Fanta Fall

Natacha Lenuzza

Elodie Lamy

Marion Brollo

Emmanuel Naline

Philippe Devillier

Etienne Thévenot

Stanislas Grassin-Delyle
email: stanislas.grassin-delyle@uvsq.fr

A split-range acquisition method for the non-targeted metabolomic profiling of human plasma with hydrophilic interaction chromatography -high-resolution mass spectrometry

Keywords: metabolomics, high-resolution mass spectrometry, hydrophilic interaction liquid chromatography, human plasma

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Metabolomics is a phenotyping approach interested in molecules located downstream (in biological terms) of those targeted by established genomics, transcriptomics and proteomics approaches. It provides novel insights into mechanisms of disease pathogenesis and is useful for the discovery of biomarkers for disease diagnosis, prognosis or for the prediction of the response to pharmacotherapy in the era of precision medicine [START_REF] Devillier | Metabolomics in the Diagnosis and Pharmacotherapy of Lung Diseases[END_REF][START_REF] Guo | Plasma metabolomic profiles enhance precision medicine for volunteers of normal health[END_REF]. As for the abovementioned classical "omics" technologies, untargeted approaches are best suited for the discovery of biomarkers and mass spectrometry (MS) has become the most convenient analytical platform for the sensitive and comprehensive metabolomic profiling of human tissues and fluids, especially with the expansion of high-resolution instruments. Plasma is the biofluid of choice for human studies, it is easy to collect and reflects the systemic metabolism.

It contains thousands of metabolites of varying abundance, molecular weight, polarity, water solubility and ionization states which may each require specific analytical conditions to be detected. Therefore, the interest of combining hydrophilic interaction liquid chromatography (HILIC) and reverse-phase (RP) liquid chromatography to improve the coverage of plasma metabolome has already been reported [START_REF] Boudah | Annotation of the human serum metabolome by coupling three liquid chromatography methods to high-resolution mass spectrometry[END_REF][START_REF] Contrepois | Optimized Analytical Procedures for the Untargeted Metabolomic Profiling of Human Urine and Plasma by Combining Hydrophilic Interaction (HILIC) and Reverse-Phase Liquid Chromatography (RPLC)-Mass Spectrometry[END_REF][START_REF] Tang | HILIC-MS for metabolomics: An attractive and complementary approach to RPLC-MS[END_REF][START_REF] Virgiliou | HILIC-MS/MS Multi-Targeted Method for Metabolomics Applications[END_REF]. Although the aim of the analysis is to focus only on molecules present in the plasma sample, the performance of untargeted analysis may also be impacted by interfering compounds from the laboratory environment, i.e. in the materials, reagents or in the analytical instruments as a residue from previous analytical runs. Welldescribed interferences are proteins, solvents, polymers, plastics and additives such as detergents or ion pairing reagents, which are known to affect MS analysis [START_REF] Keller | Interferences and contaminants encountered in modern mass spectrometry[END_REF]. Optimal acquisition parameters should therefore both maximize the detection of compounds of interest and minimize the influence of contaminants. However, despite the widespread use of metabolomic analysis, the influence of exogenous contaminants and the optimisation of mass spectrometry parameters for the metabolomic analysis of human plasma has never been investigated to date. The objectives of the present study were first to assess the main interfering compounds in a laboratory setting then to propose an analytical strategy to maximize the number of compounds detected in human plasma. HILIC was retained for chromatography since it was described to have the widest serum metabolome coverage [START_REF] Boudah | Annotation of the human serum metabolome by coupling three liquid chromatography methods to high-resolution mass spectrometry[END_REF].

MATERIALS AND METHODS

2.1.Reagents

LC-MS-grade ammonium formate and formic acid (98%) were supplied by Sigma Aldrich (Saint Quentin Fallavier, France). LC-MS-grade methanol, acetonitrile, chloroform and water were from Fisher Scientific (Illkirch, France). Deuterated internal standards (IS) were from Bertin technologies (Montigny le Bretonneux, France) (anandamide-d4, 25hydroxycholesterol-d6, 2-arachidonoylglycerol-d8) and LCG standards (St. Louis, MO, USA) (hydrocortisone-d4, glyburide-d3, fludrocortisone-d2, testosterone-d3, propoxyphene-d5, chlorpromazine-d3 and fluoxetine-d6). Plasma was obtained from the local blood bank (Etablissement Français du Sang, Rungis, France).

2.2.Sample preparation

One hundred microliters of plasma were spiked with 10 µL of the mix of deuterated internal standards (anandamide-d4, 25-hydroxycholesterol-d6, 2-arachidonoylglycerol-d8, hydrocortisone-d4 and glyburide-d3, 0.5 mg L -1 and fludrocortisone-d2, testosterone-d3, propoxyphene-d5, chlorpromazine-d3 and fluoxetine-d6, 0.05 mg L -1 ) then subjected to liquid/liquid extraction using 1 mL of methanol/chloroform/water (1:2:1 v/v/v) [START_REF] Bligh | A rapid method of total lipid extraction and purification[END_REF]. The mixture was vortexed for 30 seconds, agitated for 20 mins and centrifuged at 10000 rpm for 5 mins. 200 µL of upper phase were collected and dried under vacuum. The dried extracts were reconstituted with 75 µL of ammonium formate/acetonitrile (20:80 v/v) and transferred into injection vials for analysis. Plasma samples from 3 different healthy donors were analyzed in triplicate in each experimental condition together with blank samples consisting in mobile phase only. Samples were injected in random order.

2.3.Chromatographic separation

France, Houilles, France) was used for chromatography [START_REF] Zhang | Evaluation of mobile phase characteristics on three zwitterionic columns in hydrophilic interaction liquid chromatography mode for liquid chromatography-high resolution mass spectrometry based untargeted metabolite profiling of Leishmania parasites[END_REF]. Separation was performed under gradient elution using a mobile phase system consisting of ammonium formate 10 mM pH 3.8 (solvent A) and acetonitrile (solvent B). Starting conditions were 95% solvent B for 3 min then reaching 8% at 25 min. This plateau was kept for 5 more minutes, then back to 95% and equilibration for 10 minutes. Flow rate was 0.3 mL min -1 and oven temperature 40°C.

2.4.High-resolution mass spectrometry analysis

Compounds were detected with a Q-Exactive mass spectrometer (Thermofisher) equipped with a heated electrospray ionization (HESI) source. Nitrogen (N2-45 nitrogen generator, VWR International, Fontenay sous bois, France) was employed as sheath and auxiliary gas.

Source parameters

The HESI source was set in the positive ionization mode with a capillary temperature of 275°C. Unless otherwise stated, sheath and auxiliary gas flow rate were set at 40 and 25 arbitrary units, respectively; S-lens RF level at 100, spray voltage at 4000 V and temperature of vaporization at 100°C.

Acquisition parameters

In the first part of the study, samples were analyzed using a single full-scan m/z range (60-900) to identify the main analytical interferences. Then, four other acquisition methods were evaluated to assess the influence of splitting the scan range at masses of the main interfering compounds. Comparison was performed between the full-scan method (m/z 60-900, method M1) and different split methods (Figure 1): method M2 (3 m/z ranges, m/z 60-101; 105-213; 215-900), method M3 (3 m/z ranges, m/z 60-81; 85-213; 215-900), method M4 (3 m/z ranges, m/z 60-82; 82-213; 213-900) and method M5 (3 m/z ranges, m/z 60-300; 300-600; 600-900).

Resolution was set at 70000, AGC target at 10 6 and maximum IT at 256 ms. Data acquisition was performed using Chromeleon v 6.80 and Xcalibur v3.0.63 (Thermofisher).

