Soft Computing Techniques for Stock Market Prediction: A Literature Survey

Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market

Soft Computing Approaches to Stock Forecasting: A Survey

Soft computing techniques has been effectively applied in business, engineering, medical domain to solve problems in the past decade. However, this paper focuses on censoring the application of soft computing techniques for stock market prediction in the last decade (2010 - todate). Over a hundred published articles on stock price prediction were reviewed. The survey is done by grouping these published articles into: the stock market surveyed, input variable choices, summary of modelling technique applied, comparative studies, and summary of performance measures. This survey aptly shows that soft computing techniques are widely used and it has demonstrated widely acceptability to accurately use for predicting stock price and stock index behavior worldwide

Soft Computing Techniques for Stock Market Prediction: A Literature Survey

Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges—New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies—Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)

STOCK PRICE TREND PREDICTION USING SUPPORT VECTOR MACHINE AND CORAL REEF OPTIMIZATION ALGORITHM

Due to non-linearity and non-stationary characteristics of stock market time series data, prior approaches have not been adequate enough for predicting stock market prices. Support vector machines are classifier that have been reported in the literature as having good recognition accuracy and have been applied in the area of predicting financial stock market prices and was found efficient. It is however noted that the performance of the SVM is affected by the values of the hyper-parameters used by the SVM. There is the need to find a way for searching for the best hyper-parameters that optimizes the performance of an SVM model. Coral Reef Optimization (CRO) is one of many nature-inspired algorithms used extensively to solve optimization problems. It is very effective in solving optimization problems because it is able to achieve global optimization. This paper’s contribution is the development of Coral Reef search algorithms for the improvement of the hyper-parameters of the SVM used for stock price trend prediction. The Algorithm is validated using stock data of two banks. The results obtained out-performed un-optimized SVM, and have the same performance as that of SVM optimized with the FireFly optimization algorithm.   &nbsp

Using Population-based Metaheuristics and Trend Representative Testing to Compose Strategies for Market Timing

Market Timing is the capacity of deciding when to buy or sell a given asset on a financial market. Market Timing strategies are usually composed of components that process market context and return a recommendation whether to buy or sell. The main issues with composing market timing strategies are twofold: (i) selecting the signal generating components; and (ii) tuning their parameters. In previous work, researchers usually attempt to either tune the parameters of a set of components or select amongst a number of components with predetermined parameter values. In this paper, we approach market timing as one integrated problem and propose to solve it with two variants of Particle Swarm Optimization (PSO). We compare the performance of PSO against a Genetic Algorithm (GA), the most widely used metaheuristic in the domain of market timing. We also propose the use of trend representative testing to circumvent the issue of overfitting commonly associated with step-forward testing. Results show PSO to be competitive with GA, and that trend representative testing is an effective method of exposing strategies to various market conditions during training and testing

Building Market Timing Strategies Using Trend Representative Testing and Computational Intelligence Metaheuristics

Market timing, one of the core deciding when to buy or sell an asset of interest on a financial market. Market timing strategies can be built by using a collection of components or functions that process market context and return a recommendation on the course of action to take. In this chapter, we revisit the work presented in [20] on the application of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to the issue of market timing while using a novel approach for training and testing called Trend Representative Testing. We provide more details on the process of building trend representative datasets, as well as, introduce a new PSO variant with a different approach to pruning. Results show that the new pruning procedure is capable of reducing solution length while not adversely affecting the quality of the solutions in a statistically significant manner

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC