(Luận văn) mối quan hệ giữa quản trị vốn luân chuyển, giá trị doanh nghiệp và hạn chế tài chính

LỜI CAM ĐOAN t to Tôi xin cam đoan luận văn “Mối quan hệ quản trị vốn luân chuyển, giá trị công ng hi ty hạn chế tài chính” cơng trình tơi nghiên cứu hướng dẫn ep PGS.TS Lê Thị Lanh w Số liệu luận văn có nguồn gốc rõ ràng, đáng tin cậy xử lý cách n lo trung thực ad y th Tơi xin hồn tồn chịu trách nhiệm nội dung tính trung thực đề tài ju TP.Hồ Chí Minh, ngày 02 tháng 11 năm 2015 yi pl al n ua Tác giả n va ll fu m oi Đỗ Thị Thanh Thảo at nh z z k jm ht vb om l.c gm an Lu n va ey t re MỤC LỤC t to ng TRANG PHỤ BÌA hi LỜI CAM ĐOAN ep MỤC LỤC w DANH MỤC BẢNG BIỂU n lo ad DANH MỤC HÌNH VẼ y th TÓM TẮT ju yi CHƢƠNG 1: GIỚI THIỆU ĐỀ TÀI pl 1.1 Lý chọn đề tài al ua 1.2 Mục tiêu câu hỏi nghiên cứu n 1.3 Phương pháp nghiên cứu va n 1.4 Kết cấu nghiên cứu fu ll CHƢƠNG 2: TỔNG QUAN LÝ THUYẾT VÀ CÁC NGHIÊN CỨU TRƢỚC ĐÂY oi m at nh 2.1 Tổng quan lý thuyết 2.1.1 Vốn luân chuyển z z 2.1.2 Quản trị vốn luân chuyển vb Quản trị khoản phải thu 2.1.2.2 Quản trị hàng tồn kho 2.1.2.3 Quản trị tiền mặt chứng khoán ngắn hạn 10 2.1.2.4 Quản trị khoản phải trả 12 k jm ht 2.1.2.1 l.c gm om 2.1.3 Các nhân tố tác động tới nhu cầu vốn luân chuyển 13 Nhân tố bên 13 2.1.3.2 Nhân tố bên 15 an Lu 2.1.3.1 va n 2.2 Các nghiên cứu trước 16 Các nghiên cứu thể mối quan hệ đồng biến 17 2.2.1.2 Các nghiên cứu thể mối quan hệ nghịch biến 22 ey 2.2.1.1 t re 2.2.1 Nghiên cứu trước mối quan hệ vốn luân chuyển giá trị công ty 16 t to 2.2.2 Nghiên cứu trước đầu tư vốn luân chuyển hạn chế tài 30 ng CHƢƠNG 3: PHƢƠNG PHÁP NGHIÊN CỨU VÀ DỮ LIỆU 34 hi ep 3.1 Mơ hình 34 3.1.1 Mơ hình mối quan hệ quản trị vốn luân chuyển giá trị cơng ty 34 w n 3.1.2 Mơ hình mối quan hệ quản trị vốn luân chuyển giá trị cơng ty hạn chế tài khác 36 lo ad 3.2 Dữ liệu 40 y th 3.3 Phương pháp nghiên cứu 41 ju yi CHƢƠNG 4: KẾT QUẢ NGHIÊN CỨU VÀ THẢO LUẬN 43 pl 4.1 Phân tích thống kê mô tả 43 al n ua 4.2 Kiểm định tương quan đa cộng tuyến 44 va 4.2.1 Ma trận tương quan đơn tuyến tính cặp biến 44 n 4.2.2 Kiểm định đa cộng tuyến 46 fu ll 4.3 Kiểm định tượng phương sai thay đổi phần dư - Greene (2000) 47 m oi 4.4 Kiểm định tượng tự tương quan phần dư – Wooldridge (2002) Drukker (2003) 47 at nh z 4.5 Phân tích kết hồi quy 48 z vb 4.5.1 Kết hồi quy quan hệ quản trị vốn luân chuyển giá trị công ty 48 k jm ht 4.5.2 Kết hồi quy mối quan hệ quản trị vốn luân chuyển giá trị công ty tác động hạn chế tài khác 52 gm CHƢƠNG 5: KẾT LUẬN 58 Kết luận cho nghiên cứu 58 5.2 Hàm ý cho nhà quản lý 59 5.3 Hạn chế nghiên cứu 60 5.4 Hướng nghiên cứu tương lai 61 om l.c 5.1 n va ey t re PHỤ LỤC an Lu TÀI LIỆU THAM KHẢO XẾP HẠNG PAPER GỐC PAPER GỐC DANH MỤC BẢNG BIỂU t to Bảng 2.1: Tổng hợp nghiên cứu mối quan hệ đồng biến quản trị vốn luân ng hi chuyển giá trị công ty 20 ep Bảng 2.2: Tổng hợp nghiên cứu mối quan hệ nghịch biến quản trị vốn w luân chuyển giá trị công ty 27 n lo Bảng 3.1: Tên biến cơng thức tính biến 34 ad ju y th Bảng 3.2: Tổng hợp cách thức xác định công ty bị hạn chế tài hay khơng 38 yi Bảng 4.1: Thống kê mơ tả biến mơ hình 42 pl ua al Bảng 4.2: Kết ma trận tự tương quan 44 n Bảng 4.3: Kết kiểm tra đa cộng tuyến với nhân tử phóng đại phương sai 45 va n Bảng 4.4: Kết kiểm tra phương sai thay đổi mơ hình 46 fu ll Bảng 4.5: Kết kiểm tra tự tương quan mơ hình 47 oi m nh Bảng 4.6: Kết hồi quy mơ hình mối quan hệ đầu tư vốn luân chuyển giá at trị công ty 48 z z Bảng 4.7: Kết hồi quy mơ hình mối quan hệ đầu tư vốn luân chuyển giá vb k jm ht trị công ty hạn chế tài 51 om l.c gm an Lu n va ey t re DANH MỤC HÌNH t to Hình 2.1: Chu kỳ thương mại (NTC) ng hi Hình 2.2: Mơ mối quan hệ hình chữ U ngược vốn luân chuyển giá trị ep công ty 26 w n lo ad ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re TÓM TẮT t to Mối quan hệ quản trị vốn luân chuyển giá trị công ty nghiên ng hi cứu số lượng lớn nghiên cứu lý thuyết thực nghiệm nhiều không ep gian thời gian khác Bài nghiên cứu lần xem xét mối liên hệ w quản trị vốn luân chuyển giá trị công ty cho mẫu 259 cơng ty phi tài n niêm yết hai sàn HNX HOSE giai đoạn 2008-2014 Đa phần lo ad nghiên cứu trước mối quan hệ tuyến tính chúng, nhiên y th nghiên cứu lại cung cấp chứng tác động quản lý vốn luân ju yi chuyển đến giá trị doanh nghiệp - mối quan hệ phi tuyến có dạng hình chữ U pl ngược Nghĩa tồn mức vốn luân chuyển tối ưu để cân chi phí, lợi ích al n ua tối đa hóa giá trị cơng ty n va Bài nghiên cứu mức vốn luân chuyển tối ưu nhạy cảm với ll fu hạn chế tài khác Đúng vậy, bất cân xứng thông tin công oi m ty thị trường vốn dẫn đến tình trạng hạn chế tín dụng khoảng cách chi nh phí nguồn tài trợ nội nguồn bên ngồi, thiếu hụt thơng tin làm at thị trường đánh giá thấp công ty dự án cơng ty, từ làm gia tăng chi z z phí nguồn tài trợ bên Như vậy, mức vốn luân chuyển cao vb ht địi hỏi nhiều nguồn lực tài hơn, đồng nghĩa với việc chi phí tăng thêm, k jm đó, doanh nghiệp nhiều khả phải đối mặt với khó khăn tài nên l.c han chế tài gm trì mức vốn luân chuyển tối ưu thấp so với doanh nghiệp khả bị om Những kết nghiên cứu kim nam cho nhà quản an Lu trị việc hoạch định chiến lược quản trị vốn luân chuyển cách hiệu quả, xây dựng mức vốn luân chuyển tối ưu phù hợp với tình hình tài n ey t re Từ khóa: quản trị vốn luân chuyển, giá trị cơng ty, hạn chế tài va cơng ty CHƢƠNG 1: GIỚI THIỆU ĐỀ TÀI t to Lý chọn đề tài ng 1.1 hi Nhiều nghiên cứu trước cố gắng quản trị vốn luân ep chuyển hiệu tác động sâu sắc đến giá trị doanh nghiệp Một mức tối ưu w vốn luân chuyển tạo cân rủi ro hiệu đạt được, n lo tối thiểu hóa chi phí thực chi phí hội Vì vậy, cần phải có đầu tư ad mực cho thành phần vốn luân chuyển y th ju Hiện nay, nghiên cứu quản trị vốn luân chuyển có vai trị đặc biệt quan yi pl trọng Việt Nam cơng ty chủ yếu có quy mô vừa nhỏ, hầu hết tài sản ua al tài sản ngắn hạn, đặc biệt hàng tồn kho chiếm tỷ trọng lớn tổng tài n sản Trong đó, nợ ngắn hạn lại nguồn tài trợ chủ yếu từ bên va n hạn chế tài mà cơng ty gặp phải khó khăn việc tiếp cận ll fu nguồn tài trợ dài hạn từ thị trường vốn Bên cạnh đó, thị trường vốn Việt Nam oi m chưa phát triển ngân hàng đóng vai trị trung tâm hệ thống tài Vì at nh vậy, cơng ty có nguồn tài trợ bên ngồi thay sẵn có nên phụ thuộc phần lớn vào nguồn tài trợ nội bộ, nợ ngắn hạn ngân hàng đặc biệt tín dụng thương mại z z để tài trợ cho hoạt động công ty Đặc biệt bối cảnh kinh tế vb jm ht gặp nhiều khó khăn, giai đoạn 2008 đến 2012, hàng loạt doanh nghiệp Việt Nam phải ngừng sản xuất, đóng cửa rơi vào tình trạng khốn khó, k gm phải đối mặt với bất ổn tiềm ẩn nhiều rủi ro việc nâng cao hiệu quan tâm từ nhiều nhà quản lý, đặc biệt doanh nghiệp vừa nhỏ om l.c quản trị vốn luân chuyển để gia tăng khả sinh lợi trở thành chủ đề thu hút an Lu Thật đề tài nghiên cứu khơng phải chủ đề nghiên cứu mới, nghiệm cho nghiên cứu quản trị vốn luân chuyển Việt Nam Thông qua ey dụng phương pháp nghiên cứu đóng góp, bổ sung mặt lý thuyết thực t re hưởng hạn chế tài đến mối quan hệ trên, đồng thời nghiên cứu sử n mối quan hệ quản trị vốn luân chuyển giá trị công ty, ảnh va có ý nghĩa quan trọng theo thời gian, phát nghiên cứu kỳ vọng thay đổi cách nhìn nhận giám đốc tài t to quản trị vốn luân chuyển, từ xây dựng chiến lược quản trị vốn luân chuyển ng cách hiệu nhất, phù hợp với tình hình tài công ty hi ep 1.2 Mục tiêu câu hỏi nghiên cứu w Bài nghiên cứu tập trung vào mối quan hệ quản trị vốn luân chuyển n lo giá trị công ty Đồng thời xem xét tác động hạn chế tài đến mối quan hệ ad Bài nghiên cứu giải câu hỏi sau: y th ju  Chính sách quản trị vốn luân chuyển công ty tác động yi đến giá trị doanh nghiệp? Mối quan hệ chúng có thật tuyến tính pl ua al nhiều nghiên cứu trước hay không? n  Trong hồn cảnh giới hạn tài khác nhau, tác động n va sách quản trị vốn luân chuyển đến giá trị công ty thay đổi nào? Có ll fu khác mức vốn ln chuyển tối ưu cơng ty bị hạn chế tài oi m cơng ty nhiều khả bị hạn chế tài khơng? Phƣơng pháp nghiên cứu at nh 1.3 z Bài nghiên cứu dựa vào tài liệu sở “Working capital management, z ht vb corporate performance, and financial constraints” Sonia Baños-Caballero, jm Pedro J.García-Teruel, Pedro Martínez-Solano (2013) Trên sở tổng hợp phát k triển lý thuyết từ nghiên cứu khác giới lý gm thuyết tảng tài doanh nghiệp để tiến hành kiểm định mối quan hệ l.c quản trị vốn luân chuyển giá trị công ty tuyến tính hay phi tuyến om an Lu