Does Momentum Trading Generate Extra Downside Risk?

Momentum strategies tend to provide low returns during market crashes, and they crash themselves when the market rebounds after significant crashes. This is reflected by positive downside market betas and negative upside market betas of zero-cost momentum portfolios. Such asymmetry in upside and downside risks is unfavorable for investors and requires a risk premium. It arises mechanically because of momentum portfolio rebalancing based on trailing asset performance. The asymmetry in upside and downside risks is a robust unifying feature of momentum portfolios in various geographical and asset markets. The momentum premium can be rationalized within a standard asset-pricing framework, where upside and downside risks are priced differently.

Future of Profit Splits

In a highly integrated world where new technologies are disrupting the market, taxation and transfer pricing have gained a lot of attention because governments are seeking new ways to increase revenue collection. Although the application of the arm’s length standard is sometimes unpredictable, and its geographical approach may lead to stateless income, transfer pricing has proven to be an effective tool to protect a country’s tax base. Tax authorities and international organizations have tried to revise the transfer pricing framework through the BEPS project. However, until the new framework is settled, there is a need for an updated rational interpretation of the current methodologies to provide realistic options to allocate profits in a modern setting where multinational groups are centrally managed and intangibles are highly integrated. This Article provides such an interpretation and demonstratesits implementation with a realistic example.

An Experimental Pricing Framework for E-Commerce

Characterizing the demand curve of products is important for pricing them optimally. However, in deriving empirical demand curves, econometricians have to contend with identification issues. Furthermore, theoretical demand curves derived using standard economic theory are divorced from empirical realities: firms rarely have information on customers’ budget constraints; theoretical utility functions are seldom derived empirically. Recognizing these issues, we propose an experimental approach for determining a product’s demand curve and, in turn, its profit-maximizing price in online environments. The proposed approach yields precise estimates and is quick and inexpensive to implement.

A Comparison of Artificial Neural Networks and Bootstrap Aggregating Ensembles in a Modern Financial Derivative Pricing Framework

In this paper, the pricing performances of two learning networks, namely an artificial neural network and a bootstrap aggregating ensemble network, were compared when pricing the Johannesburg Stock Exchange (JSE) Top 40 European call options in a modern option pricing framework using a constructed implied volatility surface. In addition to this, the numerical accuracy of the better performing network was compared to a Monte Carlo simulation in a separate numerical experiment. It was found that the bootstrap aggregating ensemble network outperformed the artificial neural network and produced price estimates within the error bounds of a Monte Carlo simulation when pricing derivatives in a multi-curve framework setting.