Are Technical Trading Rules Useful to Beat the Market? Evidence from the Brazilian Stock Market

Abstract: According to the Efﬁcient Market Hypothesis, future returns cannot be forecasted based on past returns.This paper explores the proﬁtability of several trading rules vis-`a-visa buy-and-hold strategy and an ARMA predictionmodel. We use a Multilayer Perceptron network to forecast the daily return.The network is trained using a backpropagation algorithm. According to our results the ANN earns a greater return than the benchmark strategy. However,the prediction power is far from the perfect forecast. This implies that the Efﬁcient Market Hypothesis is partiallyinvalidated.Keywords: Technical trading rules, neural networks, efﬁcient market hypothesis 1. I NTRODUCTIONThe study of the informational efﬁciency is maybe one ofthe most elusive issues in ﬁnancial economics. Stock marketsallow investors to buy or to sell securities at “fair and equi-table” prices [1]. Prices will be “fair and equitable”, wheneverthe market is informationally efﬁcient. A market is efﬁcient ifprices fully reﬂect all the available information (see [2]). TheEfﬁcient Market Hypothesis (EMH) is a necessary conditionfor the existence of equilibrium in a competitive market, inwhich arbitrage opportunities cannot exist. [3] indicates thatthis deﬁnition evokes the idea that prices are the result of deci-sions by individual agents and, therefore, depend on the under-lying information. As a corollary, with the same informationset it is not possible to obtain superior returns. It implies, also,that future returns depend to a great extent not only on historicinformation but also on the new information that arrives at themarket. Therefore, an investor, whose information set is thesame of or inferior to the market information set, cannot beatthe market. In addition, investors cannot control the ﬂow oftheir informative endowment towards the market, since theirown transactions (according to its direction and volume) actas signals to the market, tending, thus, to an equalization ofthe informative sets of the different participants in the mar-ket. This produces that, in average, participants cannot beatthe market on a regular basis.Informational efﬁciency is classiﬁed in three broad cate-gories (see [2]), depending on the information set that con-veys signals to prices. The ﬁrst category is the weak efﬁcien-cy, where stock prices already reﬂect all the information con-tained in the history of past prices. The second category issemi-strong efﬁciency, where the information set is all publicknown information. Finally, the third category is strong ef-ﬁciency, where prices reﬂect all kind of information, publicand private. Although it may seem at ﬁrst sight a sign of irra-tionality, randomly evolving stock prices are a necessary con-sequence of rational investors competing to discover relevantinformation. In an update of his original literature review [4]detects the increasing interest for predictable components instock prices and returns. The ultimate goal for studying stockmarket forecasts is to gain knowledge about the dynamics thatunderlies in the return generating process.The aim of this paper is to develop a technical trading rulewhose goal is to beat the market, which will allow to test theweak form of market efﬁciency. We develop an Artiﬁcial Neu-ral Network (ANN), that follows a trading rule and we com-pare the result with a buy-and-hold strategy. If the market isefﬁcient the technical trading rule cannot outperform the mar-ket. The rationale for the use of neural networks to predictfuture movements in stock prices and returns is that investorsare rational and learn from past experience. One of the maincharacteristics of ANNs is their ability to learn, without as-suming any econometric model or relations in the time series[5]. ANNs have been used since many years in pattern recog-nition and more recently were introduced in ﬁnance to im-prove the forecasts in security markets. For a survey on theuse of ANNs in ﬁnancial markets see [6]. It was found in [7]that feedforward networks give better predictions of foreignexchange rates than the random walk model. Yao and Tan [8]uses neural networks to forecast option prices. Lin and Yeh[9] compares the performance between the Black-Scholes for-mula and a neural network model in order to forecast optionprices. Kamruzzaman and Sarker [10] uses an ANN in order toforecast exchange rates and ﬁnds that ANN outperforms oth-er forecast models such as Box and Jenkins’ Auto-RegressiveMoving Average models. Consequently, we will use an AR-MA model as a benchmark model to evaluate the performanceof the ANN. In spite of the fact that ARMA is a general uni-variate model and is based on the assumption that the timeseries is stationary [11], its use is a standard in the ﬁnanceliterature. Our paper is organized as follows: The next section ex-plains the methodology and data. Section 3 presents the resultsand Section 4 draws the main conclusions.