Dynamic Interpretation of Emerging Systemic Risks Kathleen Weiss Hanley1 and Gerard Hoberg2 1Lehigh University 2University of Southern California MFM Conference March 2017 Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Special Thanks: National Science Foundation This project was made feasible through NSF grant #1449578 Grant was funded through CIFRAM program. A special call for projects that might benefit the Office of Financial Research (OFR). We still know little about crises build, or how to predict and preempt them. Huge ramifications if progress can be made. Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Special Thanks: metaHeuristica Analytics made possible using metaHeuristica Software Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Hypothesized Informational Environment Suppose 3 states of the world: 1 Non-crisis periods. No information production predicted. Transition periods (we propose): Some info production. Crisis periods. Extensive information production. 2 3 Central Premise: Information producers in transition period will trade and their actions might be detectable. Implication: Interpretable early warning system possible. Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Theoretical Motivation Detecting information about banks is challenging. Efficient debt contracting “requires that no agent finds it profitable to produce costly information about the bank’s loans.” [Dang, Gorton, Holstrom, and Ordonez (2016)] Reasons: Costly information, loan size incentives ... Implication: Optimal bank opacity. Expect no signal in normal times. Implication: Even in transition periods, expect weak signal. Need strong power to overcome noise. Use big data. Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Properties of ideal predictive systemic risk model Automated and free of researcher bias. Interpretable without ambiguity. Can detect risks dynamically that did not appear in earlier periods. Permits flexibility to delve deeper into topics of interest. Detects risk factors well in advance of panics. Our approach makes significant headway on all 5 dimensions. Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Methods: See Paper for Details RESULT: A firm-year panel database with 18 thematic scores for each observation. Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Most Novel Innovation: Semantic Vector Analysis LDA alone is popular but difficult to interpret. Yet it can pick up “systemic” content. A second stage SVA model solves the interpretability problem. See Mikolov, Chen, Corrado, and Dean (2013) and Mikolov, Sutskever, Chen, Corrado, and Dean (2013). We are not aware of other finance papers using this technology. Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Semantic themes Important: Selected themes must be present in verbal LDA factor analysis (i.e., have a strong verbal factor structure in risk disclosures). Ensures only systematically relevant risks make the list. Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Examples of Semantic Vectors Mortgage Risk Capital Requirements Row Word Cosine Dist 1 mortgages capital 0.789 2 mortgage 0.7974 requirements 3 impac alt 0.7148 meet 0.5369 4 residential mortgage 0.7085 regulatory 0.4508 5 originated 0.6939 additional 0.4422 6 residential mortgages 0.6922 capital expenditure 0.4404 7 adjustable rate 0.6726 minimum 0.4278 8 collateralizing 0.6372 expenditures 0.4273 9 originations 0.6363 requirement 0.4228 10 fhlmc 0.6303 iubfsb 0.4166 11 fnma 0.6271 fund 0.4096 12 fannie mae 0.6231 liquidity 0.407 13 single family 0.6174 comply 0.4004 14 freddie mac 0.6156 ratios 0.3963 15 mbs 0.6142 regulations 0.3939 16 originate 0.6095 satisfy 0.39 17 newly originated 0.6069 required 0.3864 18 association fnma 0.606 guidelines 0.3836 19 mortgage backed 0.6052 regulators 0.3798 20 loan originations 0.6049 needs 0.3781 Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Result of Computational Linguistics: Large bank-year panel database Note: This database is used to construct a network of bank pairwise common exposures. Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Stock Return Covariance Matrix is Stochastic Question: Can we use big data to examine when perturbation is likely systemic risk? Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Does Risk Factor Network Explain Covariance Matrix Network? Note: RHS network indicates, verbal factor by verbal factor, do banks i and j disclose the same risks with similar intensity? Note: Verbal factors identified using LDA, and specifically from bank risks. Hence only systemically important risks are “eligible” to be part of network. Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Our emerging risk model based on pairwise covariance Run regression once per quarter. One observation is a bank-pair (i and j). Dependent variable is return covariance of i and j measured using daily returns. Independent variable of interest is semantic theme of pair defined as the product Si,j = Si Sj X are control variables including pairwise products: size, age, call report data, and industry. Covariancei,j,t = α0 + γXi,j,t + εi,j,t , (1) Covariancei,j,t = α0+β1Si,j,t,1+β2Si,j,t,2+β3Si,j,t,3+...+βT Si,j,t,18 +γXi,j,t + εi,j,t , (2) Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Our emerging risk model II Covariancei,j,t = α0+β1Si,j,t,1+β2Si,j,t,2+β3Si,j,t,3+...+βT Si,j,t,18 +γXi,j,t + εi,j,t , (3) Goal: decompose the R2 of this model into parts related to (A) accounting variables, and (B) textual risk disclosures. When R2 attributed to risk factors increases, a red flag IS RAISED. Dynamically interpret themes for channels driving increased R2. Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Data Sources We consider banks as identified by firms having SIC codes from 6000 to 6199. We exclude all other firms. CRSP (stock returns), Compustat (accounting variables). FDIC Failures and Assistance Transactions List. We also consider VIX data. Call Reports for bank-specific accounting data. metaHeuristica is used to extract risk factor discussions from bank 10-Ks from 1997 to 2014. We require the firm to have a machine readable 10-K, with some non-empty discussion of risk factors, to be included. Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Aggregate Systemic Risk Signal Our Main Result: t -statistics of R2 due to textual factors 14 12 10 8 6 4 2 199801 199901 200001 200101 200201 200301 200401 200501 200601 200701 200801 200901 201001 201101 201201 201301 20140 -2 t-statistic Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Summary of 2008 Major Risks (t-stats) Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Drill-down extended model These semantic themes were chosen for more depth on marketable securities theme Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Summary of 2015 Major Risks (t-stats) Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Extend data to 4Q 2016 «NEW» Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Extend data to 4Q 2016 «NEW» II Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Cross Section: Individual bank exposure to emerging risk Define Emerging Risk Exposure: average quarterly predicted covariance bank i has with all other banks j using the main covariance model in Equation (3) Does emerging risk exposure predict: Bank’s quarterly stock return from 9/2008 to 3/2009 and 12/2015 to 2/2016 Dummy variable indicating whether the given bank failed in 3 years after Lehman bankruptcy Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Predicting post-2008 crisis returns (9/2008-12/2012) Row Quarter Emerging Risk Exposure # Obs Predictive Timing (1) 2004 1Q 1.634 (1.13) 352 Predictive (2) 2004 2Q 1.509 (1.38) (3) 2004 3Q 3.892 (2.42) 368 (4) 2004 4Q 5.273 (1.99) (5) 2005 1Q 5.898 (2.99) 391 (6) 2005 2Q 4.864 (2.60) (7) 2005 3Q 2.571 (1.28) 421 (8) 2005 4Q 2.816 (1.05) (9) 2006 1Q -3.469 (-1.21) 410 (10) 2006 2Q -3.806 (-1.40) (11) 2006 3Q -3.325 (-1.27) 434 (12) 2006 4Q -3.310 (-1.27) (13) 2007 1Q -3.619 (-2.14) 445 (14) 2007 2Q -5.630 (-1.93) (15) 2007 3Q -4.207 (-2.83) 470 (16) 2007 4Q -5.029 (-2.50) (17) 2008 1Q -4.257 (-3.27) 