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Using the China Household Finance Survey data in 2011, the estimation results of structural equation modeling demonstrate that the respondents with higher time preference rate have a significant higher probability of investing in stocks, which implies that the short-term households will prefer stock investment. The social insurance programs and insurance policies held by the family will have a significantly direct positive effect in promoting stock investment and also a significantly direct positive effect on the respondent’s time preference, which could further indirectly increase the family’s stock investment. These results show that the safety-net built by the Chinese government, including the social security and commercial insurance, is very likely to attract more short-term investors into the stock market. These empirical results provide new evidences to explain the extreme volatility of Chinese stock market and also testify the policy effect of building an environment for people to possess property income in China.

Rescaled range analysis (R/S analysis), detrended fluctuation analysis (DFA) and detrended moving average (DMA) are widely-used methods for detection of long-range correlations in time series. Detrended cross-correlation analysis (DCCA) is a recently developed method to quantify the cross-correlations of two non-stationary time series. Another method for studying auto-correlations and cross-correlations was presented by Sergio Arianos and Anna Carbone in 2009. Recent studies have reported the susceptibility of this methods to periodic trends, which can result in spurious crossovers. In this paper, we propose the modified methods base on Laplace transform to minimizing the effect of periodic trends. The effectiveness of our techniques are demonstrated on stock data corrupted with periodic trends.

The article is about the technology brokers in Shanghai.

This paper examines the informational role of trades in the corporate bond market. Using transaction data, we compare the temporal relation between volume and volatility of returns for both bonds and stocks issued by the same firms. We find a dramatic difference between these two securities. While there is a strong positive relation between return volatility and volume for stocks, this relation is much weaker for corporate bonds. This finding holds not only for straight bonds but also for callable and convertible bonds. Empirical evidence reveals a very different relation between volatility and volume in the corporate bond market than predicted by standard microstructure models. Results show that the role of volume and trade frequency can be quite different across asset classes.

Investment in stock market involves many decision criteria and variables; hence investors are increasingly relying on the ratings provided by rating agencies to guide their stock selections. However, do these stock ratings have information value? Are these agencies' ratings valid? We establish the dominance cone principle and use the Value Line stock ratings to demonstrate the application of the dominance principle. Our results based upon limited data show that the Value Line rating does not support the notion that better rating results in better rate of return during 2006–2007.

In this paper we argue that employee stock options should be expensed on the grant date and then marked to market on subsequent reporting dates. One of the advantages of our approach is that the cumulative amount expensed for a stock option over the whole of its life does not depend on the option pricing model used. The option pricing model influences only the way in which expenses are allocated to time periods. Our paper proposes an option pricing model appropriate for employee stock options. The model explicitly considers the vesting period, the possibility that employees will leave the company during the life of the option, the inability of employees to trade their options, and dilution issues.

Stock indices are key indicators of the economy since they indicate the strength of a country’s stock market. For this reason, causality, information flow and co-movement analysis of stock indices gain importance in comparing countries’ economies. Here, we apply a novel approach by analyzing the results of two different methodologies; in wavelet coherence (WTC) analysis, the co-movement between stock indices provided and coherent areas can be shown, and information flow is indicated for five-year periods, especially on coherent zones by Transfer Entropy (TE), which detects cause-and-effect relations. This paper analyzed the information flow and co-movement among FTSE100 in the United Kingdom, the DAX in Germany and S&P500 Index in the United States stock indices. Three different results are obtained as follows: (1) DAX is on the leading side in general for five-year periods, (2) bidirectional information flows arise for every pair in the coherent periods and (3) TE-guided WTC analysis shows that TE sign change can be explained by phase angle direction obtained with WTC. These results indicate that both the methods yield proper outcomes in coherent time zones and during financial crisis like the COVID period, which we have faced for two years; for this reason, the results were also obtained for the COVID period, and in general, that shows DAX dominated other indices. We published this study to help researchers understand the connectedness between stock indices and investors avoiding risk in their stock portfolios, especially during financial crisis periods.

Sentiment analysis is a natural language processing approach that is widely implemented for many natural language processing applications such as translation, chatbots, and more. In this paper, news sentiment analysis in the stock market was reviewed. Stock market sentiment fluctuates because of events such as the reporting of quarterly financial report, macroeconomic data, government policy, etc. Traders use sentiment analysis to predict stock prices and trends. In recent years, sentiment analysis has been embedded into reinforcement learning for building an algorithmic trading system. This paper is a review paper to analyze and summarize sentiment analysis in reinforcement learning models. Specifically, the paper reviews the data, features, and approaches used in sentiment analysis. To identify the relevant journals and articles, a methodology is applied to review the sentiment analysis in the stock market. These studies are categorized according to their general similarities, differences, limitations, and field to be investigated further. Finally, the last section is the conclusion, which provides the direction for future study.

Currency is the central issue in economic history research as it is the most important measure for the progress of human society. Therefore an in-depth study of currency history is indispensable for the analysis and description of economic history. As new voyages are discovered, Britain’s GDP has increased due to numerous reasons and the most important reason is that the improved financial sector boosted economic growth. In order to research on the historical process of the development of monetary systems in ancient agricultural societies, this paper firstly discusses and provides explanations on the fluctuations of currency stock and other factors. Next, silver data in 11th to 17th century Britain is collected and analyzed and the silver stock is found to be beneficial to control currency flow and economic changes. According to the statistics of silver in Britain, it is believed that silver currency per capital, currency stock and other factors promoted growth of the domestic and national economy.