2.5.Data analysis

XCalibur RAW files were first converted to mzML and centroidized with msConvert [START_REF] Kessner | ProteoWizard: open source software for rapid proteomics tools development[END_REF], then processed using IPO [START_REF] Libiseller | IPO: a tool for automated optimization of XCMS parameters[END_REF] and XCMS (v1.50.1) [START_REF] Smith | XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification[END_REF] packages running under R. The CentWave algorithm [START_REF] Tautenhahn | Highly sensitive feature detection for high resolution LC/MS[END_REF] was used for automatic peak picking: ppm = 10, snthresh = 10, peakwidth = 15-70, mzdiff= 0.01 and prefilter = c(3, 5000), with the peakwidth parameter optimized with IPO. Alignment step was performed using the group.density method (bw = 5, mzwid = 0.01, minfrac = 0.5) and retcor.loess method (missing = n_blank+1, extra = 1). The peak table obtained at this step therefore contained the total number of peaks in study samples. Then, several filters were applied to exclude non-relevant peaks (based on analytical criteria) detected by XCMS [START_REF] Myers | One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks[END_REF] (Supplementary Figure 1): first, robust peaks were defined as peaks found in ≥2 of the samples of at least one condition; then all features with a retention time outside the window 3-30 min were discarded (retention time filter) and features detected in biological samples with a mean intensity less than 2-fold the intensity observed in blank samples, or features detected in blank samples only were also filtered out (signal filter). This group therefore constitutes the "robust, relevant peaks" group. The peak picking score (PPS) obtained with the IPO algorithm was used to assess the quality of the peak list obtained which each method. PPS was defined as the ratio of reliable peaks (RP) (which are peaks part of an isotopologue consisting of 13 C isotope peaks) weighted by the exponential factor 2, to the total number of peaks ((number of reliable peaks)² /(total number of peaks)). Therefore, if the

RESULTS AND DISCUSSION

3.1.Evaluation of interferences

Interferences originating from the laboratory environment and unrelated to plasma samples were assessed by the examination of ions present in blank samples (Figure 2). Three profiles were found for the most abundant interfering ions: intermittent interferences occurring at a specific retentions time (m/z 102.1274 and 104.1066); interferences present during a significant part of the run (m/z 167.0123 and 214.0892) and interferences present during the whole run (m/z 83.0602). The signal intensity for those interfering compounds was between 10 7 and 4.10 9 , which accounted up to a maximum of 85.7% of the total signal. The major interferences were identified as being acetonitrile (m/z 83.0602), triethylamine (m/z 102.1274) and n-butyl benzenesulfonamide (m/z 214.0892). Acetonitrile originates from mobile phase, triethylamine was previously used on the same instrument and is well-described to have a persistent memory effect in LC-MS [START_REF] Rutters | The persistent memory effect of triethylamine in the analysis of phospholipids by liquid chromatography/mass spectrometry[END_REF], and n-butyl benzenesulfonamide is a widespread plasticizer [START_REF] Keller | Interferences and contaminants encountered in modern mass spectrometry[END_REF]. Considering the signal intensity observed for these molecules, the C-trap should be filled faster with interferences, which in turn should decrease the sensitivity for compounds of interest. Therefore, the effect of splitting the acquisition m/z range was examined to avoid the acquisition of the main interfering compounds and possibly enhance the detection of metabolites.

3.2.Effect of excluding interfering compounds from acquisition

The m/z of the most intense intermittent interferences (m/z 102.1274 and 104.1066) as well as from n-butyl benzenesulfonamide (m/z 214.0892) were first excluded from the acquisition method (Figure 1; M2 acquisition method). The comparison of the triplicate analysis of plasma samples with the original M1 method and this modified M2 method resulted in the increase of the mean total number of peaks from 5,366 to 15,304 peaks, with a corresponding increase of robust, relevant peaks from 2,892 to 8,340 peaks (Table 1). 368 peaks were specific of the M1 method; 2,210 of the M2 method while 3,086 were common to both methods (Supplementary Table 1). The peaks specific to the M2 method were equally distributed over all the m/z range and mostly detected in the first 10 min or between 15-20 min of the analytical run (Figure 3A).

Then, the m/z ranges of the interference detected during the whole run (acetonitrile, m/z 83.0602) as well as from n-butyl benzenesulfonamide (m/z 214.0892) were excluded (Figure 1, method M3). For this M3 method, the total number of peaks was 14,848, corresponding to 8,203 robust, relevant peaks. 2,019 peaks were specific of the M3 method (compared to M1; Supplementary Table 1). As for method M2, these peaks were equally distributed over the m/z range and mostly detected in the first 10 min or between 15-20 min of the analytical run (Figure 3B). Altogether, these results suggest that excluding interfering m/z from the acquisition allows a better detection of the sample compounds all over the m/z -and timeranges.

3.3.Effect of m/z range splitting

Since splitting the m/z range systematically increased the number of peaks detected, we addressed whether this observation was due to the exclusion of interferences or if it was the consequence of splitting the acquisition method into several m/z ranges. The method M2 was therefore compared to method M4 where the acquisition range was split into three continuous ranges, without any gap between each range (Figure 1). As shown in Table 1, both methods allowed the detection of an equivalent number of total and robust, relevant peaks and 5,441 features were common to both methods (Supplementary Table 1). This suggests that splitting the scan range is more useful for increasing the number of detected compounds than excluding specific interferences.

3.4.Effect of m/z range size

Finally, in order to fully assess the impact of m/z range splitting, a new M5 method was designed, where the m/z interval of each of the 3 split ranges was between 240-300 units (Figure 1). Optimizing the size of m/z ranges increased the number of robust, relevant peaks from 8,272 to 11,005 (33% increase in comparison to the M4 method), as shown in Table 1.

4,781 peaks were common to both methods whereas 1,764 peaks were specific of the M5 method. The gain in the number of peaks detected is mainly in the m/z range from 300 to 900 (Supplementary Table 1 and Supplementary Figure 2). With this latest M5 method, the PPS score was the highest obtained among the 5 methods. The mass spectrometric fundamentals explaining this phenomenon for Q-Exactive instruments are related to the value of the voltage applied to the focusing lenses (RF) of the C-trap. This voltage is automatically adapted to the smallest mass value of the scan by the instrument and cannot be modified. For example, for a scan of 60 to 900 m/z it is set to allow optimal extraction of small mass ions, and therefore the extraction of high mass ions will not be optimal. By dividing the same mass range into 3 segments (60-300, 300-600 and 600-900 m/z), and thus into 3 consecutive scans, an RF value is applied for each segment and depends on the smallest mass in the segment. A first value is therefore optimized for masses close to 60, a second for masses close to 300 and a third for masses close to 600. In addition, for scans from 300 to 600 and from 600 to 900 m/z, the quadrupole filters all masses below 300 and 600 and therefore decreases the background noise generated by the mobile phase (or other contaminants), which allows the detection of more peaks of interest. Thus, the performance of using several consecutive scans on complementary mass ranges for methods intended to cover large mass ranges is better, because the value of the RF applied to the C-trap will be optimized for each scan and will allow a better extraction of the ions from each scan.

Splitting into 2 or 3 m/z ranges was also compared. With a method consisting in 2 m/z ranges (60-200 and 200-900), the number of relevant, robust peaks was 5,854, which is about 3-fold lower than with the 3-scan range method.

3.5.Consequences on metabolite annotation

Although detecting a higher number of robust, reliable peaks demonstrates increased sensitivity, understanding pathophysiological mechanisms with metabolomic analysis makes it mandatory to annotate (i.e. identify) the compounds of interest. Therefore, the number of annotated compounds after HMDB database query was compared between the initial M1 and the final M5 methods. For the M1 method, compound identification was proposed for 1,633 of the relevant, robust peaks, raising to 4,397 for the M5 method i.e. a 169% increase in the number of peaks with proposed annotation.

3.6.Consequences on signal intensity for metabolites or deuterated standards

Finally, although splitting the m/z range obviously increases the number of detected features, the impact of splitting on the signal intensity of known and unknown compounds was assessed. For deuterated internal standards, mean peak area of triplicate analysis of 3 plasma samples with the M5 method was 126 ± 52% [49-275] of the mean peak area observed with the M1 method. When comparing the signal intensity of features detected with the initial M1 and final M5 method, a mean 1.6-fold increase in intensity was observed, with 25 th and 75 th percentiles at 0.95 and 1.85, respectively (Figure 4). This increase occurred for all 3 m/z ranges and was even greater for the highest m/z, whereas splitting into 2 m/z ranges may induce a loss of signal in the low m/z range [START_REF] Ranninger | Improving global feature detectabilities through scan range splitting for untargeted metabolomics by high-performance liquid chromatography-Orbitrap mass spectrometry[END_REF].

3.7.Ionization source parameters

The parameters of the ESI strongly influence the number of detected features, as shown with the metabolomic analysis of plant samples [START_REF] Tugizimana | Mass spectrometry in untargeted liquid chromatography/mass spectrometry metabolomics: Electrospray ionisation parameters and global coverage of the metabolome[END_REF], and should therefore be optimized for each experimental setting. To assess whether compound detection could be further improved in human plasma with optimizing ionization source parameters, four sets of parameters were compared with low, medium, high or intermediate values for S-Lens RF level, spray voltage, sheath and auxiliary gas flow rate and temperature of vaporization (Table 2). Results are shown in Table 3. The intermediate set of parameters provided the highest number of robust, relevant peaks and highest PPS score.

