Search of Attention in Financial Market

This study employs correlation coefficients and the factor-augmented vector autoregressive (FAVAR) model to investigate the relationship between the stock market and investors’ sentiment measured by big data. The investors’ sentiment index is constructed from a pool of relative keyword series provided by the Baidu Index. We target two composite stock indices, namely the Hang Seng Index and the Shanghai Composite Index. We first compute the Pearson product-moment correlation coefficient to find the degree of correlation between keywords and composite stock price indices. Then, we apply the FAVAR model to obtain the impulse response of stock price to the investors’ sentiment index. Finally, we examine the leading effects of keywords on stock prices using lagged correlation coefficients. We obtain two main findings. First, a strong correlation exists between investors’ sentiment and composite stock price: Second, before and after the launch of the Shanghai-Hong Kong Stock Connect, the keywords affecting the fluctuation of the Hang Seng Index are different

The structure, functioning, and performance of the Chinese stock markets

This thesis focuses on the stocks in two major stock exchanges in China: the Shanghai Stock Exchange and the Shenzhen Stock Exchange. In order to understand the overall performance of the Chinese stock markets, the research compares the performance among Chinese stock markets, other emerging stock markets, and the S&P 500 Index during the 1990\u27s

Some historical perspectives on the Bond-Stock Earnings Yield Model for crash prediction around the world

We provide a historical perspective focusing on Ziemba's experiences and research on the bond-stock earnings yield differential model (BSEYD) starting from when he first used it in Japan in 1988 through to the present in 2014. The model has called many but not all crashes. Those called have high interest rates in long term bonds relative to the trailing earnings to price ratio. In general, when the model is in the danger zone, almost always there will be a crash. The model predicted the crashes in China, Iceland and the US in the 2006-9 period. Iceland had a drop of fully 95%. For the US the call was on June 14, 2007 and the stock market fell 56.8%. A longer term study for the US, Canada, Japan, Germany, and UK shows that over long periods being in the stock market when the bond-stock signal is not in the danger zone and in cash when it is in the danger zone provides a final wealth about double buy and hold for each of these five countries. The best use of the model is for predicting crashes. Finally we compare Shiller's high PE ratio crash model to the BSEYD model for the US market from 1962-2012. While both models add value, the BSEYD model predicts crashes better

Dynamics in the co-movement of economic growth and stock return: comparison between the United States and China

The performance of the stock market is usually regarded as the barometer of economic growth and stock return and economic growth are, therefore, believed to co-move. However, the co-movement may exhibit different characteristics in various economic systems. This paper studies the co-movement of stock return and economic growth in two representative countries, the U.S. and China, with entirely different economic systems. The degree of co-movement is measured by the correlation of stock index return and G.D.P. growth rate and a time-varying copula model is applied to capture the dynamic characteristics of the co-movement. Empirical results show that the co-movement of stock return and economic growth is relatively strong but fluctuant in the U.S. and is relatively weak but stable in China. The differences in the co-movement can be interpreted by different economic growth modes in the U.S. and Chin

Effects of WTO membership on income distribution and labour movement in China – A CGE analysis

Using a CGE model (PRCGEM) updated to 2002, the paper explores how WTO membership could affect earnings in 40 industries across 31 regions (and 8 regional blocks) of China during the period 2002–2007. Taking into account labour movement between regions within China, the direct contribution of WTO membership to overall economic growth and development is predicted to be small, with a rise in real GDP of only 6.48% short term and 5.6% long term. However, structural economic change and the WTO shock should increase regional output, especially in the established coastal economies. Regional labour movement is found to increase 69.2% at the completion of economic structural reforms. A slight decrease in the Gini coefficient for income inequality is also anticipated.applied CGE modelling; China; WTO; labour movement; inequality

The Application of Fama-French Capital Asset Pricing Model and Quantile Regression on Chinese Stock Market

Fama-French three factors asset pricing model has been well documented for the stock market cross the world. This research will apply Fama-French model to Chinese stock market using the quantile regression approach. All the portfolios are sorted by size and book-to-market ratio to mimic the market size factor and market value factor. The regression reveal that portfolios returns are positively related with market risk and investors will make more profit by holding stocks with smaller company size and higher book-to-market ratio. With the assumption that the returns are normally distributed and expected returns are linearly dependent on three factors, existing studies on Chinese stock market have used ordinary least square (OLS) method to test asset pricing models. These assumptions are not valid in most of the markets. Thus, the present study tests the three risk factors model using quantile regression with the same data set. The results of the study reveal that the when it comes to extreme values in a distribution, the OLS method becomes inefficient. Quantile regression is a better way for investors to examine the extreme values in the distribution tails

