Humans have improved their status among the other species in the world, because of their intelligence. From many centuries people have developed their abilities, skills and knowledge and today the humans have the highest technology ever on the face of the earth. Today the modern science is trying to replicate the human’s intelligence to a machine. Scientists are conducting various researches to implement cognitive abilities and skills to the machines. That study field is identified as the artificial intelligence (Yang, Shan and Bui, 2008). Evolutionary computing is a subfield of artificial intelligence. Evolutionary computing is based on the various algorithms (Yang, Shan and Bui, 2008).
These algorithms may get simple to complex, most of the time complex that would give the assistance to define probabilities and potentials that would very helpful to make decisions. The decision making process that needs a high level of various environmental factor considerations would use those algorithms. Since the evolutionary computing is using those algorithms, the subject area can be applied to the field which requires high level of various factor considerations (Yang, Shan and Bui, 2008). Often evolutionary computing is applied to various economic and financial problems such as optimisation of investment portfolios (Yang, Shan and Bui, 2008).
No one in the world would not be able to predict the behaviours of the trading floor. The prices would differ because of too many reasons and mainly the external environment would affect the organisations to differ their stock prices. Many argue that these price changes have patterns while others are arguing that it would not be any constant way to understand the trading and suggest theories (Tantar, 2013).
Many traders are following various strategies to improve their profitability on the market floor. Those trading strategies would ensure the maximum potential outcomes to the trader. Mainly the trading strategies would collect the previous knowledge of the stock market and build up new knowledge based on that collected previous knowledge (Tantar, 2013). Mainly the strategies would point out the trader when to sell and when to hold. The secret of the success on the trading floor is making the advantage of the trades when they are hitting the maximum values or the benefits. The hold should be beneficial to the trader and it should promise the future benefits of the stocks. That trading profile optimisation would be harder to create but when it is established the trader would be highly benefitted by that (Tantar, 2013).
As we discussed the trading would have a high level of probabilities. Some trading would give the promised results while others are giving the highly unexpected results. But it is a well known fact when dealing with the market floor it is essential to study the behaviour of the stock in the past to make future decisions (Tantar, 2013). The trader’s portfolio of the stock should have that previous indicator or the previous knowledge which support the future decision making of the portfolio. When the orientation is so successful the trader would be able to predict the behaviours of the portfolio and what is the optimum time to sell or hold the stocks on the trading floor. That sort of portfolio optimisation would deliver high level of benefits to the trader and give high level of support to define the strategies when trading in the market (Squillero and Burelli, 2013).
EC has been used for many subject areas. EC would assess the high level of information to give assistance to make decisions in those areas. Below it was discussed how EC was used in the trading strategies.
The stocks’ behaviour of the organisation would be assessed through the EC. This behaviour assessment would both target short term and long term. A simple chart or a timeline which expresses the price changes of the stocks over the time period would give a high level of input to the decision making. EC would use algorithms to calculate the behaviours of the stocks during the time period and provide in detail evaluation that support the decision making the process of the traders (Squillero and Burelli, 2013).
Under the EC, the organisational previous information would be assessed using various mathematical models that generally used for the purpose of predicting the behaviours of the stocks and bonds. The traders would be able to see clearly how the stocks have behaved during the recent years and identify similarities that are indicated by the EC (Chen, 2009).
EC would use both internal and external knowledge sources to get support for its decision making functions. Internal knowledge sources would provide the supporting information and knowledge that are generated through analysing the internal business functions of the organisation. The external knowledge sources would provide the supporting information and knowledge that are generated through analysing the external environmental factors which are similar to the particular business industry, the status of the country’s and world’s economy, changes in the culture and consumer behaviour etc (Chen, 2009).
Especially the EC algorithms would assess the impacts of the economy of the market and provide in detail knowledge to the traders to create a better functioning portfolio for their investment purposes. In here the organisational previous behaviour information would be highly beneficial to make decisions because the EC would be able to identify the patterns of the stock behaviour in the market because of the previous economic conditions in the market. The decision of the EC would have a high level of dependency of the information density that the system would get to make the decisions (Chen, 2009). Also, the system should be highly advised to be used up to date modern data assessment algorithms for pattern identification and decision making functions.
The EC algorithms would provide detail information for the decision making functions towards the stocks of the market (de Garis, 2004). This ability would be highly valued by the novice traders of the market. Novice traders of the market would not have a high level of knowledge about the behaviour of the stocks in the market. They would simply look at the price changes and invest their money according to the price changes of the stocks without having a simple clue about the behaviours of the stocks (de Garis, 2004).
