Quantitative Asset Management. 24/11/2015 2 Most math departments now offer finance courses 2007  2010: 61% decline in AUM Reflects loss of clients +

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Presentation transcript:

Quantitative Asset Management

24/11/ Most math departments now offer finance courses 2007  2010: 61% decline in AUM Reflects loss of clients + poor performance Different varieties: hedge funds, 130/30, long only They differ in trading frequency/portfolio turnover Quantitative hedge fund 1 in 4 has closed since 2007 Negative impact of Bear Stearns, Lehman (prime brokers for many funds)

Recent Criticisms of Quantitative Management 24/11/ The quant space is too crowd By and large the same story: value, momentum, small tilt Reliance of historical data and similar statistical methods Returns highly correlated and any value added has been competed away

LSV’s Response 24/11/ May 2010 article Research questions: Is there more crowding among quantitative managers compared to discretionary managers? How does the information ratio (industry version) between the two groups compare over time? Does the style (value/momentum..etc.) differ significantly between the two groups?

Data 24/11/  Database: eVestment Alliance, launched in 2000  Monthly data, widely used by consultants in manager searches  Self-reported data, back-filled bias  Long-only institutional managers, large cap, EAFE (i.e., LSV’s universe)  Managers self identify as either fundamental or quantitative  Style is self reported, but consultants will typically perform a holdings-based or returns-based analysis to confirm

Summary of Findings 24/11/ Sample period: Quantitative AUM US large cap has been stable at ~ 16% Number of quantitative firms Percentage of total stable at ~29% Average pairwise correlation of value added Low and comparable to fundamental managers Dispersion of in performance exists among quantitative managers

Summary of Findings 24/11/ Quantitative managers have bigger exposure to momentum Place a larger weight on momentum in ranking stocks Poor recent relative performance can be explained by this difference to a large extent From Ken French’s data library: Mkt-RfSMBHMLMom

Annual Factors (%) /11/ Mkt-RFSMBHMLRFMom Arithmetic mean Geometric mean S.D