IMFI Papers Coming Soon
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This section contains information about articles under review and waiting for publication in next issues of the journal. Regimes in Australian pension fund returns: a hidden semi-Markov approachRobert J. Bianchi, Senior Lecturer in Finance, Griffith Business School, Griffith University, Australia Abstract. Regimes are of interest to investors as they describe periods of episodic changes in returns and volatility caused by the non-normality and non-linearity characteristics of financial returns. The literature to date has examined regimes in single asset classes with little emphasis on the regime behavior of diversified (i.e. multi-asset investment) portfolios. This study examines whether lowering risk or increasing asset diversification are valid methods for investors to temper the regime behavior of their portfolios. Using a hidden semi-Markov model, we analyze the returns of two pension (i.e. superannuation) fund investment portfolios at opposite ends of the risk spectrum, namely a low risk cash-based portfolio and a moderate-to-high risk, but highly diversified, balanced portfolio. The findings show that asset class diversification does not appear to offer any noticeable benefits in relation to managing the regime behavior of investment portfolios. The findings also reveal that risk-reduction towards a cash based investment does not mitigate regimes in diversified portfolios. Do Sub-Saharan African banks with market power benefits from monetary policy?Mohammed Amidu, University of Ghana Business School, Ghana Abstract. Banking spread in Sub-Saharan Africa (SSA) is high by international standards and this Time series and neural network forecasts of daily stock pricesK. C. Tseng, Professor of Finance, California State University, USA Abstract. Time series analysis is somewhat parallel to technical analysis, but it differs from the latter by using different statistical methods and models to analyze historical stock prices and predict the future prices. With the rapid increases in algorithmic or high frequency trading in which trader make trading decisions by analyzing data patterns rather than fundamental factors affecting stock prices, both technical analyses and time series analyses become more relevant. In this study we apply the traditional time series decomposition (TSD), Holt/Winters (H/W) models, Box-Jenkins (B/J) methodology, and neural network (NN) to 50 randomly selected stocks from September 1, 1998 to December 31, 2010 with a total of 3105 observations for each company's close stock price. This sample period covers high tech boom and bust, the historical 9/11 event, housing boom and bust, and the recent serious recession and current slow recovery. During this exceptionally uncertain period of global economic and financial crises, it is expected that stock prices are extremely difficult to predict. All three time series approaches fit the data extremely well with R2 being around 0.995. For the hold-out period or out-of-sample forecasts over 60 trading days, the forecasting errors measured in terms of mean absolute percentage errors (MAPE) are lower for B/J, H/W, and normalized NN model, but forecasting errors are quite large for time series decomposition and non-normalized NN models. What is a good investment measure?Ping Hsiao, San Francisco State University, USA Abstract. Capital investment should be correlated with investment opportunity sets, future realized growth, and contemporary employee turnover, thus capital investment proxies could be validated against these benchmarks. We find that the choice of deflator is important to the performance of capital-expenditure-based proxies that are commonly applied in the literature. In addition, capital-expenditure-based proxies often underperform investment proxies constructed on some simple accounting information. R&D responds well to investment opportunities in some industries but appears to be a poor indicator of firm growth in other industries. We explore some sources of the difference in performance across various investment proxies. Short-term prior return patterns in stocks and sector returns: evidence for BRICKS marketsSanjay Sehgal, Professor of Finance, Department of Financial Studies, University of Delhi, India Abstract. In this paper, we examine if there are any prior return patterns in stock returns for BRICKS markets. Employing 6-6 portfolio formation/holding strategies, we observe strong momentum patterns for the sample markets with the exception of China. These momentum patterns disappear and in fact there are return reversals for some countries, as one elongates portfolio formation and holding windows to 12 months except for Indian market. Prior return patterns are not fully captured by CAPM as well as the Fama French three-factor model, especially for 6-6 strategy. There are prior return patterns in sector returns as was observed in case of stock returns. Hence, we augment the F-F model by including a sector momentum factor which is formed on basis of economic argument of Liu and Zhang (2008). The four-factor model is found to be a better descriptor of asset pricing but some unexplained returns may warrant a behavioral explanation for India and Russia. Our findings are relevant for global portfolio managers who are on the look out for portfolio trading strategies especially for emerging markets given their low degree of co-relation with the mature markets. The study contributes to the asset pricing anomaly literature especially for emerging markets. Aggregation of an FX order book based on complex event processingBarret Shao, Applied Mathematics and Statistics, Stony Brook University, Stony Brook, USA Abstract. Aggregating liquidity across diverse trading venues into a single consolidated order book is important for financial institutions that trade foreign exchange. But doing so poses several challenges, including stable latency performance under spurious bursts in message rate. Complex Event Processing offers an approach to this problem that yields performance and maintainability advantages over thread-based approaches. Is the progress of financial innovations a continuous spiral process?Dionisis Th. Philippas, Department of Business Administration, University of Patras, Greece Abstract. We herein examine the progress of financial innovations over the past 30 years, beginning with how they have influenced the financial system. We adopt a framework of classification that provides an overview of previous findings to examine the continuity of financial innovations. We find that the progress of financial innovations is discontinuous and is characterised by isolation and limited research studies. Finally, we highlight the main reasons why the previous literature in this area is limited and how financial innovations have not yet reached the point of diminishing returns. |
