"Can the neuro fuzzy model predict stock indexes better than its rivals?"

This paper develops a model of a trading system by using neuro fuzzy framework in order to better predict the stock index. Thirty well-known stock indexes are analyzed with the help of the model developed here. The empirical results show strong evidence of nonlinearity in the stock index by using KD technical indexes. The trading point analysis and the sensitivity analysis of trading costs show the robustness and opportunity for making further profits through using the proposed nonlinear neuro fuzzy system. The scenario analysis also shows that the proposed neuro fuzzy system performs consistently over time.

A Reality Check on Technical Trading Rule Profits in US Futures Markets

This paper investigates the profitability of technical trading rules in US futures markets over the 1985-2004 period. To account for data snooping biases, we evaluate statistical significance of performance across technical trading rules using White's Bootstrap Reality Check test and Hansen's Superior Predictive Ability test. These methods directly quantify the effect of data snooping by testing the performance of the best rule in the context of the full universe of technical trading rules. Results show that the best rules generate statistically significant economic profits only for two of 17 futures contracts traded in the US. This evidence indicates that technical trading rules generally have not been profitable in US futures markets after correcting for data snooping biases.Marketing,

The Obstinate Passion of Foreign Exchange Professionals : Technical Analysis

Technical analysis involves the prediction of future exchange rate (or other assetprice) movements from an inductive analysis of past movements. A reading of the large literature on this topic allows us to establish a set of stylised facts, including the facts that technical analysis is an important and widely used method of analysis in the foreign exchange market and that applying certain technical trading rules over a sustained period may lead to significant positive excess returns. We then analyze four arguments that have been put forward to explain the continuing widespread use of technical analysis and its apparent profitability: that the foreign exchange market may be characterised by not-fully-rational behaviour; that technical analysis may exploit the influence of central bank interventions; that technical analysis may be an efficient form of information processing ; and finally that it may provide information on nonfundamental influences on foreign exchange movements. Although all of these positions may be relevant to some degree, neither non-rationality nor official interventions seem to be widespread and persistent enough to explain the obstinate passion of foreign exchange professionals for technical analysis.foreign exchange market ; technical analysis ; market microstructure

A Note on the Use of Moving Average Trading Rules to Test For Weak from Efficiency in Capital Markets

This work focuses on the sensitivity of the performance of the moving average (MA) trading rule of technical analysis to changes in the MA length employed. Empirical analysis of daily data from NYSE, the Vienna Stock Exchange (VSE) and the Athens Stock Exchange (ASE) reveal high variability of the performance of the MA trading rule as a function of the MA length for all these markets, a result that weakens the conclusions of previous works, regarding the validity of the hypothesis of weak form market efficiency. Further, the trading rule is found to have predictive power in ASE and VSE, but not in NYSE.Efficiency of Capital Markets; Technical Analysis Trading Rules with Moving Averages; Athens Stock Exchange; New York Stock Exchange; Vienna Stock Exchange.

European exchange trading funds trading with locally weighted support vector regression

In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series

A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting

[EN] In this paper we propose and validate a trading rule based on flag pattern recognition, incorporating im- portant innovations with respect to the previous research. Firstly, we propose a dynamic window scheme that allows the stop loss and take profit to be updated on a quarterly basis. In addition, since the flag pat- tern is a trend-following pattern, we have added the EMA indicator to filter trades. This technical analysis indicator is calculated both for 15-min and 1-day timeframes, which enables short and medium terms to be considered simultaneously. We also filter the flags according to the price range on which they are de- veloped and have limited the maximum loss of each trade to 100 points. The proposed methodology was applied to 91,309 intraday observations of the DJIA index, considerably improving the results obtained in the previous proposals and those obtained by the buy & hold strategy, both for profitability and risk, and also after taking into account the transaction costs. These results seem to challenge market efficiency in line with other similar studies, in the specific analysis carried out on the DJIA index and is also limited to the setup considered.The fourth author of this work was partially supported by MINECO, Project MTM2016-75963-P.Arévalo, R.; García, J.; Guijarro, F.; Peris Manguillot, A. (2017). A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications. 81:177-192. https://doi.org/10.1016/j.eswa.2017.03.0281771928

Contrarian Technical Trading Rules: Evidence From Nairobi Stock Index

We apply several popular technical trading rules in the normal way and a contrarian way to daily data of the Nairobi Stock Index from 9/12/2006 to 4/18/2013. The contrarian usage of popular technical trading rules implies that when a technical trading indicator emits buy (sell) signals, we do the opposite and sell (buy) the index. Results from the study support the predictive power of contrarian technical trading rules. We also investigate whether a trader can use the predictive power of contrarian technical rules to beat the profitability of the buy-and-hold strategy considering both transaction costs and risk. Designing four strategies of various contrarian trading rules, we conclude that it is possible to beat the buy-and-hold strategy even considering transaction costs and risk