Explorations in LCS Models of Stock Trading

Schulenburg, Sonia; Ross, Peter

doi:10.1007/3-540-48104-4_10

Cited by 27 publications

(21 citation statements)

References 12 publications

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“…Rather we see the results presented here as providing evidence of the usefulness and potential of Learning Classifier Systems for financial trading. In this respect, these results echo those of Schulenburg and Ross [14,15,16], who also reported encouraging results from a simple Learning Classifier System architecture.…”

Section: Discussionsupporting

confidence: 85%

Foreign Exchange Trading Using a Learning Classifier System

Stone

Bull

2008

Studies in Computational Intelligence

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Abstract. We apply a simple Learning Classifier System that has previously been shown to perform well on a number of difficult continuousvalued test problems to a foreign exchange trading problem. The performance of the Learning Classifier System is compared to that of a Genetic Programming approach from the literature. The simple Learning Classifier System is able to achieve a positive excess return in simulated trading, but results are not yet fully competitive because the Learning Classifier System trades too frequently. However, the Learning Classifier System approach shows potential because returns are obtained with no offline training and the technique is inherently adaptive, unlike many of the machine learning methods currently employed for financial trading.

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Section: Discussionsupporting

confidence: 85%

Foreign Exchange Trading Using a Learning Classifier System

Stone

Bull

2008

Studies in Computational Intelligence

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“…Furthermore, other scholars have used classifier systems to analyze the trading of individual stocks using price indicators as inputs and individual stock sell signals as outputs. For instance, Liao and Chen (2001) used price and volume indicators including closing prices, 6-day average prices, and the OBV indicator as input factors, while Schulenburg and Ross (2002) used average price and volume as input factors; both obtained experimental results significantly better than both Buy & Hold and random trading strategies.…”

Section: Classifier Systemsmentioning

confidence: 96%

Constructing a dynamic stock portfolio decision-making assistance model: using the Taiwan 50 Index constituents as an example

2007

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There are several decisions in investment management process. Security selection is the most time-consuming stage. Tatical allocation is in order to take advantage of market opportunities based on short-term prediction (Amenc and Le Sourd in Portfolio theory and performance analysis. Wiley, 2003). Although it is difficult to keep track of the fluctuations of volatile financial markets, the capacity of artificial intelligence to perform spatial search and obtain feasible solutions has led to its recent widespread adoption in the resolution of financial problems. Classifier systems possess a dynamic learning mechanism, they can be used to constantly explore environmental conditions, and immediately provide appropriate decisions via self-aware learning. This study consequently employs a classifier system in conjunction with real number encoding to investigate how to obtain optimal stock portfolio based on investor adjustment cycle. We examine the constituents of the TSEC Taiwan 50 Index taking moving average (MA), stochastic indicators (KD), moving average convergence divergence (MACD), relative strength index (RSI) and Williams %R (WMS %R) as input factors, adopting investor-determined adjustment cycle to allocate capital, and then constructing stock portfolio. We have conducted empirical testing using weekly and monthly adjustment cycle; the results revealed that this study's decision-making assistance model yields average annual interest rate of 49.35%, which is significantly better than the −6.59% of a random purchase model. This research indicates that a classifier system can effectively monitor market fluctuations and help investors obtain relatively optimal returns. The assistance model proposed in this study thus can provide really helpful decision-making information to investors.

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“…It is an important aspect of finance and has led investors to develop several methods in order to accurately forecast the financial market movements. Over the years, several computational intelligent methods such as Genetic Algorithm [1], Fuzzy Logic [2], Genetic Network Programming [3], Learning Classifier Systems [4], Artificial Neural Network [5,6] have been used for financial forecasting.…”

Section: Introductionmentioning

confidence: 99%

Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm

Aluko

Smonou

Kampouridis

et al. 2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Abstract-Hyper-heuristics have successfully been applied to a vast number of search and optimization problems. One of the novelties of hyper-heuristics is the fact that they manage and automate the meta-heuristic's selection process. In this paper, we implemented and analyzed a hyper-heuristic framework on three meta-heuristics namely Simulated Annealing, Tabu Search, and Guided Local Search, which had successfully been applied in the past to a Financial Forecasting algorithm called EDDIE. EDDIE uses Genetic Programming to extract and learn from historical data in order to predict future financial market movements. Results show that the algorithm's effectiveness has improved, thus making the combination of meta-heuristics under a hyper-heuristic framework an effective Financial Forecasting approach.

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Explorations in LCS Models of Stock Trading

Cited by 27 publications

References 12 publications

Foreign Exchange Trading Using a Learning Classifier System

Foreign Exchange Trading Using a Learning Classifier System

Constructing a dynamic stock portfolio decision-making assistance model: using the Taiwan 50 Index constituents as an example

Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm

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