Abstract
In this paper, I examine how the lending relationships between banks and their borrowers affect the quality of analysts’ earnings forecasts after financial deregulation in Japan. My findings show that short-term lending relationships improve the quality of analysts’ earnings forecasts and that these earnings forecasts are useful for predicting future returns. In contrast, long-term lending relationships decay the quality of forecast and are not valuable for the prediction of future returns. These empirical results indicate that the informational advantage that commercial banks acquire is short-term and that the costs of lending relationships surpass the informational benefits in the long run.
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Notes
Puri (1996) and Gande et al. (1997) find that bond yields underwritten by commercial banks perform as well as those underwritten by investment banks. In addition, Gande et al. (1999), Roten and Mullineaux (2002, 2005) and Yasuda (2005) find that firms pay lower fees for commercial bank underwriting than for investment bank underwriting. More recently, Yasuda (2007) presents empirical evidence that Japanese firms that establish strong lending ties with commercial banks receive fee discounts.
Until the early 1990s, Japanese banks were prohibited from conducting securities business under the old Securities and Exchange Law. However, the 1999 Financial System Reform Act (FSRA) abolished these restrictions that enabled banks to freely engage in investment banking through subsidiaries.
I hand collect financial statements of parent financial institutions and gather information on their subsidiaries, which is included in supplementary schedules thereof. To assess when analysts’ affiliations are included in financial groups, I collect information on the subsidiaries of parent financial institutions every fiscal year during the sample period.
According to the Regulations for Consolidated Financial Statements, if a firm owns more than 20 % of the outstanding shares of another firm, the former must include the latter as a subsidiary.
Twenty different industry categories are defined by Nikkei Shinbun. The category is established following the Nikkei industry code.
The variable Ln(coverage) is intended to control for the extent to which an analyst devotes his or her effort to a particular stock. When the number of stocks that an analyst covers increases and he or she has to disperse his or her efforts, the possibility exists that his or her forecasts might become less accurate. The variable Ln(distance+1) is intended to control for the effect of distance from the fiscal year-end dates of analysts’ forecasts. In general, the earlier that analysts issue forecasts, the less accurate are the estimates. Firm Size, Leverage, and Volatility are intended to control for the effect of information uncertainty on earnings quality. With smaller firm sizes, higher leverage ratios, and higher volatility the estimates become less accurate. To adjust for the effect of analysts’ and brokerage firms’ reputations on earnings quality, I add the TP5Rdummy and the TP5BFdummy. I regard the top-five ranked analysts and brokerage firms in each industry as analysts and affiliations with high reputations.
Because of geographical locality, analysts affiliated with foreign banks might have some difficulties in accessing or processing corporate information.
To calculate the four-factor model alphas, I obtain intercepts by regressing the excess returns from testing the portfolios over the risk-free rate on Mkt t , SMB t , HML t , WML t . Mkt t is a value-weighted daily market return over the risk-free rate. As a proxy for the risk-free rate, I use the Japanese Uncollateralized Overnight Call Rate. Following Fama and French (1993), SMB t and HML t are constructed using the six value-weighted portfolios formed on size and the book-to-market. In contrast to Fama and French (1993), the market capitalization value at the previous month and the book-to-market value at the most recent reporting are used to sort the stocks. The monthly market capitalization breakpoint is the median TSE market equity, and the monthly book-to-market breakpoints are the 30th and 70th percentiles. I construct a momentum factor (Carhart 1997) described in French’s website. I construct six value-weighted portfolios formed on size and prior (2–12) returns. The monthly size breakpoint is the median TSE market equity. The monthly prior (2–12) return breakpoints are the 30th and 70th TSE percentiles.
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I wrote a preliminary version of this paper during my employment as an assistant professor (or lecturer) in the Graduate School of Commerce and Management at Hitotsubashi University. I am grateful for valuable comments from Yasuhiro Arikawa, Warren Bailey (the editor), Isao Ishida, Ryo Jinnai, Kozo Kiyota, Katsumasa Nishide, Wataru Ohta, Kosuke Oya, Koichi Takeda, Norio Takeoka, Wataru Tanaka, Konari Uchida, Peng Xu, an anonymous referee, and other seminar participants at Hosei University, Osaka University, and Yokohama National University. This work was supported by Grant-in-Aid for Young Scientists (B).
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Takahashi, H. The Effect of Bank-firm Relationships on Sell-side Research. J Financ Serv Res 46, 195–213 (2014). https://doi.org/10.1007/s10693-013-0170-6
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DOI: https://doi.org/10.1007/s10693-013-0170-6