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Informed or Biased? Some Evidence from Listed Fund Trading

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Abstract

The (volume-synchronized) probability of informed trading (PIN) is a relative proxy for adverse selection (flow toxicity) in securities trading. We find that (V)PIN puzzlingly explains the discount of U.S. exchange-listed funds. While (V)PIN can unintentionally represent behavioral biases, we suggest the “proportion measure of purchased futures losers or sold future winners” as a more direct proxy for behavioral biases. While the proportion measure is positively and significantly correlated with (V)PIN and the value-weighted discount of closed-end funds, it is unrelated with the price impact parameter, the adverse selection component of the bid-ask spread, and the illiquidity measure. A risk factor defined as the highest-over-lowest excess return of sorted portfolios in terms of the proportion measure, well explains the return of listed funds along with the well-known factors. Lastly, the co-movement of closed-end and exchange-traded fund pairs is pronounced for developed markets and is influenced by the proportion measure.

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Notes

  1. 1.

    The four-part CEF puzzle is as follows: (1) CEFs are usually initial-public offered (IPO’ed) at premiums; (2) after issuance, CEFs trade at discounted prices; (3) discounts are very volatile, and their fluctuation varies across time and funds; and (4) when a CEF is acquired, liquidated, or “open-ended,” its price closes at the NAV [25]. Ross [26] finds that 6–7% discounts in CEFs fairly reflect future fund expenses. De Long et al. [27] theoretically argue that the CEF discount can persist in the absence of arbitrageurs due to unhedgeable noise trader risk.

  2. 2.

    According to Baker and Wurgler [33] (p. 138): “Corporate executives have better information about the true value of their firms than outside investors. Thus, legalities aside, executives’ portfolios decisions may also reveal their views about the mispricing of their firm. If sentiment leads to correlated mispricings across firms, insider trading patterns may contain a systematic sentiment component. See Seyhun [34] for evidence on the ability of insider trading activity to predict stock returns… … rules out data that do not go back as far as stock returns data (that is, to the 1960s), which would exclude, for example, data on insider trading; micro-level data on trading behavior, and implied volatility series.”

  3. 3.

    We find fault with neither PIN nor VPIN. Rather, we focus on a facet in the financial market where an adverse selection proxy can reflect behavioral biases especially when an asset is heavily weighed by sentiment.

  4. 4.

    Similarly extending the same model of Grossman and Stiglitz [41], Chen and Choi [18] predict that, for the cross-listed pairs of Canadian firms listed on both the Toronto Stock Exchange (TSX) and the NYSE, a higher proportion of informed traders on the original listing on the TSX than on the cross-listing on the NYSE leads to the relative premium on the latter stock on the NYSE under certain conditions. Besides, see Chan et al. [17] for another application of Grossman and Stiglitz [41] to the segmentation of A- and B-shares in China.

  5. 5.

    If the listed fund (\(F\)) is a CEF, then a comparable ETF can represent the underlying assets (\(N\)). The proportion of behaviorally biased trades for fund \(i\) (\(\pi_{i}\)) can be empirically estimated as the proportion of purchased future losers or sold future winners (proportion measure) introduced in Sect. 3.1.

  6. 6.

    See Tuckman and Vila [42] for modeling arbitrageurs who face holding costs.

  7. 7.

    This is a static model, and the choice of asset for the both groups of traders is exogenous. If we allow the traders to freely switch between the fund and the underlying assets, then the fund will almost always trade at its NAV.

  8. 8.

    It can be shown that given \(\omega_{i} \equiv \omega_{i}^{B} + \omega_{i}^{R} = \pi_{i} \eta \left( {\phi_{i} - 1} \right)\tau_{S} + \eta \left( {\tau_{S} + \tau_{\upsilon } } \right)\), a direct partial derivative can be \(\partial \omega_{i} /\partial \pi_{i} = \eta \left( {\phi_{i} - 1} \right)\tau_{S} < 0\) since \(\phi_{i} \equiv \tau_{i} /\left( {\tau_{i} + h_{i}^{2} \tau_{S} } \right) \in \left( {0,1} \right).\) Consequently, for a sufficiently small difference of the proportion of behaviorally driven trades on the fund (\(\pi_{F}\)) over that on the underlyings (\(\pi_{N}\)) such that \(\Delta \pi_{i} \equiv \pi_{F} - \pi_{N} \searrow 0^{ + }\), \(\pi_{F} > \pi_{N}\) implies \(\omega_{F} < \omega_{N}\), thus \(\omega_{N} /\omega_{F} > 1\).

  9. 9.

    Depending on the magnitudes of parameters, the fund can also trade with a premium against its NAV.

  10. 10.

    There can be alternative measures of return to fund trading: Pairs trading return. In the spirit of Gatev et al. [43], for a given listed fund, by longing the shares of the discounted fund while shorting the stocks of the relatively overpriced underlying assets (NAV), the implied excess return to this pairs trading over month \(t\) is

    \({\text{ReturnArb}}_{t} \equiv {\text{Return}}_{t}^{\text{fund}} {-}{\text{Return}}_{t}^{\text{NAV}} .\)

    Alternative measure of pairs trading return. Relative to the market index (S&P 500 depository receipts, SPY), one can long and short sell a pair of relatively underpriced and overpriced funds as

    \({\text{ReturnAbs}}_{t} \equiv \left| {Return_{t}^{\text{fund}} {-}{\text{Return}}_{t}^{\text{SPY}} } \right|.\)

  11. 11.

    See Sect. 2 for the proportion of behaviorally biased trades which theoretically determines the discount of listed funds against their NAVs.

  12. 12.

    Whether the proportion measure is defined in terms of the number of trades or traded shares (trading volume), the actual estimates are mutually proximate and their impacts on the discount of listed funds qualitatively equivalent in Table 1.

  13. 13.

    URL: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

  14. 14.

    We do not use Petersen’s [49] clustering robust standard errors because PIN and VPIN are by nature the measures of informed trading, or flow toxicity, that tends to cluster around information events.

  15. 15.

    The proportion measure (\({\text{Prop}}504\)) in Panels A and B of Table 4 empirically attests the prediction of the theoretical model in Sect. 2.

  16. 16.

    From the perspective of financial technology (fintech), a robo-advisor with a trading algorithm based on the proportion measure can be suggested.

  17. 17.

    We thank Jungsuk Han for these intuitions.

  18. 18.

    See Bekaert and Urias [68] for an early discussion of emerging market closed-end funds.

References

  1. Easley D, Kiefer NM, O’hara M, Paperman JB (1996) Liquidity, information, and infrequently traded stocks. J Finan 51(4):1405–1436

    Article  Google Scholar 

  2. Easley D, Kiefer NM, O’Hara M (1997) One day in the life of a very common stock. Rev Financ Stud 10(3):805–835

    Article  Google Scholar 

  3. Easley D, Kiefer NM, O’Hara M (1997) The information content of the trading process. J Empir Finance 4(2–3):159–186

    Article  Google Scholar 

  4. Easley D, O’Hara M, Paperman J (1998) Financial analysts and information-based trade. J Finance Mark 1(2):175–201

    Article  Google Scholar 

  5. Brown P, Thomson N, Walsh D (1999) Characteristics of the order flow through an electronic open limit order book. J Int Finance Mark Instit Money 9(4):335–357

    Article  Google Scholar 

  6. Easley D, O’hara M, Saar G (2001) How stock splits affect trading: a microstructure approach. J Finance Quant Anal 25–51

    Google Scholar 

  7. Heidle HG, Huang RD (2002) Information-based trading in dealer and auction markets: an analysis of exchange listings. J Finance Quant Anal 391–424

    Google Scholar 

  8. Aslan H, Easley D, Hvidkjaer S, O’hara M (2011) The characteristics of informed trading: implications for asset pricing. J Empir Finan 18(5):782–801

    Article  Google Scholar 

  9. Easley D, Hvidkjaer S, O’hara M (2002) Is information risk a determinant of asset returns? J Finan 57(5):2185–2221

    Article  Google Scholar 

  10. Grammig J, Schiereck D, Theissen E (2001) Knowing me, knowing you: trader anonymity and informed trading in parallel markets. J Finance Mark 4(4):385–412

