This research annual publication intends to bring together investment analysis
and portfolio theory and their implementation to portfolio management. It
seeks theoretical and empirical research manuscripts with high quality in the
area of investment and portfolio analysis. The contents will consist of original
research on:
(1) the principles of portfolio management of equities and fixed-income
(2) the evaluation of portfolios (or mutual funds) of common stocks, bonds,
international assets, and options;
(3) the dynamic process of portfolio management;
(4) strategies of international investments and portfolio management;
(5) the applications of useful and important analytical techniques such as
mathematics, econometrics, statistics, and computers in the field of
investment and portfolio management.
(6) Theoretical research related to options and futures.
In addition, it also contains articles that present and examine new and important
accounting, financial, and economic data for managing and evaluating
portfolios of risky assets. Comprehensive research articles that are too long as
journal articles are welcome. This volume of annual publication consists of
twelve papers. The abstract of each chapter is as follows:
Chapter 1. Christophe Faugère and Hany Shawky develop an endogenous
growth model that incorporates random technological shocks to
the economy. These random technological shocks affect both
production and the depreciation of capital. We show the
existence of a long-run steady-state growth path, and character-
ize it. An optimal growth rate for the economy and the long-run
expected stock return are both derived. We then turn to study the
volatility of expected stock returns around this steady state. Once
tested, the model shows that deviations of de-trended capital
stock, deviations of shocks from expected values and deviations
of labor force growth from steady state together explain about
20% of the deviations of stock returns from long-term expected
values. Our estimates also implies that investors have levels of
risk aversion consistent with the literature, and that labor growth
fluctuations are not significant, due to crowding out effects.
Chapter 2. Chin W. Yang, Ken Hung and Felicia A. Yang examine the
equivalence property between the angle-maximization portfolio
technique and the Markowitz risk minimization model is proved.
Via reciprocal and monotonic transformation, they can be made
equivalent with or without different types of short sale. Since the
Markowitz portfolio model is formulated in the standard convex
quadratic programming, the equivalence property would enable
us to apply the same well-known mathematic properties to the
angle maximization model and enjoy the same convenient
computational advantage of the quadratic program (e.g., Marko-
witz’s critical line algorithm).
Chapter 3. Donald Lien and Karyl Leggio consider optimal ratios for
different lengths of hedging horizon when the highest frequency
data is generated by a cointegrated system. It is found that, after
reparameterization, a temporal aggregation of cointegrated
systems remains a cointegrated system. This result provides a
convenient method to estimate n-day hedge ratio for any integer
n. The only remaining issue concerns the possible incorrect lag
selections. Empirical results from ten futures contracts however
indicate lag selections have no effect on the estimated hedge
Chapter 4. Cheng-Few Lee and Li Li test various CAPM-based market-
timing and selectivity models, we find that about 12% of the
funds have a statistically significant Alpha with about 4% of
the funds having a significantly positive Alpha, and 8% of the
funds having a significantly negative Alpha. About 15% of funds
show significant timing ability with about 9% funds having a
significantly positive timing coefficient and 6% of the funds
having a significantly negative timing coefficient. The Asset
Allocation funds demonstrate the most timing ability and the
Aggressive Growth funds demonstrate the least timing ability.
Chapter 5. John Elder investigates the extent to which three observable
macroeconomic factors can explain the time-varying risk premia
in the short-end of the term structure. We employ an empirical
model that is motivated by a dynamic asset pricing model with
time-varying risk premia and time-invariant reward-to volatility
measures. We find that, in our model, two factors explain up to

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