CONCLUSIONS

Scientists employing high-resolution instruments for untargeted mass spectrometry analysis, such as metabolomic studies, face new challenges with respect to the simultaneous recording of a huge number of features in a given m/z range, which may be either background noise or compounds of interest. Metabolites are considered to be molecules with a molecular weight less than 1,000 Da, which implies to cover a mass range between about 50 and 1,000 Da to ensure the widest coverage. In the vast majority of methods described to date, a single acquisition range as well as generic ionization source parameters were used. Although a strategy for splitting the acquisition range into a low and high m/z ranges was previously investigated for the analysis of renal cell metabolome with reverse-phase chromatography [START_REF] Ranninger | Improving global feature detectabilities through scan range splitting for untargeted metabolomics by high-performance liquid chromatography-Orbitrap mass spectrometry[END_REF], no investigation with an interference-driven splitting strategy was reported to date for the analysis of human plasma. The results of the present study show the interest of splitting the m/z acquisition range into 3 segments for the metabolomic analysis of human plasma with hydrophilic interaction liquid chromatography coupled to high-resolution mass spectrometry.

Since acquisition range splitting outperforms excluding the acquisition of the main interferences, such strategy may be universally applied, regardless of the contaminants that may be present in each laboratory environment. The performance gain affects both the number of compounds detected and also the number of molecules for which an annotation can be proposed by querying the databases, which is the final goal of the metabolomic analysis. In addition, sensitivity was also improved throughout the entire m/z range whereas different splitting strategies may induce a loss of signal [START_REF] Ranninger | Improving global feature detectabilities through scan range splitting for untargeted metabolomics by high-performance liquid chromatography-Orbitrap mass spectrometry[END_REF]. In combination with the optimization of the ionization source parameters, the proposed method therefore allows a more comprehensive analysis of the human plasma metabolome which may be applied to pathophysiological studies.

Figure 1 :

 1 Figure 1: Schematic overview of the different acquisition methods. m/z splitting ranges

Figure 2 :

 2 Figure 2: Extracted ion chromatograms of interfering compounds. Each colour

Figure 3 :

 3 Figure 3: Comparison of features between the M1 and M2 (A) and M1 and M3 (B)

Figure 4 :

 4 Figure 4: Intensity ratio for features common to the M5 and M1 methods. The M5/M1

  Figure 1

Table 2 :

 2 Sets of parameters of the ionization source

	Parameter	Low	Medium	Intermediate	High
	S-lens RF level	50	75	100	100
	Spray voltage	1,500	3,000	4,000	5,000
	Sheath gas flow rate	10	35	40	60
	Aux gas flow rate	5	15	25	30
	Temperature of vaporization	100	200	100	300

Table 3 :

 3 Effect of ionization source parameters on peak detection

						Robust, relevant
	Method	Total peaks in blank sample (n)	Total peaks in study samples (n)	Robust peaks (n)	RT-filtered robust peaks (n)	peaks (signal-and RT-filtered robust	PPS
						peaks) (n)
	Low	2,938	7,211	5,836	5,199	3,220	2,629
	Medium	11,807	20,259	12,755	9,184	5,272	3,576
	Intermediate	7,010	16,889	13,625	10,314	6,588	4,153
	High	8,376	19,187	14,510	11,026	5,895	2,678

RT: retention time; PPS: peak picking score (the highest the better)

FUNDING SOURCES

This work was supported by the French Ministry of Higher Education and Research (PhD grant, Fanta Fall).

TABLES

Table 1: Number of peaks in each method before and after filtering.

Method

Total peaks in blank sample (n)

Total peaks in study samples (mean [min-max])

Robust peaks (mean [min-max])

RT-filtered robust peaks (mean [min-max])

Robust, relevant peaks (signal-and RT-filtered robust peaks) (mean [min-max])

PPS COMPETING INTERESTS STATEMENT

The authors have nothing to declare.


M Martin Boyer

Elicia P Cowins
email: cowinse@wlu.edu

Willie D Reddic
email: wreddic@depaul.edu

Portfolio Rebalancing Behavior with Operating Losses and Investment Regulation

Keywords: Statutory accounting principles, Property and casualty insurance, Taxexempt and taxable securities, Regulatory investment limitations JEL Classifications: G22, G28, K23

Firms should make every attempt to reduce their tax burden by, for instance, preferring higher-yield taxable investments when faced with operating losses and loweryield tax-exempt investments otherwise. We examine in this paper whether there are impediments to rebalancing which result from a firm's regulatory environment. Using an original measure of investment regulatory stringency that U.S. property and casualty insurers encounter, we find that insurers operating in more stringent regulatory environments receive a lower percentage of their investment income from taxable sources. We conclude that regulatory constraints limit insurers from rebalancing efficiently their investment portfolio in response to operational performance.

Introduction

One of the most important operational objectives of property and casualty (P&C) insurers is the generation of sufficient cash flows to ensure timely payment of future claims. To that end, P&C insurers actively manage the complementarities between their underwriting returns and their investment portfolio returns under the constraint that the insurer's uncertain liquidity needs restrict the types of investment vehicles they can use [START_REF] Lambert | Portfolio Management of Property-Liability Companies[END_REF]. These limits are a function of the insurance company's business model and at odds with its shareholders' demands for investment returns, both in terms of the potential to generate earnings from which dividends could be declared and the likelihood of positive changes in market value from which capital gains could be realized.

The optimal mix of income generating investment instruments depends on numerous external factors, such as interest rate policies and GDP growth, as well as numerous firm-specific characteristics such as the prevalence and the presence of operational gains and losses. Emphasizing the latter point, Henderschott & Koch (1980) find that in the absence of taxable income, after-tax returns on investment portfolios are maximized by fully investing in taxable securities (see also [START_REF] Buser | Empirical Determinants of the Relative Yields on Taxable and Tax-Exempt Securities[END_REF][START_REF] Cummins | Tax Management and Investment Strategies of Property-Liability Insurers[END_REF][START_REF] Pasiouras | Regulations and soundness of insurance firms: International evidence[END_REF]. Once taxable income is present, it is advantageous to shift to tax-exempts. This result suggests that P&C insurers should choose their investment mix of taxexempt and taxable instruments as a function of their underwriting performance.

The search for an optimal portfolio is not the preserve of the insurance industry. Achieving the appropriate mix of taxable and tax-exempt investments is, however, particularly salient for insurers because their ability to settle claims in a timely manner is directly tied to their ability to generate sufficient returns from their investment portfolios. Given the relative lack of correlation between a single insurer's underwriting results and returns on market-traded securities, an insurer's investment portfolio can serve as a key source of financial stability and diversification in a competitive environment.

The theoretical result in Henderschott & Koch (1980) suggests that maintaining the optimal investment portfolio requires active management whereby insurers that are incurring net underwriting losses (resp. gains) rebalance their investment portfolios in favor of taxable (resp. tax-exempt) sources of investment income that provide higher (resp. lower) pre-tax yields on average. If limitations placed on the type of investments held at any given point in time prohibit insurers from fully responding to changes in operational profitability, then a sub-optimal investment portfolio would be held.

The general investment philosophy of insurers can be qualified as conservative, 1 with bonds instinctively preferred to common shares. Despite this natural appetite for safe investments, state regulation often imposes limits on "riskier" securities, which may constrain the investment preferences of insurers. The wide range of externally imposed investment constraints that P&C insurers face provides a unique environment for studying the behavior of sophisticated and financially literate agents [START_REF] Eling | Costs and Benefits of Financial Regulation -An Empirical Assessment for Insurance Companies[END_REF]. Because investment constraints are set at the state level, researchers have access to the crosssection of U.S. states, which impose different investment limitations at different points in time. To our knowledge, our study is the first to examine the decision of P&C insurers (and other regulated entities for that matter) to invest in taxable and non-taxable securities in an environment where they face major regulatory limits on the type of instruments they are permitted to hold.

Using the Compendium of State Laws on Insurance Topics-Limitations on Insurers' Investments

published by the National Association of Insurance Commissioners, we develop an original index that accounts for the degree of regulatory investment stringency that insurers face in a given year. The index takes into consideration both the level of and the change in regulatory stringency for different types of assets for each state in which an insurer operates. The investment regulatory stringency measure we employ is thus insurer and year specific. 2 By investigating whether and how variation in regulatory environments affect insurers' portfolio management decisions, we aim to address the following two research questions:

(1) Do insurers, when faced with underwriting losses, rebalance their investment portfolios away from tax-exempt securities 3 and toward taxable securities? 1 A case in point comes from the insurance industry's use of Statutory Accounting Principles (SAP) in addition to Generally Accepted Accounting Principles (GAAP). The SAP approach is generally more conservative than GAAP because it specifically focuses on insurance companies' long-term liabilities and ability to pay claims. Using SAP generally results in lower insurer capital. 2 Licensure in several states means that a single insurer is subject to different, and changing, levels of regulatory stringency with respect to its use of different investment classes (taxable versus tax-exempt, stocks versus bonds, mortgages versus real estate, etc.). 3 Tax-exempt investments include municipal and state issued bonds so that coupon payments and capital gains associated with the selling of such bonds are non-taxable (and capital losses are not deductible).

