market and had a cascading effect on the primary and secondary markets.
The volume of IPOs
therefore decreased dramatically, from 101 IPOs in 2007 to 42 IPOs in 2008, and fell further to
only 17 IPOs in 2009.
At this critical juncture, as fear and pessimism gripped the market, it was
hoped that the commitment of reputable institutional investors through the anchor mechanism
would act as a credible signal of IPO quality and increase investors’interest in the issue.
We focus on the efficacy of the regulatory reform of introducing AIs in reviving the Indian
primary capital market and its impact on IPO valuation and performance. More specifically, we
examine whether the public knowledge of the initial price and allocation to AIs reduces ex ante
uncertainty around the IPO valuation, boosts investors’confidence in the value of the IPO, and
enhances its marketability. We invoke IPO valuation theories to explore the impact of AIs on
the valuation and performance of IPOs in India, including the certification of IPO quality, the
discretionary allocation of IPO shares, and retail and institutional investor overreaction. Under
the certification hypothesis, which draws upon information asymmetry‐based models,
knowledge of AI participation should mitigate information asymmetry, such that anchor‐
backed IPOs will be subject to lower underpricing than non‐anchor IPOs. The greater the
number of participating AIs and their allocation, the lower the underpricing. The allocation
hypothesis posits that underwriters compensate informed investors for revealing favorable
information by allocating more shares to them. However, if the number of shares to be offered
is rationed, underwriters partially adjust the final offer price upward to allow underpricing as a
reward to informed investors. Accordingly, the greater the number of participating AIs and
their allocation, the larger the price adjustment. Finally, if the reputation of the AIs leads to
oversubscription, the resulting excess demand induces greater underpricing. The aggregate
effect of the three hypotheses is indeterminate and an empirical issue.
Using a sample of 182 IPOs listed on the National Stock Exchange/BSE between August 2009
and September 2017, including 107 anchor‐backed IPOs, we examine the impact of AI
participation on underpricing, price adjustment, primary market demand, post‐IPO volatility
and liquidity, and the long‐term performance of the issuing firm. Our analyses reveal evidence
consistent with the hypotheses: the allocation to AIs (AI share) is negatively related to
underpricing, which is consistent with the certification and allocation hypotheses; and the
number of AIs (AI number), a proxy for demand by AIs, is positively related to underpricing,
which is consistent with the overreaction hypothesis. However, in the aggregate, the net effect
of AIs on underpricing is nonsignificant. Next, we test the impact of AIs on valuation and price
adjustment. The certification of IPO quality through disclosure of the initial price, the
discretionary allocation to AIs, which induces them to reveal favorable information about the
IPO, and investor overreaction are all consistent with higher IPO valuation. Our analyses reveal
that the initial AI price is related to the final price adjustment. We further find that the number
of AIs receiving an initial allocation is positively related to price adjustment. However,
inconsistent with our expectation, the share of AIs is generally negatively related to price
adjustment, resulting in a nonsignificant net effect of AIs on valuation. Finally, we find that
The Standard & Poor's (S&P) Bombay Stock Exchange Sensitive Index, or S&P BSE 30 Sensex, the most widely followed market index in India, increased by
23% in 2007 and then fell sharply, by about 63%, in 2008. The volatility in the secondary market, the global recessionary outlook, and the consequent loss of
investor confidence were a serious deterrent to the new issues market in India.
In terms of IPO proceeds, the total volume of funds raised fell from USD 9.2 billion in 2007 to USD 4.3 billion in 2008, and further to USD 3.5 billion in 2009.
One implication of the quid pro quo hypothesis is that the AIs receiving privileged allocation will be observed offering more future business to the underwriter.
Unfortunately, we cannot directly test this prediction because of lack of data. Therefore, our main focus is the efficacy of the AI concept and the impact of the
information on the identity, price, and allocation to AIs on IPO valuation.
BHATTACHARYA ET AL.EUROPEAN