A Simple Trading Strategy Based on an Artificial Neural Network
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Master’s Degreee Programme – Second Cycle (D.M. 270/2004) in Economics and Finance Final Thesis A Simple Trading Strategy based on an Artificial Neural Network Supervisor Ch. Prof. Marco Corazza Graduand Enrico Brombin Matriculation Number 842777 Academic Year 2016 / 2017 I would like to thank Professor Corazza, supervisor of this thesis, for the enthusiasm and support shown me during the writing period. Without you, this work would not have come to life. A big thanks goes to my family who, with their support, both moral and economic, have allowed me to be here, contributing to my personal development. I would also like to thank my father, whose memory constantly strengthens and motivates me. Finally, I thank my friends, in particular, Chiara, Cecilia, Andrea, Tommaso, Marco, Davide and Marco, who have always listened to me and supported me during my studies. Table of Contents Introduction .................................................................................................................... 1 Chapter 1 ........................................................................................................................... The Theory behind the trading system ................................................................. 3 1.1 Introduction .............................................................................................................. 3 1.2 Technical Analysis .................................................................................................... 4 1.3 Fundamental Analysis .............................................................................................. 8 1.4 Technical versus Fundamental Analysis .............................................................. 11 1.4.1 Assumptions ....................................................................................................... 11 1.4.2 Purposes ............................................................................................................ 12 1.4.3 Instruments ........................................................................................................ 13 1.5 Conclusions ............................................................................................................. 14 Chapter 2 ........................................................................................................................... Artificial Neural Networks ...................................................................................... 17 2.1 Introduction ............................................................................................................ 17 2.2 Biological Neural Network .................................................................................... 18 2.3 Artificial Neural Network ...................................................................................... 19 2.4 Brief resume of the literature ................................................................................ 20 2.5 Network Architectures ........................................................................................... 23 2.5.1 Single-Layer Perceptron ................................................................................... 24 2.5.2 Multi-Layer Perceptron ..................................................................................... 25 2.5.3 Activation Functions .......................................................................................... 27 2.6 Neural Network Learning ..................................................................................... 33 2.6.1 Supervised Learning and the Delta Rule ........................................................... 34 2.6.2 Unsupervised Learning ..................................................................................... 36 2.6.3 Gradient Descent Algorithm ............................................................................. 36 2.6.4 Error Back-Propagation Algorithm .................................................................. 39 2.7 Recurrent Neural Network .................................................................................... 43 2.7.1 Hopfield Network .............................................................................................. 43 2.7.2 Elman Network .................................................................................................. 45 2.7.3 Back-Propagation through time and the vanishing gradient problem .............. 47 2.7.4 Long Short Term Memory and Gated Recurrent Unit ...................................... 48 Chapter 3 ........................................................................................................................... Research and Analysis .............................................................................................. 51 3.1 Neural Network Design and Methodology ........................................................... 51 3.1.1 Variable Selection ............................................................................................. 52 3.1.2 Data Collection ................................................................................................. 68 3.1.3 Data Preprocessing, Training, validation and test sets .................................... 69 3.1.4 Network Design: Paradigms, Evaluation criteria and Training ....................... 70 3.1.5 Implementation .................................................................................................. 71 3.2 Results ..................................................................................................................... 73 Conclusions ................................................................................................................... 85 Bibliography ................................................................................................................. 87 Introduction Uncertainty, or risk, is something that has always characterized the world’s financial markets: stock market crashes, speculative bubbles, financial crises, unexpected recovery, irrational fears, the so-called black swan, and so on. Therefore, those who act in the markets have always been looking for a method to minimize the risk of losses while, possibly, maximizing the performance of their investments. Market operators have been studying artificial intelligence, and especially Artificial Neural Network (ANN), for minimizing financial risks, for more than twenty years. In this thesis, we wanted, therefore, to assess whether there is the possibility that market operators effectively use ANNs to make investment decisions. In detail, we examined two mutual funds, such as market operators, we analyzed part of their composition and using, as net inputs, various Technical Analysis indicators based on the historical opening, closing, maximum and minimum prices of the funds’ shares and, as target outputs, the historical returns of the shares, we built and trained an ANN. Subsequently, by presenting new inputs never seen before to the ANN, we tested its predictive capabilities and, using a simple trading strategy to simulate a market agent, we tested its performance and then compared them with the funds’ ones. The thesis is composed of three chapters: the first chapter describes the reasons that led us to use, as network inputs, indicators based on Technical Analysis and we discuss its validity also comparing it to the Fundamental’s one; in the second chapter, instead, we wanted to focus on the theory of ANNs, we described the theories behind them, their nature and structure and, finally, we explained different "historical" models which form the basis of the most recent networks; in the third and last chapter, on the other hand, we focused on the Technical Analysis indicators we used, the shares object of our analysis, the structure and the architecture of the ANN we have constructed and, finally, we presented and critically explained the results obtained. 1 2 Chapter 1 The Theory behind the trading system This paper describes the construction of a trading system based on an ANN. Therefore, we want to assess the significance of the theory which our trading system is based on, that is, Technical Analysis. Among the traders operating in financial markets, in fact, there are two main schools of thought, referred to Technical and Fundamental Analysis. Thus, in order to carry out this valuation, in this chapter both the theories will be described and compared. 1.1 Introduction A pillar of financial economics is represented by the Efficient Market Hypothesis. The first theoretical developments are due to Samuelson and Mandelbrot in the 60s. An efficient market is a market in which prices reflect all the publicly available information. This is equivalent to saying that agents, in making their decisions, behave in a rational way, that is, they use all the information available at the moment in which the choice is made. If current prices reflect all available information, their variations can depend only on new events that are not known and, thus, unpredictable. For this reason, it is not possible to make accurate price’s predictions, that means, there is not any market operator able to obtain particularly high profits from stocks trading. The main implication of this theory is that financial markets are structurally stable, meaning that supply and demand are in equilibrium determining the price. For the market agents that accept this theory, the instruments used to analyze the markets and their evolution are referred to Fundamental analysis.