    Article  Google Scholar 

  11. Vega C (2006) Stock price reaction to public and private information. J Financ Econ 82(1):103–133

    Article  Google Scholar 

  12. Ascioglu A, Hegde SP, McDermott JB (2008) Information asymmetry and investment–cash flow sensitivity. J Bank Finance 32(6):1036–1048

    Article  Google Scholar 

  13. Chen Q, Goldstein I, Jiang W (2007) Price informativeness and investment sensitivity to stock price. Rev Finance Stud 20(3):619–650

    Article  Google Scholar 

  14. Brockman P, Yan XS (2009) Block ownership and firm-specific information. J Bank Finance 33(2):308–316

    Article  Google Scholar 

  15. Kang M (2010) Probability of information-based trading and the January effect. J Bank Finance 34(12):2985–2994

    Article  Google Scholar 

  16. Chen Y, Zhao H (2012) Informed trading, information uncertainty, and price momentum. J Bank Finance 36(7):2095–2109

    Article  Google Scholar 

  17. Chan K, Menkveld AJ, Yang Z (2008) Information asymmetry and asset prices: Evidence from the China foreign share discount. J Finance 63(1):159–196

    Article  Google Scholar 

  18. Chen H, Choi PMS (2012) Does information vault Niagara falls? Cross-listed trading in New York and Toronto. J Empir Finance 19(2):175–199

    Article  Google Scholar 

  19. Chen H, Choi PMS, Hong Y (2013) How smooth is price discovery? Evidence from cross-listed stock trading. J Int Money Finance 32:668–699

    Article  Google Scholar 

  20. Easley D, de Prado MML, O’Hara M (2012) The volume clock: insights into the high-frequency paradigm. J Portfolio Manag 39(1):19–29

    Article  Google Scholar 

  21. Lee CM, Ready MJ (1991) Inferring trade direction from intraday data. J Finance 46(2):733–746

    Article  Google Scholar 

  22. Schultz P, Shive S (2010) Mispricing of dual-class shares: profit opportunities, arbitrage, and trading. J Financ Econ 98(3):524–549

    Article  Google Scholar 

  23. Easley D, Engle RF, O’Hara M, Wu L (2008) Time-varying arrival rates of informed and uninformed trades. J Financ Econ 6(2):171–207

    Google Scholar 

  24. Easley D, López de Prado MM, O’Hara M (2012) Flow toxicity and liquidity in a high-frequency world. Rev Financ Stud 25(5):1457–1493

    Article  Google Scholar 

  25. Lee CM, Shleifer A, Thaler RH (1991) Investor sentiment and the closed-end fund puzzle. J Finance 46(1):75–109

    Article  Google Scholar 

  26. Ross SA (2002) Neoclassical finance, alternative finance and the closed end fund puzzle. European Financ Manag 8(2):129–137

    Article  Google Scholar 

  27. De Long JB, Shleifer A, Summers LH, Waldmann RJ (1990) Noise trader risk in financial markets. J Polit Econ 98(4):703–738

    Article  Google Scholar 

  28. Easley D, Kiefer NM, O’Hara M (1996) Cream-skimming or profit-sharing? The curious role of purchased order flow. J Finance 51(3):811–833

    Article  Google Scholar 

  29. Glosten LR, Milgrom PR (1985) Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. J Financ Econ 14(1):71–100

    Article  Google Scholar 

  30. Kyle AS (1985) Continuous auctions and insider trading. Econometrica: J Econo Soc 1315–1335

    Google Scholar 

  31. Daniel K, Hirshleifer D, Subrahmanyam A (1998) Investor overconfidence, covariance risk, and predictors of securities returns. J Finance 53:1839–1886

