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Overconfidence, trading volume and liquidity effect in Asia’s Giants: evidence from pre-, during- and post-global recession

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Abstract

In this paper, we present evidence in favour of the overconfidence bias and its persistence in pre-, during and post-global recession sub-samples in China and India. The Chinese and Indian investors follow past market returns for the longer duration and trade excessively, which is posited as overconfidence bias. The global recession is facilitated as a structural break to examine the endurance of the overconfident trading activities. The Chinese investors are found to be more overconfident than the Indian investors in each sub-sample. We also explore that the Chinese and Indian investors are more overconfident in up than in down market states and overconfident trading behaviour of the Chinese investors is more than that of the Indian investors in both market states. The endogenous structure of vector autoregression also considers liquidity as one of the drivers of overconfident trading behaviour.  Besides trading volume, market liquidity also follows market returns for a short duration, but not vice versa. The lead–lag relationship of volume–volatility and liquidity–volatility is also explored by considering volatility as the exogenous variable.

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Notes

  1. World Federation of Exchanges, Market Highlights Report, First semester of 2015.

  2. https://www.forbes.com/pictures/eddk45iglh/the-worlds-biggest-stock-exchanges/#2bf1e9d96d2b accessed on 23 August 2017.

  3. Report of World Federation of Exchange.

  4. BSE Annual report 2015–16.

  5. Data is compiled from the World Federation of Exchange. Emerging economies are selected as per the MSCI Index. Data is not available for Pakistan and before the year 2012 for all the Asian emerging countries.

  6. Lo and Wang (2000) defined individual and portfolio turnover as per two-fund separation by taking an example of two-asset and two-investor where turnover is identical across all assets. The turnover (individual stocks) is defined as: \(\tau_{jt} = X_{jt} /N_{j}\), where \(\tau_{jt}\) is the turnover of stock j at the time t, \(X_{jt }\) is the share volume of security j at the time t and \(N_{j}\) is the total number of shares outstanding of stock j.

  7. The HP filter selects St to minimize for the time-series of length T: \(\sum\limits_{t = 1}^{T} {\left( {y_{t} - s_{t} } \right)^{2} } + \eta \mathop \sum \limits_{t = 2}^{T - 1} \left( {\left( {s_{t + 1} - s_{t} } \right) - \left( {s_{t} - s_{t - 1} } \right)} \right)^{2}\), where ŋ is the penalty parameter (the trend, \(s_{t}\) becomes more smooth as ŋ is increased). The common approach is to use ŋ = 14400 for monthly observations.

  8. Volatility for the month is calculated from the daily returns based on French, Schwert and Stambaugh (1987). Calculation of t month’s volatility is as \(mvolatility^{2} = \mathop \sum \limits_{\tau = 1}^{T} r_{\tau }^{2} + 2 \mathop \sum \limits_{\tau = 1}^{T} r_{\tau } r_{\tau - 1}\), where \(r_{\tau }\) is the day τ’s returns and T is the number of trading days in the month t.

  9. The readers are requested to refer the paper for detailed understanding of grid search procedure: Shen and Chiang (1999), “Retrieving the Vanishing Liquidity Effect: A Threshold Vector Autoregressive Model”, Journal of Economics and Business.

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Authors and Affiliations

  1. Indian Institute of Management Raipur, GEC Campus, IIM Building, Sejbahar, Raipur, 492015, Chhattisgarh, India

    Suman Gupta & Vinay Goyal

  2. Institute of Management Technology Hyderabad, Cherlaguda Village, RR District Survey No. 38, Shamshabad Mandal, Hyderabad, 501218, India

    Vinay Kumar Kalakbandi

  3. Indian Institute of Management, Bangalore, Bannerghatta Road, Bengaluru, Karnataka, 560076, India

    Sankarshan Basu

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  1. Suman Gupta
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  2. Vinay Goyal
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  4. Sankarshan Basu
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Correspondence to Suman Gupta.

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Gupta, S., Goyal, V., Kalakbandi, V.K. et al. Overconfidence, trading volume and liquidity effect in Asia’s Giants: evidence from pre-, during- and post-global recession. Decision 45, 235–257 (2018). https://doi.org/10.1007/s40622-018-0185-9

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