(2) Are insurers, operating in an investment-constrained regulatory environment, limited in the extent to which they rebalance their investment portfolios between taxable and tax-exempt securities based on their underwriting performance?

Whether we observe the portfolio-rebalancing behavior in a manner consistent with the theory developed by Henderschott & Koch (1980) is an empirical question. [START_REF] Cummins | Tax Management and Investment Strategies of Property-Liability Insurers[END_REF] find that when insurers are subject to the alternative minimum tax, the optimal outcome could include a failure to rebalance portfolios toward tax-exempt instruments because the alternative minimum tax eliminates the after-tax yield spread between tax-exempt and taxable investments. Similarly, [START_REF] Ponarul | Solvency Risk and the Tax Sheltering Behavior of Property-Liability Insurers[END_REF] finds that the optimal outcome could result in no shift toward tax-exempt investments even when (1) the after-tax yield on tax-exempt securities is greater than that on taxable, (2) the relative yields from taxable and tax-exempt securities are sufficiently different, and (3) default probabilities are positive. Moreover, it is not entirely obvious that solvency regulation will restrict rebalancing in the way we predict. While not a perfectly analogous study, [START_REF] Born | Insurer Profitability in Different Regulatory and Legal Environments[END_REF] examines the relation between insurers' overall profitability and the legal and regulatory environments in which they operate in the wake of the insolvency crisis that struck the industry in the mid-1980s. Our particular interest is the fact that new regulatory measures were expected to restrict investment choices and thus place downward pressure on investment income. Instead, [START_REF] Born | Insurer Profitability in Different Regulatory and Legal Environments[END_REF] finds that regulation was only weakly related to performance. More recently, a series of studies have examined the effects of the risk-based capital requirements imposed by Solvency II on the investment portfolios of life insurance companies in the European Union. Again, the expectation is that the capital requirements will serve as a binding constraint for risk and force insurers to restructure their portfolios away from equities to an extent that may harm rather than help. After constructing a balance sheet and investment portfolio for a representative European life insurance company, [START_REF] Höring | Will Solvency II Market Risk Requirements Bite? The Impact of Solvency II on Insurers' Asset Allocation[END_REF] estimates the level of capital required under two alternative models: The Solvency II standard, and the S&P rating. His results suggest the new regulatory framework would not materially affect the investment strategies of insurers because the greater capital charge against one asset class (equities) is offset by larger credits for portfolio diversification. Conversely, [START_REF] Rudschuck | Solvency II and the Investment Policy of Life Insures: Some Homework to Do For the Sales and Marketing Departments[END_REF] and [START_REF] Braun | Portfolio Optimization Under Solvency II: Implicit Constraints Imposed by the Market Risk Standard Formula[END_REF] analyze the regulatory environment under Solvency II and conclude that insurers respond to capital requirements imposed on a particular asset class by reducing their holdings of that asset class.

In our case, we find that in the year following an underwriting loss, P&C insurers report an increase in the proportion of investment income originating from taxable securities. We interpret this result as evidence that insurers rebalance their investment portfolios in response to the operational results in a manner consistent with theory and previous empirical studies. In other words, P&C insurers make taxefficient portfolio choices when faced with underwriting losses. More importantly, we find evidence that insurers operating in more stringent regulatory environments decrease their proportion of investment income originating from taxable sources regardless of their operating results. Specifically, based on an OLS regression on a pooled sample of P&C insurers, we find a negative and significant association between the year-over-year change in the percentage of investment income coming from taxable sources and our measure of investment regulatory stringency. We conclude from these results that regulation alters the relationship between tax incentives (losses) and efficient investment (portfolio rebalancing).

The remainder of the paper will proceed as follows. In the next section, we will present the conceptual background of our research design. Section 3 will focus exclusively on the description of the testable hypotheses, followed by a description of the data and the methodology used to test the hypotheses. Section 5 will present the results of our empirical analysis. Finally, we will conclude and discuss future research opportunities.

Measuring investment constraints using an investment regulatory stringency index

Regulation is an important aspect of an insurer's business environment; it affects both its underwriting and its investment activities. In particular, P&C insurers' investment activities are typically limited in terms of the proportion of the total investment assets they hold in various investment classes. Using the

National Association of Insurance Commissioners' (NAIC) Compendium of State Laws on Insurance

Topics-Limitations on Insurers' Investment, we construct an insurer-year specific regulatory stringency index based on the insurers' exposure to the regulation and the relative stringency of the investment statutes in different states and in different years.

We begin by characterizing the regulatory environment of each state as flexible or stringent with respect to a particular asset class by examining the statute in effect for a given year. 4 For example, if the statute contains key phrases such as "Not addressed" or "No Provisions" for a given investment vehicle in a given state and year, then the statute is considered "Flexible". Conversely, if the statute contains an explicit limitation such as "Combination of corporate obligations and leased property shall not exceed 40% of admitted assets" or "For common or preferred stock, 15% admitted assets, but for common stock, 10% admitted assets," the statute is classified as "Stringent" for that asset, state and year combination. Investment limitations explicitly associated with amounts invested in bonds are based on bond ratings generated by the NAIC's Securities Valuations Office.5 Some states, for instance, allow any level of investment in medium-and low-grade corporate and U.S. bonds (i.e., Flexible), while others place a restriction on these types of investments (Stringent). This exercise is carried out for each state and asset class for the years 1999 to 2012. Once we characterize each asset, state and year combination, we calculate the percentage of total investment subject to stringent limits for each insurer and year. For insurer i in year t, its investment regulatory stringency index (IRSIi,t) is calculated as:

, = 1 ∑ ∑ ( , , * , , ! " ) ∑ , ,
where i,c,t is a continuous variable representing the amount of money invested by insurer i in investment category in year t, and s,c,t is a dichotomous variable equal to one if state has a restrictive regulatory environment with respect to investment category in year and zero otherwise. By design, IRSIi,t is comprised between 0 (the insurer's entire investment portfolio is free of constraints) and 1 (the insurer's entire investment portfolio is constrained). The amount investment in tax-exempt securities is not included in the calculation of regulatory stringency to reduce the potential feedback effect between the share of taxable investment income and regulatory stringency.

The construction of IRSIi,t accounts for regulatory limits on specific asset types and captures the regulatory environment to which each insurer is specifically exposed (i.e., if insurer i is not licensed in some state, then that state's regulation is excluded from insurer i's IRSIi,t measure). IRSIi,t therefore accommodates the number of states in which an insurer operates, as well as changes to each state's investment regulatory regime as reported in the NAIC's Compendium of State Laws on Insurance Topics.

Hypotheses

Before examining how a P&C insurer's investment regulatory environment affects its asset allocation, we first investigate the relation between an insurer's operating performance and the change in its proportion of investment income originating from taxable securities. Assuming (1) firms are fully taxed,

(2) tax-exempt and taxable investments have the same maturity and default probabilities, and (3) expected returns on taxable investments exceed expected returns on tax-exempt investment pre-tax, the Henderschott & Koch (1980) model predicts that investors will favor taxable investments in the presence of net losses. In other words, with net operating losses functioning as a tax-shield, after-tax profits are maximized with investment in taxable assets. This leads us to our first hypothesis, stated in the alternative form:

Hypothesis 1: Insurers adjust their portfolio allocations in favor of taxable investments when experiencing underwriting losses.

Let us now turn to our investigation of regulatory effects. One of the goals of state regulatory bodies is to reduce the likelihood and the economic effect of insolvencies. One method employed to achieve that goal is to limit the amount and type of investments insurers can hold at any given time. This can be done by establishing a ceiling for (1) the level of premiums invested in risky assets and/or (2) the proportion of the total investment portfolio invested in riskier assets. Given the tendency for taxexempt securities to fall in the category of lower-risk & lower-return investments, we intuitively expect a positive relation between regulatory stringency and investment in tax-favored assets. Specifically, we expect P&C insurers to shift their investment portfolios toward (away from) tax-exempts as the state regulator enhances (reduces) restrictions against insurers' investment in risky assets, independent of underwriting results. It then follows that when profit-maximizing incentives to rebalance portfolios toward taxable investment arise (i.e., when P&C insurers experience underwriting losses), insurers operating in environments with greater regulatory stringency will have more difficulty rebalancing efficiently. This leads to our second and third hypotheses: Hypothesis 2: P&C insurers' operating in more (less) stringent regulatory environments have a higher proportion of tax-exempt (taxable) assets in their investment portfolios.

Hypothesis 3: When experiencing underwriting losses (gains), constrained P&C insurers rebalance their portfolio allocations less toward taxable (non-taxable) investment assets.