Global market factors that impact Baltic Dry Index

The Baltic Dry Index is used as a strategic tool by shipping companies to monitor the daily movement of freight rates for the transportation of bulk cargoes on predetermined routes for the different types of bulk carriers. Therefore, the management of shipping companies pays great attention to the factors that can contribute to the prediction of the price movement of the Baltic Dry Index. Main goal of this paper is to explore if stock market indices of United States of America (S&P 500 stock index) and China (Shanghai stock exchange Composite index), 10 Year bond yield, CRB index, WTI Crude oil and Gold as global market factors, but also as leading macroeconomic global indicators, have impact on movement of BDI. We explored period from January 1, 2003 to December 31, 2021, with monthly data for which the multiple linear regression method was used to analyse mentioned global market factors impact on BDI. The research found that the movement of S&P 500 and SSECI stock indices and CRB index had a positive impact on the movement of BDI, while the movement of Gold and WTI crude oil had negative impact on BDI for the observed period. The scientific contribution of this paper is manifested through observation and exploring relationship of mentioned global market factors with BDI, previous papers observed shorter time period and included macroeconomic indicators which are lagging, together with some global market factors

Two Essays on Investment

This dissertation consists of two essays: one looks at the time-varying relationship between earnings and price momentum, and the other looks at how liquidity and transparency affect the pricing differential between Chinese A-and Hong Kong H-share. The first essay presented in Chapter I investigates the time varying relationship between earnings momentum and price momentum. Using a Markov-switching framework, allowing for variation between high volatility and low volatility states, I find that price momentum is significantly more influenced by earnings momentum in the high volatility state. Further for price momentum I find that loser firms display a higher degree of differential response to earnings momentum across the low and high volatility states than winner firms. Limited financing and investor’s sensitivity to future investment opportunities might explain these two results. A further analysis indeed indicates that loser firms tend to be more financially constrained. Additionally, I investigate the relationship between investor sentiment and the two momentums and find that sentiment only has predictive power for price momentum profits in the low volatility state. Finally, the results are robust regardless of instrument variables. The second essay presented in Chapter 2 examines the impact of liquidity and transparency on the discount attached to H-shares from 2003 to 2011. The higher the relative illiquidity of an H-share, the more the H-share is discounted relative to the underlying A-share price. In addition, more actively traded A-shares and infrequently traded H-shares are associated with a higher H-share discount. Further, increases in the number of analysts following a firm, both in the A-and H- market, are accompanied by a lower H-share discount. Also, a firm with a higher percentage of A-share holdings by mutual funds is associated with a smaller H-share discount. Overall, the results provide support for the notion that liquidity and transparency affect the relative pricing of A- and H-shares

Two Essays on Investment

This dissertation consists of two essays: one looks at the time-varying relationship between earnings and price momentum, and the other looks at how liquidity and transparency affect the pricing differential between Chinese A-and Hong Kong H-share. The first essay presented in Chapter I investigates the time varying relationship between earnings momentum and price momentum. Using a Markov-switching framework, allowing for variation between high volatility and low volatility states, I find that price momentum is significantly more influenced by earnings momentum in the high volatility state. Further for price momentum I find that loser firms display a higher degree of differential response to earnings momentum across the low and high volatility states than winner firms. Limited financing and investor’s sensitivity to future investment opportunities might explain these two results. A further analysis indeed indicates that loser firms tend to be more financially constrained. Additionally, I investigate the relationship between investor sentiment and the two momentums and find that sentiment only has predictive power for price momentum profits in the low volatility state. Finally, the results are robust regardless of instrument variables. The second essay presented in Chapter 2 examines the impact of liquidity and transparency on the discount attached to H-shares from 2003 to 2011. The higher the relative illiquidity of an H-share, the more the H-share is discounted relative to the underlying A-share price. In addition, more actively traded A-shares and infrequently traded H-shares are associated with a higher H-share discount. Further, increases in the number of analysts following a firm, both in the A-and H- market, are accompanied by a lower H-share discount. Also, a firm with a higher percentage of A-share holdings by mutual funds is associated with a smaller H-share discount. Overall, the results provide support for the notion that liquidity and transparency affect the relative pricing of A- and H-shares

Application Of Cascade-Correlation Neural Networks In Developing Stock Selection Models For Global Equities

We investigate the potential of artificial neural networks (ANN) in the stock selection process of actively managed funds. Two ANN models are constructed to perform stock selection, using the Dow Jones (DJ) Sector Titans as the research database. The cascade-correlation algorithm of Fahlman and Lebiere (1990/1991) is combined with embedded learning rules, namely the backpropagation learning rule and the extended Kalman filter learning rule to forecast the cross-section of global equity returns. The main findings support the use of artificial neural networks for financial forecasting as an active portfolio management tool. In particular, fractile analysis and risk-adjusted return performance metrics provide evidence that the model trained via the extended Kalman filter rule had greater strength in identifying future top performers for global equities than the model trained via the backpropagation learning rule. There is no distinguishable difference between the performances of the bottom quartiles formed by both ANN models. The zero-investment portfolios formed by longing the top quartiles and simultaneously shorting the bottom quartiles or the market proxy exhibit statistically significant Jensen’s alpha and continues to accumulate positive returns over the out-of-sample period for both ANN models. On the other hand, the zero-investment portfolios formed by longing the bottom quartiles and simultaneously shorting the market proxy exhibit statistically significant Jensen’s alpha and continues to accumulate losses over the out-of-sample period for both ANN models. The implementation of the extended Kalman filter rule in training artificial neural networks for applications involving noisy financial data is recommended