No news is not good news: evidence from the intra-day return volatility–volume relationship in Shanghai Stock Exchange

Through this research, we find that the asymmetric volatility phenomenon is reversed in the Shanghai Stock Exchange during bull markets. That is, volatility increases more with good news than with bad news. This evidence is inconsistent with the US markets. Further examination of this phenomenon reveals that the positive impact of good news on volatility is driven by the return-chasing behaviour of investors during bull markets. We also find that volatility increases after stock price declines in bear markets. After controlling for liquidity shifts, we observe similar patterns in volatility in both bull and bear markets. We posit that institutional and behavioural factors are the major driving forces of observed volatility patterns in the Chinese stock market.

Interday and intraday volatility: additional evidence from the Shanghai Stock Exchange

After examining both the interday and intraday return volatility of the Shanghai Composite Stock Index, it was found that the open-to-open return variance is consistently greater than the close-to-close variance. Examining the volatility of interday returns and variance ratio tests with five-minute intervals reveals an L-shaped pattern, or more precisely, two L-shaped patterns, starting with a small hump during both the morning and the afternoon sessions, with the morning session having a much higher interday volatility than the afternoon session. This L -shaped interday volatility is supported by the similarly shaped intraday volatility pattern. This result suggests that the high volatility of intraday returns for the market open is not entirely due to the trading mechanisms (call auction in the market opening) but also due to both the accumulated overnight information and the trading halt effect. The five-minute breaks after the auction and blind auction procedures are the two major driving forces which exaggerate the high intraday volatility observed at the market open.

Do Low-Priced Stocks Drive Long-Term Contrarian Performance on the London Stock Exchange?

We investigate whether low-priced stocks drive long-term contrarian performance on the U.K. market. We find that contrarian performance at low, middle, and high price levels is positive. On the Fama-French risk adjusted basis, we find both low-priced and middle-priced losers have significantly positive returns. When we adjust returns by market and liquidity risk, only middle-priced losers maintain their positive returns. Our results reveal that low-priced stocks are not fully responsible for contrarian performance. Our empirical evidence is generally consistent with the overreaction hypothesis and behavioral models of value investing.

Momentum Trading in New York Stock Exchange (NYSE) Energy Stocks

This paper investigates whether the momentum effect exists in the NYSE energy sector. Momentum is defined as the strategy that buys (sells) these stocks that are best (worst) performers, over a pre-specified past period of time (the 'look-back' period), by constructing equally weighted portfolios. Different momentum strategies are obtained by changing the number of stocks included in these portfolios, as well as the look-back period. Next, their performance is compared against two benchmarks: the equally weighted portfolio consisting of most stocks in the NYSE energy index and the market portfolio, and the S&P500 index. The results indicate that the momentum effect is strongly present in the energy sector, and leads to highly profitable portfolios, improving the risk-reward measures and easily outperforming both benchmarks.

Volatility and VaR Forecasting in the Madrid Stock Exchange

This paper provides an empirical study to assess the forecasting performance of a wide range of models for predicting volatility and VaR in the Madrid Stock Exchange. The models performance was measured by using different loss functions and criteria. The results show that FIAPARCH processes capture and forecast more accurately the dynamics of IBEX-35 returns volatility. It is also observed that assuming a heavy-tailed distribution does not improve models ability for predicting volatility. However, when the aim is forecasting VaR, we find evidence of that the Student’s t FIAPARCH outperforms the models it nests the lower the target quantile.

How differently do our non-large companies listed on the Portuguese Stock Exchange perform?

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Pricing of Liquidity Risks in London Stock Exchange

This thesis aims to investigate pricing of liquidity risks in London Stock Exchange. Liquidity Adjusted Capital Asset Pricing Model i.e. LCAPM developed by Acharya and Pedersen (2005) is being applied to test the influence of various liquidity risks on stock returns in London Stock Exchange. The Liquidity Adjusted Capital Asset Pricing model provides a unified framework for the testing of liquidity risks. All the common stocks listed and delisted for the period of 2000 to 2014 are included in the data sample. The study has incorporated three different measures of liquidity – Percent Quoted Spread, Amihud (2002) and Turnover. The reason behind the application of three different liquidity measures is the multi-dimensional nature of liquidity. Firm fixed effects panel regression is applied for the estimation of LCAPM. However, the results are robust according to Fama-Macbeth regressions. The results of the study indicates that liquidity risks in the form of (i) level of liquidity, (ii) commonality in liquidity (iii) flight to liquidity, (iv) depressed wealth effect and market return as well as aggregate liquidity risk are priced at London Stock Exchange. However, the results are sensitive to the choice of liquidity measures.