Skip to main content
SpringerLink
Log in

An empirical investigation of large trader market manipulation in derivatives markets

  • Published:
Review of Derivatives Research Aims and scope Submit manuscript

Abstract

Using account-level transaction data in options and futures markets, we investigate the existence of market manipulation, which is the ability of large traders to trade strategically, impacting prices and making abnormal profits. First, large trader’s option positions have a quantity impact on the underlying asset’s price. Second, large traders generate significantly positive alphas from trading options and futures. Among the different investor types, proprietary dealers generate the largest positive alphas. Third, these abnormal returns are consistent with strategic trading and cross-market manipulation. The evidence supports market manipulation across the options and futures markets, but not within the futures market itself.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. See “South Korea Sanctions Deutsche Unit for Market Manipulation” by Se Young Lee and Alison Tudor, The Wall Street Journal, February 24th, 2011.

  2. See “HAP Trading, chief fined $1.5 mln for options manipulation CHICAGO Mon May 12, 2014”, Reuters, May 12th, 2014.

  3. There are three major strands to this empirical market manipulation literature. The first strand investigates asset price patterns resulting from manipulation (see, e.g., Carhart et al. 2002; Hillion and Suominen 2004; Ni et al. 2005; Blocher et al. 2011; Ben-David et al. 2013; Comerton-Forde and Putniņš 2014). The second strand studies known manipulation cases (see, e.g., Mei et al. 2004; Jiang et al. 2005; Merrick et al. 2005; Aggarwal and Wu 2006; Allen et al. 2006; Comerton-Forde and Putniņš 2011; Atanasov et al. 2015). The third strand provides an examination of market manipulation using account-level or transaction-level data (see, e.g., Khwaja and Mian 2005; Chow et al. 2013).

  4. Our notion of market manipulation does not imply the trading activity is illegal, and we do not examine the welfare effects of market manipulation. As common to the literature, we use the term “large” to differentiate a trader whose trades have a quantity impact on the price from a “small” trader whose trades do not. Of course, a quantity impact is related to the size of trade, clarifying the use of the word “large.”

  5. Order flow information is included within our definition of market manipulation, i.e., trading strategically knowing the quantity impact of a trade on the price due to order flow information.

  6. In theory, manipulators are differentiated from other types of traders with the key difference of a quantity impact on the price. While other types of traders (such as arbitrageurs and liquidity suppliers) are price takers, manipulation requires a quantity impact on the price where the trader trades with knowledge of its effect.

  7. See Sect. 2.1 for institutional background of Taiwan Futures Exchange (TAIFEX).

  8. Pan and Poteshman (2006) use index option markets to examine whether investors possess information about future market-wide stock price movements. They do not find any evidence of informed trading in the index option market.

  9. See Sect. 2.1 and Footnote 22 for transaction costs in Taiwan futures and options markets.

  10. This trading advantage is sometimes called trading based on order flow information, as opposed to fundamental value information.

  11. We emphasize that strategic trading based on a quantity impact of a trade is manipulation. Our results are consistent with this interpretation, and they do not support the hypothesis that large traders are price takers and liquidity providers.

  12. According to Jarrow (1992), arbitrage pricing theory invokes the price taking paradigm. The theory of manipulation studies arbitrage when traders affect prices. This generalization requires distinguishing between “paper” wealth and “real” wealth when valuing a trader’s position. For a price taker, these values are identical; but for a large trader they are distinct.

  13. Although options and futures have different implicit leverage in the contracts, we measure both futures and options returns based on notional (contract) value. The exchanges purposefully impose margins to equalize the likelihood of losses due to these leverage differences across contract types. Consequently, the percentage returns of options (based on contract value) are comparable to the percentage returns of futures (based on contract value).

  14. While studies such as Manaster and Rendleman (1982), Bhattacharya (1987), Anthony (1988), Easley et al. (1998), Chakravarty et al. (2004), Pan and Poteshman (2006), Roll et al. (2010), and Johnson and So (2012) find that particular types of options order flows can predict future stock prices, others such as Stephan and Whaley (1990), Chan et al. (1993), and O’Connor (1999) find the opposite.

  15. Compared to futures market manipulation, manipulation in options markets has also been relatively less studied (Roch 2012). Our paper provides new evidence in this area as well.

  16. Ni et al. (2005) rely on estimated profits of proprietary traders and may ignore the cost of manipulation. In contrast, our unique data allows us to compute the large traders’ (including proprietary traders’) actual profits, which include trading and other costs.