Sau đó, nghiên cứu kiểm định tác động hạn chế tài đến mối quan hệ quản trị vốn luân chuyển giá trị công ty Bài nghiên cứu ey doanh nghiệp t re trợ bên ngoài, khả tiếp cận thị trường vốn tình trạng khánh kiệt tài n bên (có hay khơng chi trả cổ tức, tỷ lệ chi trả cổ tức cao hay thấp), chi phí tài va dùng thước đo khác để đo lường hạn chế tài chính, nguồn tài trợ Đặc biệt, nghiên cứu sử dụng mơ hình GMM hai bước đề xuất t to Arellaro Bond (1991) với mục đích kiểm sốt vấn đề nội sinh xảy Việc ng kiểm sốt vấn đề nội sinh cho phép nghiên cứu khẳng định mối quan hệ có hi ep ảnh hưởng quản trị vốn luân chuyển lên giá trị công ty do chiều tác động ngược lại, giá trị công ty ảnh hưởng đến vốn luân chuyển w n 1.4 Kết cấu nghiên cứu lo ad Cấu trúc nghiên cứu gồm năm chương sau: y th ju  Chương 1: Giới thiệu nghiên cứu yi  Chương 2: Tổng quan lý thuyết nghiên cứu trước Phần phát pl ua al triển dự đoán mối quan hệ lõm quản trị vốn luân chuyển giá trị công n ty Và vạch điều kiện ảnh hưởng có điều kiện tài đến n va mối quan hệ ll fu  Chương 3: Mơ tả mơ hình thực nghiệm liệu oi m  Chương 4: Kết mối quan hệ quản trị vốn luân chuyển giá trị nh cơng ty Việt Nam Và phân tích cách thay đổi mức vốn tối ưu at doanh nghiệp nhiều khả phải đối mặt với khó khăn tài z z  Chương 5: Kết luận k jm ht vb om l.c gm an Lu n va ey t re CHƢƠNG 2: TỔNG QUAN LÝ THUYẾT VÀ CÁC NGHIÊN CỨU t to TRƢỚC ĐÂY ng hi 2.1 Tổng quan lý thuyết ep 2.1.1 Vốn luân chuyển w n Theo Guthmann Dougall (1948), vốn luân chuyển theo nghĩa rộng giá lo ad trị toàn tài sản luân chuyển - tài sản gắn liền với chu kỳ kinh doanh y th công ty Trong chu kỳ kinh doanh, chúng chuyển hóa qua tất hình ju thái tồn từ tiền mặt đến hàng tồn kho, khoản phải thu trở hình thái yi pl ban đầu tiền mặt Vốn luân chuyển vận động theo chu kì: tiền - dự trữ sản xuất - al ua bán thành phẩm - thành phẩm - tiền Vịng ln chuyển vốn tính từ lúc bỏ tiền n mua nguyên, nhiên, vật liệu, sức lao động đến lúc tiêu thụ thành phẩm (bán hàng) va n thu hồi tiền Trong thời gian này, vốn thay đổi hình thái (tiền - hàng - tiền) trở ll fu lại hình thái ban đầu (tiền tệ) m oi Vốn luân chuyển phân thành vốn luân chuyển hoạt động (operational nh at working capital) vốn luân chuyển tài (financial working capital) Vốn luân z chuyển hoạt động bao gồm khoản phải thu, hàng tồn kho khoản phải trả, z ht vb tối ưu hóa chịu ảnh hưởng hoạt động công ty Các phần jm cịn lại, tức tiền mặt, chứng khốn thị trường, khoản trả trước tất khoản k nợ ngắn hạn khác định tài công ty Nghiên cứu tập gm trung hồn tồn vào vốn ln chuyển hoạt động, định nghĩa đơn giản om l.c khoản phải thu cộng hàng tồn kho trừ khoản phải trả an Lu Một công ty tập trung tài sản dạng vốn ln chuyển tính khoản cơng ty giảm Nói cách khác, việc quản lý vốn luân tương lai Một mức độ tối ưu vốn luân chuyển tạo cân rủi ro ey thiểu mà không gây ảnh hưởng đến tăng trưởng doanh số bán hàng t re hóa vốn ln chuyển; cơng ty khơng thể giảm vốn ln chuyển đến mức tối n nợ khoản tài ngắn hạn giảm chi phí tài Vấn đề nằm việc tối ưu va chuyển hiệu cho phép công ty đầu tư vào tăng trưởng tương lai, trả Dynamic panel-data estimation, two-step system GMM t to ng hi ep Group variable: company Time variable : year Number of instruments = 86 Wald chi2(8) = 72.34 Prob > chi2 = 0.000 Coef ntc ntc_chicotuc ntc2 ntc2_chicotuc size lev growth roa _cons 4389756 -.2386342 -.0523634 0350734 -.1599174 2.125701 10.24243 9588945 8882001 w q Number of obs Number of groups Obs per group: avg max Std Err z P>|z| = = = = = 1511 257 5.88 [95% Conf Interval] n lo ad ju y th yi 1341713 0817004 0176255 0150677 0318571 4285587 2.533977 4313165 4241723 pl 0.001 0.003 0.003 0.020 0.000 0.000 0.000 0.026 0.036 1760046 -.3987641 -.0869088 0055413 -.2223561 1.285742 5.275924 1135297 0568378 7019466 -.0785043 -.0178181 0646056 -.0974786 2.965661 15.20893 1.804259 1.719562 n ua al 3.27 -2.92 -2.97 2.33 -5.02 4.96 4.04 2.22 2.09 va Warning: Uncorrected two-step standard errors are unreliable n fu ll Instruments for first differences equation Standard D.(D.ntc_chicotuc D.size L.size L.ntc L.ntc2) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/6).(ntc_chicotuc size ntc ntc2) Instruments for levels equation Standard D.ntc_chicotuc D.size L.size L.ntc L.ntc2 _cons oi m at nh z z Pr > z = Pr > z = 0.238 0.211 Prob > chi2 = 0.302 om l.c gm Prob > chi2 = n ey t re 0.579 0.018 va Difference-in-Hansen tests of exogeneity of instrument subsets: iv(D.ntc_chicotuc D.size L.size L.ntc L.ntc2) Hansen test excluding group: chi2(72) = 68.97 Prob > chi2 = Difference (null H = exogenous): chi2(5) = 13.68 Prob > chi2 = 0.309 an Lu overid restrictions: chi2(77) = 82.93 but not weakened by many instruments.) overid restrictions: chi2(77) = 82.65 weakened by many instruments.) -1.18 -1.25 k Sargan test of (Not robust, Hansen test of (Robust, but jm ht vb Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Dynamic panel-data estimation, two-step system GMM t to ng hi ep Group variable: company Time variable : year Number of instruments = 83 Wald chi2(8) = 66.23 Prob > chi2 = 0.000 Number of obs Number of groups Obs per group: avg max Coef ntc ntc_tylechitrac~c ntc2 ntc2_tylechitra~c size lev growth roa _cons -.0339051 1524116 0361564 -.0463092 -.0523817 700425 7.145065 2.248652 6957262 Std Err z P>|z| 1511 257 5.88 [95% Conf Interval] w q = = = = = n lo ad ju y th yi pl 222431 2097391 0468344 0458307 0187979 2671732 1.875756 4126476 224008 0.879 0.467 0.440 0.312 0.005 0.009 0.000 0.000 0.002 -.4698618 -.2586695 -.0556374 -.1361356 -.0892249 1767751 3.468651 1.439877 2566787 4020515 5634927 1279502 0435173 -.0155386 1.224075 10.82148 3.057426 1.134774 ua al -0.15 0.73 0.77 -1.01 -2.79 2.62 3.81 5.45 3.11 n Warning: Uncorrected two-step standard errors are unreliable va n Instruments for first differences equation Standard D.(D.size L.size L.ntc L.ntc2) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/6).(size ntc ntc2) Instruments for levels equation Standard D.size L.size L.ntc L.ntc2 _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.(size ntc ntc2) ll fu oi m at nh z z Pr > z = Pr > z = 0.237 0.493 gm Prob > chi2 = 0.988 Prob > chi2 = 0.230 ey t re 0.204 0.536 n chi2 = chi2 = 0.040 0.992 va chi2 = chi2 = an Lu Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(56) = 75.87 Prob > Difference (null H = exogenous): chi2(18) = 6.78 Prob > iv(D.size L.size L.ntc L.ntc2) Hansen test excluding group: chi2(70) = 79.51 Prob > Difference (null H = exogenous): chi2(4) = 3.13 Prob > om l.c overid restrictions: chi2(74) = 49.32 but not weakened by many instruments.) overid restrictions: chi2(74) = 82.65 weakened by many instruments.) -1.18 -0.69 k Sargan test of (Not robust, Hansen test of (Robust, but jm ht vb Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Dynamic panel-data estimation, two-step system GMM t to ng hi ep Group variable: company Time variable : year Number of instruments = 85 Wald chi2(8) = 45.00 Prob > chi2 = 0.000 Coef ntc ntc_dongtien ntc2 ntc2_dongtien size lev growth roa _cons 3541694 -.3887066 -.019848 0284753 -.067654 5468241 7.098315 -1.757538 1.186067 Std Err z P>|z| = = = = = 1511 257 5.88 [95% Conf Interval] w q Number of obs Number of groups Obs per group: avg max n lo ad ju y th yi 2043255 162325 0311134 0293855 0291343 2439652 4.165206 8281042 4294688 pl 0.083 0.017 0.524 0.333 0.020 0.025 0.088 0.034 0.006 -.0463012 -.7068577 -.0808292 -.0291192 -.1247561 068661 -1.065338 -3.380593 3443236 75464 -.0705555 0411333 0860698 -.0105518 1.024987 15.26197 -.1344838 2.02781 ua al 1.73 -2.39 -0.64 0.97 -2.32 2.24 1.70 -2.12 2.76 n Warning: Uncorrected two-step standard errors are unreliable va n Instruments for first differences equation Standard D.(L.lev L.size L.ntc L.ntc2) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/6).