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

Kim, Jaehwan; Shamsuddin, Abul

doi:10.1080/14697688.2014.1002419

Cited by 7 publications

(8 citation statements)

References 64 publications

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“…The AMH implies that trading strategies may become unsuccessful for a time and then return to profitability, when environmental conditions become more conducive to such strategies. The more these strategies are exploited by market participants, the less successful they become (Urquhart and Hudson, 2013;Kim and Shamsuddin, 2015). The time-adaptive HAR volatility leverage of this study accounts for the heterogeneity in the market agents and in a sense captures traders' behavioural biases that are implied by AMH.…”

Section: Trading Performance With Har Volatility Leveragementioning

confidence: 98%

Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities

Stasinakis

Psaradellis

Verousis

2016

Quantitative Finance

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In this study a Krill Herd-Support Vector Regression (KH-vSVR) model is introduced. The Krill Herd (KH) algorithm is a novel metaheuristic optimization technique inspired by the behaviour of krill herds. The KH optimizes the SVR parameters by balancing the search between local and global optima. The proposed model is applied to the task of forecasting and trading three commodity Exchange Traded Funds (ETFs) on a daily basis over the period 2012-2014. The inputs of the KH-vSVR models are selected through the Model Confidence Set (MCS) from a large pool of linear predictors. The KH-vSVR's statistical and trading performance is benchmarked against traditionally adjusted SVR structures and the best linear predictor. In addition to a simple strategy, a time-varying leverage trading strategy is applied based on Heterogeneous Autoregressive (HAR) volatility estimations. It is shown that the KH-vSVR outperforms its counterparts in terms of statistical accuracy and trading efficiency, while the leverage strategy is found to be successful.

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Section: Trading Performance With Har Volatility Leveragementioning

confidence: 98%

Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities

Stasinakis

Psaradellis

Verousis

2016

Quantitative Finance

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“…We consider three panel variance ratio tests proposed by Kim and Shamsuddin (). All of the three tests control for cross‐sectional dependency and the corresponding p‐values of the tests are obtained by wild bootstrapping; therefore, from the aspect of econometrics, the test results are reliable in finite samples.…”

Section: Panel Unit Root Tests and Panel Variance Ratio Testsmentioning

confidence: 99%

Can Information and Communication Technology Improve Stock Market Efficiency? A Cross‐country Study

Lee

Tsai

Chen

et al. 2018

Bulletin of Econ Res

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The rapid advance of information and communication technology (ICT) has revolutionized the dissemination of stock market information. Based on the noise trading theory, this study discusses whether the changes brought by ICT have promoted the transparency of stock market information or instead flooded the stock market with misinformation. A cross‐country panel dataset of 71 countries from 2002 to 2014 was established. The empirical methodologies include panel unit root tests, panel variance ratio tests, and panel multiple regressions. The results of panel unit root tests and panel variance ratio tests show that stock markets in countries with high ICT diffusion are efficient while stock markets in countries with low or medium ICT diffusion are not all efficient. The results of panel regressions further show that the effect of ICT diffusion in reducing market noises was more significant than its effect in magnifying the noises.

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“…3 For example, variance ratio test (Lo & MacKinlay, 1988), multiple variance ratio test (Chow & Denning, 1993;Whang & Kim, 2003), nonparametric ranks-and signs-based variance ratio tests (Wright, 2000), wild-bootstrapped variance ratio test (Kim, 2006), and automatic variance ratio (AVR) test (Choi, 1999). The latest development in this area is the univariate (Kim, 2009) and panel versions (Kim & Shamsuddin, 2015) of the wild-bootstrapped AVR.…”

Section: Introductionmentioning

confidence: 99%

Equity return predictability, its determinants, and profitable trading strategies

Rahman

Khan

Vigne

et al. 2020

Journal of Forecasting

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This paper explains cross-market variations in the degree of return predictability using the extreme bounds analysis (EBA). The EBA addresses model uncertainty in identifying robust determinant(s) of cross-sectional return predictability. Additionally, the paper develops two profitable trading strategies based on return predictability evidence. The result reveals that among the 13 determinants of the cross-sectional variation of return predictability, only value of stock traded (a measure of liquidity) is found to have robust explanatory power by Leamer's (1985) EBA. However, Sala-i-Martin's (1997) EBA reports that value of stock traded, gross domestic product (GDP) per capita, level of information and communication technology (ICT) development, governance quality, and corruption perception are robust determinants. We further find that a strategy of buying (selling) aggregate market portfolios of the countries with the highest positive (negative) return predictability statistic in the past 24 months generates statistically significant positive returns in the subsequent 3 to 12 months. In the individual country level, a trading rule of buying (selling) the respective country's aggregate market portfolio, when the return predictability statistic turns out positive (negative), outperforms the conventional buy-and-hold strategy for many countries.

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

Cited by 7 publications

References 64 publications

Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities

Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities

Can Information and Communication Technology Improve Stock Market Efficiency? A Cross‐country Study

Equity return predictability, its determinants, and profitable trading strategies

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