  17. According to Futures Industry Association, the TAIFEX was among the top-50 derivatives exchanges in the world and ranked 18th in 2014 by number of contracts traded and/or cleared.

  18. The TAIFEX has no market makers or specialists and operates in an order-driven electronic environment where futures prices are determined by limit and market orders.

  19. In 2014, TXO was ranked 6th among equity index options contracts in the world by number of contracts traded.

  20. Our data are obtained from the Taiwan Stock Exchange Corporation (www.tse.com.tw) and the Taiwan Futures Exchange (www.taifex.com.tw).

  21. Note that in Taiwan, index futures contracts are tradeable but the underlying spot index is non-tradeable. Also, futures markets are similar to spot markets where all types of traders can trade (in contrast, the options market is mainly traded by institutional traders in Taiwan). As such, we use futures as the “tradeable” underlying to estimate actual trading profit of large traders in options and futures markets.

  22. The trading costs per contract in Taiwan futures and options markets are based on a fixed-amount commission fee per contract and a transaction tax, a fixed rate on contract value. Also, there is no tax on trading gains in Taiwan futures and options markets. For example, the single-trip trading cost of a TAIEX Futures, including the transaction tax based on 0.002% of the contract value and a fixed commission fee per contract, is about $3.20 USD per contract. The single-trip trading cost of the TAIEX Options, including the transaction tax based on 0.1% of the contract value and a fixed commission fee per contract, is about $0.83 USD per contract (for at-the-money contracts).

  23. The TX (TXO) is a futures (European type option) contract whose value is equal to the index level of the TAIEX multiplied by 200 (50) NT$. Since the TAIEX is non-tradeable, we employ the TX in the pricing model of the TXO while dividends are already impounded into the TX price.

  24. The delta of a put option is negative, and therefore the effects of both call and put options do not neutralize.

  25. The average cost method is defined as follows. After each acquisition of additional position, the moving average unit cost is computed by dividing the total cost of existing position by the total number of existing contracts. For example, suppose that an investor holds 10 contracts of TAIEX futures with average cost (in Taiwan NT dollar) equal to 9250 at the end of the previous trading day. This investor purchases 2 contracts with cost equal to 9200 and 9180 respectively today. The moving average cost for this investor’s position after the purchase becomes 9240 (which is (10*9250 + 9200 + 9180)/12).

  26. In this computation, the trader may trade in only one market, in which case the return is just for that market.

  27. According to Jarrow (1992), the theory of manipulation requires distinguishing between “paper” wealth and “real” wealth when valuing a trader’s position. Paper wealth is defined as the value of the speculator’s position evaluated at the prices supported by the large trader. Real wealth is the value of the large trader’s position after liquidation (i.e., return to zero holdings). See Sect. 5.3 for the analyses and results that differentiate market manipulation from mispricings.

  28. This profit reflects the returns from trading when the trader closes the position. The realized return is observed on a daily basis for all days that the position is open.

  29. The last column of Panel A reveals that the number-1 trader’s total profits are 1.44 times of those of the next best trader and 2.39 times of the 3rd best trader. Also, the number-1 trader’s total profits are 19.57 times of those of the 50th best trader and 44.96 times of the 100th-best trader.

  30. Further examination (results not reported here) reveals that total profit made by all of the large traders in our sample (consisting of 30,253 accounts) is $1.09 billion USD. The top-100 large traders’ total profit is 88% larger than the total profit of all large traders in our sample.

  31. In Sect. 3.4, we further examine the performance of large traders who trade simultaneously in both options and futures markets.

  32. The realized returns reported in Table 1 are winsorized by top- and bottom-1% to avoid any outlier effects—unlike Tables 14 and 15 in Appendix where we use the outliers (most profitable trades/accounts) to illustrate the large investor’s profits. The median realized returns in both dollar and percentage terms (unreported in Table 1) are zeros for total realized returns, options realized returns, and futures realized returns. The t-statistics (reported in Table 1) indicate that all mean realized returns are significantly different from (greater than) zero at the 1%.

  33. We estimate that the total losses (from year 2007 to 2012) are − $1,532,520,206 in Taiwan NT dollar, equivalent to − $51.08 million USD (assume that current exchange rate of 1 USD = 30 Taiwan NT dollar).

