Cultural Consonance in Leisure,Leisure Satisfaction,Life Satisfaction,and Self-Rated Health in Urban Taiwan

The purpose of this study threefold: to determine whether a shared cultural model of the importance of a set of leisure activities to a good leisure life existed in urban Taiwan, the degree to which cultural consonance in leisure mediates the relationship between leisure constraints and leisure satisfaction, and the degree to which leisure satisfaction affects life satisfaction and self-rated health. Results indicate that a cultural model of the importance of leisure activities to a good leisure life existed among sample members. Second, higher levels of self-reported participation in leisure activities that are culturally agreed upon as more important for a good leisure life are more strongly associated with leisure satisfaction than are activities culturally agreed upon as less important. Finally, leisure satisfaction strongly predicts both life satisfaction and self-rated health.     

Profit or politics? Understanding renationalizations in Russia

This study examines firm-level determinants of the government incentives to acquire controlling stakes in private companies. Using a novel hand-collected dataset of 153 largest listed and unlisted Russian companies, I investigate the methods and the rationales of a 2004–2008 wave of selected nationalizations in a post-privatization market. I find robust evidence that formerly privatized and domestically-owned companies in strategically important sectors face the highest risks of corporate control transfers from private to state hands. I also find that the corporate tax evasion is another significant determinant of a firm nationalization. Contrary to commonly held beliefs, there is little evidence that renationalizations in Russia are driven by firm profitability factors: the government neither systematically “cherry-picks” best performers nor addresses market failures by rescuing national champions in financial distress. These results contribute to t'he politics and finance literature by providing new firm-level evidence on the importance of strategic nationalism in the government's decision to intervene into the corporate control structures.     

Ultimate ownership and control in Russia

I investigate ultimate control and ownership patterns in Russian publicly traded companies. I show that these companies are controlled either by the state or by anonymous private owners. Federal and regional governments’ control is exercised through extensive use of pyramids. Private owners widely exploit legal loopholes that allow them to mask their holdings and identities through nominee and foreign offshore arrangements. The comparison of formal and informal ownership disclosure reveals that the typical anonymous owners are insiders and that in virtually all cases the market participants “know” who the real owners are. Collectively, the evidence suggests that the legal weaknesses in disclosure requirements are important determinants of country-specific ownership and control structures.     

Condition and Prospects of the Development of the Housing and Utilities Sphere in the Mari El Republic

The article has considered the conceptual foundations of business management in the housing and utilities sphere. The condition of housing stock in the Mari El Republic, as well as housing and the state of utility facilities and systems, has been monitored. A forecast for providing the Republic’s population with social housing and the priority regional project of the comfortable urban environment formation has been presented.     

Are semi-presidential constitutions bad for democratic performance?

Many countries since 1990 have adopted semi-presidential constitutions, which are often considered to be problematic, primarily because of the potential for conflict between the assembly-supported government and the popularly elected president. Such conflicts are said to lead to unstable governments, policy paralysis and the eventual undermining of the democratic regime. Using data for all parliamentary and semi-presidential democracies between 1946 and 2006, we examine the effect of semi-presidential constitutions on the duration of prime ministers’ tenure in office, government accountability with respect to economic outcomes, and democratic survival. We also examine (for a smaller sample of post-communist countries) the impact of these constitutions on the progress of structural reforms. We find that the observed higher instability of prime ministers in semi-presidential democracies is more due to the electoral system than to the presence of a popularly elected president. We also find that semi-presidential constitutions have little impact on the government’s accountability to economic outcomes and on the survival of democratic regimes. Finally, we find that neither a weak president nor a weak government is optimal for the progress of economic reforms in post-communist countries. Regarding economic reforms, the optimal allocation of constitutional powers between the president and the government grants both significant powers.     

Size, value and liquidity. Do They Really Matter on an Emerging Stock Market?

The paper extends the evidence on factors determining stock prices on emerging markets by focusing on the most advanced stock market in Central and Eastern Europe, the Polish market. Besides market, size and value factors, we investigate whether liquidity is a priced risk factor, addressing the hypothesis of its particular relevance in emerging markets. Our results support existing evidence for developed markets regarding market, size, and value factors. Contrary to the expectation that liquidity is a priced factor on emerging markets, we do not find evidence supporting this hypothesis. Analyzing specific market characteristics, we consider possible explanations behind these findings.     

Funding of basic research in Russia: Modern realities and forecasts

This paper reviews the conceptual provisions, state of the art, and forecasting in the field of basic research funding in Russia.     

Technological bias at the exchange rate market

Prediction of exchange rates has been a topic for debate in economic literature since the late 1980s. The recent development of machine learning techniques has spurred a plethora of studies that further improves the prediction models for currency markets. This high-tech progress may create challenges for market efficiency along with information asymmetry and irrationality of decision-making. This technological bias emerges from the fact that recent innovative approaches have been used to solve trading tasks and to find the best trading strategies. This paper demonstrates that traders can leverage technological bias for financial market forecasting. Those traders who adapt faster to the changes in market innovations will get excess returns. To support this hypothesis we compare the performance of deep learning methods, shallow neural networks with baseline prediction methods and a random walk model using daily closing rate between three currency pairs: Euro and US Dollar (EUR/USD), British Pound and US Dollar (GBP/USD), and US Dollar and Japanese Yen (USD/JPY). The results demonstrate that deep learning achieves higher accuracy than alternate methods. The shallow neural network outperforms the random walk model, but cannot surpass ARIMA accuracy significantly. The paper discusses possible outcomes of the technological shift for financial market development and accounting conforming also to adaptive market hypothesis.     

PUBLIC MANAGEMENT REFORM UNDER STRESS - The Ukrainian civil service experience

This article presents a heuristic continuum for the development and reform of civil service systems, drawing implications for the usefulness of western civil service reforms to the current Ukrainian situation. The authors argue that the current reform ideology common to western democracies may not be appropriate when applied to Ukraine and other nascent states.     

Deep networks for predicting direction of change in foreign exchange rates

Trillions of dollars are traded daily on the foreign exchange (forex) market, making it the largest financial market in the world. Accurate forecasting of forex rates is a necessary element in any effective hedging or speculation strategy in the forex market. Time series models and shallow neural networks provide acceptable point estimates for future rates but are poor at predicting the direction of change and, hence, are not very useful for supporting profitable trading strategies. Machine learning classifiers trained on input features crafted based on domain knowledge produce marginally better results. The recent success of deep networks is partially attributable to their ability to learn abstract features from raw data. This motivates us to investigate the ability of deep convolution neural networks to predict the direction of change in forex rates. Exchange rates for the currency pairs EUR/USD, GBP/USD and JPY/USD are used in experiments. Results demonstrate that trained deep networks achieve satisfactory out‐of‐sample prediction accuracy.