474 (18) 2008 2Q -6.034 (-3.46) (19) 2008 3Q -5.574 (-4.59) 495 Non Predictive (20) 2008 4Q -3.363 (-2.60) 497 (21) 2009 1Q -5.843 (-3.69) 525 (22) 2009 2Q -5.229 (-3.24) (23) 2009 3Q -5.105 (-2.68) 536 (24) 2009 4Q -5.010 (-2.49) 529 Non-Predictive Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Predicting current period returns (12/2015-2/2016) Row Quarter Emerging Risk Exposure # Obs Predictive Timing (1) 2010 1Q -0.925 (-3.44) 335 Predictive (2) 2010 2Q -0.779 (-2.54) (3) 2010 3Q -0.526 (-1.14) 342 (4) 2010 4Q -0.332 (-0.51) (5) 2011 1Q -1.388 (-1.89) 353 (6) 2011 2Q -1.120 (-1.98) 352 (7) 2011 3Q -0.837 (-1.04) 358 (8) 2011 4Q -0.056 (-0.15) (9) 2012 1Q 0.005 (0.04) 349 (10) 2012 2Q -0.237 (-0.57) (11) 2012 3Q 0.120 (0.30) 360 (12) 2012 4Q 0.044 (0.14) (13) 2013 1Q -0.686 (-1.55) 351 (14) 2013 2Q -0.668 (-1.55) (15) 2013 3Q 0.155 (0.47) 368 (16) 2013 4Q -0.617 (-0.94) (17) 2014 1Q -0.467 (-1.08) 356 (18) 2014 2Q -0.901 (-1.04) (19) 2014 3Q -1.565 (-1.97) 367 (20) 2014 4Q -1.105 (-2.61) (21) 2015 1Q -0.758 (-1.16) (22) 2015 2Q -1.208 (-3.38) (23) 2015 3Q -1.773 (-2.69) 378 (24) 2015 4Q -1.374 (-2.82) 377 Non Predictive Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Predicting bank failures Row Quarter Emerging Risk Exposure Log Assets Loans Assets Loss/ Assets Cap- ital (1) 2004 1Q -0.000 (-0.13) -0.005 (-0.77) 0.149 (4.63) -10.533 (-2.77) -0.207 (-0.79) (2) 2004 2Q 0.003 (1.09) -0.005 (-0.71) 0.149 (4.57) -7.843 (-1.75) -0.200 (-0.77) (3) 2004 3Q -0.001 (-0.21) -0.005 (-0.81) 0.149 (4.66) -10.753 (-2.12) -0.208 (-0.78) (4) 2004 4Q -0.004 (-1.58) -0.006 (-0.88) 0.150 (4.73) -13.791 (-2.24) -0.209 (-0.78) (5) 2005 1Q 0.005 (1.01) -0.001 (-0.20) 0.160 (5.10) -1.510 (-0.30) -0.376 (-2.61) (6) 2005 2Q 0.003 (0.78) -0.002 (-0.34) 0.159 (5.31) -2.191 (-0.35) -0.371 (-2.69) (7) 2005 3Q 0.007 (2.41) -0.001 (-0.13) 0.159 (5.23) -0.322 (-0.05) -0.379 (-2.81) (8) 2005 4Q 0.007 (2.32) -0.001 (-0.12) 0.159 (5.22) 0.444 (0.07) -0.379 (-2.83) (9) 2006 1Q 0.000 (0.04) -0.009 (-1.11) 0.220 (7.17) -8.166 (-1.08) -0.408 (-2.86) (10) 2006 2Q 0.012 (1.71) -0.005 (-0.61) 0.221 (7.13) 4.409 (1.31) -0.419 (-3.31) (11) 2006 3Q 0.014 (2.50) -0.004 (-0.56) 0.222 (7.25) 2.109 (0.69) -0.418 (-3.45) (12) 2006 4Q 0.019 (5.03) -0.001 (-0.17) 0.221 (7.14) 2.094 (0.52) -0.422 (-3.73) (13) 2007 1Q 0.020 (4.92) -0.003 (-0.25) 0.224 (6.02) -0.147 (-0.01) -0.290 (-2.65) (14) 2007 2Q 0.025 (6.90) -0.001 (-0.06) 0.223 (6.45) 7.694 (0.55) -0.291 (-2.63) (15) 2007 3Q 0.023 (6.18) -0.002 (-0.14) 0.223 (6.21) -0.159 (-0.01) -0.287 (-2.53) (16) 2007 4Q 0.028 (6.39) 0.219 (6.25) 3.324 (0.25) -0.268 (-2.30) (17) 2008 1Q 0.020 (5.13) -0.004 (-0.54) 0.222 (2.47) -42.064 (-2.15) -0.036 (-0.42) (18) 2008 2Q 0.020 (5.32) -0.003 (-0.47) 0.224 (2.48) -38.468 (-1.92) -0.028 (-0.33) (19) 2008 3Q 0.023 (4.30) -0.002 (-0.27) 0.224 (2.55) -42.117 (-2.28) -0.057 (-0.71) (20) 2008 4Q 0.018 (3.82) -0.006 (-0.87) 0.224 (2.52) -35.426 (-1.76) -0.039 (-0.47) (21) 2009 1Q 0.025 (6.82) -0.008 (-1.51) 0.136 (3.77) -118.07 (-16.64) -0.829 (-2.95) (22) 2009 2Q 0.009 (8.76) -0.011 (-2.03) 0.141 (3.83) -107.40 (-18.47) -0.873 (-3.20) (23) 2009 3Q 0.019 (6.42) -0.012 (-2.21) 0.141 (3.41) -114.10 (-18.97) -0.850 (-3.05) (24) 2009 4Q 0.001 (0.22) -0.011 (-2.11) 0.142 (3.93) -101.97 (-13.68) -0.869 (-3.25) Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks

Conclusions We propose a dynamic model of emerging systemic risks based on computational linguistic analysis of financial firm disclosures and return covariances. Benefits of model: Provides little or no signal in “normal times”. Provides aggregate measure of trading on systemic risks. When systemic risk is building, produces interpretable information about specific channels. Model is dynamic and reveals risks researcher might be unaware of. Yet SVA also allows researcher to drill down. Suggests interpretable early warning system is possible. Also suggests SEC’s risk factor disclosure program is valuable (not a priori clear from existing work). Hanley and Hoberg (2016) Dynamic Emerging Systemic Risks