The cross-section of stock returns in an early stock market

Using a new dataset which contains monthly data on 1015 stocks traded on the London Stock Exchange between 1825 and 1870, we investigate the cross section of stock returns in this early capital market. Unique features of this market allow us to evaluate the veracity of several popular explanations of asset pricing behavior. Using portfolio analysis and Fama–MacBeth regressions, we find that stock characteristics such as beta, illiquidity, dividend yield, and past-year return performance are all positively correlated with stock returns. However, market capitalization and past-three-year return performance have no significant correlation with stock returns.

Effect of European Sovereign debt crisis on banks’ stock market performances: application to Portuguese data

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance 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.

A stochastic dynamic programming approach for pricing options on stock-index futures

The aim of this thesis is to price options on equity index futures with an application to standard options on S&P 500 futures traded on the Chicago Mercantile Exchange. Our methodology is based on stochastic dynamic programming, which can accommodate European as well as American options. The model accommodates dividends from the underlying asset. It also captures the optimal exercise strategy and the fair value of the option. This approach is an alternative to available numerical pricing methods such as binomial trees, finite differences, and ad-hoc numerical approximation techniques. Our numerical and empirical investigations demonstrate convergence, robustness, and efficiency. We use this methodology to value exchange-listed options. The European option premiums thus obtained are compared to Black's closed-form formula. They are accurate to four digits. The American option premiums also have a similar level of accuracy compared to premiums obtained using finite differences and binomial trees with a large number of time steps. The proposed model accounts for deterministic, seasonally varying dividend yield. In pricing futures options, we discover that what matters is the sum of the dividend yields over the life of the futures contract and not their distribution.

The Impact of a Corporate Name Change on Stock Price and Trading Volume of Canadian Companies

This thesis examines the impact of a corporate name change on stock price and trading volume of Canadian companies around the announcement date, the approval date, and the adoption date over the time period from 1997 to 2011. Name changes are classified into six categories: major and minor, structural and pure, diversified and focused, accompanied with a change in ticker symbol and without a change in ticker symbol, “Gold” name addition and deletion, and different reasons for name changes (e.g., merger and acquisition, change of structure, change of strategy, and better image). The thesis uses the standard event study methodology to perform abnormal return and trading volume analyses. In addition, regression analysis is employed to examine which type of a name change has the largest impact on cumulative abnormal returns. Sample stocks exhibit a significant positive abnormal return one-day prior to the approval day and one day after the adoption date. Around the approval date we observe significant abnormal returns for stocks with a structural name change. On the day after the adoption date we document abnormal returns for stocks with major, minor, structural, pure, focused, and ticker symbol name changes. If a merger or acquisition is the reason for a name change, companies tend to experience a significant positive abnormal return one-day before the approval date and on the adoption date. If a change of structure is the reason for a name change, companies exhibit a significant positive abnormal return on the approval date and a significant negative abnormal return on the adoption date. In case of a change of strategy as the reason for a name change, companies show a significant negative abnormal return around the approval date and a significant positive abnormal return around the adoption date.

Return and Volatility performance comparison of Ethical and Non-ethical publicly-listed financial services companies

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Time series multistep-ahead predictability estimation and ranking

A predictability index was defined as the ratio of the variance of the optimal prediction to the variance of the original time series by Granger and Anderson (1976) and Bhansali (1989). A new simplified algorithm for estimating the predictability index is introduced and the new estimator is shown to be a simple and effective tool in applications of predictability ranking and as an aid in the preliminary analysis of time series. The relationship between the predictability index and the position of the poles and lag p of a time series which can be modelled as an AR(p) model are also investigated. The effectiveness of the algorithm is demonstrated using numerical examples including an application to stock prices.

