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In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio. [ABSTRACT FROM AUTHOR]

In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio.

Making investment decisions in modern conditions is quite complicated. On the example of the construction industry, the article tests the approach based on WACC and WARA analyses. The risk and profitability of the specified markets were analyzed based on the Efficient Frontier method. The Polish market and the group of Euronext markets were selected for comparison. The necessity of calculation of WARA based on only significant assets was determined. The differentiation between WARA and WACC in the Polish construction market can be overcome by maximizing goodwill and intangible assets and minimizing tangible assets. It reveals problems not in the stock market, but in the industry and the economy as a whole, particularly the lack of functioning markets such as the stock market for individual assets. The role of intangibles is different for Euronext markets. On the other hand, the requirement of the international accounting standards to transfer goodwill from assets to "other income" in case of a negative value (bad will) makes adequate consideration of goodwill in the WARA model virtually impossible. Another problem is a calculation of relative weights of assets in conditions of occurrence of both positive and negative values, which creates prerequisites for different approaches and interpretations. Building material that is used in the construction industry tries to improve the environment. the source of building materials are natural resources such as sandstone. [ABSTRACT FROM AUTHOR]

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Objective: Analyzing the behavior of Tehran Stock Market, based on the daily asset return for the duration between 1387 and 1397 has been the main aim of this research.Methods: Tehran Stock Market daily asset return can be considered as a time-series and therefore all existing models can be applied to it. Considering the distributional and temporal properties of such series, it can be shown that the series is stationary. Hence, Hidden Semi-Markov Model, which is widely used for analyzing time series, could be employed to analyze this series.Results: Based on Kolmogorov-Smirnov test and Akaike and Bayesian indices, the best density function for the series is a three parameter Gussian Mixture. Moreover, employing three-state Hidden Semi-Markov Model would be the suitable method for modeling. In addition, it was found that Tehran Stock Market followed three states namely bull, bear, and sidewalk and the definitions for such states have been given, while the probability of being in each state has also been provided.Conclusion: Tehran Stock Market was in sidewalk state around half of the analyzed duration and the luckiest state after both bear and bull states was sidewalk. The market almost never came to bull state after the bear state. Moreover, the chance of getting into bear state from sidewalk was three times more than the chance of getting into the bull market.

Copyright of Scientific Bulletin of National Mining University is the property of National Mining University, State Higher Educational Institution and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Estimation of the return distribution has a crucial role in Risk measurement and since the precision of risk measures depends on the precision of the return distribution, truly estimation of return distribution has attracted a huge attention. Although using Stochastic Volatility models with parametric assumptions for estimation and illustration of the volatilities has been common in research, these assumptions usually result in careless estimations. So in the following research a semiparametric approach has been used for estimation of the volatility by using a normal mixture dirichlet process. In this paper the distribution of the logarithm of the squared returns of banking index of Tehran Stock Exchange has been estimated by using mixtures of normal family and employing an MCMC algorithm. Finally, the results has been compared to the Basic stochastic volatility model. The results show that when the return distribution is skewed, estimates of volatility using the model can differ dramatically from those using a Normal return distribution. Furthermore, when return distribution is similar to a normal distribution, the results of this model are similar to the results of the parametric model.