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Allocating budget optimally to marketing channels is an increasingly difficult venture. This difficulty is compounded by an increase in the number of marketing channels, a rise in siloed data between marketing technologies, and a decrease in individually identifiable data due to legislated privacy policies. The authors explore the rich attribution modeling literature and discuss the different model types and approaches previously used by practitioners and researchers. They also investigate the changing landscape of marketing attribution, discuss the advantages and disadvantages of different data handling approaches (i.e., aggregate vs. individualistic data), and present a research agenda for future attribution research.

This paper provides a fuller characterization of the analytical upper bounds for American options than has been available to date. We establish properties required of analytical upper bounds without any direct reliance on the exercise boundary. A class of generalized European claims on the same underlying asset is then proposed as upper bounds. This set contains the existing closed form bounds of Margrabe, (1978) and Chen and Yeh (2002) as special cases and allows randomization of the maturity payoff. Owing to the European nature of the bounds, across-strike arbitrage conditions on option prices seem to carry over to the bounds. Among other things, European option spreads may be viewed as ratio positions on the early exercise option. To tighten the upper bound, we propose a quasi-bound that holds as an upper bound for most situations of interest and seems to offer considerable improvement over the currently available closed form bounds. As an approximation, the discounted value of Chen and Yeh's (2002) bound holds some promise. We also discuss implications for parametric and nonparametric empirical option pricing. Sample option quotes for the European (XEO) and the American (OEX) options on the S&P 100 Index appear well behaved with respect to the upper bound properties but the bid–ask spreads are too wide to permit a synthetic short position in the early exercise option.

We design experimental economies based on a New Keynesian Dynamic Stochastic General Equilibrium (DSGE) model. We apply shocks to tastes, productivity, and interest rate policy, and measure the persistence of these shocks. We find that, in a setting where goods are perfect substitutes, there is little persistence of output shocks compared to treatments with monopolistic competition, which perform similarly irrespective of whether or not menu costs are present. Discretionary central banking is associated with greater persistence than automated instrumental rules.

The aim of this chapter is to develop a strong research base for the academia and the industry to understand the importance of data analytics in International Trade. This chapter focuses on the case of cotton trade from India and explores different methodologies developed by the World Bank and International Trade Center to analyze the Big Data available on export and import. Through Big Data analysis, this chapter attempts to find out the export performance, market demand, export potential, and attractive markets for Indian cotton. This chapter also explores the trade competitiveness of Indian cotton over the years. The data through appropriate analysis can address some simple yet complicated questions in trade like what export potential the commodity holds, if the commodity is competitive or not in international market, what are new markets to look up to, and other similar questions. In other words, this information could make huge difference in decision-making of traders and policymakers directly, and farmers indirectly.

Current US tax laws provide investors an incentive to time the sales of their bonds to minimize tax liability. This gives rise to a tax-timing option that affects bond value. In reality, corporate bond investors’ tax-timing strategy is complicated by risk of default. Existing term structure models have ignored the effect of the tax-timing option, and how much corporate bond value is due to the tax-timing option is unknown. In this chapter, we assess the effects of taxes and stochastic interest rates on the timing option value and equilibrium price of corporate bonds by considering discount and premium amortization, multiple trading dates, transaction costs, and changes in the level and volatility of interest rates. We find that the value of the tax-timing option accounts for a substantial proportion of corporate bond price even when interest rate volatility is low. Ignoring the timing option value results in overestimation of credit spread, and underestimation of default probability and the marginal investor’s income tax rate. These estimation biases generally increase with bond maturity and credit risk.

Purpose – Marketers are under increasing pressure to demonstrate the financial return associated with marketing expenditures. Concurrently, more attempts at measuring return on investment from marketing as well as achieving other long-term goals such as building brand equity and increasing shareholder value have been made. As a result of this emphasis, the degree to which advertising budgets are spent efficiently and the impact of these expenditures on the bottom line are an important topic to study.

Methodology/approach – This study applies data envelopment analysis (DEA) to a group of large firms to assess the degree to which companies spend advertising dollars efficiently and to examine the impact of advertising efficiency on investor behavior and, ultimately, stock prices.

Findings – The analysis reveals that firms that advertise more efficiently are rewarded by investors by positive stock returns.

Research limitations/implications (if applicable) – The study is limited to large enterprises with strong brands within a time frame of only four years.

Practical implications (if applicable) – The results imply that it is advisable for marketing managers not to limit their focus to increasing market-based assets at any cost. The efficiency of their efforts can send a positive signal to investors and contribute to shareholder value enhancement.

Originality/value of the chapter – The chapter finds investors to pay attention not only to the effectiveness of advertising activities but also to their efficiency. The study also demonstrates how DEA and stock return response modeling can be combined to investigate the link between advertising efficiency and investor behavior.