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The Application of Artificial intelligence on Finance and Investing

Since economy and economic situations usually have uncertain behaviors, it is hard to predict its trend with traditional approaches. As the uncertain behaviors change by the time, people need to solve nonlinear and time variant problems. Artificial intelligence was introduced to solve similar problems. According to Wikipedia, artificial intelligence is defined as intelligence displayed by machines. Nowadays, three famous artificial intelligence techniques have been mainly applied on real financial problems to achieve the goal of predicting economic situations, including artificial neural networks, expert systems and hybrid intelligence systems.

In artificial intelligence, an expert system (ES) is a computer system which has the similar decision-making ability of a real person expert. There are two sybsystems in one single expert system, the knowledge base and the inference engine. The knowledge base stores facts and if-then rules, while the inference engine applies those rules to the existing facts to generate the new facts, which is the conclusion.

In the area of investment and tax advice, ES works as an assistant for intensive works to give advice on investment, insurance, tax, assets, and so on. The work usually starts from data entry, followed by input of ideas by the planner (Humpert and Holley 78). Then the programme will run on the knowledge base and inference engine. During the work, the inference engine continuously examines the status of the knowledge base, and determines the order where inferences are made. the Finally, a preferred option will be generated as a report. Different from other mathematical models, ES can be summarized with following characteristics. First, ES is not only able to apply mathematical or analog schemes, but also can handle factual or heuristic knowledge. Second, the knowledge base can be continuous updated based on the prior knowledge and the input as the evidence. Third, the ES can also handle simply qualitative information. Fourth, ES is able to cope with uncertain, unreliable or even mission data (Bahrammirzaee 1165).

With these advantages, ES became popular on finance since the 1970s. in 1987, a review of ES on finance presented a variety of ES application in finance, investment, accounting, taxation, and administration since 1977. In 1988, a similar review was published about the application of ES on finance, including investment and tax advice, financial planning, risk assessment and banking practice. After that, a series of review of ES application on finance have been published (Wang et al. 144–145). In 2010, a recent review summarized the main application of ES in financial domain as follows.

The first application is credit evaluation. As credit is the basis of the conditions and the amount of a loan, a loan officer has to track the customer’s credit history carefully. This task is repetitive and unstructured. The advantage of ES in credit evaluation is its high speed and accuracy. For example, it is well known that American Express credit card application do not need to wait a long time for the results. The reason is that they utilize ES to process the requests. After using ES, their bad guess rate dropped from 15% to 4%, which reflects the high accuracy of ES (Bahrammirzaee 1165). During the 1980s, multiple ES systems were developed to manage banking loans. In 1986, a credit-evaluation ES with MuLISP was developed by the academy of economics in Wroclaw, Poland. In 1989, some French industrial companies started to use a knowledge-based decision support system (KB/DSS), FINISM, to conduct financial analysis and planning. In the 21st century, various ES system are continuously developed to meet different demands of loans. In 2001, ALEES was developed to evaluate agricultural loan incorporating with qualitative and quantitative assessment. In 2003, CEEES began to work on granting credit lines to applicant firms. By the comparison to existing methods in finance, developed ESs exhibited better performance on high efficiency and accuracy.

The second promising application of ES is financial prediction and planning. In this area, ES helps marketing executives to generate attractive finance plans for customers who are “interested in making large scale investments in products, services, and to back it up with convincing arguments that take into account the conflicting interests in business and finance” (Wang et al. 88–89). In this area, FAME system is a famous one for financial marketing. It runs on Lisp and provide financial marketing recommendations for mainframe computer business. FAME was written with several subdomains, including customer information gathering, capacity analysis, cash flow generation, financial analysis and explanation and advice. In this system, the costumer can question any part of the explanation only by pointing to it on the screen. Another developed ES is FINEVA, which is a multicriteria knowledge-based ES to assess firm performance and viability. The inference engine of this system utilizes both backward and forward chaining method. The output of this system can suggest the ranking of the analyzed firm based on class of risk.

In recent decades, artificial neural networks (ANN) emerged and found extensive acceptance in many disciplines for modeling complicated practical problems. ANNs was inspired from biological nervous systems and brain structure. As a computational modeling tools, artificial neural networks have been applied for a large number of economic situations. Currently, ANN represents powerful solutions for subjective information processing, decision-making, forecasting and other related problems. Especially in recent years, ANN become a popular tool for economy prediction.

Stock prices is highly-noisy because stock markets are affected by tons of factors. Predicting stock price with the high-noisy data directly will lead to large errors. In 2011, a Chinese team developed a Wavelet De-noising-based Back Propagation neural network (WDBP) to predict the stock prices. The simulation with Shanghai Composite Index from 1993 to 2009 demonstrated that the application of the WDBP neural network to stock price prediction is effective and accurate (Wang et al. 1016).

Financial markets are complicated nonlinear systems with subtleties and interactions hard to comprehend. Therefore, ANNs is extensively used in financial prediction and planning. As the knowledge of economy developing, ANNs were improved step by step. In 1996, a paper was published to explain the developing of a neural network to forecast financial and economic time series in details. In their theory, there were eight steps to design a neural network forecasting model, including variable selection, data collection, data preprocessing, training, testing, and validation sets, neural network paradigms, evaluation criteria, neural network training and implementation(Kaastra and Boyd 215).

