DYNAMIC INCENTIVES FOR BUY-SIDE ANALYSTS Maher Said with Rahul Deb (University of Toronto) and Mallesh Pai (Rice University) August 2019
MOTIVATION Analyst research plays an important role in modern capital markets. Analysts obtain information from public records, corporate fjlings, and other sources. Institutions and investors rely on this research to aid in investment decisions. Our goal is to try to understand some of the incentives at play in these environments. Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts
MOTIVATION We focus on buy-side analysts that gather and provide information to fund managers for their exclusive use within the fjrm. Fund managers rely on these analysts for investment ideas, advice, and recommendations. These buy-side analysts difger in their ability and access to information. = How should a fund manager incentivize analysts and maximize fund profjts? Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts They behave strategically to enhance the perception of their ability. ⇒ The manager may be operating on biased or misleading advice! 1. In the short term ⇐ ⇒ maximize the value of information. 2. In the long term ⇐ ⇒ maximize the human capital of her fund.
BASIC MODEL: ENVIRONMENT State: Players: Outcome: Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts Horizon: ▶ Single principal (fund manager) and single agent (analyst). ▶ Principal commits to a retention/promotion policy (no transfers). ▶ Analyst observes and communicates information over T periods. ▶ An event is publicly realized in period T + 1 , and the principal implements policy. ▶ Persistent state of the world ω ∈ Ω , | Ω | = n ; each state is equally likely. ▶ A public outcome r ∈ Ω is realized in period T + 1 . � γ if r = ω, ▶ The outcome is noisy but informative: Pr ( r | ω ) = 1 − γ if r ̸ = ω. n − 1
BASIC MODEL: AGENT Types: otherwise. Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts Signals: ▶ Analyst’s private type θ ∈ { h, l } . ▶ Both types are equally likely. ▶ At each t ≤ T , agent privately observes a signal s t ∈ S := Ω ∪ { ϕ } . ▶ Learning is an “all-or-nothing” Bernoulli arrival process: 1 if s t = ω and s t − 1 = ω, if s t = ω and s t − 1 ̸ = ω, α θ Pr ( s t | ω, s t − 1 ) = 1 − α θ if s t = ϕ and s t − 1 ̸ = ω, 0 ▶ The high type is a faster learner: 0 < α l < α h < 1 .
BASIC MODEL: PREFERENCES Principal’s preferences: Agent’s preferences: Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts ▶ The analyst has reputationally motivated career concerns. � 1 if retained/promoted , ▶ Her payofg is 0 otherwise . ▶ The fund manager has dual concerns Π( V, W ) , increasing in both. ▶ Ex post value of information (i.e., “trading profjts”) is V ( π 1 , . . . , π T , r ) , where π t is the period- t belief about ω . 1 if type h is retained , ▶ Value of human capital is W := − 1 if type l is retained , 0 otherwise .
TWO CRITICAL ASSUMPTIONS 1. Analyst cares only about probability of retention/promotion; no transfers. concentrated at the top end of the tenure/fund hierarchy. 2. Analyst ability is refmected in speed (and not quality) of learning. low-type…. The heterogeneity stems from difgerential ability to produce new information.” (Crane and Crotty, forthcoming JF ) (2018) for diffjculties with high-dimensional state spaces. Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts ▶ Evidence shows substantial heterogeneity in the use of high-powered incentives; ▶ Compensation driven primarily by promotions (eventually to fund manager). ▶ Also: “skin in the game” only makes things easier for the manager. ▶ “Analysts exhibit heterogeneous skill—some are high-type, and some are ▶ That said, difgerential quality of information may be natural. See Deb-Pai-Said
REVELATION PRINCIPLE The revelation principle applies, so the principal can do no better than the payofg she gets Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts In our setting, a direct mechanism is a policy X (˜ s T , r ) ∈ [0 , 1] . θ, ˜ ▶ The agent reports a private type ˜ θ at time 0. ▶ She then reports a signal ˜ s t at each t . ▶ The public outcome r is realized at T + 1 . from an optimal incentive compatible direct mechanism. ▶ Agent must be incentivized to report all private information truthfully. ▶ It requires an agent to truthfully report that she is unskilled!
