Chile constitutional convention may struggle to agree

Significance The process of drafting a new constitution is scheduled to be completed by July 4. A period of public hearings is drawing to a close and the convention’s seven permanent commissions are starting to discuss, draft and vote on articles to be submitted to the plenary. Impacts The constitutional process will be one of the defining issues of the new president’s mandate. Investor sentiment will remain cautious until there is more clarity on the new constitution’s impact on business. The right’s lack of representation in the convention may undermine its credibility.

The effect of investor sentiment and the structure of shareholder ownership on corporate investment

Purpose This paper aims to investigate the relations amongst investor sentiment, the structure of shareholder ownership and corporate investment.Design/methodology/approach This paper develops a theoretical model, proposes hypotheses based on the predictions of the model and conducts empirical tests. The primary method is panel regression with fixed effects. The sample covers the US data for the period between 1980 and 2018.Findings This paper finds that firms with a higher proportion of retail investors invest more than otherwise similar firms. In the low-sentiment periods, the financially constrained firms invest less than the non-financially constraint firms. The positive effect of residual retail ownership on the investment level is higher for firms with a higher idiosyncratic risk.Practical implications The results suggest that larger share ownership of the relatively informed institutional investors may serve as a mechanism that could reduce the degree of overinvestment caused by higher investor sentiment and the over-optimistic of the relatively uninformed investors.Originality/value This paper provides an incremental theoretical and empirical contribution to the relations amongst investor sentiment, corporate investment and the structure of shareholder ownership.

The emotional cost-of-carry: Chinese investor sentiment and equity index futures basis

PurposeWhile the classic futures pricing tool works well for capital markets that are less affected by sentiment, it needs further modification in China's case as retail investors constitute a large portion of the Chinese stock market participants. Their expectations of the rate of return are prone to emotional swings. This paper, therefore, explores the role of investor sentiment in explaining futures basis changes via the channel of implied discount rates.Design/methodology/approachUsing Chinese equity market data from 2010 to 2019, the authors augment the cost-of-carry model for pricing stock index futures by incorporating the investor sentiment factor. This design allows us to estimate the basis in a better way that reflects the relationship between the underlying index price and its futures price.FindingsThe authors find strong evidence that the measure of Chinese investor sentiment drives the abnormal fluctuations in the basis of China's stock index futures. Moreover, this driving force turns out to be much less prominent for large-cap stocks, liquid contracting frequencies, regulatory loosening periods and mature markets, further verifying the sentiment argument for basis mispricing.Originality/valueThis study contributes to the literature by relying on investor sentiment measures to explain the persistent discount anomaly of index futures basis in China. This finding is of great importance for Chinese investors with the intention to implement arbitrage, hedging and speculation strategies.

The Influence of Research Reports on Stock Returns: The Mediating Effect of Machine-Learning-Based Investor Sentiment

This paper investigates whether and how the research reports issued by securities companies affect stock returns from the perspective of investor sentiment in China. By collecting research reports and investor comments from a popular Chinese investor community, i.e., East Money, we derive two indices that represent the information contained in research reports: one is the attention of research reports and the other is the average stock rating given by research reports; then we develop an investor sentiment indicator using the machine learning method. Based on behavioral finance theory, we hypothesize that research reports have a significant effect on stock returns and investor sentiment plays a mediating role in it. The empirical analysis results confirm the above hypotheses. Specifically, the average stock rating given by research reports can better predict future stock returns, and investor sentiment plays a partial mediating role in the relationship between stock rating and stock returns.

Research on the Application of Generative Adversarial Networks in the Generation of Stock Market Forecast Trend Images

Investors make capital investment by buying stocks and expect to get a certain income from the stock market. When buying stocks, they need to draw up investment plans based on various information such as stock market historical transaction data and related news data of listed companies and collect and analyze these data. The data are relatively cumbersome and require a lot of time and effort. If you only rely on subjective analysis, the reference factors are often not comprehensive enough. At the same time, Internet social media, such as the speech in stock forums, also affect the judgment and behavior of investors, and investor sentiment will have a positive or negative effect on the stock market. This has an impact on the trend of stock prices. Therefore, this article proposes a stock market prediction model that uses data preprocessing technology based on past stock market transaction data to establish a stock market prediction model, and secondly, an image description generation model based on a generative confrontation network is designed. The model includes a generator and a discriminator. A time-varying preattention mechanism is proposed in the generator. This mechanism allows each image feature to pay attention to the image features of other stock markets to predict stock market trends so that the decoder can better understand the relational information in the image. The discriminator is based on the recurrent neural network and considers the degree of matching between the input sentence and the 4 reference sentences and the image features. Experiments show that the accuracy of the model is higher than that of the stock pretrend forecast model based on historical data, which proves the effectiveness of the data used in this paper in the stock price trend forecast.