The effect of regulation on portfolio investment is not consistently observed empirically (see [START_REF] Born | Insurer Profitability in Different Regulatory and Legal Environments[END_REF]. Ultimately, whether P&C insurers' portfolio decisions are constrained as a result of regulation is an empirical question. We discuss the data next.

Data and Methodology

The initial sample consists of both affiliated and unaffiliated single P&C insurers operating in the United States between 1999 and 2012. The NAIC annual statement database includes all financial information needed to calculate the dependent variable, Portfolio Rebalancing (or $ % , ), which is the change in an insurer's earned taxable investment income6 from year -1 to year scaled by each year's total The final sample consists of 2,700 unique P&C insurers between 2000 and 2012 (the year 1999 had to be deleted because of our use of lagged variables), for a total of 17,241 observations.

Main independent variables

The two independent variables of interest are the current and the previous years' underwriting income (Underwritingt and Underwritingt-1), which are defined as an insurer's operating income scaled by total net admitted assets, and our index of investment regulatory stringency (IRSIi,t). The IRSIi,t variable (see Section 2) is constructed using three NAIC publications:

1-The Compendium of State Laws-Limitations on Insurers' Investment to measure a state's regulatory investment environment; 2-The Exhibit of Net Investment Income Pages, from which we obtained insurers' earned investment income from taxable and tax-exempt sources; 3-Schedule T: Exhibit of Premiums Written, from which we deduced where P&C insurers were licensed.

Control variables

Many econometric models employed to test the paper's main hypotheses include controls for confounding factors that either operate as additional portfolio rebalancing constraints, or support competing narratives of P&C insurers' incentives to rebalance their investment portfolios.

Because one critical role of insurance regulators is to mitigate insurer insolvency, we account for the regulatory scrutiny associated with an insurer's level of solvency. More specifically, we consider the Risk-Based Capital (RBC) requirement as our regulatory scrutiny measure. RBC has two components: 1-A formula that establishes a minimum level of capital required to guard against insolvency, and 2-A law that automatically grants authority to state regulators to take specific actions based on the severity of the insurer's impairment. 7 [START_REF] Kwon | Can Insurance Firms Easily Exit from the Market? A Global Comparative Analysis of Regulatory Structures[END_REF] find that insurance regulators tend to prioritize policyholders' interests over that of shareholders (i.e., the regulators' primary concerns are to minimize insurance market disruption, and to facilitate and expedite the payment of claims). The regulatory control of a troubled insurer's operations could therefore affect the posited relation between underwriting results and portfolio rebalancing behavior. The solvency proxy we employ, Regulatory Compliance, is a dichotomous variable equal to 1 if the RBC ratio suggests no regulatory intervention is necessary and 0 otherwise, even if there are five distinct outcomes.

We also control for the insurers' liquidity needs and the riskiness of their investment strategy. 8 The insurers' need for cash or near cash securities is likely related to their need to pay claims. Aggregate casualty losses in excess of those originally forecasted will require a greater shift toward taxable investment income as the sale of invested assets is more likely to generate taxable capital gains. We therefore measure the insurers' need for liquid assets using the variable called Loss Paid, which we define as the ratio of an insurer's total claims paid to its net admitted assets [START_REF] Harrington | Price Cutting in Liability Insurance Markets[END_REF][START_REF] Cummins | Price, Financial Quality, and Capital Flows in Insurance Markets[END_REF][START_REF] Danzon | The Crisis in Medical Malpractice Insurance[END_REF]. With respect to the riskiness of the insurers' portfolio, we expect insurers whose investment income is more likely generated by capital gains to hold more non-taxable securities since they have more freedom in deciding when to realize those taxable capital gains. To minimize their tax liability, insurers will choose to realize their capital gains in years 7 The insurance economic literature recognizes that three important financial solvency measures have been developed: Financial Analysis and Solvency Tracking (FAST) score [START_REF] Cummins | Regulatory Solvency Prediction in Property-Liability Insurance: Risk-Based Capital, Audit Ratios, and Cash Flow Simulation[END_REF][START_REF] Cummins | Capital Adequacy and Insurance Risk-Based Capital System[END_REF], the Insurance Regulatory Information Systems (IRIS) ratios [START_REF] Petroni | Optimistic Reporting in the Property-Casualty Insurance Industry[END_REF][START_REF] Beaver | Management of the Loss Reserve Accrual and the Distribution of Earnings in the Property-Casualty Insurance Industry[END_REF]Gaver and Patterson, 1999, 2004[START_REF] Gaver | The Influence of Large Client on Office-Level Auditor Oversight: Evidence from the Property-Casualty Insurance Industry[END_REF], and the Risk-Based Capital (RBC) ratios. Despite the fact that [START_REF] Grace | Risk-Based Capital and Solvency Screening in Property-Liability Insurance: Hypotheses and Empirical Tests[END_REF] and [START_REF] Cummins | Insolvency Experience, Risk Based Capital, and Prompt Corrective Action in Property-Liability Insurance[END_REF][START_REF] Cummins | Regulatory Solvency Prediction in Property-Liability Insurance: Risk-Based Capital, Audit Ratios, and Cash Flow Simulation[END_REF] find little evidence that RBC ratios help predict insurers insolvencies, regulators still make use of RBC calculations to determine whether any form of remedial action is needed. For a more thorough overview, see www.naic.org/cipr_topics/topic_risk_based_capital.htm. See [START_REF] Feldblum | NAIC Property/Casualty Insurance Company Risk-Based Capital Requirements[END_REF] for a critique. 8 The authors would like to thank anonymous referees for suggesting to use these additional control variables.

when their operating income is negative. The potential importance of taxable capital gains is measured using the variable called Investment Risk, which is calculated as the sum of the value of common stock and of real estate investments, divided by the insurer's net admitted investment assets (e.g., see [START_REF] Harrington | A regression-based methodology for solvency surveillance in the property-liability insurance industry[END_REF][START_REF] Pottier | Property-liability insurer financial strength ratings: differences across rating agencies[END_REF].

Our fourth control variable accounts for the fact that insurers should rebalance their portfolios toward tax-exempt investments when facing higher tax rates. We control for the influence of taxes (Tax Rate) by including the sum of federal and foreign income taxes incurred (excluding tax on capital gains) divided by net income.

We finally control for insurer Size, as calculated by the log of total admitted assets [START_REF] Wang | The Relationship between External Financing Activities and Earnings Management: Evidence from Enterprise Risk Management[END_REF], and recent revenue Growth, as calculated using the one-year percent increase in net premiums written.

Both variables are included to control for variations in liquidity incentives to rebalance. Although the incentive to smooth income in the insurance industry has been discussed in many papers (see [START_REF] Anderson | Effects of Under and Overvaluations in Loss Reserves[END_REF][START_REF] Weiss | A Multivariate Analysis of Loss Reserving Estimates in Property-Liability Insurers[END_REF]Grace, 1990;and Grace & Leverty, 2012 inter alia), we exclude this as a control in our model because net income includes both income from operations (underwriting) and investment income, the main component of our dependent variable. Including income smoothing in our model would present important challenges in our attempt to draw inferences with respect to our variables of interest.

Methodology

To investigate the relationship between portfolio rebalancing ($ % , ), underwriting performance (Underwritingi,t) and an insurer's investment regulatory environment (IRSIi,t), we estimate the following insurer (Γ ) and year (Λ ) fixed effect regression model ( indexes insurers and indexes years):

, = 89 , + ;< , + Γ + Λ + = , ,
where , is the dependent variable ($ % , ), 9 , is the vector of the main variables of interest (in particular IRSIi,t), and < , is a vector of control variables. Robust standard errors are used.

Negative estimates for 8 or 8 > (Underwritingi,t and Underwritingi,t-1) would provide support for Hypothesis 1, in which an insurer's investment income from taxable sources should be negatively related to its operating profitability. Negative values for 8 and/or 8 > mean that insurers facing positive (resp. negative) operational returns reduce (resp. increase) the proportion of investment income they receive from taxable sources. In other words, the worse the underwriting performance, the more likely the insurer is to invest in taxable instruments, because underwriting losses can be used to offset positive returns from taxable investments.

With respect to the effect of IRSIi,t, a negative coefficient of 8 ? would provide support for Hypothesis 2, which suggests that there is a link between the regulatory environment insurers face and their portfolio rebalancing decisions. In contrast with the analysis conducted with respect to Hypothesis 1, no paper has examined the effect of regulatory stringency on the distribution of P&Cs investment income between taxable and tax-exempt sources. The construction and use of the IRSIi,t variable in explaining P&C insurers' portfolio rebalancing behavior are the paper's main contributions.