(lev size ntc ntc2) Instruments for levels equation Standard L.lev L.size L.ntc L.ntc2 _cons ll fu oi m at nh z z vb -1.18 -1.18 Pr > z = Pr > z = 0.237 0.238 Prob > chi2 = 0.578 gm Prob > chi2 = 0.897 om l.c overid restrictions: chi2(76) = 72.96 but not weakened by many instruments.) overid restrictions: chi2(76) = 60.87 weakened by many instruments.) k Sargan test of (Not robust, Hansen test of (Robust, but jm ht Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = an Lu Difference-in-Hansen tests of exogeneity of instrument subsets: iv(L.lev L.size L.ntc L.ntc2) Hansen test excluding group: chi2(72) = 50.62 Prob > chi2 = Difference (null H = exogenous): chi2(4) = 10.25 Prob > chi2 = 0.974 0.036 n va ey t re Dynamic panel-data estimation, two-step system GMM t to ng hi ep Group variable: company Time variable : year Number of instruments = 58 Wald chi2(8) = 93.78 Prob > chi2 = 0.000 Number of obs Number of groups Obs per group: avg max Coef ntc ntc_chiphitrano~i ntc2 ntc2_chiphitran~i size lev growth roa _cons 070391 -.2727672 -.0073455 0511709 -.0084442 6597859 1.287085 3.235574 3734016 Std Err z P>|z| 1775 258 6.88 [95% Conf Interval] w q = = = = = n lo ad ju y th yi pl 0331926 075326 003473 0162623 0245583 245513 6511255 449564 3596234 0.034 0.000 0.034 0.002 0.731 0.007 0.048 0.000 0.299 0053348 -.4204035 -.0141526 0192974 -.0565777 1785893 0109025 2.354444 -.3314473 1354473 -.125131 -.0005385 0830444 0396893 1.140983 2.563268 4.116703 1.078251 ua al 2.12 -3.62 -2.12 3.15 -0.34 2.69 1.98 7.20 1.04 n Warning: Uncorrected two-step standard errors are unreliable va n Instruments for first differences equation Standard D.(ntc chicotuc growth) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/6).(ntc ntc2) Instruments for levels equation Standard ntc chicotuc growth _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.(ntc ntc2) ll fu oi m at nh z z Pr > z = Pr > z = 0.235 0.403 gm Prob > chi2 = 0.758 Prob > chi2 = 0.000 om an Lu 0.000 0.354 ey chi2 = chi2 = t re 0.000 0.092 n chi2 = chi2 = va Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(37) = 80.86 Prob > Difference (null H = exogenous): chi2(12) = 18.88 Prob > iv(ntc chicotuc growth) Hansen test excluding group: chi2(46) = 96.48 Prob > Difference (null H = exogenous): chi2(3) = 3.25 Prob > l.c overid restrictions: chi2(49) = 41.78 but not weakened by many instruments.) overid restrictions: chi2(49) = 99.74 weakened by many instruments.) -1.19 -0.84 k Sargan test of (Not robust, Hansen test of (Robust, but jm ht vb Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Dynamic panel-data estimation, two-step system GMM t to ng hi ep Group variable: company Time variable : year Number of instruments = 59 Wald chi2(8) = 181.83 Prob > chi2 = 0.000 Number of obs Number of groups Obs per group: avg max Coef ntc ntc_kntralaivay ntc2 ntc2_kntralaivay size lev growth roa _cons 6530958 -.479006 -.0872799 0771128 0331894 4087507 1.605805 3.345124 -.5534702 Std Err z P>|z| 1775 258 6.88 [95% Conf Interval] w q = = = = = n lo ad ju y th yi pl 1043856 0936601 0144142 0140137 0176385 2045275 5896135 3115905 3120492 0.000 0.000 0.000 0.000 0.060 0.046 0.006 0.000 0.076 4485038 -.6625764 -.1155313 0496465 -.0013813 0078841 4501841 2.734418 -1.165075 8576877 -.2954356 -.0590286 1045791 0677602 8096173 2.761427 3.95583 058135 ua al 6.26 -5.11 -6.06 5.50 1.88 2.00 2.72 10.74 -1.77 n Warning: Uncorrected two-step standard errors are unreliable va n Instruments for first differences equation Standard D.(ntc chicotuc growth tlchitrctc) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/6).(ntc ntc2) Instruments for levels equation Standard ntc chicotuc growth tlchitrctc _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.(ntc ntc2) ll fu oi m at nh z z Pr > z = Pr > z = 0.235 0.289 gm Prob > chi2 = 0.322 Prob > chi2 = 0.000 ey t re 0.000 0.323 n chi2 = chi2 = 0.000 0.002 va chi2 = chi2 = an Lu Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(38) = 108.29 Prob > Difference (null H = exogenous): chi2(12) = 30.54 Prob > iv(ntc chicotuc growth tlchitrctc) Hansen test excluding group: chi2(46) = 134.16 Prob > Difference (null H = exogenous): chi2(4) = 4.67 Prob > om l.c overid restrictions: chi2(50) = 54.07 but not weakened by many instruments.) overid restrictions: chi2(50) = 138.83 weakened by many instruments.) -1.19 -1.06 k Sargan test of (Not robust, Hansen test of (Robust, but jm ht vb Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Dynamic panel-data estimation, two-step system GMM t to ng hi ep Group variable: company Time variable : year Number of instruments = 60 Wald chi2(8) = 109.62 Prob > chi2 = 0.000 Number of obs Number of groups Obs per group: avg max Coef ntc ntc_zscoredcf ntc2 ntc2_zscoredcf size lev growth roa _cons 2808526 -.166299 -.0215075 0100423 0358258 9340969 1.278564 2.546634 -.6519516 Std Err z P>|z| 1775 258 6.88 [95% Conf Interval] w q = = = = = n lo ad ju y th yi pl 4.75 -2.44 -3.83 1.28 1.25 3.83 1.89 6.72 -1.49 0.000 0.015 0.000 0.202 0.210 0.000 0.059 0.000 0.135 1649631 -.2997061 -.0325248 -.0053942 -.0201423 4559264 -.0501819 1.803601 -1.507808 3967421 -.0328919 -.0104902 0254788 0917938 1.412267 2.60731 3.289667 203905 ua al 0591284 0680661 0056212 0078759 0285556 243969 6779439 3791053 4366696 n Warning: Uncorrected two-step standard errors are unreliable va n Instruments for first differences equation Standard D.(ntc chicotuc growth tlchitrctc chiphtitrbnngoi) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/6).(ntc ntc2) Instruments for levels equation Standard ntc chicotuc growth tlchitrctc chiphtitrbnngoi _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.(ntc ntc2) ll fu oi m at nh z z Pr > z = Pr > z = 0.237 0.939 gm Prob > chi2 = 0.422 Prob > chi2 = 0.000 ey t re 0.000 0.399 n chi2 = chi2 = 0.000 0.850 va chi2 = chi2 = an Lu Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(39) = 92.27 Prob > Difference (null H = exogenous): chi2(12) = 7.12 Prob > iv(ntc chicotuc growth tlchitrctc chiphtitrbnngoi) Hansen test excluding group: chi2(46) = 94.25 Prob > Difference (null H = exogenous): chi2(5) = 5.14 Prob > om l.c overid restrictions: chi2(51) = 52.32 but not weakened by many instruments.) overid restrictions: chi2(51) = 99.39 weakened by many instruments.) -1.18 0.08 k Sargan test of (Not robust, Hansen test of (Robust, but jm ht vb Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Dynamic panel-data estimation, two-step system GMM t to ng hi ep Group variable: company Time variable : year Number of instruments = 62 Wald chi2(8) = 49.27 Prob > chi2 = 0.000 Coef ntc ntc_quymo ntc2 ntc2_quymo size lev growth roa _cons 3054992 -.2892461 -.0286138 0252194 -.202016 1382231 1.370019 2.416488 2.989072 Std Err z P>|z| = = = = = 1775 258 6.88 [95% Conf Interval] w q Number of obs Number of groups Obs per group: avg max n lo ad ju y th 1091187 0939263 0144308 0135925 0533775 1219903 6749718 4497846 6662639 yi pl 0.005 0.002 0.047 0.064 0.000 0.257 0.042 0.000 0.000 0916305 -.4733383 -.0568976 -.0014215 -.306634 -.1008735 0470986 1.534926 1.683218 519368 -.105154 -.00033 0518603 -.0973981 3773197 2.69294 3.298049 4.294925 ua al 2.80 -3.08 -1.98 1.86 -3.78 1.13 2.03 5.37 4.49 n Warning: Uncorrected two-step standard errors are unreliable va n Instruments for first differences equation Standard D.(ntc chicotuc growth tlchitrctc chiphtitrbnngoi lev chiphitranongoai) GMM-type (missing=0, separate instruments for each period unless collapsed) L(1/6).(ntc ntc2) Instruments for levels equation Standard ntc chicotuc growth tlchitrctc chiphtitrbnngoi lev chiphitranongoai _cons GMM-type (missing=0, separate instruments for each period unless collapsed) D.