Search results

This study explores the Granger causal relationship between return and volume in the KOSPI200 spot and option markets for the period from December 13. 2002 to December 9. 2004. using minute-by-minute data. Specifically, we examine the lead-lag relationship among OPtion volume, option return, cash volume, and cash return to determine whether option volume and return impact cash return.

Our results show that option volume has no direct impact on cash return as cash return unilaterally leads option volume‘ While option volume impacts cash volume. cash return unilaterally leads cash volume. implying no indirect impact of option volume on cash return.

However, there is evidence that option return impacts cash return directly, given a bilateral causality between option return and casll return. Option return also impacts cash volume, but again cash volume has no impact on cash return. meaning no indirect impact of option return on cash return. Our findings were generally robust across days of the week and different maturities. Finally, we analyzed lead-lag relationship within the option market. and found a bilateral causality between option volume and option return. This implies that option volume may impact cash return indirectly via option return.

We use stochastic dominance to test whether investors should prefer riskier securities as the investment horizon lengthens. Simulated return distributions for stocks, bonds, and U.S. Treasury bills are generated for holding periods of one to 20 years and stochastic dominance tests are run to establish preferences among the alternative portfolios. With independent returns, we find no evidence that high-risk securities (stocks) dominate low-risk securities (bonds) as the investment horizon lengthens. Under the assumption that security returns are correlated across time, we find that common stocks dominate corporate bonds and U.S. Treasury bills for sufficiently long investment horizons.

This study presents evidence of a statistically significant negative correlation between crude oil and equities over the past 20 years. Including proper proportions of negatively correlated assets in a diversified portfolio can improve the ratio of reward relative to risk, and therefore, adding crude oil with equities into a diversified portfolio can provide superior portfolio performance, compared with equities alone. Because crude oil prices held stable for nearly a century before the oil crisis of 1973, and oil derivatives did not begin trading actively on public markets until the 1980s, the diversification value of oil is a relatively new phenomenon. Also contributing to the phenomenon, the majority of oil reserves and the majority of crude oil production capacity worldwide are held by entities that are not traded in public equity markets, and therefore, the diversification benefits of oil cannot be fully realized by holding a portion of the global market portfolio of equities.

Overnight risk is of particular interest for many market participants including traders who provide liquidity to the market, but also to market participants with longer investment horizons who want to determine whether a given risk–return tradeoff can justify possible intermediate portfolio hedging transactions. Overnight risk may in particular play a highly significant role in emerging markets, given that information is incorporated into prices at a slower rate and liquidity may hinder a quick unwinding of portfolio positions.

Historical stock prices have long been used to evaluate a stock’s future returns as well as the risks associated with those returns. Similarly, historical dividends have been used to evaluate the intrinsic value of a stock using, among other methods, a dividend discount model. In this chapter, we propose an alternate use of the dividend discount model to enable an investor to assess the risks associated with a particular stock based on its dividend history. In traditional applications of the dividend discount model for stock valuation, the value of a stock is the net present value of its future cash dividends. We propose an alternative approach in which we calculate the internal rate of return for a stream of future cash dividends assuming the current stock price. We use a bootstrapping approach to generate a stream of future cash dividends, and use a Monte Carlo simulation approach to run multiple trials of the model. The probability distribution of the internal rates of return obtained from the simulation model provides an investor with an expected percentage return and the standard deviation of the return for the stock. This allows an investor to not only compare the expected internal rates of return for a group of stocks but to also evaluate the associated risks. We illustrate this internal rate of return approach using stocks that make up the Dow Jones Industrial Average.

Art return indices are usually estimated based only on a few means of artistic expression (mainly paintings and drawings). Other forms of expression (e.g. sculptures and installations) are generally ignored, in part because they are three-dimensional and, hence, more difficult to measure. We analyze the price determinants as well as the return and risk of three artistic expressions (paintings, drawings and sculptures) executed by Fernando Botero, the most expensive living Latin American artist, to analyze the degree to which their risk and return attributes differ throughout a 20-year period.

