Search at the Margin

2017 ◽  
Vol 107 (10) ◽  
pp. 3146-3181
Author(s):  
José A. Carrasco ◽  
Lones Smith
Keyword(s):  
Opportunity Cost ◽  
Bargaining Power ◽  
Value Function ◽  
Search Theory ◽  
Reservation Price ◽  
Search Frictions ◽  
Marginal Value ◽  
Strictly Convex ◽  
Holding Costs

We extend search theory to multiple indivisible units and perfectly divisible assets, solving them respectively with induction and recursion. Buyer demands and prices are random, and the seller can partially exercise orders. With divisible assets, the Bellman value function is increasing and strictly concave, and the optimal reservation price falls in the position, reflecting increasing holding costs (opportunity cost of delaying optionality for inframarginal units). The marginal value exists, and is strictly convex with a falling purchase cap density. Our model is amenable to price-quantity bargaining; e.g., greater buyer bargaining power is tantamount to greater search frictions. (JEL C61, C78, D25, D83, G31)

scholarly journals Robo-Advising: Learning Investors’ Risk Preferences via Portfolio Choices*

2020 ◽  
Author(s):  
Humoud Alsabah ◽  
Agostino Capponi ◽  
Octavio Ruiz Lacedelli ◽  
Matt Stern
Keyword(s):  
Opportunity Cost ◽  
Value Function ◽  
Risk Preference ◽  
Portfolio Decisions ◽  
Learning Framework ◽  
Portfolio Choices ◽  
Trading Decisions ◽  
Exploration Exploitation ◽  
The Value Function ◽  
Over Time

Abstract We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor’s risk preference but learns it over time by observing her portfolio choices in different market environments. We develop an exploration–exploitation algorithm that trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor’s risk aversion. We show that the approximate value function constructed by the algorithm converges to the value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor’s mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor’s opportunity cost for making portfolio decisions.

scholarly journals Rational taxation in an open access fishery model

2017 ◽  
Vol 27 (1) ◽  
pp. 5-27
Author(s):  
Dmitry B. Rokhlin ◽  
Anatoly Usov
Keyword(s):  
Fishery Management ◽  
Value Function ◽  
Cooperative Behavior ◽  
Golden Rule ◽  
Growth Function ◽  
Single Population ◽  
Marginal Value ◽  
Resource Stock ◽  
Fishery Model ◽  
Taxation Scheme

Abstract We consider a model of fishery management, where n agents exploit a single population with strictly concave continuously differentiable growth function of Verhulst type. If the agent actions are coordinated and directed towards the maximization of the discounted cooperative revenue, then the biomass stabilizes at the level, defined by the well known “golden rule”. We show that for independent myopic harvesting agents such optimal (or ε-optimal) cooperative behavior can be stimulated by the proportional tax, depending on the resource stock, and equal to the marginal value function of the cooperative problem. To implement this taxation scheme we prove that the mentioned value function is strictly concave and continuously differentiable, although the instantaneous individual revenues may be neither concave nor differentiable.

Why Do Vacant Houses Sell for Less: Holding Costs, Bargaining Power or Stigma?

2010 ◽  
Vol 39 (1) ◽  
pp. 19-43 ◽  
Author(s):  
Geoffrey K. Turnbull ◽  
Velma Zahirovic-Herbert
Keyword(s):  
Bargaining Power ◽  
Holding Costs

scholarly journals A generic marginal value function for natural areas

2016 ◽  
Vol 58 (1) ◽  
pp. 159-179 ◽  
Author(s):  
Mark J. Koetse ◽  
Erik T. Verhoef ◽  
Luke M. Brander
Keyword(s):  
Value Function ◽  
Natural Areas ◽  
Marginal Value

A Simple OTC Pricing Model

Dark Markets ◽  
2012 ◽  
Author(s):  
Darrell Duffie
Keyword(s):  
Asset Pricing ◽  
Risk Aversion ◽  
Bargaining Power ◽  
Asset Price ◽  
Pricing Model ◽  
Search Frictions ◽  
Over The Counter ◽  
Supply Shocks ◽  
Hedging Demand ◽  
Over Time

This chapter presents a simple introduction to asset pricing in over-the-counter markets. Investors search for opportunities to trade and bargain with counterparties, each counterparty being aware that failure to conduct a trade could lead to a costly new search for a counterparty. In equilibrium, whenever there is gain from trade, the opportunity to search for a new counterparty is dominated by trading at the equilibrium asset price. The asset price reflects the degree of search frictions. Under conditions, illiquidity premia are higher when counterparties are harder to find, when sellers have less bargaining power, when the fraction of qualified owners is smaller, and when risk aversion, volatility, or hedging demand is larger. Supply shocks cause prices to jump, and then “recover” over time, with a pattern that depends on the degree of search frictions. The chapter shows how the equilibrium bargaining powers of the counterparties are determined by search opportunities using the approach of Rubinstein and Wolinsky (1985).

scholarly journals Sorting through Search and Matching Models in Economics

2017 ◽  
Vol 55 (2) ◽  
pp. 493-544 ◽  
Author(s):  
Hector Chade ◽  
Jan Eeckhout ◽  
Lones Smith
Keyword(s):  
College Student ◽  
Search Theory ◽  
Assortative Matching ◽  
Transferable Utility ◽  
Search And Matching ◽  
Search Frictions ◽  
Informational Frictions ◽  
Search And Matching Models ◽  
Student Sorting ◽  
Matching Models

Toward understanding assortative matching, this is a self-contained introduction to research on search and matching. We first explore the nontransferable and perfectly transferable utility matching paradigms, and then a unifying imperfectly transferable utility matching model. Motivated by some unrealistic predictions of frictionless matching, we flesh out the foundational economics of search theory. We then revisit the original matching paradigms with search frictions. We finally allow informational frictions that often arise, such as in college-student sorting. (JEL C78, D82, D83, I23, J12)

Retesting the Shape-of-Voice Value Function

2005 ◽  
Author(s):  
Jaewon Ko ◽  
Layne Paddock ◽  
Kees Van den Bos ◽  
Gary J. Greguras ◽  
Kidok Nam ◽  
...  
Keyword(s):  
Value Function
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