Technical trading revisited: False discoveries, persistence tests, and transaction costs

Bajgrowicz, Pierre; Scaillet, Olivier

doi:10.1016/j.jfineco.2012.06.001

Cited by 226 publications

(173 citation statements)

References 62 publications

(20 reference statements)

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“…It is therefore of interest to know if they can really expect significant profits from such investment techniques or if excess returns are solely due to luck. Second, working with the DJIA enables us to compare our results (to a certain extent) with those obtained previously in popular studies (mainly, Sullivan et al, 1999;Bajgrowicz and Scaillet, 2012).…”

Section: Empirical Illustrationmentioning

confidence: 70%

“…Under these assumptions, for a broad class of nonlinear statistics, this technique provides consistent estimators of the considered statistics (see Kreiss and Paparoditis, 2011, for more details). Starting from the initial series of log returns R, we build block bootstrap training samples by executing the following steps (Sullivan et al, 1999;Bajgrowicz and Scaillet, 2012):…”

Section: Alternative To the Residuals-based Bootstrap: The Block Bootmentioning

confidence: 99%

“…Because the drawing is made by blocks, the unselected observations are also a set of blocks of average length 1=a, but with their original order of appearance conserved. Here, we select a D 0:1 as in Sullivan et al (1999) and Bajgrowicz and Scaillet (2012), after a trial-and-error phase. This stage could be improved by using an automatic procedure of selection, as in Politis and White (2004), but this question is beyond the present research.…”

Section: Repeat Stepmentioning

confidence: 99%

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Estimating the Out‐of‐Sample Predictive Ability of Trading Rules: A Robust Bootstrap Approach

Hambuckers

Heuchenne

2015

Journal of Forecasting

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In this paper, we provide a novel way to estimate the out-of-sample predictive ability of a trading rule. Usually, this ability is estimated using a sample splitting scheme, true out-of-sample data being rarely available. We argue that this method makes a poor use of the available data and creates data mining possibilities. Instead, we introduce an alternative .632 bootstrap approach. This method enables to build insample and out-of-sample bootstrap datasets that do not overlap but exhibit the same time dependencies. We show in a simulation study that this technique drastically reduces the mean squared error of the estimated predictive ability. We illustrate our methodology on IBM, MSFT and DJIA stock prices, where we compare 11 trading rules specifications. For the considered datasets, two different filter rule specifications have the highest out-of-sample mean excess returns. However, all tested rules cannot beat a simple buy-and-hold strategy when trading at a daily frequency. JEL: C52, C53, C15, C18

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Section: Empirical Illustrationmentioning

confidence: 70%

Section: Alternative To the Residuals-based Bootstrap: The Block Bootmentioning

confidence: 99%

Section: Repeat Stepmentioning

confidence: 99%

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Estimating the Out‐of‐Sample Predictive Ability of Trading Rules: A Robust Bootstrap Approach

Hambuckers

Heuchenne

2015

Journal of Forecasting

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“…At the same time, the reuse of this data period may cause the problem of data snooping, highlighted by Lo and MacKinlay (1990), White (2000) and the references therein. To illustrate the problem of data snooping using the example of Bajgrowicz and Scaillet (2012) 'imagine putting enough monkeys on typewriters and that one of the monkeys writes the Iliad in ancient Greek. Because of the sheer size of the sample, you are likely to find a lucky monkey once in a while.…”

Section: Reality Checkmentioning

confidence: 99%

Predictability of Equity Models

Chicaroli¹,

Pereira

2015

Journal of Forecasting

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In this study, we verify the existence of predictability in the Brazilian equity market.Unlike other studies in the same sense, which evaluate original series for each stock, we evaluate synthetic series created on the basis of linear models of stocks.Following Burgess (1999), we use the "stepwise regression" model for the formation of models of each stock. We then use the variance ratio profile together with a Monte Carlo simulation for the selection of models with potential predictability. Unlike Burgess (1999), we carry out White's Reality Check (2000) in order to verify the existence of positive returns for the period outside the sample. We use the strategies proposed by Sullivan, Timmermann & White (1999) and Hsu & Kuan (2005) amounting to 26,410 simulated strategies. Finally, using the bootstrap methodology, with 1,000 simulations, we find strong evidence of predictability in the models, including transaction costs.

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“…Since the market information is changing rapidly, TA is regarded as the short-term toll. Some of the most influential studies that investigate usefullnes and effects of TA application in financial market trading are [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

An Automated System for Stock Market Trading Based on Logical Clustering

et al. 2018

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Abstract:In this paper a novel clustering-based system for automated stock market trading is introduced. It relies on interpolative Boolean algebra as underlying mathematical framework used to construct logical clustering method which is the central component of the system. The system uses fundamental analysis ratios, more precisely market valuation ratios, as clustering variables to differentiate between undervaluated and overvaluated stocks. To structure investment portfolio, the proposed system uses special weighting formulas which automatically diversify investment funds. Finally, a simple trading simulation engine is developed to test our system on real market data. The proposed system was tested on Belgrade Stock Exchange historical data and was able to achieve a high rate of return and to outperform the BelexLine market index as a benchmark variable. The paper has also provided in-depth analysis of the system's investment decision making process which reveals some exciting insights.

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Technical trading revisited: False discoveries, persistence tests, and transaction costs

Cited by 226 publications

References 62 publications

Estimating the Out‐of‐Sample Predictive Ability of Trading Rules: A Robust Bootstrap Approach

Estimating the Out‐of‐Sample Predictive Ability of Trading Rules: A Robust Bootstrap Approach

Predictability of Equity Models

An Automated System for Stock Market Trading Based on Logical Clustering

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