We analyzed all paintings, drawings and sculptures executed by Botero and sold at Sotheby’s and Christie’s between 2000 and 2020 (a total of 707 artworks). The data and the images of each artwork were obtained from the web pages of these two auction houses. A hedonic regression was run to explain the price of each artwork and use explanatory variables that are standard in the literature. Art price indices for paintings, drawings and sculptures were constructed using the year-dummy variables estimated in the regressions. We performed a similar analysis for another artist, Carlos Cruz-Diez, as a robustness to our results.

The performance of Botero’s sculptures through time differs markedly from that of his paintings and drawings. Our results suggest that it is possible that returns estimated in the literature could suffer from a bias, as they have usually ignored the performance of sculptures and other artistic expressions. Botero’s paintings provided a return that was comparable to those of his sculptures (3.36% and 3.20%, respectively), they were two times as high as those of his drawings (1.68%). On the other hand, whereas paintings and drawings had similar annual standard deviations (26% and 25.22%, respectively), sculptures had a much smaller standard deviation (16.96%).

A limitation of the hedonic regression method lies in the need to have a significant and diverse sample to identify the true effect of each variable on the price of a good. Another limitation is that we were only able to use art prices from auctions, as this is the only comprehensive source of art price data that is publicly available. These two limitations are shared by all the studies that use the hedonic pricing model.

Our results have practical applications for art collectors and investors, as well as for artists, galleries and, in general, for the whole art market ecosystem. The risk and return attributes of the various artistic expressions of an artist can be different, and thus it makes sense to analyze each one of them individually, as well as their correlations with the other artistic expressions and with traditional and other alternative investments.

The art market is part of what is known as the “orange economy” (also known as the Creative Economy). According to the World Bank, the economic value of the creative sector is not well known or appreciated, even though cultural, creative and artistic activities are vital for our sense of well-being.

To the best of our knowledge, this is the first paper that compares the financial performance of paintings, drawings and sculptures for the case of a specific artist. We chose Botero for three reasons. First, he is a Latin American living artist who has achieved the highest levels of international sales. Second, Botero has worked extensively on various artistic expressions (oil paintings, drawings on different materials and sculptures) throughout his life, a characteristic that is essential to be able to carry out our study. Third, there is a long record of auction sales for each of Botero’s artistic expressions.

Los índices de rentabilidad del arte generalmente se estiman basándose únicamente en unos pocos medios de expresión artística (principalmente pinturas y dibujos). Otras formas de expresión artística (por ejemplo, esculturas e instalaciones) generalmente se ignoran, en parte porque son tridimensionales y, por tanto, más difíciles de medir. Analizamos los determinantes del precio, así como el retorno y el riesgo de tres expresiones artísticas (pinturas, dibujos y esculturas) ejecutadas por Fernando Botero, el artista latinoamericano vivo más caro, para analizar en qué medida sus atributos de riesgo y retorno difieren a lo largo del tiempo, en un período de 20 años.

Analizamos todas las pinturas, dibujos y esculturas ejecutadas por Botero y vendidas en Sotheby’s y Christie’s entre 2000 y 2020 (un total de 707 obras de arte). Los datos y las imágenes de cada obra se obtuvieron de las páginas web de estas dos casas de subastas. Se realizó una regresión hedonante para explicar el precio de cada obra de arte y se utilizaron variables explicativas estándar en la literatura. Los índices de precios de arte para pinturas, dibujos y esculturas se construyeron utilizando variables ficticias anuales estimadas en las regresiones. Realizamos un análisis similar para otro artista, Carlos Cruz-Diez, como análisis de robustez de nuestros resultados.

El desempeño de las esculturas de Botero a través del tiempo difiere marcadamente del de sus pinturas y dibujos. Nuestros resultados sugieren que es posible que los retornos estimados en la literatura sufran un sesgo, ya que generalmente han ignorado el desempeño de esculturas y otras expresiones artísticas. Las pinturas de Botero proporcionaron un retorno comparable al de sus esculturas (3.36% y 3.20%, respectivamente), pero fueron dos veces superiores a los de sus dibujos (1.68%). Por otro lado, mientras que las pinturas y los dibujos tuvieron desviaciones estándar anuales similares (26% y 25.22%, respectivamente), las esculturas tuvieron una desviación estándar mucho menor (16.96%).