(ntc ntc2) ll fu oi m at nh z z Pr > z = Pr > z = 0.238 0.137 gm Prob > chi2 = 0.591 Prob > chi2 = 0.001 om l.c overid restrictions: chi2(53) = 50.02 but not weakened by many instruments.) overid restrictions: chi2(53) = 93.40 weakened by many instruments.) -1.18 -1.49 k Sargan test of (Not robust, Hansen test of (Robust, but jm ht vb Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = an Lu n va ey t re Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(41) = 79.41 Prob > chi2 = 0.000 Difference (null H = exogenous): chi2(12) = 13.99 Prob > chi2 = 0.301 iv(ntc chicotuc growth tlchitrctc chiphtitrbnngoi lev chiphitranongoai) Hansen test excluding group: chi2(46) = 82.44 Prob > chi2 = 0.001 Difference (null H = exogenous): chi2(7) = 10.97 Prob > chi2 = 0.140 XẾP HẠNG PAPER t to  Tên nghiên cứu: Sonia Bos-Caballero, Pedro J.García-Teruel, Pedro ng hi Martínez-Solano (2013) “Working capital management, corporate ep performance, and financial constraints”  Tạp chí: Journal of Business Research w n lo ad ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm an Lu n va ey t re JBR-07710; No of Pages Journal of Business Research xxx (2013) xxx–xxx Contents lists available at SciVerse ScienceDirect t to ng Journal of Business Research hi ep w Working capital management, corporate performance, and financial constraints☆ n lo ad Sonia Baños-Caballero 1, Pedro J García-Teruel ⁎, Pedro Martínez-Solano i n f o n ua al n va ll fu Keywords: Working capital Corporate performance Financial constraints This paper examines the linkage between working capital management and corporate performance for a sample of non-financial UK companies In contrast to previous studies, the findings provide strong support for an inverted U-shaped relation between investment in working capital and firm performance, which implies the existence of an optimal level of investment in working capital that balances costs and benefits and maximizes a firm's value The results suggest that managers should avoid negative effects on firm performance because of lost sales and lost discounts for early payments or additional financing expenses The paper also analyzes whether the optimal working capital level is sensitive to alternative measures of financial constraints The findings show that this optimum is lower for firms more likely to be financially constrained © 2013 Elsevier Inc All rights reserved pl Article history: Received June 2012 Received in revised form December 2012 Accepted January 2013 Available online xxxx a b s t r a c t yi a r t i c l e ju y th Department of Management and Finance, Faculty of Economics and Business, University of Murcia, Murcia, Spain which increase their probability of going bankrupt (Kieschnick, LaPlante, & Moussawi, 2011) Combining these positive and negative working capital effects leads to the prediction of a nonlinear relation between investment in working capital and firm value The hypothesis in this paper is that an inverted U-shaped relation may result if both effects are sufficiently strong Authors like Schiff and Lieber (1974), Smith (1980) and Kim and Chung (1990) suggest that working capital decisions affect firm performance In this line, Wang (2002) finds that firms from Japan and Taiwan with higher values hold a significantly lower investment in working capital than firms with lower values Kieschnick et al (2011) study the relation between working capital management and firm value They take Faulkender and Wang (2006) as their baseline valuation model and analyze how shareholders of US corporations value an additional dollar invested in net operating working capital by using a stock's excess return as proxy for firm value Their results show that, on average, an additional dollar invested in net operating working capital is worth less than a dollar held in cash They also find that an increase in net operating working capital, on average, would reduce the excess stock return and they show that this reduction would be greater for those firms with limited access to external finance Since market imperfections increase the cost of outside capital relative to internally generated funds (Greenwald, Stiglitz, & Weiss, 1984; Jensen & Meckling, 1976; Myers & Majluf, 1984) and may result in debt rationing (Stiglitz & Weiss, 1981), Fazzari et al (1988) suggest that firms' investment may depend on financial factors such as the availability of internal finance, access to capital markets or cost of financing Fazzari and Petersen (1993) suggest in their analysis that investment in working capital is more sensitive to financing constraints than investment in fixed capital However, while the above study focuses on the influence of an additional investment in working capital on firm value, our paper examines the functional form of the relation between investment in oi at nh z z k jm ht vb om l.c gm an Lu n va The literature on investment decisions evolved through many theoretical and empirical contributions A number of studies show a direct relation between investment and firm value (Burton, Lonie, & Power, 1999; Chung, Wright, & Charoenwong, 1998; McConnell & Muscarella, 1985) Additionally, since the seminal work by Modigliani and Miller (1958) showing that investment and financing decisions are independent, extensive literature based on capital-market imperfections has appeared that supports the relation between these two decisions (Fazzari, Hubbard, & Petersen, 1988; Hubbard, 1998) Despite the importance of the interrelations between the individual components of working capital when evaluating their influence on corporate performance (Kim & Chung, 1990; Sartoris & Hill, 1983; Schiff & Lieber, 1974), few studies of empirical evidence for the valuation effects of investment in working capital and, more specifically, the possible influence of financing on this relation exist Studies on working capital management fall into two competing views of working capital investment Under one view, higher working capital levels allow firms to increase their sales and obtain greater discounts for early payments (Deloof, 2003) and, hence, may increase firms' value Alternatively, higher working capital levels require financing and, consequently, firms face additional financing expenses, m Introduction Please cite this article as: Baños-Caballero, S., et al., Working capital management, corporate performance, and financial constraints, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.01.016 ey 0148-2963/$ – see front matter © 2013 Elsevier Inc All rights reserved http://dx.doi.org/10.1016/j.jbusres.2013.01.016 t re ☆ The authors are grateful to Juan Pedro Sánchez-Ballesta and Ginés Hernández-Canovas for their valuable comments and suggestions made on this manuscript This research is part of the project 15358/PHCS/10 financed by Fundación Séneca Science and Technology Agency of the Region of Murcia (Spain) – (Program: PCTIRM 11-14) The authors also acknowledge financial support from Fundación CajaMurcia ⁎ Corresponding author Tel.: +34 868887828; fax: +34 868887537 E-mail addresses: sbanos@um.es (S Bos-Caballero), pjteruel@um.es (P.J García-Teruel), pmsolano@um.es (P Martínez-Solano) Tel.: +34 868883798; fax: +34 868887537 Tel.: +34 868883747; fax: +34 868887537 S Baños-Caballero et al / Journal of Business Research xxx (2013) xxx–xxx t to ng hi ep w n lo ad ju y th yi pl n ua al n va at nh z z k jm ht vb om l.c gm 2.2 Investment in working capital and financial constraints an Lu If the results verify the hypothesis that there is an inverted U-shaped relation between working capital and performance of a firm, one would expect the optimal level of investment in working capital to differ between firms more or less likely to face financing constraints Modigliani and Miller (1958) argue that in a frictionless world, companies can always obtain external financing without problems and, hence, their investment does not depend on the availability of internal capital Once capital market imperfections (i.e., informational asymmetries and agency costs) are present, capital market frictions increase the cost of outside capital relative to internally generated funds (Greenwald et al., 1984; Jensen & Meckling, 1976; Myers & Majluf, 1984) Consequently, external capital does not provide a perfect substitute for internal funds Stiglitz and Weiss (1981) also describe how asymmetric information may result in debt rationing In this line, Fazzari et al (1988) suggest that the firms' investment may depend on financial factors such as the availability of internal finance, access to capital markets or cost of financing ey t re Please cite this article as: Baños-Caballero, S., et al., Working capital management, corporate performance, and financial constraints, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.01.016 n va The investment in receivable accounts and inventories represents an important proportion of a firm's assets, while trade credit is an important source of funds for most firms Cuñat (2007) reports that trade credit represents about 41% of the total debt and about half the short term debt in UK medium sized firms There is substantial literature on credit policy and inventory management, but few attempts to integrate both credit policy and inventory management decisions, even though Schiff and Lieber (1974), Sartoris and Hill (1983), and Kim and Chung (1990) show the importance of taking into account the interactions between the various working capital elements (i.e