  34. In addition, there are 318 large trader accounts that are unclassified as to the investor type.

  35. To test for statistical differences in the investor type trading performances we perform an ANOVA F-test, a Bartlett's test of equal variances, and a Kruskal–Wallis equality-of-populations rank test. These tests (results not reported here) all show that the realized returns across trader types are significantly different.

  36. This assumption of cross-market trades also implies a non-zero trading position in both markets. In order to generate realized returns, traders must change/close their positions. See also Table 16 in Appendix for further examples. As a robustness check (results not reported here), we define cross-market trades as observations with non-zero deltas in both futures and in options on the same day. We find similar results and conclusions based on this alternative definition of cross-market trades.

  37. Note that the realized returns are reported without leverage; with leverage, the actual magnitude of the realized returns would be approximately 56.4% because the margin is around 10% of the overall contract value (i.e., the calculation of the presented returns is based on contract value).

  38. We perform unit root tests on the time-series of the futures prices of the underlying index and delta demand of large traders. The result of the unit root test reveals that delta demand is stationary but futures prices are non-stationary (and they are stationary in first-differences). As such, we estimate the VAR system with the relation between delta demand and the change in the (first difference of) future prices.

  39. As a robustness test (results not reported here), we test the optimal lags of the VAR system, which is between 5 and 10 lags. As such, we estimate the VAR system using 5 lags and found similar results.

  40. The result in Panel A of Table 5 does not necessarily support the synchronous market condition because the large traders’ total trading positions may include trading positions in futures market only, in options market only, or in both options and futures markets (i.e., cross-market trades). As shown in Table 5, large traders’ trading in the options market Granger causes futures prices (see Panel B) but large traders’ trading in the futures market does not Granger causes futures prices (see Panel C).

  41. However, we find that large traders’ holdings depend on prices, suggesting that one could be picking up a type of mean reversion in this test.

  42. If one employs the standard approach of using a multiple-beta model to adjust for systematic risk, then a recent result of Jarrow and Protter (2013a, b) is that such mispricings—positive alphas—are equivalent to the existence of arbitrage opportunities or dominated securities.

  43. As shown in Jarrow and Protter (2013b), a linear factor structure is sufficient to characterize systematic risk, independent of whether a traded asset is a primary asset (e.g., index) or a derivative.

  44. As discussed in footnote 34, there are 318 large trader accounts which are unclassified as to the investor type. Further examination (results not reported here) reveals that the estimated alpha is insignificant for large investors with the unclassified investor type.

  45. As a robustness check (results not reported here), we find similar results using an alternative definition of cross-market trades, which is defined as observations with non-zero deltas in both futures and in options on the same day.

  46. Based on the information provided by TAIFEX, the percentage transaction cost is calculated as: (fixed cost + transaction tax)/contract value, where the fixed cost is a constant commission fee per contract, the transaction tax (for a single-trip) is 0.002% of the contract value for futures and 0.1% for options, and the contract value depends on the index level (for futures) or premium (for options). See also Footnote 22.

  47. According to Jarrow (1992), arbitrage pricing theory invokes the price taking paradigm while the theory of manipulation studies traders’ impacts on prices. This generalization requires distinguishing between “paper” wealth and “real” wealth when valuing a trader’s position. For a price taker, the “paper” wealth and “real” wealth are identical; but for a large trader these values are distinct.

  48. If there is manipulation, returns on trading days should exceed returns on non-trading days. This shows that real profits exceed paper profits, i.e., trading strategically has value. To see why, consider the following. Suppose a trade occurs on day 1 to take advantage of an arbitrage opportunity, and a trade occurs on day 10 to liquidate the position. No trades occur on days 2-9. The return on day 1 will be zero if the arbitrage remains or positive if it disappears by the end of the day. The return on day 10 will be zero if the arbitrage has disappeared between days 1 and 9. It will be positive if the arbitrage disappears on day 10. We see that in some circumstances the trading days do not have abnormal returns, they can appear in the non-trading days as the mispricing disappears. But, it is possible that trading days earn abnormal profits. This is the partial confounding with manipulation profits. In contrast, with manipulation abnormal profits are only earned on trading days.

  49. See Table 16 in the Appendix for an example of identifying a trading cycle for each investor account and how we compute the realized return and unrealized return for each account.

  50. In Table 11, we winsorized the realized returns by top- and bottom-1% to avoid outliers observed in realized returns.

References

  • Aggarwal, R. K., & Wu, G. (2006). Stock market manipulations. Journal of Business, 79(4), 1915–1954.