Una limitación del método de regresión hedónica radica en la necesidad de contar con una muestra significativa y diversa para identificar el verdadero efecto de cada variable sobre el precio de un bien. Otra limitación consiste en que solo pudimos utilizar precios de arte de subastas, ya que esta es la única fuente completa de datos sobre precios de arte que está disponible públicamente. Estas dos limitaciones son compartidas por todos los estudios que utilizan el modelo de precios hedónico.

Nuestros resultados tienen aplicaciones prácticas para coleccionistas e inversores de arte, así como también para artistas, galerías y, en general, para todo el ecosistema del mercado del arte. Los atributos de riesgo y retorno de las diversas expresiones de un artista pueden ser diferentes, por lo que tiene sentido analizar cada una de ellas individualmente, así como sus correlaciones con las otras expresiones artísticas y con las inversiones tradicionales y otras alternativas.

El mercado del arte forma parte de lo que se conoce como “economía naranja” (también conocida como Economía Creativa). Según el Banco Mundial, el valor económico del sector creativo no es bien conocido ni apreciado, a pesar de que las actividades culturales, creativas y artísticas son vitales para nuestra sensación de bienestar.

Hasta donde hemos podido comprobar, este es el primer artículo que compara el desempeño financiero de pinturas, dibujos y esculturas para el caso de un artista específico. Elegimos a Botero por tres razones. En primer lugar, es el artista vivo latinoamericano que ha alcanzado los mayores niveles de ventas internacionales. En segundo lugar, Botero ha trabajado extensamente en diversas expresiones artísticas (óleos, dibujos sobre distintos materiales y esculturas) a lo largo de su vida, característica que resulta fundamental para poder realizar nuestro estudio. En tercer lugar, existe un largo historial de ventas en subasta de cada una de las expresiones artísticas de Botero.

This study investigates the paradox of lotteries in financial markets, challenging traditional utility models predicated on rational behavior amid uncertainty. It explores why investors are drawn to lotteries despite the potential trade-off between risk-adjusted returns and sporadically substantial gains.

Employing a multifaceted approach, the study first scrutinizes diverse theories elucidating the perplexing behavior of lottery investors. Subsequently, it assesses the premium attached to lottery stock shares in the Brazilian financial market using distinct methodologies, thereby offering a comprehensive analysis of this phenomenon. Finally, the study estimates the risk premium associated with the lottery stocks applying an extended Fama–French multifactor model and searching for evidence of overlap with other risk-based anomalies.

This research unveils theories underpinning seemingly irrational investor behavior vis-à-vis lotteries, revealing the motivations propelling investors to willingly exchange risk-adjusted returns for the allure of substantial but infrequent gains. Empirical evidence delineates the extent of the premium paid for lottery stocks in the Brazilian market.

The study’s novelty lies in its amalgamation of theoretical exploration, empirical analysis and the application of the Fama–French factor model to gauge the risk premium associated with lottery-related behavior. Furthermore, its investigation of lottery stocks within the Brazilian market introduces a distinctive dimension, elucidating market dynamics and investor behaviors unique to the region.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

This empirical study aims to examine the COVID impact on the rate of return to schooling in 20 US industries.

An extended Mincer earnings equation with the COVID dummy variable and dummy interactive terms is used to examine the COVID effect on the rate of return to schooling for different industries. We use Heckman selection model to account for sample selection bias.

During COVID years, the change in the wage differential between college-and-above and below-college workers is different for industries, which leads to different changes in the rate or return to schooling among the 20 industries. During COVID, the rate of return to schooling increased for seven industries, decreased for seven industries and remained the same for six industries.

There is a lack of empirical tests of recession effects on the rate of return to schooling focusing on industry differentials. This study fills the research gap in this field. Our results will contribute to the ongoing discussion of the COVID impact on wages and returns from human capital investment.