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Lower tick sizes and futures pricing efficiency: evidence from the emerging Malaysian market

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Abstract

We provide robust evidence of the impact on spot market liquidity and the pricing efficiency of FBM-FKLI index futures following the introduction of lower tick sizes for the stocks listed in the Bursa Malaysia. Our findings show a significant increase in unexpected trading volume and the speed of mean reversion of the futures mispricing. We find that the increase in the unexpected trading volume of the underlying stocks helps in reducing inter-market price discrepancies. The findings offer new evidence that lowering of tick sizes improves pricing efficiency in the Malaysian futures market.

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Notes

  1. FBM-FKLI is the code name for the index futures contract. Its value is derived from FBM-KLCI stock index which tracks the performance of the 30 constituent stocks with the largest market capitalisation.

  2. Hybrid-driven markets comprise both quote and order driven markets.

  3. In order driven markets the risk of non-execution of trades increases since it is easier for traders to front-run the queue in the limit order book. As a consequence, traders are more reluctant to submit limit orders. Thus, the impact of tick size reduction may be larger due to the fact that limit orders are the primary source of liquidity in a pure order-driven market like Malaysia.

  4. This is particularly relevant since empirical evidence suggests that ETFs facilitate arbitrageurs in executing short arbitrage strategies (see for example, Kurov and Lasser 2002; Park and Switzer 1995; Switzer et al. 2000).

  5. The binding constraint hypothesis predicts that the spread widens (narrows) when stocks move to a larger (smaller) tick category (Harris 1994).

  6. Switzer et al. (2000) have shown that failure to use the actual dividend of the constituent stocks that make-up the index could lead to measurement error in the cost-of-carry model. Thus, it is important to obtain the actual dividend yield in calculating the index futures intrinsic value.

  7. The intrinsic stock index price is calculated by discounting the observed price of the nearby index futures contract.

  8. The model is fitted using an ARIMA (10, 0, 0) model to account for the momentum effect and autocorrelation. Due to the presence of ARCH effects, a GARCH (1,1) model is fitted to account for the conditional variance. Modelling diagnostics indicate that the residuals from the estimated model follow a white noise process (results not reported but can be made available on request).

  9. The Kuala Lumpur Interbank Offered Rate (KLIBOR) is the average interest rate at which term deposits are offered between prime banks in the Malaysian wholesale money market.

  10. MGS are coupon-bearing, long-term bonds issued by the Malaysian Government which are the most actively traded government securities.

  11. PDS are issued by corporations under conventional or Islamic principles. PDS can be commercial papers (CPs), medium term notes (MTNs), bonds, asset-backed securities (ABS), amongst others.

  12. We are unaware of any studies that have examined day of the week effect on volume in the Malaysian market. Brooks and Persand (2001) find significant positive Monday average returns and significant negative Tuesday returns in the Malaysian market.

  13. The 100-day moving average series captures minor adjustments to changes in the anticipated trading volume.

  14. \(g_{i} = 1\) corresponds to full adjustment towards equilibrium prices, while \(g_{i} < 1\) and \(g_{i} > 1\) corresponds to under-reaction and over-reaction respectively.

  15. The extant literature uses nearby contracts (see for example, Pok et al. 2009; Switzer et al. 2000; Theobald and Yallup 1998).

  16. Despite the fact that the Capital Market Masterplan (CMP) was officially launched in February 2001, the 15 recommendations that directly affect derivatives trading were only implemented from the beginning of January 2002. Thus, we choose 2 January 2002 as the starting date for our sample.

  17. Indeed, Choy and Zhang (2010) find that in Hong Kong, the futures market plays a dominant role in price discovery.

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Authors and Affiliations

  1. Centre for Research in Finance, Cranfield School of Management, Cranfield University, Cranfield, MK43 0AL, UK

    Sunil S. Poshakwale

  2. Universiti Malaysia Sabah, Beg Berkunci 2073, 88899, Kota Kinabalu, Malaysia

    Jude W. Taunson

  3. University of Birmingham, Birmingham, B15 2TT, UK

    Anandadeep Mandal

  4. Emeritus Professor, University of Birmingham, Birmingham, B15 2TT, UK

    Michael Theobald

Authors
  1. Sunil S. Poshakwale
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  2. Jude W. Taunson
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  3. Anandadeep Mandal
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  4. Michael Theobald
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Corresponding author

Correspondence to Anandadeep Mandal.

Appendix

Appendix

Summary statistics for debt and equity market explanatory variables

 

\(SR\)

\(TS\)

\(QS\)

Stocks (%/day)

Levels

 N

2704

2704

2704

2703

 Mean

2.977

1.210

1.137

0.034

 Std. dev.

0.464

0.744

0.171

0.796

 Minimum

2.030

− 0.506

0.598

− 9.979

 Maximum

3.647

2.695

2.032

4.259

First differences

 N

2703

2703

2703

 

 Mean

0.000

− 0.000

− 0.000

 

 Std. dev.

0.023

0.065

0.079

 

 Minimum

− 0.784

− 0.604

− 0.676

 

 Maximum

0.307

0.738

0.588

 
  1. \(SR\): Yield on overnight Kuala Lumpur Inter-Bank Offered Rate (KLIBOR), the proxy for short-term interest rates
  2. \(TS\): Yield spread between the constant maturity 10-year Treasury bond and the overnight KLIBOR, the proxy for term spread
  3. \(QS\): Yield spread between Bank Negara Malaysia (BNM), i.e. The Central Bank of Malaysia, Grade AAA 10-year Private Debt Securities (PDS) and the constant maturity 10-year Treasury bond, the proxy for quality spread

See Tables 9 and 10.

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Poshakwale, S.S., Taunson, J.W., Mandal, A. et al. Lower tick sizes and futures pricing efficiency: evidence from the emerging Malaysian market. Rev Quant Finan Acc 53, 1135–1163 (2019). https://doi.org/10.1007/s11156-018-0777-7

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