A Review of Artificial Intelligence and Biologically Inspired Computational Approaches to Solving Issues in Narrative Financial Disclosure

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1 A Review of Artificial Intelligence and Biologically Inspired Computational Approaches to Solving Issues in Narrative Financial Disclosure Saliha Minhas 1, Soujanya Poria 2, Prof. Amir Hussain 1, Prof Khalid Hussainey 1, 1 Dept. of Computing Science, Univerity of Stirling, FK9 4LA, Scotland 2 Dept. of Computing Science, Jadavpur University, West Bengal, India szm@stir.ac.uk, ahu@cs.stir.ac.uk, soujanya.poria@ieee.org, khaled.hussainey@stir.ac.uk Abstract. Indisputably, financial reporting has a key role to play in the efficient workings of capitalist economies. Problems related to agency and asymmetric information (Jensen and Meckling, 1976) would abound and cripple financial markets, as it has done when left unchecked (Enron, WorldCom and Tyco). However for too long, quantitative data has monopolised the assessment and prediction role within this arena and this has contributed to the failures, borne out by research (Kumar & Ravi, 2007). As qualitative data proliferates, containing value relevant information it needs to be factored into the analysis. This paper reviews work on financial narrative disclosures and looks at conventional artificial intelligence and more recent biologically inspired computational approaches to catapult the domain to more progressive methods of using linguistic data in evaluations. Keywords: Narrative Financial Disclosure, Biologically Inspired, SenticNet

2 1 Introduction Efficient allocation of resources in an economy, capital market development, liquidity in the market, decreased cost of capital, lower return volatility, and high analyst forecast accuracy (Kothari, Li and Short, 2009) To reach this aim, narrative disclosures can play a significant role, especially given the deluge of unstructured content that abounds and could contain value relevant information. However, despite this potential by far analysis and prediction has been predominantly performed using quantitative data. There is a reason why the saying number s don t lie prevails and not the text doesn t lie. Numerical data is just easier to deal with, whereas text is riddled with ambiguities and difficult to decipher for humans, let alone computers. However, given the deluge, taming text must be done. Within the Financial Reporting domain it would significantly enhance decision making powers of stakeholders. The next section reviews research work in narrative financial reporting to appreciate the task at hand. Thereafter, significant artificial intelligence/biologically inspired approaches are examined to assess their capabilities to decipher content. In particular, the potential of Sentic Computing (Cambria and Hussain, 2012) cyrstalized into SenticNet, an opinion mining engine will be examined. This is built on ontological affective common sense representation and seeks to mimic cognitive processes in handling linguistic data. Preliminary results from this engine run on extracts of company annual reports will be given. 2 Literature Review - Narrative Disclosures Despite being overshadowed by the reams of work in numerical analysis, deciphering narrative disclosure in accounting documents has had its proponents. A catalogue of significant research in this field is given in appendix one. This research can be examined from two angles, delineated below:

3 2.1. A review of methodology applied to understand the narrative content. Merkl-Davies et al (2013) developed a taxonomy to give structure to the relationship between research paradigms and text analysis approaches. They correctly point out that content analysis is the main textual analysis approach used within accounting narrative domain. It involves draw[ing] inferences from data by systematically identifying characteristics within the data (Clatworthy et al, 2001). Within content analysis, two approaches are typically taken: form orientated analysis, which involves routine counting of words or concrete references, and meaning orientated'' analysis, which focuses on analysis of the underlying themes in the texts under investigation (Smith and Taffler, 2000). Form orientated can be quantitative, use of proxies is common to enable statistical analysis or it can be qualitative, searching for occurrence of predefined content categories within texts. As Davies et al (2013) argue what methodology is chosen depends on the aims of the research. Other studies (Li, 2008; Jones and Shoemaker, 1994) have looked at readability of narratives to typically check if the management obfuscation hypothesis is being played out. This is assessed by a readability formula that counts language variables in a text in order to provide a measure of reading difficulty for readers. Still others (Demers and Vega, 2008; Brennan et al, 2008) have looked at rhetorical manipulation, use of certain linguistic choices to influence meaning. This would again be relevant in the area of impression management and fraudulent activity. Another nuance within content analysis work is that it can be performed either with a dictionary or a statistical based approach. In the former, dictionaries are employed to pick up the meaning and polarity of the text. For example (Kothari, Li and Short, 2009) put words into categories through use of a word list and then ascertained polarity (positive/negative) using the General Inquirer dictionary. Whereas, (Feldman et al., 2010) used Loughran and Macdonald s (2011) financial word list with polarity for detecting (positive/negative) tone. This has been touted as being more relevant for the financial domain. Typically once polarity of text has been determined, the results are quantified and used within regression analysis. There is a number of significant studies performed in this manner (Tetlock, 2007, Kothari, Li and Short, 2009, Huang, 2011). The use of dictionaries can be cumbersome as they need to be updated and require expert manual effort. Statistical approaches use a variety of generative, probabilistic models based on bag of words approach and maximizing the likelihood of the observed word being in a class to gauge meaning (Manning and Shutze, 1999). The main criticism is the lack of lexical analysis done, therefore poor capture of semantics.