International evidence on the predictability of returns to securitized real estate assets: econometric models versus neural networks

The performance of various statistical models and commonly used financial indicators for forecasting securitised real estate returns are examined for five European countries: the UK, Belgium, the Netherlands, France and Italy. Within a VAR framework, it is demonstrated that the gilt-equity yield ratio is in most cases a better predictor of securitized returns than the term structure or the dividend yield. In particular, investors should consider in their real estate return models the predictability of the gilt-equity yield ratio in Belgium, the Netherlands and France, and the term structure of interest rates in France. Predictions obtained from the VAR and univariate time-series models are compared with the predictions of an artificial neural network model. It is found that, whilst no single model is universally superior across all series, accuracy measures and horizons considered, the neural network model is generally able to offer the most accurate predictions for 1-month horizons. For quarterly and half-yearly forecasts, the random walk with a drift is the most successful for the UK, Belgian and Dutch returns and the neural network for French and Italian returns. Although this study underscores market context and forecast horizon as parameters relevant to the choice of the forecast model, it strongly indicates that analysts should exploit the potential of neural networks and assess more fully their forecast performance against more traditional models.

Seasonality in Southeast Asian stock markets: some new evidence on day-of-the-week effects

This paper examines the evidence for a day-of-the-week effect in five Southeast Asian stock markets: South Korea, Malaysia, the Philippines, Taiwan and Thailand. Findings indicate significant seasonality for three of the five markets. Market risk, proxied by the return on the FTA World Price Index, is not sufficient to explain this calendar anomaly. Although an extension of the risk-return equation to incorporate interactive seasonal dummy variables can explain some significant day-of-the-week effects, market risk alone appears insufficient to characterize this phenomenon.

Information signalling of common stock repurchase announcements: Australian evidence

Stock repurchases (or share buy-backs) have become increasingly popular among Australian companies. One of the main aims of announcing a stock repurchase by a listed company is signalling the market that its shares are currently underpriced. When market reacts to the signal, price of the shares is expected to increase immediately after the announcement. While there are several ways of repurchasing shares, 'on-market buy-backs' is the most popular method of stock repurchases in Australia. Australian listed companies have announced more than two hundred on-market share buy-backs over the past three years. The aim of this paper is to examine the information signalling effects of these on-market buy-back announcements. If the signal is considered positively (negatively) by the market, the price of the repurchasing company's shares should increase (decrease) immediately after the announcement. If there is no information content in the announcement, the price will remain the same. In this study, signalling effect of share buy-back announcements was examined using most recent Australian data. The total population of on-market buy-back announcements during the period from January 1, 2000 to March 10, 2003 was included. The abnormal market return over the short-run (announcement day and 9 trading days centred on the announcement date) was computed using the All Ordinaries Accumulation Index as the reference portfolio. The daily Abnormal Returns (AR) and Cumulative Abnormal Returns (CAR) during the event period were computed. The results strongly support the information-signalling hypothesis of share buy..backs. Australian market generally considers announcement of on~market share repurchases as signalling of insider information that shares are currently underpriced.

Simultaneous volatility transmissions and spillover effects: U.S. and Hong Kong stock and futures markets

Contemporaneous transmission effects across volatilities of the Hong Kong Stock and Index futures markets and futures volume of trade are tested by employing a structural systems approach. Competing measures of volatility spillover, constructed from the overnight U.S. S&P500 index futures, are tested and found to impact on the Hong Kong asset return volatility and volume of trade patterns. The examples utilize intra-day 15-min sampled data from this medium-sized Asia Pacific equity and derivative exchange. Both the intra- and inter-day patterns in the Hong Kong market are allowed for in the estimation process.

How profitable is the Indian stock market?

In this paper, using a range of technical trading and momentum trading strategies, we show that the Indian stock market is profitable. We find robust evidence that investing in some sectors is relatively more profitable than investing in others. We show that sectoral heterogeneity with respect to profitability is a result of the gradual diffusion of information from the market to the sectors. Specifically, we show that while the market predicts returns of sectors, the magnitude of predictability varies with sectors. Our results are robust to a range of trading strategies. © 2014 Elsevier B.V.

Has oil price predicted stock returns for over a century?

This paper contributes to the debate on the role of oil prices in predicting stock returns. The novelty of the paper is that it considers monthly time-series historical data that span over 150. years (1859:10-2013:12) and applies a predictive regression model that accommodates three salient features of the data, namely, a persistent and endogenous oil price, and model heteroscedasticity. Three key findings are unraveled: first, oil price predicts US stock returns. Second, in-sample evidence is corroborated by out-sample evidence of predictability. Third, both positive and negative oil price changes are important predictors of US stock returns, with negative changes relatively more important. Our results are robust to the use of different estimators and choice of in-sample periods.