Trading Behavior of Institutional Investors and Stock Index Futures Returns in Taiwan

Lai, Hung-Cheng; Wang, Kuan-Min

doi:10.1080/15427560.2015.1095751

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…They examined the relation in a multivariate structural Vector Autoregressive (VAR) framework and found that FIIs have significantly guided the market performance through their positive feedback trading behaviour. Lai et al (2015) conducted an empirical study of the relationship between three institutional investors’ trading behaviours and Taiwan stock index futures. The result showed that FIIs in the future market are negative feedback traders while the investment trusts are positive feedback traders.…”

Section: Literature Reviewmentioning

confidence: 99%

Do Local Investors Exhibit Smart Value Investment? Empirical Evidence from India

Chauhan

Chaklader

2020

Global Business Review

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We have studied the investment behaviour of ‘foreign institutional investors’ (FIIs) and ‘domestic institutional investors’ (DIIs) in the Indian stock market for positive feedback trading and smart money value investments. We have collected the daily investment of FII and DII in Indian stock markets along with NIFTY daily returns. We have used multivariate causality approach VAR and found that FII investments follow positive feedback trading approach whereas DII follow smart money value investments in the Indian stock market.

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Section: Literature Reviewmentioning

confidence: 99%

Do Local Investors Exhibit Smart Value Investment? Empirical Evidence from India

Chauhan

Chaklader

2020

Global Business Review

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“…, 2007; Kurov, 2008; Salm and Schuppli, 2010; Antoniou et al. , 2011; Lai and Wang, 2014, 2015; Smales, 2016; Chen and Yang, 2021) confirming empirically the significance of (predominantly, positive) feedback trading in that segment [83]. At the macro level, positive feedback traders often appear more active in index futures during market slumps (likely due to index futures being utilized for portfolio insurance – Salm and Schuppli, 2010; Antoniou et al.…”

Section: Empirical Evidencementioning

confidence: 59%

“…In the case of Taiwan, earlier evidence by Cheng et al. (2007) suggested that positive feedback trading in index futures was confined to retail traders and dealers at the weekly frequency during 2001–2002, with no other trader-type found to feedback trade; later evidence (Lai and Wang, 2014, 2015), however, denotes that foreign investors (investment trusts) negative (positive) feedback traded during the 2008–2013 window at the daily frequency. Finally, with respect to South Korea, Ghysels and Seon (2005) found that foreign and domestic institutional (domestic institutional and retail) investors positive (negative) feedback traded in the futures market at the daily frequency prior to the outbreak of (during) the Asian crisis in 1997.…”

Section: Empirical Evidencementioning

confidence: 99%

Feedback trading: a review of theory and empirical evidence

Economou

Gavriilidis

Gębka

et al. 2022

RBF

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PurposeThe purpose of this paper is to comprehensively review a large and heterogeneous body of academic literature on investors' feedback trading, one of the most popular trading patterns observed historically in financial markets. Specifically, the authors aim to synthesize the diverse theoretical approaches to feedback trading in order to provide a detailed discussion of its various determinants, and to systematically review the empirical literature across various asset classes to gauge whether their feedback trading entails discernible patterns and the determinants that motivate them.Design/methodology/approachGiven the high degree of heterogeneity of both theoretical and empirical approaches, the authors adopt a semi-systematic type of approach to review the feedback trading literature, inspired by the RAMESES protocol for meta-narrative reviews. The final sample consists of 243 papers covering diverse asset classes, investor types and geographies.FindingsThe authors find feedback trading to be very widely observed over time and across markets internationally. Institutional investors engage in feedback trading in a herd-like manner, and most noticeably in small domestic stocks and emerging markets. Regulatory changes and financial crises affect the intensity of their feedback trades. Retail investors are mostly contrarian and underperform their institutional counterparts, while the latter's trades can be often motivated by market sentiment.Originality/valueThe authors provide a detailed overview of various possible theoretical determinants, both behavioural and non-behavioural, of feedback trading, as well as a comprehensive overview and synthesis of the empirical literature. The authors also propose a series of possible directions for future research.

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“…Since then, the ability of feedback trading to induce return autocorrelation and its impact on destabilizing asset prices and market inefficiency have received much debate in most financial markets (Black, 1986). This includes several developed stock markets other than the US (Koutmos, 1997), foreign exchange markets (Laopodis, 2005; Tayeha and Kallinterakis, 2022), emerging equity markets (Aguirre and Saidi, 1999), stock index futures markets (Salm and Schuppli, 2010; Lai and Wang, 2015), Exchange-Traded Fund (ETF), contracts (Chau et al , 2011; Charteris et al , 2014), coal and electricity market (Chau et al , 2015), Bitcoin (Wang et al , 2022) and real estate (Kyriakou et al , 2020). Nonetheless, even with this increasing importance and interest, little is known about feedback trading in the cryptocurrency markets except for Bitcoin.…”

Section: Introductionmentioning

confidence: 99%

Feedback trading in the cryptocurrency market

Ahmed,

Alsamman,

Chebbi

2023

SEF

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Purpose This paper aims to investigate feedback trading and autocorrelation behavior in the cryptocurrency market. Design/methodology/approach It uses the GJR-GARCH model to investigate feedback trading in the cryptocurrency market. Findings The findings show a negative relationship between trading volume and autocorrelation in the cryptocurrency market. The GJR-GARCH model shows that only the USD Coin and Binance USD show an asymmetric effect or leverage effect. Interestingly, other cryptocurrencies such as Ethereum, Binance Coin, Ripple, Solana, Cardano and Bitcoin Cash show the opposite behavior of the leverage effect. The findings of the GJR-GARCH model also show positive feedback trading for USD Coin, Binance USD, Ripple, Solana and Bitcoin Cash and negative feedback trading for Ethereum and Cardano only. Originality/value This paper contributes to the literature by extending Sentana and Wadhwani (1992) to explore the presence of feedback trading in the cryptocurrency market using a sample of the most active cryptocurrencies other than Bitcoin, namely, Ethereum, USD coin, Binance Coin, Binance USD, Ripple, Cardano, Solana and Bitcoin Cash.

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Trading Behavior of Institutional Investors and Stock Index Futures Returns in Taiwan

Cited by 5 publications

References 31 publications

Do Local Investors Exhibit Smart Value Investment? Empirical Evidence from India

Do Local Investors Exhibit Smart Value Investment? Empirical Evidence from India

Feedback trading: a review of theory and empirical evidence

Feedback trading in the cryptocurrency market

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