    Article  Google Scholar 

  • Allen, F., Litov, L., & Mei, J. (2006). Large traders, price manipulation, and limits to arbitrage: An anatomy of market corners. Review of Finance, 10(4), 645–693.

    Article  Google Scholar 

  • Amin, K. I., & Lee, C. (1997). Option trading, price discovery, and earnings news dissemination. Contemporary Accounting Research, 14(2), 153–192.

    Article  Google Scholar 

  • Anthony, J. H. (1988). The interrelation of stock and options market trading-volume data. Journal of Finance, 43(4), 949–964.

    Article  Google Scholar 

  • Atanasov, V., Davies, R. J., & Merrick, J. J. (2015). Financial intermediaries in the midst of market manipulation: Did they protect the fool or help the knave? Journal of Corporate Finance, 34, 210–234.

    Article  Google Scholar 

  • Baron, M., Brogaard, J., & Kirilenko, A. A. (2014). The trading profits of high frequency traders. Working Paper. Available at SSRN: http://ssrn.com/abstract=2433118 or http://dx.doi.org/10.2139/ssrn.2433118.

  • Ben-David, I., Franzoni, F., Landier, A., & Moussawi, R. (2013). Do hedge funds manipulate stock prices? Journal of Finance, 68(6), 2383–2434.

    Article  Google Scholar 

  • Bhattacharya, M. (1987). Price changes of related securities: The case of call options and stocks. Journal of Financial and Quantitative Analysis, 22(1), 1–15.

    Article  Google Scholar 

  • Blocher, J., Engelberg, J., & Reed, A. V. (2011). The long and the short of it: Evidence of year-end price manipulation by short sellers. In AFA 2012 Chicago Meetings Paper. Available at SSRN: http://ssrn.com/abstract=1364835 or http://dx.doi.org/10.2139/ssrn.1364835.

  • Cao, C., Chen, Z., & Griffin, J. (2005). Informational content of option volume prior to takeovers. Journal of Business, 78(3), 1073–1109.

    Article  Google Scholar 

  • Carhart, M., Kaniel, R., Musto, D., & Reed, A. (2002). Leaning for the tape: Evidence of gaming behavior in equity mutual funds. Journal of Finance, 57(2), 661–693.

    Article  Google Scholar 

  • Chakravarty, S., Gulen, H., & Mayhew, S. (2004). Informed trading in stock and option markets. Journal of Finance, 59(3), 1235–1257.

    Article  Google Scholar 

  • Chan, K., Chung, Y. P., & Johnson, H. (1993). Why option prices lag stock prices: A trading-based explanation. Journal of Finance, 48(5), 1957–1967.

    Article  Google Scholar 

  • Chow, E. H., Hung, C., Liu, C. S., & Shiu, C. (2013). Expiration day effects and futures trading profits: Evidence from Taiwan. Review of Quantitative Finance and Accounting, 41(3), 441–462.

    Article  Google Scholar 

  • Comerton-Forde, C., & Putniņš, T. (2011). Measuring closing price manipulation. Journal of Financial Intermediation, 20(2), 135–158.

    Article  Google Scholar 

  • Comerton-Forde, C., & Putniņš, T. (2014). Stock price manipulation: Prevalence and determinants. Review of Finance, 18(1), 23–66.

    Article  Google Scholar 

  • Easley, D., O’Hara, M., & Srinivas, P. (1998). Option volume and stock prices: Evidence on where informed traders trade. Journal of Finance, 53(2), 431–465.

    Article  Google Scholar 

  • Hillion, P., & Suominen, M. (2004). The manipulation of closing prices. Journal of Financial Markets, 7, 351–375.

    Article  Google Scholar 

  • Hu, J. (2014). Does option trading convey stock price information? Journal of Financial Economics, 111(3), 625–645.

    Article  Google Scholar 

  • Jarrow, R. A. (1992). Market manipulation, bubbles, corners, and short squeezes. Journal of Financial and Quantitative Analysis, 27(3), 311–336.

    Article  Google Scholar 

  • Jarrow, R. A. (1994). Derivative security markets, market manipulation, and option pricing theory. Journal of Financial and Quantitative Analysis, 29(2), 241–261.

    Article  Google Scholar 

  • Jarrow, R. A., & Protter, P. E. (2013a). Positive alphas, abnormal performance, and illusory arbitrage. Mathematical Finance, 23(1), 39–56.

