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The positive feedback trading phenomenon is significant in Chinese market, and,buying-winners effect is much stronger than selling-losers effect. We call it rise-favor asymmetry of positive feedback trading. This paper studies if this rise-favor asymmetry has asset pricing power. This paper constructs reversal factor, positive feedback factor, and rise-favor asymmetry factor based on Fama-French three factor,and uses a sample that spans from Jan 1998 to Oct 2016 and contains daily data of all A shares that become a listed company before 2010 to do empirical tests. The paper finds that, the performance of rise-favor asymmetry factor is quite different from that of positive feedback factor and reversal factor; Although these three factors all have some pricing power individually, the pricing power of positive feedback factor and reversal factor is weak in the multi-factor pricing model, and only rise-favor asymmetry factor can significantly promote the pricing power of multi-factor model; The pricing power of rise-favor asymmetry factor does not result from price-rising chasing behavior, price-dropping chasing behavior, liquidity risk premium, or investor sentiment. In sum, rise-favor asymmetry is a new pricing factor that different from traditional factors, and its pricing power may relate to the market’s compensation on the risk of irrational speculation. [ABSTRACT FROM AUTHOR]

In order to make optimal trip scheme based on travelers' perception of trip cost, which was described by sensitivity coefficients applied here, the influencing relationship among trip cost, personal feature and trip decision was simulated by structural equation model. The path coefficients was calibrated by stated preference survey data as recommended value of sensitivity coefficients. Considering the travelers' choice preference and assuming that travel time obeyed the normal distribution, the solving result of optimal departure time model showed that optimal departure time depended on desired arriving time, expectations of travel time and variance, relative value difference of time cost between early losses and late losses. The methodology here was useful for making personalized and optimized trip scheme. [ABSTRACT FROM AUTHOR]

In the process of multi-period portfolio selection, the frequent adjustment of investment proportion will produce a series of transaction costs, which is an important factor affecting the decision-making of investment. Online portfolio belongs to the multi-period portfolio; the proportion of investment needs to be adjusted in accordance with the investment stage, rather than a one-time determination. This paper introduces the transaction costs into the online portfolio model, and further applies the weak aggregating algorithm to integrated finite expert advice to design online portfolio strategy. Firstly, this paper discusses the situation that expert advice is fixedly investing on single stock at every period and obtains the single aggregating strategy with transaction costs; and proves the conclusion that comparing with best expert, there exists asymptotical lower bound on the difference between their average cumulative gains. Secondly, this paper discusses the situation that the expert advice is fixedly investing on different numbers of stocks at every period, and obtains the mixed aggregating strategy with transaction costs, and provides the competitive performance of this strategy. Based on the transaction data of New York stock market, numerical examples are given to prove that the single aggregating strategy and mixed aggregating strategy with transaction costs can achieve almost the same performance as the best expert advice, and the effect of transaction costs on strategies' performance is also analyzed. [ABSTRACT FROM AUTHOR]