Finally, assuming that investment regulatory constraints limit the speed at which insurers can rebalance their investment portfolios, a 8 @ coefficient (Underwritingi,t-1 * IRSIi,t) that is significantly different from zero would provide support for the possibility that insurers in less (resp. more) stringent regulatory environments have greater (lower) flexibility to rebalance in response to the tax incentives of the previous year's underwriting income. In other words, if it is true that investment regulatory stringency limits rebalancing, then firms that have experienced negative (resp. positive) underwriting performance and are incentivized to hold more taxable (resp. tax-exempt) investment products would be prohibited from rebalancing completely. This means that the sign of the 8 @ coefficient should depend on whether P&C insurers suffered an underwriting loss (which would entail a positive 8 @ if the hypothesis is correct) or experienced a gain (8 @ negative, if the hypothesis is correct).

Empirical Results

Table 1 shows descriptive statistics for the unbalanced panel of 17,241 insurer-year observations. On average, P&C insurers rebalance their portfolios away from taxable securities by -0.4% (or -0.004), although the median is almost exactly zero. In any given year, half the insurers increase their position in tax-exempt securities whereas the other half reduce their position.

-Insert Table 1 here-Turning our attention to our independent variables of interest, the average underwriting income scaled by total admitted assets (Underwritingt) is -0.002, which means that approximately half of the insurers have positive underwriting results. With respect to IRSIi,t, over half of an insurer's investment portfolio is subject to some restrictive regulatory measures (IRSIi,t has a mean of 0.528 and a median of 0.526).

Figure 1 presents the distribution of $ % , (the more central distribution, labeled Rebalancing) and Underwritingi,t (the wider distribution, eponymous) for the entire sample. Although both variables are essentially centered at 0 and have similar standard deviations (0.057 and 0.060 respectively), the distribution of $ % , has much more distribution mass in the center and in the tails. In other words, the fatter tail of $ % , suggests that if a firm decides to rebalance its investment portfolio, it does so drastically. states and investing in one or two types of securities that are subject to similar regulatory environments. These "small in size and scope" insurers have an IRSIi,t that is exactly equal to zero or to one.

In terms of the control variables, approximately 95% of P&C insurers are in regulatory compliance (Regulatory Compliance), which is consistent with prior studies [START_REF] Grace | Risk-Based Capital and Solvency Screening in Property-Liability Insurance: Hypotheses and Empirical Tests[END_REF][START_REF] Cummins | Insolvency Experience, Risk Based Capital, and Prompt Corrective Action in Property-Liability Insurance[END_REF][START_REF] Cummins | Regulatory Solvency Prediction in Property-Liability Insurance: Risk-Based Capital, Audit Ratios, and Cash Flow Simulation[END_REF]. P&C insurers' average tax rate-as calculated by the taxes paid on net income-is approximately 22.6%. The payment of claims consumes an average of 22.4% of admitted assets. Insurers hold 12.2% of admitted assets in the form of investments in common stocks and real estate holdings. Finally, the growth of the underwriting businesses (Growth) is on average 9.0 percent per year, whereas the log of the average firm size is 11.5, which represents an average firm size (when measured in terms of assets) of over 100 million dollars. 2. Consistent with Hypothesis 1, the correlation between Portfolio Rebalancing and Underwriting is negative and significant at the 5% level. Of all the variables used in this paper, the one which has the highest Pearson correlation with Portfolio Rebalancing ($ % , ) is IRSIi,t. A negative and significant correlation between IRSIi,t and $ % , suggests that insurers' investment income shift toward tax-exempt sources in response to more stringent regulatory environments. This negative correlation is consistent with the notion that insurers look for less regulated asset classes (such as cash and tax-exempt bonds) when the level of regulatory scrutiny increases. With respect to IRSIi,t, we observe a strong correlation with asset size: Larger firms are more likely to have a greater fraction of their investment subject to regulatory oversight.

-Insert Table 2 here-

Underwriting performance

Column 1 of Table 3 presents the regression coefficient estimates based on the entire sample. All regressions include year and firm fixed effects, and use robust standard errors.

-Insert Table 3 here-

The results from the pooled sample show that, with respect to operational profitability, only the lagged underwriting variable (Underwritingt-1) is significantly associated with Rebalancing. Contemporaneous underwriting performance (Underwritingt) does not seem to play a role in determining the taxable nature of investment income. Taken together, this result tells us that when P&C insurers experience strong underwriting performance in a given year, they then see a decrease in the percentage of their investment income from taxable sources in the following year. P&C insurers reduce the percentage of their total taxable investment income, presumably to compensate for incremental tax liabilities associated with their profitable insurance operations. This negative relationship between underwriting performance and the proportion of investment income was also found in [START_REF] Cummins | Tax Management and Investment Strategies of Property-Liability Insurers[END_REF] and [START_REF] Jiang | Dynamics of Underwriting Profits: Evidence from the U.S. Insurance Market[END_REF] inter alia.

When the sample is partitioned based on an insurer's previous year's underwriting income (i.e., whether the insurer in year t-1 experienced an underwriting loss in Column 2 or gain in Column 3), we see that firms with underwriting losses (Column 2) continue to respond to tax incentives as predicted by economic theory (i.e., the coefficient on Underwritingt-1 is negative): The better (i.e., the less negative) the underwriting results, the greater is the change in the proportion of investment income originating from taxable sources. For insurers with underwriting gains, however, the proportion of investment income received from taxable sources does not seem to respond to underwriting performance. Taking the results of Columns 2 and 3 together, we conclude that only insurers that experienced poor underwriting results in the previous year realign their investment portfolios in the current year. Finally, contemporaneous underwriting performance has no effect on the taxable nature of P&C insurers' investment income.

The posited tax incentives for portfolio management are confirmed by the negative effect tax rates have on portfolio rebalancing. When we partition observations based on the previous year's underwriting return, the implied tax rate is significant only for P&C insurers that experienced a profitable underwriting year (Model 3). There does not seem to be an effect of Tax Rate on portfolio rebalancing unless the previous year's underwriting performance was positive.

Regulatory stringency

With respect to our second hypothesis, results in Column 1 of Table 3 show a negative IRSIi,t coefficient. This means that in the face of more stringent regulatory environments, insurers liquidate taxable investment instruments toward tax-exempt instruments. This negative relationship is consistent with the view that insurers, when faced with major limitations on their investment portfolios, prefer to invest in specific securities that are less subject to regulatory oversight, such as tax-exempt securities.

When we partition the sample based on whether insurers experienced underwriting gains or losses, we find in both partitions9 that investment regulatory stringency induces portfolio rebalancing toward less taxable sources of investment income. Moreover, the economic effect of investment regulatory stringency seems to be quite independent of insurers' underwriting results; the coefficient of -0.019 for IRSIi,t in the loss partition (Column 2) is statistically similar to the -0.020 for IRSIi,t in the gains partition (Column 3). This tells us that the more stringent is the insurer's investment regulatory environment, the more likely will it transition away from taxable and higher-yield sources of investment income such as corporate bonds, and shares of preferred and common stock.

Economically, the size of the coefficients in Column 1 tells us that P&C insurers in the 75th percentile in terms of performance (0.027) and regulatory stringency (0.706) would reduce the percentage of taxable investment income that they receive by 1.55% (-0.027*0.052-0.020*0.706=-0.0155) compared to firms with the median underwriting performance and that face no investment regulatory constraints (that will see no change since median underwriting is zero and there are no constraints; i.e. 0*0.052-0.020*0=0).

Moving from the 75th percentile to the 25th percentile in terms of regulatory stringency (going from and index value of 0.706 to 0.328) would reduce the variation in the proportion of taxable investment income by 0.80% (-0.027*0.052-0.020*0.328=-0.0080). The same relative movement in the distribution of regulatory constraints (from the 75th to the 25th percentile) has a significantly larger effect when achieved with respect to the investment regulatory stringency P&C insurers face (-0.020*(0.706-0.328) = -0.0076) than with respect to insurers' underwriting performance (-0.052*(0.027+0.027) = -0.0028).

Apart from firm size (Size), IRSIi,t and Investment Riski,t are the only variables that have a significant effect on portfolio rebalancing in all three partitions. This suggests that only firm size, the stringency of investment regulations, and investment risk affect P&C insurers' portfolio holdings irrespective of insurers' performance. In particular, we note that Regulatory Compliance, Growth, and Loss Paid do not matter in portfolio rebalancing.

