Desarrollo de la teoría de perspectivas de meta de logro.

Finally, we compare the cross-sectional predictive ability of AVE-CM MF-CFER to that of IVS and DOTS. The former is expected to perform better (and certainly not worse) than the other two measures for the purposes of predicting stock returns (Propositions 1.2.1 and 1.2.2). We follow Bali and Hovakimian (2009) and calculate IVS by taking the average of the IVS of available pairs of call and put options across different strikes and maturities. We construct DOTS in line with Goncalves-Pinto et al. (2019). Appendix 1.C describes the construction of IVS and DOTS in detail.

Table 1.5 reports the average returns, and alphas for the spread portfolios formed on AVE-CM MF-CFER, IVS and DOTS. For any given sorting criterion, t-statistics for each average return and alpha are reported within parentheses. Within square brackets, we report thet-statistics of the difference between the performance measures of the CFER-sorted and IVS- (DOTS)-sorted portfolios. For this comparison, the null hypothesis is that each pair of sorting criteria yields equal results and the alternative

hypothesis is that the CFER-sorted portfolio outperforms. Regarding the IVS-sorted spread portfolio, we can see that the CFER-sorted spread portfolio earns a greater average return by 47 bps and greater alphas by 45–49 bps. Moreover,these differences are statistically significant. When compared with the DOTS-sorted portfolio, again the CFER-sorted spread portfolio earns a greater average return by 19 bps and greater alphas by 27–42 bps, although the economic and statistical differences are generally smaller than the CFER versus IVS case. This result is expected because DOTS is not subject to the vega scaling point encountered in IVS, and the additional term ut over our sample period is small.19 In sum, in line with the previous literature, both IVS and DOTS predict stock returns, yet MF-CFER outperforms IVS. This confirms Propositions1.2.1 and 1.2.2.

[Table 1.5 about here.]

Next, we explore whether the economic mechanism for the predictability of CFER (compensation for market frictions) is distinct from the one employed to explain the predictability of IVS. Cremers and Weinbaum(2010) argue that the predictive power of IVS is due to informed option trading. In the case where informed traders receive optimistic (pessimistic) information on the future stock performance, they prefer to buy (sell) call options and/or sell (buy) put options to utilize their information rather than trading the underlying stock. As a result, new information is incorporated into option prices prior to the underlying stock price causing deviations from put-call parity. However, the informed option trading view and our friction-based view can be complementary because the existence of market frictions is a necessary condition for the informed trading story to hold; if the market is frictionless, deviations from put-call parity and hence a non-zero CFER would not occur.

To investigate further whether the predictive power of MF-CFER reflects informed option trading, we test the following conjecture. If the predictability of MF-CFER is due to informed option trading, then it should be stronger in stocks which underlie the more liquid options; Easley et al. (1998) model suggests that informed traders will trade in options which have higher liquidity. To test our conjecture, we conduct

19 _{We calculateu}

tas DOT St(K)−CF ERM Ft,t+1(K)/R 0

t,t+1 and find that the monthly time series

of the median ofut is close to half of the net risk-free rate, which is close to zero over our sample

a dependent bivariate sort, where we first sort stocks into five groups based on their respective aggregate option trading volume on the sorting date, and then within each group we sort stocks based on the estimated CFER into quintile portfolios. If the predictability of MF-CFER is due to informed option trading, then we expect that the CFER-spread portfolios of the higher option trading bins should earn more significant returns (i.e., predictive power) compared to those of lower option trading bins.

Table 1.6reports the results as well as the average volume and liquidity character- istics of the stocks in the individual option trading volume bins. We can see that the greatest average return and alpha are obtained for the smallest option trading vol- ume bin, which is not consistent with the informed option trading view. The average return (alpha) of the MF-CFER spread portfolio in the lowest trading volume bin is 1.32 (1.31) versus 0.91 (1.04) in the biggest trading volume bin; the t-statistics also decrease from about 6 to about 3.2 as we move from the lowest to the highest trading volume bin. We can also see that stocks in smaller option trading volume bins are smaller, have wider bid-ask spread, and lower liquidity, that is, these stocks are sub- ject to larger market frictions. Therefore, our friction-based view is more consistent with our findings; the CFER-spread portfolio of the smallest option trading volume bin earns the highest return because its constituent stocks are subject to larger mar- ket frictions. This implies that the degree of market frictions for trading stocks are more pertinent to the return predictability than option trading activity.20

[Table 1.6 about here.]

Our findings above are in accordance with Goncalves-Pinto et al. (2019) in two ways. First, they find that the predictive power of IVS and DOTS does not differ between stocks which have non-zero option trading volume and those which have zero option trading volume. This finding and our empirical result above suggest that deviations from put-call parity convey information on future stock returns even when there is small or even no option trading volume. Second, they document that the predictive power of deviations from put-call parity is a manifestation of price 20_{We repeat a similar bivariate sorting exercise using IVS instead of MF-CFER. We obtain the}

same pattern, that is, the predictability of IVS is stronger in lower option trading bins. This is consistent with our theoretical result that IVS is an approximation (monotonic transformation) of MF-CFER.

pressure in the underlying stock market than of informed option trading. They find that temporary buying (selling) pressure in the stock market tends to mean lower (higher) DOTS. This mechanism can be accommodated in our theoretical framework by viewing the agent in our model as a stock market-maker. For example, when the market-maker faces selling pressure, she provides liquidity by buying the stock. In this case, she demands higher return (positive CFER) to be compensated for transaction costs and other friction-related costs (Proposition 1.2.3).