Abstract

In the financial market, there are a large number of indicators used to describe the change of stock price, which provides a good data basis for our stock price forecast. Different stocks are affected by different factors due to their different industry types and regions. Therefore, it is very important to find a multi factor combination suitable for a particular stock to predict the price of the stock. This paper proposes to use Genetic Algorithm(GA) for feature selection and develop an optimized Long Short-Term Memory(LSTM) neural network stock prediction model. Firstly, we use the GA to obtain a factors importance ranking. Then, the optimal combination of factors is obtained from this ranking with the method of trial and error. Finally, we use the combination of optimal factors and LSTM model for stock prediction. Thorough empirical studies based upon the China construction bank dataset and the CSI 300 stock dataset demonstrate that the GA-LSTM model can outperform all baseline models for time series prediction.

Introduction

Buying and selling stocks is a very interesting thing. Because stocks can make investors get big profits, but vice versa. To get a big profit, investors need to analyze in predicting stock prices. However, predicting stock prices is a difficult thing to do because stock prices fluctuate rapidly all the time. So that investors need to predict the stock price as short as possible.

Genetic Algorithm or GA is a metaheuristic inspired by the process of natural selection that belongs to the larger class of Evolutionary Algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover, and selection.

The process in genetic algorithms begins with the initialization stage, which is to create random individuals who have a certain gene arrangement (chromosome). These chromosomes represent the solution to the problem. The next stage is reproduction to produce offspring from the existing individuals in the population. After reproduction, new individuals will be born so that the number of individuals increases. Each chromosome has a fitness, the greater the fitness the better the chromosome will be used as a solution. This stage of calculating fitness is called the evaluation stage. The final stage is selection, selecting individuals from the population set and offspring.

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Genetic Algorithm (GA): Proposed Model

Application of GA in the context of data mining is generally for the task of hypothesis testing and refinement, where the user poses some hypothesis and the system first evaluate the hypothesis and seek to refine it. Hypothesis refinement, where the user poses some hypothesis and the system first evaluates the hypothesis and then seeks to refine it. Hypothesis refinement is achieved by “seeding” the system with the hypothesis and then allowing some or all parts of it to vary. One can use a variety of evaluation functions to determine the fitness of a candidate refinement. The important aspect of the GA application is the encoding of the hypothesis and evaluation function for fitness. Another way to use data mining is to design a hybrid techniques by blending one of the know techniques with GA.

GA is search algorithm based on the mechanics of natural selection and genetics and they combine survival of the fittest among string structures to form a search algorithm (L. Davis, 1991). GA is particularly suitable for multi-parameter optimization problems with an objective function subject to numerous hard and soft constraints. The main idea of GA is to start with a population of solutions to a problem, and attempt to produce new generations of solutions which are better than the previous ones. GA operates through a simple cycle consisting of the following four stages: initialization, selection, crossover, and mutation. Figure shows the basic steps of proposed genetic algorithms model.

Trading Rule

Although there are an infinite number of possible rules by which we could trade, it seems that only a few of them would have made a profit. To find the rule that would have yielded the most profit had it been used to trade stocks on a given set of historical data, firstly, we develop trading rules of this general form is as shown below.

General form of trading rules

If today’s value of the indicator 1 is greater than or equal to (less than) A1,
And change since the last day’s value of the indicator 2 is Greater than or equal to (less than) A2,
And last day’s value of the indicator 3 is greater than or equal to (less than) A3
And last day’s value of the indicator 4 is greater than or equal to (less than) A4,
And last day’s value of the indicator 5 is greater than or equal to (less than) A5,
And today’s value of the indicator 6 is greater than or equal to (less than) A6,

There are five conditions that are evaluated for each trading day. If the all of five conditions are satisfied, then the model will produce ‘buy’ signal on that day, otherwise it will suggest ‘sell.’ A1 to A6 denotes the cutoff values. The cutoff values range from 0 to 1, and represent the percentage of the data source's range. For example, if RSI (relative strength index) ranges from 0 to 100, then a cutoff value of 0.0 would match a RSI of 0, a cutoff value of 1.0 would match a RSI of 100, and a cutoff value of 0.5 would match a RSI of 50. This allows the rules to refer to any data source, regardless of the values it takes on. We consider additional flexibility regarding the indicator component of the rule structure such as ‘today’s value,’ ‘last day’s value,’ and ‘change since the last day’s value.’ Translating this in its full form, for example, would yield the following statement:

Trading rules

If today’s value of ROC is greater than or equal to 30.0,
And change since the last day’s value of RSI is less than 60.0,
And last day’s value of stochastic %D is less than 51.0,
And last day’s value of A/D oscillator is less than 12.5,
And last day’s value of MACD is less than 14.9,
And today’s value of stochastic %K is less than 75.9,

Then buy else sell

Above rule structure is summarized in Table 1. In Table 1, ‘which data’ means data source the rule refers to, and ‘modifier’ means a modifier value that determines if the value itself should be examined, or if the last day's value or the change since the last day should be examined. There has been much debate regarding the development of trading system using historical data. We agree that the future is never exactly like the past; however, a common investment approach is to employ systems that would probably have worked well in the past and that seem to have a reasonable chance of doing well in the future. So, we define a goal of the system as finding a rule which would have yielded the highest return over a certain time period.