With respect to Regulatory Compliance, we proposed that regulatory control of a troubled insurer's operations would induce regulators to favor payments of claims over profit maximization goals. Insurers in worse financial shape would thus need to secure their investment even if it comes at the cost of having less dividends to distribute to shareholders. Generally, taxable securities generate higher yields, so Regulatory Compliance was expected to have a positive sign. This does not seem to be the case, however, as being in compliance or not seems to be unrelated to the taxable nature of investment income. Growth, which can be achieved through lower premiums to increase market share, was expected to increase the proportion of investment income from taxable sources. Finally Loss Paid was initially expected to increase the proportion of taxable investment income because the sale of assets to pay for casualty losses is more likely to generate taxable capital gains. Similar to our findings with respect to Regulatory Compliance we find no relationship between either Growth or Loss Paid, and the change in the taxable aspects of an insurer's investment portfolio.

Interaction between regulation and underwriting performance

We now consider the interaction between underwriting performance and the level of investment regulatory stringency in determining insurers' investment rebalancing decisions. Our hypothesis is that insurers operating in more stringent regulatory environments would find it harder to rebalance optimally in response to poorer underwriting performance. The results in Table 4 do not support that hypothesis as an insurer's regulatory environment does not have a different effect on portfolio rebalancing when controlling explicitly for underwriting performance (i.e., Underwritingt-1 * IRSIi,t is not different from zero).

-Insert Table 4 here-When we partition the data based on whether the previous year's underwriting income is positive or negative, we again find that Underwritingt-1 * IRSIi,t is not different from zero. Including the interactive term does not change the main message of the paper, which is that greater regulatory stringency reduces the degree to which insurers rebalance their investment portfolios toward taxable securities (IRSIi,t<0).

Robustness

In our main regression results in Table 3, we presented the effect of regulatory stringency on the change in taxable investment income expressed as a proportion of all investment income. We were not considering separately the different types of investment income such as dividend, interest, and capital gains. One possible problem of doing so is that realized capital gains (and losses) are, by definition, earned when a security is sold, which means that an action by the insurer is necessary for such capital gains (and losses) to be realized. When we examine the importance of capital gain investment income as a percentage of total investment income (table not provided), we note that taxable capital gains represent on average approximately one-third of total taxable investment income, compared to taxexempt capital gains which represent on average only 7% of total tax-exempt investment income.

In Table 5, we address this potential investment action by examining changes in investment income associated only with capital gains (Model 1) and excluding capital gains (Model 2). 10 The dependent variable in Model 1 (resp. Model 2) is constructed as the one-year variation in investment income attributable to taxable capital gains (resp. excluding taxable capital gains) as a proportion of total capital gain (resp. investment income excluding capital gains). The reason we separate realized capital gains from other types of investment income is that they require a more active rebalancing of the insurer's portfolio since capital gains (and losses) can be realized only when the security held is sold. This is not true for other sources of investment income. The question is then "when are insurers more likely to realize capital gains?" We contend that capital gains are more likely to be realized when losses are greater than expected. If indeed capital gains are more likely to occur following important losses, then such a source of investment income can hardly be expected to be related to regulatory stringency. We therefore expect regulatory stringency to be more problematic for those sources of income that are not directly associated with capital gains and losses. Consequently we expect the IRSIi,t coefficient to be more negative in Model 2 than in Model 1.

-Insert Table 5 here- 5 support our intuition and our hypotheses in many ways. In particular, we see in the case of capital gains only (Model 1) that none of the independent variables have any power (at the 5% level) in explaining the change in the proportion of total taxable capital gain investment income, which be due to the fact that more than half of the sample firms report exactly zero in tax-exempt capital gains. In contrast, when all capital gains are discarded from the calculation of taxable and non-taxable investment income (Model 2), the effect of regulatory stringency on portfolio rebalancing remains negative and significant. Taken together, the results in Table 5 tell us that stringent investment allocation regulation reduces the proportion of investment income coming from taxable sources, and that this reduction is not attributable to changes in realized capital gains. This is essentially the same result we had before when we were examining all sources of investment income together: The more regulatory stringent is the investment environment, the more likely will an insurer's investment income originate from tax-exempt sources. We also partitioned the data based on underwriting performance to examine if having had underwriting gains or losses has any effect on the source of investment income.

Results in Table

Results (untabulated) essentially remain the same: IRSIi,t is only significant in explaining the proportion of taxable investment income from sources other than capital gains.

We present in Table 6 results based on the same regression models as in Table 3, but after partitioning the data as a function of two major shocks that befell the economy in general on the one hand, and the P&C insurance industry specifically on the other hand. More specifically, the first (resp. second) result column in Table 6 uses data points prior (posterior) to the financial crisis, whereas the third (resp. fourth) result column uses data points prior (posterior) to hurricane Katrina. The results are essentially the same in the different partitions and are similar to those found in our primary analysis. Therefore, there does not appear to be important changes in the way regulation affects investment income following important catastrophes. Although it is true that the coefficient on IRSIit becomes more negative after each event, the difference does not seem to be statistically significant.

The last table (Table 7) examines whether investment regulatory stringency has any effect on firm growth. The logic is that if insurers are forced to hold a suboptimal portfolio, then firm growth should be impeded because investment returns will not be high enough to fuel firm capital growth. This hypothesis is validated in Table 7 as the negative and significant coefficient on IRSIit confirms that investment regulation impedes a firm's growth in direct premiums written.

The "robustness" verifications we presented in the last three tables of the paper confirm our initial analysis that regulatory constraints on asset allocation have a significant economic effect on how insurers generate investment income. This means that it is quite possible that an insurer would have chosen a different asset allocation if regulation was not there to limit insurers' asset allocation.

Final remarks

In this paper, we analyzed the investment behavior of property and casualty insurers in an environment where they do not have full liberty to invest collected premiums in all asset classes to generate cash flows to cover future losses. Our analysis of a large sample of P&C insurers indicated that the level of regulatory stringency on P&C insurers' investment portfolios favored the holding of tax-exempt securities to the detriment of taxable securities, at least inasmuch as investment income is a proxy for investment holdings. The economic effect of investment regulatory stringency was essentially the same whether insurers experienced underwriting gains or losses and whether we looked at all the sources of investment income or only at some subset (capital gains or non-capital gains). Moreover, insurer behavior with respect to regulatory stringency did not change following major catastrophes such as the 2009 financial crisis or hurricane Katrina in 2005. To our knowledge, no study has ever documented the effect of investment regulatory constraints on portfolio rebalancing strategies.

Our results also tell us that insurers respond to tax incentives by receiving more income from taxable sources when underwriting performance is poor. The effect of underwriting performance on the insurers' investment strategy, although significant, is economically smaller than that of regulatory restrictions in the nature of the securities that P&C insurers are allowed to hold.

A few caveats are necessary before drawing far-reaching inferences. The first is that our portfolio rebalancing proxy measures the change in investment income instead of the actual holdings of securities. It is possible that the associations we found between investment regulatory constraints and investment income have more to do with factors related to the magnitude of returns on taxable investment rather than P&C insurers' decisions to rebalance. The second caveat is that our flexibility variable was derived based on insurers' potential regulatory environments, not necessarily their actual environments. This proxy was again a second best, given that the information we had access to did not provide enough detail to itemize P&C insurers' actual investment portfolios.

Our contribution to the body of knowledge in financial and insurance economics goes beyond the simple acknowledgement that regulation reduces insurers' freedom to choose what would have been their "preferred" asset allocation. The fact that more stringently regulated insurers invest a higher fraction of their portfolios in non-taxable securities raises the question of whether their resulting asset allocation is dangerously suboptimal given their operations. Even more dangerous is the fact that regulatory stringency may lead to insurers holding a portfolio that is under-diversified. If it is indeed the case that insurers hold a sub-optimal portfolio, then either they are bearing more risk of insolvency in the event that a major loss occurs, or they will be forced to increase premiums to maintain an acceptable level of insolvency risk. Another possibility is that as insurers are disproportionately holding non-taxable securities, which are less numerous than taxable securities, it increases their systemic risk since a shock to the non-taxable securities market will affect all stringently regulated insurers in a way that regulators may not have foreseen (see in particular [START_REF] Chaderina | The Dark Side of Liquid Bonds in Fire Sales[END_REF]. This is a potential cost to all taxpayers if they are asked to foot the bill of insolvent insurers. Finally, the fact that regulated insurers are holding more tax-exempt securities means that they are paying less in income tax, which, in a general equilibrium sense and in addition to the cost to taxpayers of insurer insolvency, increases the taxpayers' overall burden. ). Underwriting is defined as the operating income for an insurer scaled by total net admitted assets. IRSI is the percent of investment classes subject to the regulatory investment limitations per state, per year. Regulatory Compliance is a dichotomous variable equal to 1 if the appropriate RBC ratio suggest "No Action" should be taken by the regulator, and 0 otherwise. Loss Paid is calculated as the loss paid dividend by net admitted assets. Investment Risk is calculated as the dollar ratio of total common stock and real estate investments to net admitted investment assets. Tax Rate is defined as federal and foreign income taxes incurred, excluding tax on capital gains, divided by net income. Growth is the one-year percent increase in net premium. Size is the logarithm of admitted assets. Size is the log of total admitted assets.