receivable accounts, inventories and payable accounts) Lewellen, McConnel, and Scott (1980) demonstrate that under perfect financial markets, trade credit decisions not serve to increase firm value However, capital markets are not perfect and, consequently, several papers demonstrate the influence of trade credit and inventories on firm value (see, for instance, Bao & Bao, 2004; Emery, 1984) The idea that working capital management affects firm value also seems to enjoy wide acceptance, although the empirical evidence on the valuation effects of investment in working capital is scarce There are various explanations for the incentives of firms to hold positive working capital Firstly, a higher investment in extended trade credit and inventories might increase corporate performance for several reasons According to Blinder and Maccini (1991), larger inventories can reduce supply costs and price fluctuations and prevent m 2.1 Working capital and corporate performance ll fu Working capital, corporate performance and financing interruptions in the production process and loss of business due to scarcity of products They also allow firms better service for their customers and avoid high production costs arising from high fluctuations in production (Schiff & Lieber, 1974) Granting trade credit, on the other hand, might also increase a firm's sales, because it can serve as an effective price cut (Brennan, Maksimovic, & Zechner, 1988; Petersen & Rajan, 1997); it encourages customers to acquire merchandise at times of low demand (Emery, 1987); it strengthens long-term supplier–customer relationships (Ng, Smith, & Smith, 1999; Wilner, 2000); it allows buyers to verify product and services quality prior to payment (Lee & Stowe, 1993; Smith, 1987) Hence, it reduces the asymmetric information between buyer and seller Indeed, Shipley and Davis (1991), and Deloof and Jegers (1996) suggest that trade credit is an important supplier selection criterion when it is hard to differentiate products Emery (1984) suggests that trade credit is a more profitable short-term investment than marketable securities Secondly, working capital may also act as a stock of precautionary liquidity, providing insurance against future shortfalls in cash (Fazzari & Petersen, 1993) Finally, from the point of view of accounts payable, Ng et al (1999) and Wilner (2000) also demonstrate that a firm may obtain important discounts for early payments when it reduces its supplier financing However, there are also possible adverse effects of investment in working capital which may lead to a negative impact on firm value at certain working capital levels Firstly, keeping stock available supposes costs such as warehouse rent, insurance and security expenses, which tend to rise as the level of inventory increases (Kim & Chung, 1990) Secondly, since a greater working capital level indicates a need for additional capital, which firms must finance, it involves financing costs and opportunity costs On the one hand, companies that hold a higher working capital level also face more interest expenses as a result (Kieschnick et al., 2011) and, therefore, more credit risk As working capital increases, it is more likely that firms will experience financial distress and face the threat of bankruptcy This gives firms with high investment in working capital incentives to reduce working capital levels and minimize the risk of financial distress and costly bankruptcy On the other hand, keeping high working capital levels means that money is locked up in working capital (Deloof, 2003), so large investment in working capital might also hamper the ability of firms to take up other value-enhancing projects These positive and negative working capital effects indicate that the working capital decisions involve a trade-off Consequently, we expect firms to have an optimal working capital level that balances these costs and benefits and maximizes their value Specifically, we expect corporate performance to rise as working capital increases until a certain working capital level is reached Conversely, we expect that, beyond this optimum, the relation between working capital and performance will become negative oi working capital and corporate performance Given that financing conditions might play an important role in this relation, we also study whether firms' financing constraints affect the above relation To our knowledge, our paper is the first to analyze the functional form of this relation as well as the possible influence of financial constraints on it We use non-financial companies from the United Kingdom UK capital markets are well developed (Schmidt & Tyrell, 1997) and present more than 80% of daily business transactions on credit terms (Summers & Wilson, 2000) In fact, Cuñat (2007) indicates that trade credit represents about 41% of the total debt and about half the short term debt in UK medium sized firms This study contributes to the working capital management literature in a number of ways First, we offer new evidence on the effect of working capital management on corporate performance, by taking into account the possible non-linearities of this relation Second, the paper investigates the relation between investment in working capital and firm performance according to the financing constraints of the firms Third, we estimate the models by using panel data methodology in order to eliminate the unobservable heterogeneity Lastly, we use the generalized method of moments (GMM) to deal with the possible endogeneity problems Our results indicate that there is an inverted U-shaped relation between working capital and firm performance That is, investment in working capital and corporate performance relate positively at low levels of working capital and negatively at higher levels We also find that the results hold when firms are classified according to a variety of characteristics designed to measure the level of financial constraints borne by firms The findings show that the optimum is sensitive to the financing constraints of the firms and that under each of our classification schemes optimal working capital level is lower for those firms that are more likely to be financially constrained The structure of the paper is as follows The next section develops the predicted concave relation between working capital and corporate performance and outlines the possible influence of financing conditions on this relationship In Section we describe our empirical model and data We present our results in Section and analyze how the optimum changes between firms more or less likely to face financing constraints Section concludes S Baños-Caballero et al / Journal of Business Research xxx (2013) xxx–xxx t to (2006), which is a linear combination of six factors: cash flow, a dividend payer dummy, leverage, firm size, industry sales growth, and firm sales growth A greater index means a firm has less access to external capital markets Thus, we consider a firm as being more (less) financially constrained when its WW index is above (below) the median value of this index in our sample ng Fazzari and Petersen (1993) suggest that investments in working capital are more sensitive to financing constraints than investments in fixed capital Accordingly, since a positive working capital level needs financing, one would expect the optimal level of working capital to be lower for more financially constrained firms In this line, empirical evidence demonstrates that investment in working capital depends on a firm's financing conditions Specifically, Hill, Kelly, and Highfield (2010) show that firms with greater internal financing capacity and capital market access hold a higher working capital level To test the effect of financial constraints on the optimal level of working capital, we estimate the optimal working capital investment for various firm subsamples, partitioned on the basis of the likelihood that firms have constrained access to external financing There are several measures in previous studies to separate firms that are suffering from financial constraints from those that are not, but it is still a matter of debate as to which measure is the best Thus, we classify firms according to the following proxies for the existence of financing constraints: hi ep Finally, we