Frank Russell Company and The Yasuda Fire and Marine Insurance Co., Ltd., developed an asset/liability management model using multistage stochastic programming. It determines an optimal investment strategy that incorporates a multiperiod approach and enables the decision makers to define risks in tangible operational terms. It also handles the complex regulations imposed by Japanese insurance laws and practices. The most important goal is to produce a high-income return to pay annual interest on savings-type insurance policies without sacrificing the goal of maximizing the long-term wealth of the firm. During the first two years of use, fiscal 1991 and 1992, the investment strategy devised by the model yielded extra income of 42 basis points (¥8.7 billion or US$79 million).
This paper tests models of mutual fund market timing that allow the manager's payo! function to depend on returns in excess of a benchmark, and distinguish timing based on publicly available information from timing based on "ner information. We simultaneously estimate parameters which describe the public information environment, the manager's risk aversion, and the precision of the fund's market-timing signal. Using a sample of more than 400 U.S. mutual funds for 1976}94, our "ndings suggest that mutual funds behave as highly risk averse, benchmark investors. Conditioning on public information improves the model speci"cation. After controlling for the public 0304-405X/99/$ -see front matter 1999 Elsevier Science S.A. All rights reserved. PII: S 0 3 0 4 -4 0 5 X ( 9 9 ) 0 0 0 0 6 -9 information, we "nd no evidence that funds have signi"cant market-timing ability.1999 Elsevier Science S.A. All rights reserved.JEL classi"cation: D82; G11; G12; G14; G23
This paper discusses technical aspects of the Russell-Yasuda Kasai financial planning model. These include the models for the discrete distribution scenario generation processes for the uncertain parameters of the model, the mathematical approach used to develop the infinite-horizon end-effects part of the model, a comparison of algorithms used in the model's solution, and a comparison of the multistage stochastic linear programming model with the previous technology, static mean-variance analysis. Experience and benefits of the model in Yasuda-Kasai's financial planning process is also discussed.
This paper tests models of mutual fund market timing that allow the manager's payo! function to depend on returns in excess of a benchmark, and distinguish timing based on publicly available information from timing based on "ner information. We simultaneously estimate parameters which describe the public information environment, the manager's risk aversion, and the precision of the fund's market-timing signal. Using a sample of more than 400 U.S. mutual funds for 1976}94, our "ndings suggest that mutual funds behave as highly risk averse, benchmark investors. Conditioning on public information improves the model speci"cation. After controlling for the public 0304-405X/99/$ -see front matter 1999 Elsevier Science S.A. All rights reserved. PII: S 0 3 0 4 -4 0 5 X ( 9 9 ) 0 0 0 0 6 -9 information, we "nd no evidence that funds have signi"cant market-timing ability.1999 Elsevier Science S.A. All rights reserved.JEL classi"cation: D82; G11; G12; G14; G23
Abstract. In a Semi-Strong Form (SSF) Efficient Market, asset prices should respond quickly and completely to the public release of new information. In the period from his election on 11/8/16 to his swearing in ceremony on 1/20/17, President-elect Trump posted numerous statements ('tweets') on his Twitter messaging service account that identified ten publicly traded firms. In the absence of new information, the Efficient Market Hypothesis (EMH) predicts that these announcements should have little or no price impact on the common stocks of these firms. Using standard event study methods, we find that positive (negative) content tweets elicited positive (negative) abnormal returns on the event date and virtually all of this effect is from the opening stock price to the close. Within five trading days, the CARs are no longer statistically significant. President-elect Trump's tweets were associated with increases in trading volume and Google Search activity. Taken as a whole, the price and trading volume response, combined with Google Search activity is consistent with hypothesis that it was small/noise traders who were acting on President-elect Trump's tweets and that their impacts were transitory.
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