Abstract: A number of papers have solved for the optimal dynamic portfolio strategy when expected returns are time-varying and trading is costly, but only for agents with myopic utility. Non-myopic agents benefit from hedging against shocks to the investment opportunity set even when transaction costs are zero (Merton, 1969, 1971). In this paper, we propose a solution to the dynamic portfolio allocation problem for non-myopic agents faced with a stochastic investment opportunity set, when trading is costly. We show that the agent's optimal policy is to trade toward an "aim" portfolio, the makeup of which depends both on transaction costs and on each asset's correlation with changes in the investment opportunity set. The speed at which the agent should trade towards the aim portfolio depends both on the shock's persistence and on the extent to which the shock can be effectively hedged.
Bio: Mehmet Sağlam is the Johnson Associate Professor of Finance at the Carl H. Lindner College of Business at University of Cincinnati. He received a B.Sc. from Cornell University and Ph.D. from Columbia Business School. His research focuses on high-frequency trading and portfolio choice with trading costs. Prior to joining University of Cincinnati, he spent one year at Bendheim Center for Finance at Princeton University as a postdoctoral research associate.
His research has appeared in top journals such as Journal of Financial Economics, Management Science, Journal of Econometrics, Operations Research, and Journal of Financial and Quantitative Analysis. He has presented his research at the annual meeting of American Finance Association (AFA) and Western Finance Association (WFA).
In addition to his academic experience, he worked in the quantitative trading groups at BlackRock, JP Morgan Asset Management and Bank of America Merrill Lynch and worked as a management consultant at FMCG. He has provided consulting services to high-frequency trading and investment management companies.
Zoom: https://us02web.zoom.us/j/82937108090?pwd=MUJDNFQ2dFRJVWxaRk0rc2pwNjQ3dz09
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