REVELATION PRINCIPLE In our employment/organizational setting, this is prima facie impractical. talk to each other and to research fjrms. We therefore assume the fund manager’s contracting/commitment power is limited. Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts ▶ Even if the commitment to promote a self-admitted low-skill type was credible, managers ▶ External reputational hit is a costly impediment to career mobility. ▶ May also face legal/regulatory prohibitions (e.g., EEO). ▶ We rule out the use of full direct revelation mechanisms. ▶ Instead we focus on a class of “indirect” contracts. ▶ Key restriction: the manager does not solicit information about types.
OUR GAME Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts At each t = 1 , . . . , T , the agent sends a message ˜ s t ∈ S . Agent histories: The set of agent histories is H A = ∪ T t =1 S t × S t − 1 . A typical period- t element is h A t = ( s t , ˜ s t − 1 ) . Principal histories: The set of relevant public histories is H P = S T × Ω . A typical element is h P = (˜ s T , r ) . Agent’s strategy: σ θ : H A → ∆( S ) determines the distribution of messages at each history. s T , r ) ∈ [0 , 1] is the decision to retain/promote the agent (or not). Principal’s strategy: χ (˜ ▶ The principal fully commits to χ . ▶ We explicitly consider stochastic mechanisms.
RECAP private signal payofgs realized. implemented; realized; publicly Outcome private signal Agent observes Principal Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts Agent observes retention policy commits to Nature draws Agent learns state ω ∈ Ω ; s 1 ∈ S and s T , r ) ∈ [0 , 1] ; χ (˜ type θ ∈ { h, l } ; reports ˜ s 1 ∈ S ; s T , r ) Policy χ (˜ r ∈ Ω s T ∈ S and reports ˜ s T ∈ S ;
MAIN QUESTIONS OF INTEREST How much information can the principal elicit? How much screening is possible? What exactly is the tradeofg between learning and screening? What does the optimal mechanism look like? Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts
PREVIEW OF RESULTS With a nonstrategic analyst, the principal uses a deterministic test that relies only on speed . Despite not using a DRM, the principal can induce the analyst to immediately and truthfully reveal all signals. The principal provides incentives for reporting no learning, and also for providing risky or contrarian advice. Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts ▶ But with a strategic analyst, rewarding speed alone is not optimal. ▶ It is too easy to “manufacture” information. Instead, the principal screens using accuracy , with stochastic penalties for slow learning.
RELATED LITERATURE Forecasters: Ottaviani-Sørensen (2006a,b,c), Marinovic-Ottaviani-Sørensen (2013). Analysts: Hong-Kubik-Solomon (2000), Hong-Kubik (2003). Dynamic mechanism design: Battaglini (2005), Pavan-Segal-Toikka (2014). …without money: Guo-Hörner (2018), Deb-Pai-Said (2018). Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts Testing experts: Foster-Vohra (1998), Olszewski (2015).
BENCHMARK: PUBLIC SIGNALS Consider the “fjrst-best” benchmark where the agent’s signals are public. likelihood ratio test Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts portfolio decision. ▶ No private information about the state = ⇒ retention decision is decoupled from ▶ Principal can maximize V without worrying about incentives. ▶ And principal can maximize W without worrying about information. ▶ Therefore, principal’s payofg is Π FB := Π( V FB , W FB ) . The measure of type separation for any retention rule χ is � � � � Pr ( r, s T ) χ ( s T , r ) Pr ( θ = h | r, s T ) − Pr ( θ = l | r, s T ) W = s T ∈S T r ∈ Ω = 1 � � � Pr ( r, s T | θ = h ) − Pr ( r, s T | θ = l ) � χ ( s T , r ) . 2 � �� � s T ∈S T r ∈ Ω
BENCHMARK: PUBLIC SIGNALS ln such that the analyst is retained if, and only if, an informative Theorem Deb, Pai, and Said (2019): Dynamic Incentives for Buy-Side Analysts In the public signal benchmark, the optimal retention policy χ FB is characterized by a cutofg � �� � � ¯ α h 1 − α l k := 1 + ln α l 1 − α h signal arrives in some period t ≤ ¯ k . The fjrst-best screens purely on the speed of learning: ▶ Only the arrival time of the fjrst informative signal (if any) matters. ▶ There is no benefjt to randomization. ▶ The analyst is not penalized for events out of her control. ▶ But she is never retained/promoted if information doesn’t arrive.
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