In standard portfolio theory, an investor is typically taken as having one stochastic objective, to maximize the random variable of portfolio return. But in this paper, we focus on investors whose purpose is to build, more broadly, a “suitable portfolio” taking additional concerns into account. Such investors would have additional stochastic and deterministic objectives that might include liquidity, dividends, number of securities in a portfolio, social responsibility, and so forth. To accommodate such investors, we develop a multiple criteria portfolio selection formulation, corroborate its appropriateness by examining the sensitivity of the nondominated frontier to various factors, and observe the conversion of the nondominated frontier to a nondominated surface. Furthermore, multiple criteria enable us to provide an explanation as to why the “market portfolio,” so often found deep below the nondominated frontier, is roughly where one would expect it to be with multiple criteria. After commenting on solvability issues, the paper concludes with the idea that what is the “modern portfolio theory” of today might well be interpreted as a projection onto two-space of a real multiple criteria portfolio selection problem from higher dimensional space.

http://media.terry.uga.edu/documents/finance/standard_portfolio.pdf

Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection

Earnings forecasting in a global stock selection model and efficient portfolio construction and management

Stock selection models often use analysts’ expectations, momentum, and fundamental data. We found support for composite modeling using these sources of data for global stocks during 1997-2011. We found additional evidence to support the use of SunGard APT and Axioma multi-factor models for portfolio construction and risk control. Three levels of testing of stock selection and portfolio construction models were developed and estimated. We created portfolios for January 1997-December 2011. We report three conclusions: (1) analyst’s forecasts information has been rewarded by the global market from January 1997-December 2011; (2) analysts’ forecasts can be combined with reported fundamental data, such as earnings, book value, cash flow, and sales, and momentum in a stock selection model to identify mispriced securities; and (3) the portfolio returns of the multi-factor risk-controlled portfolios allow us to reject the null hypothesis for the data mining corrections test. The earnings forecasting variable dominates our composite model in terms of its impact on stock selection.

Earnings Forecasting in a Global Stock Selection Model and Efficient Portfolio Construction and Management

Comparing Japanese and U.S. securities markets, the paper shows that survivor bias affecting quantitative analysis is relatively minor in Japan and substantial in the U.S. The paper suggests that the constraint levels and estimation procedures that did best in both countries in the past will do well in the future.

A Comparison of Some Aspects of the U.S. and Japanese Equity Markets

The present study aims to investigate the effects of macroeconomic variables on stock price crash risk in the economically uncertain conditions of Iran’s market. This study also seeks to examine whether there is a significant relationship between some firm characteristics and falling stock prices. The sample of the study includes 152 Iranian companies listed on the Tehran Stock Exchange (TSE) between 2014 and 2019. Furthermore, the research model has been estimated using a fixed effect pattern, and the DUVOL (down-to-up volatility) measure is defined as a proxy for stock price crash risk. Consistent with our expectations, the results show that there is a positive association between the inflation and unemployment rates and stock price crash risk, whereas the GDP and exchange rates are correlated negatively with crash risk. In fact, with rising inflation and unemployment, on the one hand, the amount of savings and the purchasing power of the people have decreased, and on the other hand, it has reduced the sales of companies due to the increase in the pricing of manufactured products. In Iran’s economically uncertain situation due to sanctions, managers are trying to overstate financial performance and conceal bad news to have better access to financing; so, when the total amount of bad news accumulated over time reaches a tipping point, it leads to a stock crash. It also appears that when the exchange rate rises, Iranian investors prefer to buy companies’ shares to maintain the purchasing power of their money. Outcomes also confirm that larger firms and those with higher Return on Assets (ROA) are more sensitive to crash risk.

/pdf/macroeconomic-factors-and-stock-price-crash-risk-do-managers-5c8v24syxj.pdf

Macroeconomic Factors and Stock Price Crash Risk: Do Managers Withhold Bad News in the Crisis-Ridden Iran Market?

In this study, we produce mean-variance efficient portfolios for various universes in the U.S. equity market, and show that the use of a composite of analyst earnings forecast, revisions, and breadth variable as a portfolio tilt variable and an R&D quadratic term enhances stockholder wealth. The use of the R&D screen creates portfolios in which total active return generally rise relative to the use of the analyst variable. Stock selection may not necessarily rise as risk index and sector index returns are affected by the use of the R&D quadratic term. R&D expenditures of corporations may be integrated into a mean-variance efficient portfolio creation system to enhance stockholder returns and wealth. The use of an R&D variable enhances stockholder wealth relative to the use of capital expenditures or dividends as the quadratic term. The stockholder return implications of the R&D quadratic variable are particularly interesting given that most corporations allocate more of their resources to capital expenditures than R&D.

The optimization of efficient portfolios: the case for an r&d quadratic term

Earnings forecasting data has been a consistent, and highly statistically significant, source of excess returns. This chapter discusses a composite model of earnings forecasts, revisions, and breadth, CTEF, a model of forecasted earnings acceleration, was developed in 1997 to identify mispriced stocks. Our most important result is that the forecasted earnings acceleration variable has produced statistically significant Active and Specific Returns in the Post-Global Financial Crisis Period. Simple earnings revisions and forecasted yields have not enhanced returns in the past 7–20 years, leading many financial observers to declare earnings research passe. We disagree! Moreover, earnings forecasting models complement fundamental data (earnings, book value, cash flow, sales, dividends, liquidity) and price momentum strategies in a composite model for stock selection. The composite model strategy excess returns are greater in international stocks than in US stocks. The models reported in Guerard and Mark (2003) are highly statistically significant in its post-publication time period, including booms, recessions, and highly volatile market conditions.

Earnings Forecasts and Revisions, Price Momentum, and Fundamental Data: Further Explorations of Financial Anomalies

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Andrew Mark

Author Tools

Chat about Author

Papers

The optimization of efficient portfolios: the case for an r&d quadratic term

Earnings Forecasts and Revisions, Price Momentum, and Fundamental Data: Further Explorations of Financial Anomalies