In this survey work, we explored the possibility of predicting financial markets using machine learning techniques, with a focus on the application of genetic programming (GP) to the definition of profitable trading strategies for stock prices, stock indexes, and foreign exchange rates (FX rates). Financial markets are complex systems, the study of which has spanned over the years, both from a theoretical standpoint and from a more practical perspective. While many, in the theoretical discussion, support the essential unpredictability of financial markets, others suggest that stock prices and FX rates are in fact predictable, and developed analytical models to prove so. Building mainly on technical analysis, the study of historical prices in the belief that patterns do tend to repeat themselves, computer scientists over the years applied different machine learning techniques to produce relevant predictions and strategies. Based on our research, the machine learning techniques that most commonly are applied to make daily forecasts are GP and artificial neural networks (ANNs), with fuzzy logic-based methods gaining popularity in recent years. Results of the different techniques are comparable, in that they are mixed and inadequate to establish a clear outperforming method. We selected GP applications to focus on. In most research papers that we reviewed, GP is used as the search algorithm to find profitable trading strategies in stock and foreign exchange markets. We present and discuss the different applications from a number of different perspectives, starting from the motivations that guided researchers to adopt GP, then moving to the definition of inputs and building blocks of the GP algorithm, and concluding with the solutions adopted to deal with the issue of adapting the trading models to the dynamic conditions of financial markets. GP appears to be a relevant approach to the prediction of financial markets, with clear advantages over other methods. The research conducted so far is rather solid, and the open issues and proposed future developments well-defined.
In questo lavoro, esploriamo la possibilità di prevedere l’andamento dei mercati finanziari usando tecniche di machine learning, presentando una rassegna della letteratura pertinente; ci concentriamo, in particolare, sull’applicazione di genetic programming per la definizione di strategie di investimento profittevoli che riguardino prezzi di azioni, indici azionari e tassi di cambio tra valute. I mercati finanziari sono sistemi complessi, lo studio dei quali si è sviluppato nel corso degli anni sia da un punto di vista strettamente teorico, sia da una prospettiva più applicata. Da una parte, molti sostengono la sostanziale impredicibilità dei mercati finanziari, dall’altra alcuni suggeriscono che prezzi e tassi possano essere effettivamente previsti, e hanno sviluppato modelli analitici per dimostrarlo. Partendo dall’analisi tecnica (technical analysis), cioè dallo studio delle serie storiche dei prezzi nella convinzione che alcuni pattern si ripetano nel tempo, tecniche di machine learning sono state impiegate per produrre previsioni e strategie rilevanti. Sulla base della nostra ricerca, le tecniche principalmente utilizzate per le previsioni giornaliere sono genetic programming e reti neurali, con metodi basati su logica fuzzy che hanno acquistato popolarità negli ultimi anni. Le differenti tecniche forniscono risultati comparabili, positivi solo in parte e tendenzialmente inadeguati a stabilire quale sia il migliore approccio da utilizzare. Abbiamo scelto di focalizzarci sulle applicazioni di genetic programming. Nella maggior parte dei documenti di ricerca analizzati, genetic programming viene utilizzato per trovare strategie di investimento favorevoli nel mercato azionario e monetario. Discutiamo le differenti applicazioni da diversi punti di vista: le motivazioni che hanno guidato la scelta dei ricercatori di adottare genetic programming, la definizione dei dati in ingresso e delle primitive dell’algoritmo di ricerca genetica, e le soluzioni adottate per risolvere il problema di adattare i modelli di previsione alle condizioni dinamiche dei mercati finanziari. Riteniamo che genetic programming sia un metodo di previsione adatto allo scopo. La ricerca condotta finora è abbastanza solida, e i punti ancora aperti e le proposte di ulteriore sviluppo ben definiti.
Surveying financial markets prediction : a focus on genetic programming applications
ANTONINI, PAOLO
2017/2018
Abstract
In this survey work, we explored the possibility of predicting financial markets using machine learning techniques, with a focus on the application of genetic programming (GP) to the definition of profitable trading strategies for stock prices, stock indexes, and foreign exchange rates (FX rates). Financial markets are complex systems, the study of which has spanned over the years, both from a theoretical standpoint and from a more practical perspective. While many, in the theoretical discussion, support the essential unpredictability of financial markets, others suggest that stock prices and FX rates are in fact predictable, and developed analytical models to prove so. Building mainly on technical analysis, the study of historical prices in the belief that patterns do tend to repeat themselves, computer scientists over the years applied different machine learning techniques to produce relevant predictions and strategies. Based on our research, the machine learning techniques that most commonly are applied to make daily forecasts are GP and artificial neural networks (ANNs), with fuzzy logic-based methods gaining popularity in recent years. Results of the different techniques are comparable, in that they are mixed and inadequate to establish a clear outperforming method. We selected GP applications to focus on. In most research papers that we reviewed, GP is used as the search algorithm to find profitable trading strategies in stock and foreign exchange markets. We present and discuss the different applications from a number of different perspectives, starting from the motivations that guided researchers to adopt GP, then moving to the definition of inputs and building blocks of the GP algorithm, and concluding with the solutions adopted to deal with the issue of adapting the trading models to the dynamic conditions of financial markets. GP appears to be a relevant approach to the prediction of financial markets, with clear advantages over other methods. The research conducted so far is rather solid, and the open issues and proposed future developments well-defined.File | Dimensione | Formato | |
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https://hdl.handle.net/10589/137728