Flexible Non-Flexible

Contract Loans 1) Not addressed.

2) Generally, without notification to and approval of director, an insurer may not engage in transaction in which an officer or director has a financial interest, including loans. However, insurer may make policy loans, advances for reasonable business expenses, and loans with relocation.

1) No loan upon pledge of securities shall exceed 90% market value of collateral pledged. 2) Insurer may make loans not expressed permitted under this chapter, if the loan qualifies as a second investment, and in an aggregate amount such loans do not exceed 10% of the insurer's assets. 3) investments not specifically prohibited, greater of unrestricted surplus and lesser surplus and lesser of 10% admitted assets or 50% surplus as regards policyholders.

Cash, Cash Equivalents and Short-Term Investments* 1) Not addressed.

2) Not addressed.

Derivatives Instruments 1) Not addressed.

2) No provision.

3) Insurer may purchase or sell financial futures contract for the purpose of hedging against economic risk associated with a company asset or liability.

1) For hedging transactions, 7.5% admitted assets in total statement value of options, caps, floors, and warrants not attached. 2) Insurer may engage in certain hedging transactions, but an insurer shall not devote more than 10% of the excess of its capital and surplus over the minimum requirements of a new stock or mutual company to qualify for a certificate of authority to the hedging transaction. 3) No hedging transactions.

Other Invested Assets 1) Not specifically addressed. 2) Except as otherwise prohibited, insurer may make investment that are prudent with respect to business and diversification assets. 3) After satisfying minimum investment requirements, insurer may make investments not otherwise prohibited, without limitation.

1) For investments not specifically prohibited, greater of unrestricted surplus and lesser of 10% admitted assets or 50% surplus as regards policyholders. 2) For loans or investments not expressly prohibited, 10% admitted assets; 75% of total capital and surplus; with specific exemptions. 3) For loans or other investments permitted, in total amount not exceeding 10% admitted assets.

Write-in for Investments* 1) Not addressed. 1) Not addressed.

Note: The table presents limits on investments and the determining factor of whether it should be classified as flexible or nonflexible. Based on my criteria, there are multiple states that changed their investing limitations from flexible to non-flexible and vice versa. For example, from 2008 to 2009 the state of Illinois went from a flexible investment state concerning their US Government, Tax-free, and Other Bonds to non-flexible investment state since the state of Illinois (Department of Insurance) decided to place boundaries investment limit on how much a P&C insurer can allocate to "admitted assets". In addition, some states only allow quality debt securities holding or place a limit on how much low quality debt securities an insurer can invest in as rated by the NAIC. Source: NAIC's Compendium of State Laws -Limitations on Insurers' Investment.

  *+, (-(./+ 0 '()*+, (-(./+ 0 1'()*+, *2*3 (-(./+ 0 -'()*+, (-(./+ 045 '()*+, (-(./+ 045 1'()*+, *2*3 (-(./+ 045 .

Figure 1 :

 1 Figure 1: Distribution of Rebalancing and Underwriting variables

Figure 2 :

 2 Figure 2: Histogram of the main independent variable of interest, IRSI

Table 1 . Descriptive Statistics

 1

	Percentiles

Note: The table presents summary statistics for the years 2000-2012. There are 17,241 insurer-year observations. Portfolio Rebalance is the difference between the current year's (t) ratio of earned taxable investment income to total investment income (

Table 2 . Pairwise Correlation

 2 Note: This table provides the pairwise correlations years 2000 to 2012. Pearson correlations are shown below the diagonal and Spearman correlation shown above diagonal (italic). Coefficients in bold indicate significance at the 5% level.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	(1) Portfolio Rebalancing		-0.093	-0.073 0.016 0.026 0.052 -0.054 0.027 0.027
	(2) Underwriting t	-0.061		0.049 -0.037 -0.163 -0.042 0.241 -0.037 0.038
	(3) IRSI	-0.057	0.046		0.082 0.061 0.186 -0.102 -0.019 0.165
	(4) Regulatory Compliance	0.017	-0.054	0.084		0.292 0.142 0.001 0.043 0.336
	(5) Loss Paid	0.031	-0.174	0.052	0.280		0.019 -0.065 -0.020 0.421
	(6) Investment Risk	0.036	-0.046	0.143	0.097 -0.081		-0.083 -0.001 0.231
	(7) Tax Rate	-0.036	0.088	-0.032 -0.000 -0.016 -0.047		0.146 -0.002
	(8) Growth	-0.010	-0.033	-0.034 0.023 -0.038 -0.028 0.057		-0.013
	(9) Size	0.039	0.049	0.154	0.345 -0.403 0.183 -0.001 -0.032

Table 3 . Fixed Effect Regression: Pooled Sample, Loss Partitioned, Gains Partitioned

 3

	Estimation of the effects of underwriting income and of investment regulatory stringency (IRSI) on the
	change in the proportion of investment income originating from taxable securities (Portfolio
	Rebalancing) as measured by
	$ % =

Table 5 . Fixed Effect Regression: Sources of Investment Income

 5 Estimation of the effects of underwriting income and of investment regulatory stringency (IRSI) on the change in the proportion of realized capital gains originating from taxable securities as a proportion of total capital gains in Model 1, and on the change in the proportion of investment income originating from taxable securities but excluding taxable capital gains as a proportion of total investment income but excluding capital gains in Model 2. The dependent variable in Model 1 is therefore All variables are defined in Table1. Standard errors are clustered by insurer and year(Peterson, 2009). *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. All variables are Windsorized using the Belsley-Kuh-Welsch (BKW) test to identify a data point as an influential outlier. A negative (resp. positive) coefficient means that a greater proportion of investment income is received from taxexempt (resp. taxable) sources in year t than in year t-1.

		$ % =	A+(/ B+, (C (/ (-(./+ 0 D2 (/ A+(/ B+, (C (/ E( * 0	-	A+(/ B+, (C (/ (-(./+ 045 D2 (/ A+(/ B+, (C (/ E( * 045	.
	The dependent variable in Model 2 is
	$ % =	'()*+, (-(./+ 0 3 A+(/ B+, (C (/ (-(./+ 0 D2 (/ +()*+, 0 3D2 (/ )+(/ B+, (C (/ E( * 0	-	'()*+, (-(./+ 045 3 A+(/ B+, (C (/ (-(./+ 045 D2 (/ +()*+, 045 3D2 (/ )+(/ B+, (C (/ E( * 045	.
						Portfolio Rebalancing
			(1)			(2)
			Capital Gains Only		Excluding Capital Gains
	Variable		Coef.	t-stat	Coef.	t-stat
	Intercept		0.024	1.05	0.903	4.47***
	Underwritingt		0.055	1.25	0.618	1.61
	Underwritingt-1		-0.073	-1.79*	-1.157	-2.51**
	IRSI		-0.002	-0.26	-0.244	-4.51***
	Regulatory Compliance	-0.015	-1.31	0.003	0.03
	Loss Paid		0.008	0.84	0.276	1.29
	Investment Risk		0.020	1.61	0.212	1.29
	Tax Rate		-0.005	-1.41	0.055	1.43
	Growth		-0.011	-1.58	0.083	1.40
	Size		-0.001	-0.40	-0.018	-1.06
	Clustering Firm?		Yes			Yes
	Clustering Year?		Yes			Yes
	Prob > F		0.0000			0.0000
	R-Sqr		0.0011			0.0025

One of the authors discussed the statutes with insurance state departments (specifically the legal department) to fully understand the wording of the laws and how it pertains to the investment limitations of P&C insurers. The Appendix provides the criteria we used to assign a level of regulatory investment limitation for all asset classes.

"The NAIC's Securities Valuation Office (SVO) is responsible for the day-to-day credit quality assessment and valuation of securities owned by state regulated insurance companies. The SVO conducts credit analysis on these securities for the purpose of assigning an NAIC 'class rating' designation." The designations are not produced to aid investment decisions for investors(NAIC, 2014).

A potentially better measure of portfolio rebalancing would be the change in taxable assets per se. The data source we use does not provide sufficient information to classify investments in this manner.

Partitioning on the previous and the current year's underwriting performances (i.e., two negative years or two positive years) gives approximately the same results as in Columns 2 and 3 of Table3.

Acknowledgements

We thank the Social Science and Humanities Research Council (grant # 435-2018-1536) for partially financing this project, which was started when the third author was a visiting scholar in the Department of Finance at HEC Montréal. The first author wished to acknowledge the continuing support of the Direction de la recherche at HEC Montréal and of CIRANO.