Autocorrelation and partial price adjustment

Anderson, Robert M.; Eom, Kyong Shik; Hahn, Sang Buhm; Park, Jong-Ho

doi:10.1016/j.jempfin.2013.08.003

Cited by 30 publications

(26 citation statements)

References 21 publications

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“…Rösch, Subrahmanyam, and van Dijk (2013) provide evidence that such informational efficiency metrics measured at intraday horizons are highly correlated with lowfrequency measures of informational efficiency and are different from liquidity measures. Anderson, Eom, Hahn, and Park (2013) find that partial price adjustment (slow price adjustment and overshooting), which implies a degree of informational inefficiency, is a major source of positive and negative autocorrelations. In robustness tests we confirm that our results hold at lower frequencies (estimating the measures for each stock-month using daily data), although such tests have lower statistical power and less precision.…”

Section: Informational Efficiencymentioning

confidence: 96%

“…The estimated effect of dark trading is to make the autocorrelations and variance ratios more negative (these results are not tabulated), consistent with a decrease in informational efficiency. An interpretation of these results is that prices (midquotes) tend to overreact to new information or order flow and subsequently reverse the overreaction (Anderson, Eom, Hahn, and Park, 2013), and high levels of dark trading tend to exacerbate the inefficient overreactions and reversals. Negative autocorrelations also arise from imperfect riskbearing capacity of liquidity providers (e.g., Ho and Stoll, 1981).…”

Section: Autocorrelationmentioning

confidence: 99%

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Dark trading and price discovery

Comerton‐Forde

Putniņš

2015

Journal of Financial Economics

167

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a b s t r a c tRegulators globally are concerned that dark trading harms price discovery. We show that dark trades are less informed than lit trades. High levels of dark trading increase adverse selection risk on the lit exchange by increasing the concentration of informed traders. Using both high-and low-frequency measures of informational efficiency we find that low levels of non-block dark trading are benign or even beneficial for informational efficiency, but high levels are harmful. In contrast, we find no evidence that block trades in the dark impede price discovery.

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Section: Informational Efficiencymentioning

confidence: 96%

Section: Autocorrelationmentioning

confidence: 99%

Dark trading and price discovery

Comerton‐Forde

Putniņš

2015

Journal of Financial Economics

167

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“…When designing an HFT model, an important challenge that a model designer is faced with is the claim that return autocorrelations in HFT can have both genuine and spurious elements (Anderson, 2011;Anderson et al, 2013). The latter is attributed to market microstructure noise (McAleer and Medeiros, 2008), mainly resulting from non-synchronous trading effect and bid-ask bounce.…”

Section: High Frequency Data and Technical Indicatorsmentioning

confidence: 99%

“…This noise is attributed as one of the key modelling challenges and sources of uncertainty in HFT (Anderson, 2011;Anderson et al, 2013;Rechenthin and Street, 2013). T1 fuzzy sets cannot fully represent the uncertainty associated with the inputs since as a contradiction, the membership function of a T1 fuzzy set has no uncertainty associated with it.…”

Section: Generalizing Anfis Model To T2 Flsmentioning

confidence: 99%

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Vella

2016

Expert Systems with Applications

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Highlights• We investigate the viability of Type-2 fuzzy systems in high frequency trading.• We propose Type-2 models based on a generalisation of the popular ANFIS model.• Type-2 models score significant risk adjusted performance improvements over Type-1.• Benefits of Type-2 models increase with higher trading frequencies. AbstractIn this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT.

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“…On the other hand, return autocorrelation arising from time-varying expected returns and partial price adjustment is termed as genuine autocorrelation (see Campbell et al 1997). In this paper, we use the term weak-form efficiency and return predictability interchangeably, as autocorrelations in US stock returns are primarily attributed to partial price adjustment rather than market microstructure biases or time-varying expected returns (see Mech 1993, Anderson et al 2013. and stability of US macroeconomic fundamentals.…”

Section: Introductionmentioning

confidence: 99%

A closer look at return predictability of the US stock market: evidence from new panel variance ratio tests

Kim

Shamsuddin

2015

Quantitative Finance

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This paper examines the return predictability of the US stock market using portfolios sorted by size, book-to-market ratio and industry. We use novel panel variance ratio tests, based on the wild bootstrap proposed in this paper, which exhibit desirable size and power properties in small samples. We have found evidence that stock returns have been highly predictable from 1964 to 1996, except for a period leading to the 1987 crash and its aftermath. After 1997, stock returns have been unpredictable overall. At a disaggregated level, we find evidence that large-cap portfolios have been priced more efficiently than small-or medium-cap portfolios; and that the stock returns from high-tech industries are far less predictable than those from non-high-tech industries.

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Autocorrelation and partial price adjustment

Cited by 30 publications

References 21 publications

Dark trading and price discovery

Dark trading and price discovery

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

A closer look at return predictability of the US stock market: evidence from new panel variance ratio tests

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