    Article  Google Scholar 

  32. Gervais S, Odean T (2001) Learning to be overconfident. Rev Financ Stud 14(1):1–27

    Article  Google Scholar 

  33. Baker M, Wurgler J (2007) Investor sentiment in the stock market. J Econ Perspect 21(2):129–152

    Article  Google Scholar 

  34. Seyhun H (1998) Nejat, investment intelligence from insider trading. MIT Press, Cambridge, MA

    Book  Google Scholar 

  35. Duarte J, Young L (2009) Why is PIN priced? J Financ Econ 91(2):119–138

    Google Scholar 

  36. Odean T (1999) Do investors trade too much? Am Econ Rev 89(5):1279–1298

    Article  Google Scholar 

  37. Glosten LR, Harris LE (1988) Estimating the components of the bid/ask spread. J Financ Econ 21(1):123–142

    Article  Google Scholar 

  38. Amihud Y (2002) Illiquidity and stock returns: cross-section and time-series effects. J Financ Mark 5(1):31–56

    Article  Google Scholar 

  39. Fama EF, French KR (1993) Common risk factors in the returns on stocks and bonds. J Financ Econ

    Google Scholar 

  40. Carhart MM (1997) On persistence in mutual fund performance. J Finance 52(1):57–82

    Article  Google Scholar 

  41. Grossman SJ, Stiglitz JE (1980) On the impossibility of informationally efficient markets. Am Econ Rev 70(3):393–408

    Google Scholar 

  42. Tuckman B, Vila JL (1992) Arbitrage with holding costs: a utility-based approach. J Finance 47(4):1283–1302

    Article  Google Scholar 

  43. Gatev E, Goetzmann WN, Rouwenhorst KG (2006) Pairs trading: performance of a relative-value arbitrage rule. Rev Financ Stud 19(3):797–827

    Article  Google Scholar 

  44. Hansen LP (1982) Large sample properties of generalized method of moments estimators. Econometrica: J Econ Soc 1029–1054

    Google Scholar 

  45. Neal R, Wheatley SM (1998) Adverse selection and bid–ask spreads: evidence from closed-end funds. J Financ Mark 1(1):121–149

    Article  Google Scholar 

  46. Shleifer A, Vishny RW (1997) The limits of arbitrage. J Finance 52(1):35–55

    Article  Google Scholar 

  47. Newey WK, West KD (1986) A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. National Bureau of Economic Research

    Google Scholar 

  48. Newey WK, West KD (1994) Automatic lag selection in covariance matrix estimation. Rev Econ Stud 61(4):631–653

    Article  MathSciNet  MATH  Google Scholar 

  49. Petersen MA (2009) Estimating standard errors in finance panel data sets: comparing approaches. Rev Financ Stud 22(1):435–480

    Article  Google Scholar 

  50. Choi PMS, Choi JH (2018) Is individual trading priced in stocks? J Int Money Finance 85:76–92

    Article  Google Scholar 

  51. Choi PMS, Choi JH, Chung CY (2020) Do individual traders undermine firm valuation? Finance Res Lett 101567

    Google Scholar 

  52. Park C, Choi PMS, Choi JH (2019) Is individual trading priced in the preferred stock discount? Emerg Mark Rev 38:326–346

    Article  Google Scholar 

  53. Aktas N, De Bodt E, Declerck F, Van Oppens H (2007) The PIN anomaly around M&A announcements. J Financ Mark 10(2):169–191

    Article  Google Scholar 

  54. Easley D, Hvidkjaer S, O’hara M (2010) Factoring information into returns. J Financ Quant Anal 293–309

    Google Scholar 

  55. Akay OO, Cyree KB, Griffiths MD, Winters DB (2012) What does PIN identify? Evidence from the T-bill market. J Financ Mark 15(1):29–46

    Article  Google Scholar 

  56. Boehmer E, Grammig J, Theissen E (2007) Estimating the probability of informed trading—does trade misclassification matter? J Financ Mark 10(1):26–47

    Article  Google Scholar 

  57. Lin H-WW, Ke W-C (2011) A computing bias in estimating the probability of informed trading. J Financ Mark 14(4):625–640

    Article  Google Scholar 

  58. Yan Y, Zhang S (2012) An improved estimation method and empirical properties of the probability of informed trading. J Bank Finance 36(2):454–467

