Market capitalization is often used as the weighting methodology for broad market indexes to reflect the performances of large established firms in the market. The market capitalization of a firm is a price-sensitive measure of firm size that self-adjusts to reflect the firms intrinsic value in an efficient capital market. In the presence of investor overreaction, the price-sensitive cap-weighted indexes cease to be mean-variance efficient in that they overweigh overvalued assets and under weigh undervalued assets. Fundamental indexation, proposed by Arnott, Hsu and Moore (2005), argue that fundamental values of a firm such as book value, revenues and earnings are price-insensitive, and hence are not subject to the systematic overshooting of asset prices through noise trading. The aim of this paper is to test whether fundamental-weighted indexes are more mean-variance efficient proxies for large established firms in the global equity market compared to cap-weighted indexes over an extensive 18-year period from 1991 to 2008. Test results show that fundamental-weighted indexes outperform cap-weighted indexes over two sub-periods as well as the overall examination period, during an expansionary market and in turbulent times. A strong negative relationship between the degree of index concentration and the index performance is detected for cap-weighted indexes while no such relationship is detected for the fundamental-weighted indexes. Our results suggest that price-insensitive fundamental-weighted indexes are more mean-variance efficient proxies for the performances of large firms for global equities relative to cap-weighted indexes. By removing the price-element in measuring firm size, the small firm anomaly is not present in fundamental-weighted indexes.

This paper reviews the development of capital market theories based on the assumption of capital market efficiency, which includes the efficient market hypothesis (EMH), modern portfolio theory (MPT), the capital asset pricing model (CAPM), the implications of MPT in asset allocation decisions, criticisms regarding the market portfolio and the development of the arbitrage pricing theory (APT

Empirical literature suggests that stock-picking of fund managers do not provide economic benefits in addition to passively-replicated style benchmarks. This paper constructs a 4-factor style model using the Morgan Stanley Capital International (MSCI) World Index and the global size, value and momentum proxies to replicate the style benchmark returns of 12 actively-managed global equity funds based on the return-decomposition approach of Sharpe (1992). In line with prior literature, it is found that the returns of the global equity funds under investigation are primarily driven by their respective style benchmarks. The selection returns of the analyzed funds are insignificant after adjustments for the inherent style risks. We thus conclude that active stock-picking of fund managers do not provide significant value in addition to asset and style allocation decisions.

We investigate the potential of artificial neural networks (ANN) in the stock selection process of actively managed funds. Two ANN models are constructed to perform stock selection, using the Dow Jones (DJ) Sector Titans as the research database. The cascade-correlation algorithm of Fahlman and Lebiere (1990/1991) is combined with embedded learning rules, namely the backpropagation learning rule and the extended Kalman filter learning rule to forecast the cross-section of global equity returns. The main findings support the use of artificial neural networks for financial forecasting as an active portfolio management tool. In particular, fractile analysis and risk-adjusted return performance metrics provide evidence that the model trained via the extended Kalman filter rule had greater strength in identifying future top performers for global equities than the model trained via the backpropagation learning rule. There is no distinguishable difference between the performances of the bottom quartiles formed by both ANN models. The zero-investment portfolios formed by longing the top quartiles and simultaneously shorting the bottom quartiles or the market proxy exhibit statistically significant Jensens alpha and continues to accumulate positive returns over the out-of-sample period for both ANN models. On the other hand, the zero-investment portfolios formed by longing the bottom quartiles and simultaneously shorting the market proxy exhibit statistically significant Jensens alpha and continues to accumulate losses over the out-of-sample period for both ANN models. The implementation of the extended Kalman filter rule in training artificial neural networks for applications involving noisy financial data is recommended.

Although the ability of the Fama and French (1993) 3-factor model in explaining style-based portfolio returns have been widely tested, no such test has been conducted on sector-based portfolios. The study conducted by Hsieh and Hodnett (2011) indicate that the resource sector yields significant abnormal returns under the capital asset pricing model (CAPM) over the period from 1999 to 2009. In addition, the book value-to-market ratio and market capitalization are found to have pervasive effects on the pricing of sector returns for global equities. Motivated by this insight, we undertake to test the ability of the Fama and French (1993) 3-factor model in explaining the variations in the global sector returns. Our test results indicate that the market risk premium is the most significant factor that drives the returns in all sectors under review. Although the positive abnormal returns of the resource sector dissipates under the 3-factor model, the industrial sector and the information technology (I.T.) sector yield abnormal returns under the 3-factor model. Unlike the empirical findings on the style portfolios, the signs and statistical significance of the exposures to the value and size risk premiums are not consistent across all sectors. This finding suggests that sector exposures are more unique and distinctive compared to the style portfolios. It could be argued that since most of the style portfolios are directly related to the value and size anomalies, any factor model that incorporates risk premiums on these anomalies would significantly explain the style portfolio returns. However, the ability of such factor model in explaining returns on portfolios formed using methodologies other than style anomalies, such as sector portfolio returns, would be questionable. Taking into account the rising global integration, sector allocation might be more effective in terms of global active portfolio management or international diversification than style allocation and country allocation.