also classify firms according to two measures for bankruptcy risk that a firm presents (interest coverage and Z-score) because a firm in financial distress is more likely to face a higher degree of financial constraints: w n Interest coverage This variable is a common measure of a firm's bankruptcy risk and financial constraints (see, for example, Whited, 1992) Firms go into two groups on the basis of their interest coverage ratio, which comes from the calculation of the ratio earnings before interest and tax to financial expenses The greater this ratio, the fewer problems the firm would have in repaying its debt and the firm's earnings before interest and tax would cover the interest payment Hence, companies that have an interest coverage ratio below (above) the sample median are more (less) likely to be financially constrained Z-score We also consider Z-score in order to capture the probability of financial distress of firms, which can also influence a firm's access to credit and, therefore, might limit its investment We use the re-estimation of Altman's (1968) model by Begley, Mings, and Watts (1996) Thus, firms with below-median scores (low Z-score) are financially constrained, while above-median firms (high Z-score) are financially unconstrained lo ad ju y th yi pl n ua al n va ll fu m oi Model and data at nh 3.1 Specification of the model and methodology z According to the previous section, there are reasons which justify that the relation between working capital and firm performance may be non-monotonic Specifically, we expect a concave relation to exist In order to test the proposed functional form, we analyze a quadratic model Following Shin and Soenen (1998), we use the net trade cycle (NTC) as a measure of working capital management We regress corporate performance against net trade cycle (NTC) and its square (NTC 2) Additional variables are also present in the performance regression model to control for other potential influences on the performance of the firm Specifically, the variables are firm size (SIZE), leverage (LEV), opportunity growth (GROWTH), and return on assets (ROA) Therefore, we estimate the following model: z k jm ht vb l.c gm om Q i;t ẳ ỵ NTCi;t ỵ NTCi;t ỵ SIZEi;t ỵ LEVi;t þ β5 GROWTHi;t þ β6 ROA þ λt þ ηi þ εi;t ð1Þ an Lu where Qi,t is the corporate performance Following Agrawal and Knoeber (1996), Himmelberg, Hubbard, and Palia (1999), Thomsen, Pedersen, and Kvist (2006), Florackis, Kostakis, and Ozkan (2009), and Wu (2011) among others, the calculation of corporate performance is the ratio of the sum of the market value of equity and the book value of debt to the book value of assets This variable mitigates most of the shortcomings inherent in accounting profit ratio, since accounting practices affect accounting profit ratios and capital market valuation appropriately incorporates firm risk and minimizes any distortions introduced by tax laws and accounting conventions (Smirlock, Gilligan, & Marshall, 1984) Perfect and Wiles (1994) demonstrate that the improvements over this variable obtained with the estimation of Tobin's q based on replacement costs are limited According to Shin and Soenen (1998), NTC comes from: NTC = (accounts receivable/sales)∗365+(inventories/sales)∗365−(accounts n va ey Please cite this article as: Baños-Caballero, S., et al., Working capital management, corporate performance, and financial constraints, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.01.016 t re Dividends Following Fazzari et al (1988) we use this variable to identify a firm's degree of financial constraints Financially constrained firms tend not to pay dividends (or to pay lower dividends) to reduce the probability of raising external funds in the future Thus, we first split the data into zero-dividend and positive-dividend groups We expect that zero-dividend firms are the most likely to face financial constraints Accordingly, non-dividend paying (dividend paying) companies are financially constrained (unconstrained) Secondly, following Almeida, Campello, and Weisbach (2004), and Faulkender and Wang (2006), we also categorize firms according to their dividend payout ratio (measured by dividends/net profit) Thus, we consider that firms with a dividend payout ratio above the sample median are less financially constrained than those with a payout ratio below the sample median Cash Flow We have also categorized firms according to their cash flow, similar to the approach by Moyen (2004), which suggests that, unlike the dividends, this variable allows one to focus on the firm's beginning-of-the-period funds, since dividends also take into account the investment and financial decisions taken by the firms during that period This variable is defined as the ratio of earnings before interest and tax plus depreciation to total assets Firms with a cash flow above the sample median are assumed to be less likely to face financing constraints Size Many studies use this variable as an inverse proxy of financial constraints (Almeida et al., 2004; Carpenter, Fazzari, & Petersen, 1994; Faulkender & Wang, 2006) following the notion that smaller firms face higher informational asymmetry and agency costs and, hence, will be more financially constrained In this line, Whited (1992) indicates that larger firms have better access to capital markets, so they face lower borrowing constraints and lower costs of external financing Therefore, we separate firms according to their size, measured by the natural logarithm of sales, and we consider firms with size above (below) the sample median to be less (more) likely to be financially constrained Cost of external financing Fazzari et al (1988) consider firms as constrained when external financing is too expensive Thus, firms are also more or less likely to face financial constraints when considering their external financing cost, calculated by the ratio financial expenses/total debt In particular, companies with costs of external financing above (below) the sample median are more (less) likely to be financially constrained Whited and Wu Index We also group our companies according to the external finance constraints index constructed by Whited and Wu S Baños-Caballero et al / Journal of Business Research xxx (2013) xxx–xxx Table Correlation matrix t to payable/sales)∗365 Hence, it is a dynamic measure of ongoing liquidity management that provides an easy estimate for additional financing needs with regard to working capital, with a shorter NTC meaning a lower investment in working capital We use this variable to avoid the deficiencies of traditional liquidity ratios such as current ratio and quick ratio We measure firm size (SIZE) as the natural logarithm of sales; leverage (LEV) by the ratio of total debt to total assets; growth opportunities (GROWTH) is the ratio (book value of intangibles assets / total assets); and the measurement of return on assets (ROA) is through the ratio earnings before interest and taxes over total assets The parameter λt is a time dummy variable that aims to capture the influence of economic factors that may also affect corporate performance but which companies cannot control ηi is the unobservable heterogeneity or the firm's unobservable individual effects, so we can control for the particular characteristics of each firm Finally, εi,t is the random disturbance We also control for industry effects by introducing industry dummy variables The coefficients on net trade cycle variables allow us to determine the inflection point in the net trade cycle-corporate performance relation, because this comes from: − β1/2β2 Since we expect NTC and corporate performance to relate positively at low levels of working capital and negatively at higher levels, the hypothesis is that β2 is negative, because it would indicate that firms have an optimal working capital level that balances the costs and benefits of holding working capital and maximizes their performance We tested our hypothesis on the effect of working capital management on firm performance with the panel data methodology, because of the benefits it provides First, it allows us to control for unobservable heterogeneity and, therefore, eliminate the risk of obtaining biased results arising from this heterogeneity (Hsiao, 1985) Firms are heterogeneous