base for portfolio management. Finally, the paper conducts an analysis of volatility using dynamic volatility models such as GARCH, GJR and EGARCH. The results confirm that volatility is persistent over time and the asymmetry of volatility is small for daily returns.

scholarly journals Value Driven Project Portfolio Management: the study features. Part 2. Application methodology

2017 ◽  
Vol 6 (1) ◽  
pp. 3-14
Author(s):  
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Dmitriy Gergert ◽  
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E. Ketova
Keyword(s):  
Project Management ◽  
Portfolio Management ◽  
Fast Moving ◽  
Successful Implementation ◽  
Project Portfolio ◽  
Project Portfolio Management ◽  
Correct Application ◽  
Over Time

In todays fast-moving world, project management gradually covers one activity after another. Increasingly, projects are the major components of the organization. Over time, projects are becoming more complex and dynamic, often, to achieve this goal it is necessary to implement a whole range of projects. Successful implementation of such projects, in most cases is only possible when using the approach to management based on values, the correct application of which makes it possible to optimize and balance the activities of the organization to achieve strategic and tactical objectives. This article discusses the application of value driven project portfolio methodology

Intelligent Portfolio Management via NLP Analysis of Financial 10-k Statements

2020 ◽  
Vol 11 (6) ◽  
pp. 13-25
Author(s):  
Purva Singh
Keyword(s):  
Portfolio Management ◽  
Strong Correlation ◽  
Word List ◽  
Research Community ◽  
Sharpe Ratio ◽  
Cosine Similarity ◽  
Alpha Factor ◽  
Critical Sections ◽  
Sentiment Word ◽  
Over Time

The paper attempts to analyze if the sentiment stability of financial 10-K reports over time can determine the company’s future mean returns. A diverse portfolio of stocks was selected to test this hypothesis. The proposed framework downloads 10-K reports of the companies from SEC’s EDGAR database. It passes them through the preprocessing pipeline to extract critical sections of the filings to perform NLP analysis. Using Loughran and McDonald sentiment word list, the framework generates sentiment TF-IDF from the 10-K documents to calculate the cosine similarity between two consecutive 10-K reports and proposes to leverage this cosine similarity as the alpha factor. For analyzing the effectiveness of our alpha factor at predicting future returns, the framework uses the alphalens library to perform factor return analysis, turnover analysis, and for comparing the Sharpe ratio of potential alpha factors. The results show that there exists a strong correlation between the sentiment stability of our portfolio’s 10-K statements and its future mean returns. For the benefit of the research community, the code and Jupyter notebooks related to this paper have been open-sourced on Github1.

scholarly journals The Efficiency of Credit Portfolio Management in Pakistan’s Banking Sector

2016 ◽  
Vol 4 (2) ◽  
pp. 51-72 ◽  
Author(s):  
Syed M. Waqar Azeem Naqvi ◽  
Tahseen M. Khan ◽  
Sayyid Salman Rizavi
Keyword(s):  
Portfolio Management ◽  
Banking Sector ◽  
Foreign Banks ◽  
Aggregate Level ◽  
Managerial Decision ◽  
Nonperforming Loans ◽  
Public Banks ◽  
Credit Portfolio ◽  
Over Time ◽  
Credit Portfolio Management

This study highlights the differences in performance of commercial banks operating in Pakistan in the context of credit portfolio management. Specifically, we look at their credit allocation policies and outcomes in the shape of nonperforming loans (NPLs). We categorize a sample of 34 banks into four major groups: public, private, Islamic and foreign banks. The study tests several hypotheses related to the overall efficiency of banks’ credit portfolio management over time as well as the drivers of NPLs and priority sectors for lending across these four categories. The findings broadly suggest that public banks tend to suffer most from NPLs, whereas Islamic and foreign banks manage their portfolios more efficiently. NPLs are highest in the priority lending sectors across all types of banks, which underscores the inefficiency of managerial decision-making when managing credit portfolios. Over time, at an aggregate level, all four types of banks have become less efficient, as reflected by the increase in NPLs as a percentage of gross credit and assets.

Debt Portfolio Management

2019 ◽  
pp. 679-698
Author(s):  
Zachary Jersky ◽  
He Li
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Portfolio Management ◽  
Management Strategies ◽  
Initial Formation ◽  
Passive Strategies ◽  
Portfolio Managers ◽  
Common Risk Factors ◽  
The Common ◽  
Over Time

Debt portfolio management has received increasing attention over time as both academics and practitioners have become aware of its unique challenges. This chapter discusses the common risk factors faced by debt portfolio managers and introduces a set of portfolio management strategies that are targeted at addressing major debt portfolio risks in order to achieve common portfolio management goals. These strategies differ in both style and objective. Passive strategies only require investor effort and decision-making at the initial formation of the portfolio, whereas active strategies require frequent restructuring and rebalancing of the portfolio. Some strategies aim at funding liabilities, while others attempt to seek total return. The chapter also provides a discussion of the application of modern portfolio theory within the context of debt portfolio management.

Strategies for choosing an uncertainty budget in log-robust portfolio management

2019 ◽  
Vol 06 (02) ◽  
pp. 1950011
Author(s):  
Yuntaek Pae ◽  
Navid Sabbaghi
Keyword(s):  
Portfolio Management ◽  
Lower Risk ◽  
Trading Volume ◽  
Optimization Problems ◽  
Uncertainty Budget ◽  
Trading Costs ◽  
Robust Portfolio Optimization ◽  
Robust Portfolio ◽  
Uncertainty Parameters ◽  
Over Time

This paper proposes six strategies for deciding upon “budget of uncertainty” parameters as input to a sequence of robust (portfolio) optimization problems over time, the solutions of which are a sequence of portfolios (i.e., a portfolio trajectory). Using 10 French Library datasets, 1 the performance of the portfolio trajectories resulting from these strategies are compared with one another and the 1/[Formula: see text] strategy. Before accounting for trading costs, all strategies result in portfolio trajectories that produce higher profit than the 1/[Formula: see text] strategy. Even after accounting for trading costs (of 1% of trading volume), two of the strategies result in portfolio trajectories that have higher profit and lower risk compared to the 1/[Formula: see text] strategy. Furthermore, we find that equal-weighted indices are better assets to manage than value-weighted indices in terms of achieving larger returns and lower risks.

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