    Article  Google Scholar 

  59. De Long JB, Shleifer A, Summers LH, Waldmann RJ (1990) Positive feedback investment strategies and destabilizing rational speculation. J Finance 45(2):379–395

    Article  Google Scholar 

  60. Avery C, Zemsky P (1998) Multidimensional uncertainty and herd behavior in financial markets. Am Econ Rev 724–748

    Google Scholar 

  61. Jegadeesh N, Titman S (1993) Returns to buying winners and selling losers: implications for stock market efficiency. J Finance 48(1):65–91

    Article  Google Scholar 

  62. Bachelier L (1900) Théorie de la spéculation. Annales scientifiques de l’École normale supérieure 21–86

    Google Scholar 

  63. Harris M, Raviv A (1993) Differences of opinion make a horse race. Rev Financ Stud 6(3):473–506

    Article  Google Scholar 

  64. Kandel E, Pearson ND (1995) Differential interpretation of public signals and trade in speculative markets. J Polit Econ 103(4):831–872

    Article  Google Scholar 

  65. Bessembinder H, Chan K, Seguin PJ (1996) An empirical examination of information, differences of opinion, and trading activity. J Financ Econ 40(1):105–134

    Article  Google Scholar 

  66. Bamber LS, Barron OE, Stober TL (1999) Differential interpretations and trading volume. J Financ Quant Anal 369–386

    Google Scholar 

  67. Sarkar A, Schwartz RA (2009) Market sidedness: Insights into motives for trade initiation. J Finance 64(1):375–423

    Article  Google Scholar 

  68. Bekaert G, Urias MS (1996) Diversification, integration and emerging market closed-end funds. J Finance 51(3):835–869

    Article  Google Scholar 

  69. Levine R, Zervos S (1999) Stock markets, banks, and economic growth. Am Econ Rev 537–558

    Google Scholar 

  70. Beck T, Levine R (2004) Stock markets, banks, and growth: panel evidence. J Bank Finance 28(3):423–442

    Article  Google Scholar 

  71. Bris A, Goetzmann WN, Zhu N (2007) Efficiency and the bear: short sales and markets around the world. J Finance 62(3):1029–1079

    Article  Google Scholar 

  72. Barber BM, Odean T (2000) Trading is hazardous to your wealth: the common stock investment performance of individual investors. J Finance 55(2):773–806

    Article  Google Scholar 

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Acknowledgements

Special thanks are due to Warren Bailey (de facto co-author), David Easley, Andrew Karolyi, and Maureen O’Hara for their teachings. We also thank Yong-Ho Cheon, Jungsuk Han, Soeren Hvidkjaer, Byoung Uk Kang, Gi Hyun Kim, Alok Kumar, Albert S. Kyle, Jaehoon Lee, Albert Menkveld, and Yinggang Zhou for invaluable discussion and feedback. The authors conducted part of this research as visiting scholars at the Johnson Graduate School of Management, Cornell University, and P.M.S. Choi as a doctoral student under Warren Bailey’s advisory. P.M.S. Choi is grateful for the grants provided by the Fulbright Scholarship Program and Ewha Womans University. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5A2A03053680). Jaysang Ahn, Ethan Jaesuh Kim, Sabin Kim, SaMin Kim, Young Jin Kim, Hyun Jun Lee, and Jaehyun Rhee provided excellent research assistance. We also appreciate technical help from Mancang Dong and Mike Leiter. Standard disclaimer rules apply, and all errors are of our own.

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  1. College of Business Administration, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760, South Korea

    Paul Moon Sub Choi

  2. Department of Business Management, The State University of New York, 119-2 Songdo Moonhwa-ro, Yeonsu-gu, Incheon, 21985, South Korea

    Joung Hwa Choi

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  1. Ewha Womans University, Seoul, Korea (Republic of)

    Paul Moon Sub Choi

  2. The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

    Seth H. Huang

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Choi, P.M.S., Choi, J.H. (2021). Informed or Biased? Some Evidence from Listed Fund Trading. In: Choi, P.M.S., Huang, S.H. (eds) Fintech with Artificial Intelligence, Big Data, and Blockchain. Blockchain Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-33-6137-9_11

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