and there are always characteristics that might influence their value that are difficult to measure or are hard to obtain, and which are not in our model (Himmelberg et al., 1999) Second, panel data also allows us to avoid the problem of possible endogeneity, which might be present in our analyses and could seriously affect the estimation results The endogeneity problems arise because it is possible that the observed relationships between firm performance and firm-specific characteristics reflect not only the effect of independent variables on a firm's performance but also the effect of corporate performance on those variables Shocks affecting performance are also likely to affect some other firm-specific characteristics We therefore estimated our models using the two-step generalized method of moments (GMM) estimator based on Arellano and Bond (1991), which allows us to control for endogeneity by using instruments Specifically, we have used all the right-hand-side variables in the models, lagged up to four times, as instruments in the difference equations ng Q NTC SIZE LEV GROWTH ROA hi ep Q NTC SIZE LEV GROWTH ROA 1.0000 0.1478a 0.0138 −0.0229 0.0116 0.2562a 1.0000 −0.1818a −0.2126a −0.0371 0.1032a 1.0000 0.3118a −0.0435c 0.3065a 1.0000 −0.1347a −0.0007 1.0000 −0.1545a 1.0000 w Q represents the corporate performance; NTC the net trade cycle; SIZE the size; LEV the leverage; GROWTH the growth opportunities; and ROA the return on assets a Indicates significance at 1% level b Indicates significance at 5% level c Indicates significance at 10% level n lo ad ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb Empirical evidence l.c gm 4.1 Effects of working capital management on firm performance om 3.2 Data and summary statistics Median Perc 90 0.8675 −1.8250 9.5025 0.3300 0.0141 −0.0498 1.3098 52.2906 12.1041 0.5717 0.1592 0.0687 2.2711 107.6327 14.8708 0.8048 0.5157 0.1571 Q represents the corporate performance; NTC the net trade cycle; SIZE is the natural logarithm of total sales; LEV the leverage; GROWTH the growth opportunities; and ROA the return on assets “We also find an inverted U-shaped relation between firm performance and each individual component of net trade cycle (accounts receivable to sales ratio, inventories to sales ratio and accounts payable to sales ratio).” “We also find this concave relation between working capital and firm performance when using the ordinary least squares (OLS) and the two-stage least squares (2SLS) estimation method These results hold when we use measures of accounting profitability (earnings before tax over sales, net profit over sales, and earnings before interest and taxes over sales) to measure a firm's performance.” Please cite this article as: Baños-Caballero, S., et al., Working capital management, corporate performance, and financial constraints, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.01.016 ey Perc 10 0.7343 54.4139 2.0233 0.1774 0.1950 0.1182 t re Standard deviation 1.4874 56.4772 12.1233 0.5687 0.2119 0.0559 n Mean va Table Summary statistics The results obtained from Eq (1) appear in Table Consistent with predictions, they confirm a large and statistically significant inverted U-shaped relation between corporate performance and working capital, since the coefficient for the NTC variable is positive (β1 > 0), and that for its square is negative (β2 b 0) Therefore, our findings indicate that at working capital levels below the optimal level the effects of higher sales and discounts for early payments dominate and, hence, working capital has a positive impact on firm performance Conversely, the opportunity cost and financing cost effects dominate when the firm has a working capital level above this optimum and, consequently, the relation between working capital an Lu The data in this paper are from the Osiris database The sample comprises non-financial quoted firms from the United Kingdom for the period 2001–2007 Q NTC SIZE LEV GROWTH ROA The information was refined Specifically, we eliminated firms with lost values, cases with errors in the accounting data and extreme values presented by all variables We also required firms to have presented data for at least five consecutive years, which is a necessary condition to have a sufficient number of periods to be able to test for second-order serial correlation This left an unbalanced panel of 258 firms (1606 observations) A t test confirms that there are no significant differences between the mean NTC of our sample (56.48) and the mean NTC of non-financial quoted firms from the United Kingdom (54.85) for the period analyzed (p-value is 0.7808) Neither are there significant differences (p-value of 0.3071) between the mean market to book ratio of our sample (1.49) and the mean market to book ratio for non-financial quoted firms from the United Kingdom (1.48) Table reports some descriptive statistics for corporate performance, net trade cycle, and the control variables Market to book ratio is on average 1.48, while the median is 1.30 The mean net trade cycle is 56.47 days (median is 52.29 days) On average debt finances 56.87% of total assets, the mean growth opportunities ratio is 0.21, and mean return on assets is only 5.59% (median is 6.87%) Table displays correlations among variables used in the subsequent analyses In addition, we used a formal test to ensure that the multicollinearity problem is not present in our analyses Specifically, we calculated the variance inflation factor (VIF) for each independent variable in our models The largest VIF value is 2.87, which confirms that there is no multicollinearity problem in our sample, because it is far from (Studenmund, 1997) S Baños-Caballero et al / Journal of Business Research xxx (2013) xxx–xxx market may result in credit rationing and a wedge between the costs of internal and external financing, because insufficient information lowers the market's assessment of the firm and of its projects and raises the firm's cost of external financing Thus, since a higher working capital level needs financing, which would mean additional expenses, we expect firms more likely to face financial constraints to have a lower optimal working capital level than those that are less likely In order to test whether or not the optimal working capital level of more financially constrained firms differs from that of less constrained ones, Eq (1) is extended by incorporating a dummy variable that distinguishes between firms more likely to face financing constraints and those that are less likely according to the different classifications commented on above Specifically, DFC is a dummy variable that takes a value of for firms more financially constrained, and otherwise Thus, we propose the following specification: Table Estimation results of net trade cycle-firm performance relation t to ng hi 0.0391b (2.41) −0.0292a (−5.90) −0.0470 (−1.41) 0.4843a (4.49) 1.0798a (6.31) −0.0395 (−0.43) −0.74 108.28 (102) 1606 ep NTC NTC2 SIZE LEV GROWTH ROA m2 Hansen test Observations w The dependent variable is the corporate performance; NTC is the net trade cycle divided by 100 and NTC2 its square; SIZE the size; LEV the leverage; GROWTH the growth opportunities; and ROA the return on assets Time and industry dummies are included in the estimations, but not reported Z statistic in brackets m2 is a serial correlation test of second-order using residuals of first differences, asymptotically distributed as N(0,1) under null hypothesis of no serial correlation Hansen test is a test of over-identifying restrictions distributed asymptotically under null hypothesis of validity of instruments as Chi-squared Degrees of freedom in brackets a Indicates significance at 1% level b Indicates significance at 5% level c Indicates significance at 10% level n lo ad y th ju     Q i;t ẳ ỵ ỵ DFCi;t NTCi;t ỵ ỵ DFCi;t NTCi;t ỵ SIZEi;t yi ỵ4 LEVi;t ỵ GROWTHi;t þ β6 ROA þ λt þ ηi þ ε i;t : ð2Þ pl ua al n and firm performance becomes negative The coefficients for net trade cycle variables allow us to determine for our sample the turning point in the relationship between performance of firms and net trade cycle Specifically, we find a turning point of 66.95 days n va ll fu 4.2 Financial constraints and optimal working capital level oi m at nh Once we have verified that