Day Trader Behavior and Performance: Evidence from Taiwan Futures Market

Cheng, Teng Yuan; Lin, Chao Hsien; Li, Hung-Chih; Lai, Syouching; Watkins, Kerry

doi:10.1080/1540496x.2016.1172205

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…In addition, since the momentum strategy is widely used among investors in the TAIEX futures market (Cheng, Lin, Li, Lai & Watkins, 2016), we expect positive relations between lagged order imbalance and current return would be discovered in our research. Furthermore, Yu and Chen (1999) indicate that GARCH (1, 1) is more appropriate for stock index futures' change process of price and volume.…”

Section: Introductionmentioning

confidence: 86%

U.S. Quantitative Easing Policy Effect on TAIEX Futures Market Efficiency

Huang¹,

Su²,

Chen³

2017

AEF

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This paper examines the market efficiency of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) Futures after the announcement of Quantitative Easing (QE) policy. Order imbalance is used to explore the relationship between return and order imbalance. We find that under the unconditional OLS model, lagged order imbalances almost have no significantly positive predictive power for current return. Nonetheless, on the trading day after the announcement of QE 1 policy, one-minute interval data show that the lagged order imbalance has predictive power for current return. Under the conditional OLS model, the reversed relation between current return and lagged order imbalance is not universal; on the other hand, after the announcement of QE 2 policy, the reversed relation between current return and lagged order imbalance is more common. Moreover, under volatility-GARCH (1, 1), one-minute interval data shows significantly positive relation between order imbalance and volatility.

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Section: Introductionmentioning

confidence: 86%

U.S. Quantitative Easing Policy Effect on TAIEX Futures Market Efficiency

Huang¹,

Su²,

Chen³

2017

AEF

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“…However, some studies provide evidence that is inconsistent with this view. For futures in Taiwan, Cheng et al (2016) find that most individual day traders follow positive feedback trading strategies. In addition, Harris and Schultz (1998) demonstrate that individual day traders, who use the small order execution system of the Nasdaq, tend to follow a momentum strategy.…”

Section: Intraday Tradingmentioning

confidence: 93%

Feedback Trading: The Intraday Case of Retail Derivatives

Baule,

Frijns,

Schlie

2024

Journal of Futures Markets

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We analyze retail order flow in terms of intraday feedback trading patterns. Using a unique data set of exchange trades and high‐frequency quotes, we first provide evidence that retail investors actively and consciously respond to short‐term intraday returns in a negative feedback, contrarian fashion. Second, we show that some retail investors also feedback trade on tick‐by‐tick returns. Third, we find that on average this behavior leads to significant losses on the day they open a position. These losses are primarily due to the bid‐ask spread and to investors' timing inability, but not to market makers taking advantage of investors.

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“…Traders need to set a certain level of gain or loss for their decision framework and be disciplined with this rule. Violating the law with overconfidence can cause their asset to underperform and be considered a disposition effect (Cheng et al, 2016;Prosad et al, 2015).…”

Section: Framing Effect and Disposition Effectmentioning

confidence: 99%

Framing effect and disposition effect: investment decisions tools to understand bounded rationality

Kiky,

Atahau,

Mahastanti

et al. 2024

RBF

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PurposeThis paper aims to explore the development of investment decision tools by understanding the rationality behind the disposition effect. We suspect that not all disposition decisions are irrational. The decisions should be evaluated based on the bounded rationality of the individuals’ target and tolerance level, which is not covered in previous literature. Adding the context of individual preference (target and tolerance) in their decision could improve the classic measurement of disposition effect.Design/methodology/approachThe laboratory web experiment is prepared to collect the responses in holding and selling the stocks within 14 days. Two groups of Gen Z investors are observed. The control group makes a decision based on their judgment without any system recommendation. In contrast, the second group gets help inputting their target and tolerance. Furthermore, the framing effect is also applied as a reminder of their target and tolerance to induce more holding decisions on gain but selling on loss.FindingsThe framing effect is adequate to mitigate the disposition effect but only at the early day of observation. Bounded rationality explains the rationality of liquidating the gain because the participants have reached their goal. The framing effect is not moderated by days to affect the disposition effect; over time, the disposition effect tends to be higher. A new measurement of the disposition effect in the context of bounded rationality is better than the original disposition effect coefficient.Practical implicationsGen Z investors need a system aid to help their investment decisions set their target and tolerance to mitigate the disposition effect. Investment firms can make a premium feature based on real-time market data for investors to manage their assets rationally in the long run. Bounded rationality theory offers more flexibility in understanding the gap between profit maximization and irrational decisions in behavioral finance. The government can use this finding to develop a suitable policy and ecosystem to help beginner investors understand investment risk and manage their assets based on subjective risk tolerance.Originality/valueThe classic Proportion Gain Realized (PGR) and Proportion Loss Realized (PLR) measurements cannot accommodate several contexts of users’ targets and tolerance in their choices, which we argue need to be re-evaluated with bounded rationality. Therefore, this article proposed new measurements that account for the users’ target and tolerance level to evaluate the rationality of their decision.

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Day Trader Behavior and Performance: Evidence from Taiwan Futures Market

Cited by 9 publications

References 32 publications

U.S. Quantitative Easing Policy Effect on TAIEX Futures Market Efficiency

U.S. Quantitative Easing Policy Effect on TAIEX Futures Market Efficiency

Feedback Trading: The Intraday Case of Retail Derivatives

Framing effect and disposition effect: investment decisions tools to understand bounded rationality

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