It demonstrates how to use advanced risk analysis and techniques to construct a specific portfolio given client's risk/return preferences
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Wednesday 27 June 2007
Tuesday 5 June 2007
Sunday 3 June 2007
Saturday 2 June 2007
Bond Market Integration
Using government bond market data for the
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Market Underreaction - Behavioural Finance Explanation?
We investigate the stock price reaction to UK going-concern audit report disclosures in the calendar year subsequent to publication. Over this period our firm population underperforms by between 24% and 31% depending on the benchmark adopted. This market underreaction to such an unambiguous bad news release is not a post-earnings announcement drift phenomenon; it is also robust to other potentially confounding explanations. However, whatever the reasons for such stock mispricing, we find costly arbitrage prevents rational investors forcing prices back into line with fundamental value. Our results have implications for the market's ability to impound bad news appropriately and the incompleteness of arbitrage in such small "loser" firm situations.
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Constructing Diversified Fund Portfolio
This paper analyzes two major constraints placed on investors when implementing diversification across mutual funds. First, diversification is considered in a benefit/cost context. As long as the marginal benefit obtained from diversification is larger than the cost associated, it is worthy of increasing the diversification level for fund investors. Second, several risk statistics are examined as the number of funds in the portfolio increase. Diversifying across mutual funds substantially reduces portfolio standard deviation, kurtosis and VaR but also causes an undesirable decrease in return skewness. As such, the goal of an investor who wants to increase skewness would be to hold a less diversified portfolio. Jointly depending on the above two constraints, I suggest that a moderate level of diversification across mutual funds, say, a five-fund portfolio, can achieve most of the advantages associated with diversification.
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Sector Betting or Sector Timing?
Previous research demonstrates that actively managed mutual funds exhibit negative market timing abilities. However, few studies have investigated the sector timing abilities of these fund managers. By implementing a multi-factor model based on Sharpe (1992), I derive a time series of mutual fund sector risk exposure coefficients which are demonstrated to be good proxies for actual fund exposures to the eight sectors examined in this study. Based on these sector risk exposures, sector timing measures are derived and used to explore whether fund managers in general, or certain sub-groups of fund managers in particular, exhibit successful sector timing abilities. My sample covers 485 randomly selected mutual funds listed on Datastream for the period from April, 1997 to July, 2002. I conclude that mutual funds as a whole exhibit some evidence of negative sector timing abilities. However, particular groups of funds, such as aggressive growth funds, appear to possess better sector timing abilities than other types of funds, and this pattern is more manifest after controlling for market downturn conditions.
What is the Wind Behind this Sail?
There is a considerable body of literature that examines the behaviour of institutional investors as a potential source of market price movement. Most existing studies focus on the market timing abilities of active fund managers and find mixed evidence for their fund timing skills. However, few studies have investigated fund manager timing abilities within segments of the market, such as factor timing and sector timing. This study investigates the style timing behaviour of
Specifically, I examine the timing activities of actively managed mutual funds within different market segments based on such established systematic risk factors as size, book-to-market, momentum, and across different fund styles such as, aggressive growth, growth and income, and small company funds etc.
Mutual fund timing strategy can be viewed as the fund manager’s response to his/her private information regarding future factor premiums. Instead of directly observing how fund managers make their timing decisions, an alternative approach is to look at the direct outcomes of their decisions, which are related to the factor timing loadings derived from a factor timing model. I significantly expand on the work of Bollen and Busse (2001) and Volkman (1999) by combining systematic risk factors unique to equity markets with timing factors unique to actively managed portfolios. Within this empirical timing-activity evaluation framework, I additionally investigate fund timing behaviour in the context of Morningstar star rating performance record, investment objectives, fund age, turnover, and load expense, etc.
This Ph.D. is an original contribution to the literature of fund timing activities, which seeks to contribute to our understanding in terms of investigating mutual fund mangers’ timing strategies with respect to specific systematic risk factors and their evolution over time. This research has important implications both for extant asset pricing theories and for practitioners especially in evaluation of portfolio performance and investigation of fund managers’ timing activities.
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Fund Manager Risk Behaviors Study
The quantification of speculative risk appears to be useful for measuring risk in diversified portfolio, such as mutual funds. Based on the measure of systematic skewness, I explore the alleged shift among mutual funds toward more highly aggressive investment policies and the apparent increase in the diversity of investment policies from fund to fund. My sample covers 485 randomly selected mutual funds listed on Datastream for the period from April, 1997 to July, 2002. The risk taking behavior by mutual fund managers are investigated by linking to different market conditions and managerial incentives based on fund flow-performance relationship. Fund managers with enhanced performance tend to decrease the speculative risk of fund portfolios while fund managers with deteriorative performance tend to increase the speculative risk of fund portfolios. However, implementing speculative investment strategies and hence increasing portfolio speculative risk may not come without cost. The evidence of this article has some important implications with respect to behavioral finance.
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Advanced Risk Explorer
This tool is created in Excel by using VBA programming techqiues with access to Bloomberg database server. Basicall, it provides all sorts advanced quantitative risk analysis on a single equity or fund.
Feel free to download it and test it on your own machine. Please note that this tool requires Bloomberg access and Bloomberg API.
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Feel free to download it and test it on your own machine. Please note that this tool requires Bloomberg access and Bloomberg API.
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