The third type of artificial intelligence is hybrid intelligent systems. As every single approach has its own strengths and shortcoming, many researchers seek to integrate some of them to gain better performance. Hybridization refers to mixing different functions in order to perform a complex task. Hybrid intelligent system not only represents the combination of different intelligent techniques, but also uses with conventional computer systems and spreadsheets and databases. The degree of interaction among the modules in a hybrid system can be varied from loosely coupled, transformational models, tightly coupled models to fully coupled models.

As a variety of ANNs have been developed, a state-of-the-art review divided ANNs to two types by the different data mining methods and different goals. The first type is for verification in which they system is limited to verifying a user’s hypothesis. The second type is discovery where the system finds new patterns. The review found the Rough Sets Theory of Pawlak, as a new knowledge discover tool, had many advantages for financial prediction. Firstly, the rough sets model is based on the original data only. Secondly, the rough sets model is a tool suitable for analyzing both quantitative and qualitative problems. Third, it is able to discover hidden facts in data and express them in the natural language of decision rules. Fourth, the set of decision rules derived by the rough sets model provides a generalized description of the knowledge contained in the financial information tables. Fifth, the decision rules from the model are based on facts. Sixth, the results are easy to understand without professional interpretation of technical parameters.

With so many strength, rough set theory and artificial neural network were merged to propose a hybrid intelligent system to predict the failure of firms according to the past financial performance data. In this hybrid approach, the developer said they get reduced information table, which implies that the number of evaluation criteria was reduced with no information loss. The rules developed by rough set analysis suggested the best prediction accuracy if a case does not match any of the rules. Analysis of 2400 Korean firms during the period 1994-1997 validated the effectiveness of this hybrid approach (Ahn et al. 65–67).

Another trial for hybrid approach was on stock price forecasting. Genetic fuzzy systems (GFS) and ANN was integrated to construct a stock price forecasting expert system. The first step of this hybrid approach is stepwise regression analysis in which factors most influencing the stock prices will be determined. The second step was dividing the raw data into k clusters by self-organizing map neural networks. Finally, all clusters were fed to independent GFS models. The evaluation results suggested that this hybrid approach performed well on mean absolute percentage error. Therefore, the developers believed it is a suitable tool for stock price forecasting problems(Hadavandi et al. 800–801).

Although most people believe the application of artificial intelligence will be the main trend in prediction of economy and economic situations, there are some risks and shortcomings limiting this application. The first shortcoming is the high expense of the artificial intelligence system. The adoption of an artificial intelligence system is not only cost for the creation of smart technologies, but also the need for repair and ongoing maintenance (dcomisso n.p). The high cost limits the widely use of artificial intelligence adoption, particularly in small- and medium-scale companies. Second, the current artificial intelligence systems are not totally reliable for economic decisions. People would like to use the results of the AI systems only for reference instead of judgement (Artifical Intelligence). For example, several banks have introduced AI systems for credit evaluation. When people want to apply a new credit card, the system will make the decision of approval or disapproval in several minutes. However, the decisions can be overthrown totally by the judgement of human. That is why the manual calls after the decision usually works when people believe the decision is not correct. Moreover, there are a variety of networks using for prediction of economy and economic situations without any gold standard. Since any network has the pros and cons, people are hard to say which network is better. When the results of networks are different, it is hard for human to make decision only depending on those results of artificial intelligence.

But the need to solve highly nonlinear, time variant problems has been growing rapidly(Humpert and Holley 80–83). Conventional and traditional mathematical model, even single intelligent technique cannot meet the demands of prediction to economy and economic situations. Conclusively, the intense demands in this area attracts application of artificial intelligent techniques, such as fuzzy logic, ANN, ES, and recently hybrid intelligent system.

Works Cited

Ahn, B. S., et al. “The Integrated Methodology of Rough Set Theory and Artificial Neural Network for Business Failure Prediction.” Expert Systems with Applications, vol. 18, no. 2, 2000, pp. 65–74.

Artifical Intelligence. Artificial Intelligence: The Pros and Cons | Arrk Group. https://www.arrkgroup.com/thought-leadership/artificial-intelligence-the-advantages-and-disadvantages/. Accessed 16 Nov. 2017.

Bahrammirzaee, Arash. “A Comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems.” Neural Computing and Applications, vol. 19, no. 8, 2010, pp. 1165–1195.

dcomisso. “Risks and Limitations of Artificial Intelligence in Business.” Nibusinessinfo.Co.Uk, 16 June 2017, https://www.nibusinessinfo.co.uk/content/risks-and-limitations-artificial-intelligence-business.

Hadavandi, Esmaeil, et al. “Integration of Genetic Fuzzy Systems and Artificial Neural Networks for Stock Price Forecasting.” Knowledge-Based Systems, vol. 23, no. 8, 2010, pp. 800–808.

Humpert, B., and P. Holley. “Expert Systems in Finance Planning.” Expert Systems, vol. 5, no. 2, 1988, pp. 78–101.

Kaastra, Iebeling, and Milton Boyd. “Designing a Neural Network for Forecasting Financial and Economic Time Series.” Neurocomputing, vol. 10, no. 3, 1996, pp. 215–236.

Wang, Jian-Zhou, et al. “Forecasting Stock Indices with Back Propagation Neural Network.” Expert Systems with Applications, vol. 38, no. 11, 2011, pp. 14346–14355.