    Article  Google Scholar 

  • Jarrow, R. A., & Protter, P. E. (2013b). Positive alphas and a generalized multiple-factor asset pricing model. Working Paper.

  • Jiang, G., Mahoney, P., & Mei, J. (2005). Market manipulation: A comprehensive study of stock pools. Journal of Financial Economics, 77(1), 147–170.

    Article  Google Scholar 

  • Johnson, T. L., & So, E. C. (2012). The option to stock volume ratio and future returns. Journal of Financial Economics, 106(2), 262–286.

    Article  Google Scholar 

  • Khwaja, A. I., & Mian, A. (2005). Unchecked intermediaries: Price manipulation in an emerging stock market. Journal of Financial Economics, 78(1), 203–241.

    Article  Google Scholar 

  • Manaster, S., & Rendleman, R., Jr. (1982). Option prices as predictors of equilibrium stock prices. Journal of Finance, 37(4), 1043–1058.

    Article  Google Scholar 

  • Mei, J., Wu, G., & Zhou, C. (2004). Behavior based manipulation: Theory and prosecution evidence. New York University Working Paper.

  • Merrick, J. J., Naik, N. Y., & Yadav, P. K. (2005). Strategic trading behavior and price distortion in a manipulated market: Anatomy of a squeeze. Journal of Financial Economics, 77(1), 171–218.

    Article  Google Scholar 

  • Ni, S., Pearson, N., & Poteshman, A. M. (2005). Stock price clustering on option expiration dates. Journal of Financial Economics, 78(1), 49–87.

    Article  Google Scholar 

  • O’Connor, M. L. (1999). The cross-sectional relationship between trading costs and lead/lag effects in stock & option markets. Financial Review, 34(4), 95–118.

    Article  Google Scholar 

  • Pan, J., & Poteshman, A. (2006). The information in options volume for future stock prices. Review of Financial Studies, 19(3), 871–908.

    Article  Google Scholar 

  • Putniņš, T. J. (2011). Market manipulation: A survey. Journal of Economic Surveys, 26(5), 952–967.

    Article  Google Scholar 

  • Roch, A. F. (2012). Price manipulation and American options in illiquid markets. Available at SSRN: http://ssrn.com/abstract=1923865 or http://dx.doi.org/10.2139/ssrn.1923865.

  • Roll, R., Schwartz, E., & Subrahmanyam, A. (2010). O/S: The relative trading activity in options and stock. Journal of Financial Economics, 96(1), 1–17.

    Article  Google Scholar 

  • Stephan, J., & Whaley, R. E. (1990). Intraday price changes and trading volume relations in the stock and stock option markets. Journal of Finance, 45(1), 191–220.

    Article  Google Scholar 

Download references

Acknowledgements

We thank Jed DeVaro and the participants at the Department of Accounting and Finance Research Workshop and the Department of Economics Seminar, California State University, East Bay for helpful comments. Fung would like to acknowledge the support of the Jack and Susan Acosta Professorship at California State University, East Bay. Tsai would like to acknowledge the financial support from the Ministry of Science and Technology, Taiwan, R.O.C. (Grant No. 104-2410-H-003-005).

Author information

Authors and Affiliations

  1. Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, NY, 14853, USA

    Robert Jarrow

  2. Kamakura Corporation, Honolulu, HI, 96815, USA

    Robert Jarrow

  3. College of Business and Economics, California State University, East Bay, Hayward, CA, 94588, USA

    Scott Fung

  4. Graduate Institute of Management, National Taiwan Normal University, Taipei, Taiwan

    Shih-Chuan Tsai

  5. Taiwan Futures Exchange, Taipei, Taiwan

    Shih-Chuan Tsai

Authors
  1. Robert Jarrow
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Scott Fung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shih-Chuan Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Robert Jarrow or Shih-Chuan Tsai.

Appendix

Appendix

See Tables 13, 14, 15 and 16.

Table 13 Datasets
Full size table
Table 14 Ranking of most profitable trades
Full size table
Table 15 Ranking of top traders
Full size table
Table 16 Example of trading cycle for each trading account
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jarrow, R., Fung, S. & Tsai, SC. An empirical investigation of large trader market manipulation in derivatives markets. Rev Deriv Res 21, 331–374 (2018). https://doi.org/10.1007/s11147-018-9143-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11147-018-9143-0

Keywords

JEL Classification

Search

Navigation