firms have an optimal working capital level that maximizes their performance, our aim is also to explore the possible effect of financing on this optimal level As we commented above, asymmetric information between the firm and the capital All dependent and independent variables are as previously defined By construction, the expression − β1/2β2 measures the optimal working capital investment of less financially constrained firms The optimum of more financially constrained firms comes from − (β1 + δ1)/2(β2 + δ2) Table shows the regression results for more financially constrained and less financially constrained firms categorized according to the classification schemes above The results indicate the existence of a concave relation between working capital and firm performance for less financially constrained firms This table also includes an F-test in order to check whether the coefficients of the NTC variable are significant for more financially constrained firms Specifically, for these firms, F1 test indicates whether the NTC coefficient (i.e (β1 + δ1)) is significant, z z Table Financial constraints and net trade cycle-firm performance relation GROWTH Z-score grouping 0.3260a (6.50) −0.3306a (−6.39) −0.1358a (−7.48) 0.1227a (6.77) −0.0315 (−1.54) 0.5044a (8.20) 0.7552a (7.21) 0.0601 (1.05) 0.19 26.36 −0.57 142.45 (136) 1606 0.1091a (3.32) −0.0804a (−2.81) −0.0530a (−3.27) 0.0367b (2.36) −0.0520b (−2.32) 0.4682a (6.28) 0.4060a (3.65) 0.1107 (1.60) 5.67 23.86 −0.51 143.81 (136) 1606 0.1982a (5.92) −0.1812a (−6.00) −0.1047a (−7.83) 0.0832a (6.38) −0.0911a (−4.25) 0.5908a (7.58) 0.8067a (6.96) −0.0393 (−0.57) 1.83 30.36 −0.51 133.26 (136) 1606 0.1751a (2.77) −0.1825a (−2.97) −0.0862a (−3.53) 0.0672a (2.79) −0.0448c (−1.79) 0.3841a (5.28) 1.0104a (7.16) 0.0950 (1.31) 0.35 36.68 −0.73 139.34 (136) 1606 0.0324b (2.26) −0.0457c (−1.76) −0.0198a (−5.14) −0.0241a (−2.81) −0.0497b (−2.25) 0.4917a (7.57) 0.7432a (5.96) 0.0984 (1.37) 0.18 27.13 −0.64 143.98 (136) 1606 0.2724a (5.93) −0.2650a (−5.87) −0.1832a (−4.51) 0.1666a (4.10) −0.0255 (−1.06) 0.5861a (6.97) 0.7972a (5.94) 0.1320n (1.76) 0.32 18.54 −0.56 144.14(128) 1606 0.2025a (5.11) −0.1824a (−5.10) −0.0998a (−7.56) 0.0892a (5.81) −0.0603a (−2.70) 0.6720a (7.95) 0.6460a (5.75) −0.0893 (−1.20) 2.44 5.64 −0.65 137.20 (136) 1606 0.1879a (4.69) −0.1557a (−3.97) −0.1006a (−7.29) 0.0787a (5.73) −0.0602a (−2.59) 0.5212a (7.52) 0.8110a (5.88) 0.0566 (0.81) 6.50 52.45 −0.61 133.24 (136) 1606 n Please cite this article as: Baños-Caballero, S., et al., Working capital management, corporate performance, and financial constraints, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.01.016 ey The dependent variable is the corporate performance; NTC is the net trade cycle divided by 100 and NTC2 its square; SIZE the size; LEV the leverage; GROWTH the growth opportunities; and ROA the return on assets DFC is a dummy variable equals for firms more likely to be financially constrained and otherwise Time and industry dummies are included in the estimations, but not reported Z statistic in brackets F1 is a F-test for the linear restriction test under the following null hypothesis: H0: (β1 +δ1)=0 F2 is a F-test for the linear restriction test under the following null hypothesis: H0: (β2 +δ2)=0 m2 is a serial correlation test of second-order using residuals of first differences, asymptotically distributed as N(0,1) under null hypothesis of no serial correlation Hansen test is a test of over-identifying restrictions distributed asymptotically under null hypothesis of validity of instruments as Chi-squared Degrees of freedom in brackets a Indicates significance at 1% level b Indicates significance at 5%level c Indicates significance at 10% level t re F1 F2 m2 Hansen test Observations Interest coverage grouping va ROA Whited and Wu Index grouping an Lu LEV External financing cost grouping om SIZE Size grouping l.c NTC2 ∗ DFC Cash flow grouping gm NTC2 Payout ratio grouping k NTC ∗ DFC Dividend paying grouping jm NTC ht vb Financial constraints criteria S Baños-Caballero et al / Journal of Business Research xxx (2013) xxx–xxx while the F2 test check whether the NTC2 coefficient (i.e (β2 + δ2)) is significant Since the F2 test indicates that the NTC2 coefficient of more constrained firms is negative and significant in all the classifications used, it shows that the concave relation also holds for these firms However, the optimal investment in working capital depends on the financing constraints borne by firms When financing conditions are present in the analysis, the results indicate that the optimal level of working capital is lower for those firms more likely to be financially constrained This may be mainly because of the higher financing costs of those firms and their greater capital rationing, since the lower the investment in working capital, the lower the need for external financing Therefore, the approach we propose here allows us to understand why the level of financial constraints borne by a company influences its investment in working capital decisions Specifically, it would allow us to justify the results of previous studies, which find that investment in working capital depends on internal financing resources, external financing costs, capital market access and financial distress of the firms that future studies on working capital should control for financial constraints t to ng References hi ep w n lo ad ju y th yi pl ua al Conclusions n n va ll fu m oi at nh z z k jm ht vb om l.c gm an Lu ey t re Please cite this article as: Baños-Caballero, S., et al., Working capital management, corporate performance, and financial constraints, Journal of Business Research (2013), http://dx.doi.org/10.1016/j.jbusres.2013.01.016 n va The aim of this paper is to provide empirical evidence for the relation between working capital and corporate performance Although few studies empirically examine whether there is an association between investment in working capital and firm value, the idea that working capital management influences firm value enjoys widespread acceptance We use a panel data model and employ the GMM method of estimation, which allows us to control for unobservable heterogeneity and for potential endogeneity problems In contrast to previous findings, our main contribution here is to study the functional form of the above-mentioned relation This analysis, which the literature has not considered previously, reveals that there is an inverted U-shaped relation between working capital and corporate performance, which implies that there exists an optimal level of investment in working capital that balances costs and benefits and maximizes a firm's performance This supports the idea that at lower levels of working capital managers would prefer to increase the investment in working capital in order to increase firms' sales and the discounts for early payments received from its suppliers However, there is a level of working capital at which a higher investment begins to be negative in terms of value creation due to the additional interest expenses and, hence, the higher probability of bankruptcy and credit risk of firms Thus, firm managers should aim to keep as close to the optimal level as possible and try to avoid any deviations from it that destroy firm value Following Fazzari and Petersen (1993) and Hill et al (2010), who suggest that investment in working capital is sensitive to firms' capital market access, we also analyze whether financing constraints influence the optimal level of investment in working capital Our findings indicate that, although the concave relation between working capital and firm performance always holds, the optimal working capital level of firms that are more likely to be financially constrained is lower than that of less constrained firms In addition, this result is robust to various proxies of financial constraints It justifies the impact of internally generated funds and the access to external financing on companies' working capital investment decisions that previous studies reported There are several implications of our study which may be relevant for managers and research on investment in working capital First, our results suggest that managers should be concerned 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