Those traders would make money but most of the time they would end up in making losses. Since the EC algorithms are providing in detail information to purchase the stocks the novice traders would be able to get their portfolio together with high level of potential of making profits. Also, the EC would give a high level of assistance in the decision making functions of the trading floor. It would indicate when to buy the stocks and also the traders can follow up the stocks that they are interested in buying with the help of EC. EC would indicate the best time to buy the stock or simply the trader can purchase the stocks when the price is low. The various unfavourable market condition would decrease the value of the stocks and the EC would indicate the potential duration of the stock holding time to traders. Also, the EC would define the positive market conditions to sell the stock to get the benefits of the trading floor to the trader.
The EC would be very helpful to the trader to identify the potentials of his stock portfolio. The organisation has been analysed by the system and give the maximum information and decision to the traders to make in detail decision for the investment choices in the trading floor. The trader can optimise his portfolio with that information and get a high level of potential for his portfolio. Various internal and external data sources would be assessed by the system to improve its knowledge to decision making and using those decisions of the system the traders would be able to make better optimisation for their portfolios.
According to the study it was clear that the EC technologies are still in the primary stages of the decision making capabilities. The field of artificial intelligence is still developing and because of that its subset areas are still developing (Esparcia-Alcázar, 2013). If the EC technologies can adapt high end self thinking and decision making abilities the capabilities of the EC would unimaginable. By then the EC would be able provide high end solutions for the market functions and also it would be able to identify unseen factors and logics of the economy.
Currently the EC technologies are conducting high level of analysis tasks in the market. Those information that gained through the analysis functions would be very useful to make decisions in the market. Also the mathematical algorithms that the system is using would provide guidance for the improvements of the investment portfolio. Still the system is not be able to provide the exact predictions of the market potentials. The system is limited to the previously gathered knowledge.
The current status of the EC can be used for the purpose of investment decision making functions. The investor or the trader would be able to assess the organisational status according to the analysis of the EC system and decide whether that he is going to make the right decision in the trading floor (Esparcia-Alcázar, 2013). Also the EC would be able to provide various decision making suggestions to the user by analysing the previous data and the current market conditions. Using those data the trader would be able to optimise the investment opportunities. The trader would be able to use his decision making and cognitive capabilities with the system abilities to make the best decision in the market. Currently the abilities of the EC is highly appreciated in the trading floor as well.
The technology should be improved to implement the full automation functions in the market. The high level of decision making abilities of the system would give additional capabilities to the system as well. The highly improved self decision making capabilities would provide much flexibility to the investor. That is the future development suggestions to the system. If the system is capable of making an own decision the system can make the decisions when to buy, sell and hold the stocks. The user would be able to set up the decision making level of the system and the user would be able to allow the system to make overall decisions about the system by its own. This is similar to having a savings account in a bank. The system would automatically generate the income to the trader without having a high level of involvement of the trader to the trading functions of the trading floor (Squillero and Burelli, 2013).
In conclusion, the EC technologies are providing high level of assistance to the traders in the trading floor. The algorithms that the EC is using are much helpful to identify the various patterns and potentials of the market. That is very helpful to the traders when optimising their portfolio. They would be able to select the high potentials of the investment pool when investing their money in stocks. The EC would use various knowledge sources to define the strategies for the successful trading in the market. But still, the technology is not much capable of providing the best solutions in the market (Squillero and Burelli, 2013). The EC technologies are capable of providing the suggestion of best potential but still the technology cannot provide the highly accurate predictions all the time. The decisions should be taken by the humans and therefore the EC should be developed more to work by its own when it comes to making the decisions in the trading.
- Chen, S. (2009). Evolutionary computation in economics and finance. 1st ed.
- de Garis, H. (2004). Introduction to Evolutionary Computing. Evolutionary Computation, 12(2), pp.269-271.
- Esparcia-Alcázar, A. (2013). Applications of evolutionary computation. Berlin: Springer.
- Iba, H. and Aranha, C. (2012). Practical applications of evolutionary computation to financial engineering. Berlin: Springer.
- Squillero, G. and Burelli, P. (2013). Applications of evolutionary computation. 1st ed.
- Tantar, E. (2013). EVOLVE– A bridge between probability, set oriented numerics and evolutionary computation. Berlin: Springer.
- Yang, A., Shan, Y. and Bui, L. (2008). Success in evolutionary computation. Berlin: Springer.