Market capitalization

About: Market capitalization is a research topic. Over the lifetime, 3583 publications have been published within this topic receiving 77288 citations. The topic is also known as: market cap & market value.

The Boom and Gloom of Real Estate Markets

Abstract: Real estate markets around the world have earned a complicated reputation. On the one hand, real estate markets offer investors a wide spectrum of profitable investments opportunities, investments that nowadays can be executed by simply buying shares of stock listed by real estate investment companies. In the first half of this inaugural address, the boom of these real estate stocks is discussed. In less than three decades, the listed real estate market developed into a sector with almost 400 listed firms worldwide, representing a sum aggregate market capitalization of around one trillion dollars by the end of 2007. Three relevant lessons regarding these international real estate stocks are discussed in the first fifteen pages of this booklet, lessons offered by real estate research from the Rotterdam School of Management. On the other hand, real estate markets are notorious for attracting entrepreneurs with bad intentions, seeking for opportunities to circumvent the strong arm of the law. These activities have yielded many headlines in the daily press and have given real estate a gloomy reputation. The dynamics of foreclosure auction of homes is an example of a source of negative headlines, stressing that the suboptimal organization of these auctions prohibits distressed sellers from earning a fair price for their home. In the second part of this address, I focus on an empirical test of the matter. By analyzing over 700 auctioned homes the dynamics of the auction system is discussed objectively. This offers a fair view on the problems at hand and searches for way to improve the system in the near future.

The Impact of Macroeconomic and Financial Variables on Market Risk: Evidence from International Equity Returns

Abstract: Using a GARCH approach, we estimate a time-varying two-factor international asset pricing model for the weekly equity index returns of 16 OECD countries. We find significant time-variation in the exposure (beta) of country equity index returns to the world market index and in the risk-adjusted excess returns (alpha). We then explain these world market betas and alphas using a number of country-specific macroeconomic and financial variables with a panel approach. We find that several variables including imports, exports, inflation, market capitalisation, dividend yields and price-to-book ratios significantly affect a country’s exposure to world market risk. Similar conclusions are obtained by using lagged explanatory variables, and thus these variables may be useful as predictors of world market risks. Several variables also significantly impact the risk-adjusted excess returns over this time period. Our results are robust to a number of alternative specifications. We further discuss some economic hypotheses that may explain these relationships.

Psychology of the Stock Market

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Prediction of stock performance by using logistic regression model: evidence from Pakistan Stock Exchange (PSX)

Abstract: The key purpose behind the study is to use logistic regression model to predict stock performance. For this purpose different financial and accounting ratios were used as independent variables and stock performance (either “good” or “poor”) as dependent variable. The result shows that financial and accounting ratios significantly predict the stock performance. Our study consists on the sample period of annual data from 2011-2015 and comprises of 109 listed non-financial firms of Pakistan’s Stock Exchange (PSX). Our sample was shortlisted on the basis of available data of Market Capitalization. Our research examines sales growth, debt to equity ratio, book to price ratio, earning per share, return on equity and current ratio for the prediction of stock performance. The findings indicate that our prediction was 89.77 percent accurate for prediction good as well as bad performance of stock. Although we did not consider macroeconomic variable to forecast stock return performance but our six firm specific accounting and financial ratios were good enough to predict stock performance. This study shows that Logistic regression model can be used by investors, individual as well as institutions or fund managers to enhance their ability to predict “good or poor” stock.