4 Smith and Taffler (2000) used OCP and SPSS to perform content analysis. They attempted to capture both themes and words. They looked for certain keywords that denote profitability, dividends, resignations etc. They also captured themes by examining combinations of words that would fit into designated thematic components. Keywords and captured themes are given numerical scores to correlate with bankruptcy (tested using failed firms). The scores are fed into linear discriminant models which show that chairman s statement in annual reports highly correlative with bankruptcy. Beattie et al (2004) use content analysis software to code each sentence into one of ten categories and into four dimensions. Coders go through the software to aid in this process. Although methodology is comprehensive in the review of annual report narratives, it was labour intensive. Hussainey (2003) attempted to move away from manual analysis, with a view to capturing forward looking statements in narratives by drawing up keyword list that were indicative of this class. There are a number of other similar manual/semi-automated content analysis performed (Botosan, 1997; Ahmed et al.,1999; Kothari, Li and Short, 2009; Li, 2011). The main problem with these approaches is that it requires human interaction and judgement and is thus subject to reliability issues (such as inter coder agreement on terms), it is error prone and expensive. Therefore samples sizes tend to be small. There has been some recent work in the accounting narrative domain using a more complete computerised content analysis and is now delineated. Li (2010) categorised 30,000 sentences containing Forward Looking Statement (FLS) into two dimension (positive/negative) and content (profitability, operations, liquidity). This training data is fed into a Naïve Bayes algorithm which is used to categorize the tone and content of about 13 million FLS. The problem with this approach is that no lexical analysis is done, each word is deemed independent of any other word and categorisation is often done using frequencies computed as prior/conditional probabilities. This is simplistic/naïve from a linguistic point of view. Demers and Vega (2008) use dictionaries to measure linguistic tone to determine optimism and certainty in narratives. This is then build into a Bayesian learning model which predicts that sentiments expressed as linked to stock returns. Similarly, Tetlock (2007) and Feldman (2008) detect the relationship between sentiment expressed in narratives and stock market reactions. The former looks at frequencies of negative words, whereas the latter uses dictionaries to determine polarity.

5 Brown and Tucker (2010) used the vector space model of information retrieval to determine similarities in accounting statements. The similarity of any two documents is measured by the angle between the two vectors representing the documents, a smaller angle indicates more similar documents. TF-IDF weighting function was used within the vectors, this picks up words that are more salient in document as compared to the corpus. This vector space comparison can also be done at sentence level. Qiu et al (2004) build a Support Vector Machine (SVM) based predictive model based on textual content and experimented with different feature selection methods. Balakrishnan et al (2010) again use a bag of word approach with TF/IDF scores to develop a predictive model where linguistic data predicts performance. Detection of financial misreporting has attracted research work, given the rise in fraudulent activity. Hobson et al (2012) build on cognitive dissonance theory (Festinger, 1957) - psychological discomfort when one s actions and beliefs are discrepant. They look for emotions such as fear, uncertainty, guilt, shame which give a high cognitive dissonance marker score that are indicative of deception. They apply this approach to non-verbal cues to CEO speech samples. Goel (2008) build an SVM classifier used to detect fraud. Linguistic features such as active/passive voice (look at personal pronouns used), uncertainty markers, readability index, tone, usage of proper nouns, type-token ratio etc were used. This helped to distinguish fraudulent annual reports from non-fraudulent reports. Generally researchers perform linguistic analysis identifying a set of lying words that pertain to deceptive language (Larcker and Zakolyukina, 2011; Newman et al., 2003; Loughran and McDonald, 2009). The above research has still failed to capture the true potential of natural language processing capabilities. In particular, to guage tone, sentiment in narratives, to distinguish it from factual statement falls short of opinion mining and sentiment analysis potential. Some of these technologies will be examined in the Section 3. In general, there is still a paucity of research that uses Biologically Inspired/Artificial Intelligence approaches that can utilise the knowledge gained from the research into accounting narratives (see appendix one) and that can extract meaning from text.

6 2.2 A review of the findings of methodology applied. Appendix one, gives an overview of salient research in the accounting narrative research. As said by Qui et al (2004) these studies emanate from the intuitive recognition of a link between the textual report content and corporate performance. This indeed is characteristic of the findings outlined in appendix one, the narratives are correlated with variables, some of which are proxied eg Kothari et al (2009) used the cost of capital, stock return volatility and analyst forecast dispersion to proxy for firm risk. In the majority of cases it is found that the narratives have information content of a predictive nature (see appendix one) or that it sheds light on management actions or explains industry specific disclosure practices. In all the text analysis of narratives conducted the researchers were keen to pick up forward looking information and tone as it relays messages that can be significant for stock markets and industry analysts. This was primarily done using keyword searches, which can be improved upon by concept based opinion mining tools (Cambria and Hussain, 2012). Recently the literature has been dominated by looking for linguistic cues that are indicative of impression management or fraud. Of primary importance in any textual analysis is the picking out of the most representative features. This has been more of a challenge given the ambiguous nature of these phenomena. Merkl-Davies and Brennan (2007) identified six strategies used for concealment. Two of these obfuscate bad news by manipulating verbal information and four strategies emphasize good news by manipulating verbal and/or numerical information. The challenge is to develop features that would pick up such semantics. For example Goal (2008), used word frequencies, syntactic and surface level features for fraud detection. The research delineated in appendix one also indicates that agency and information asymmetry, proprietary cost, self-attribution bias are all found to be empirically linked to disclosure. One of the main ways that researchers have used (Feldman, 2010; Tetlock, 2007; Li, 2010) to capture firm intention is tone. Therefore any analysis and prediction that is done using narrative content needs to take into account the industry background, management motivations and biases. For example we know that in highly competitive industries or firms operating in a highly litigious environment, poor performance are all likely to lead to less disclosure or attempts to blame external circumstances for unfavourable results. Any approach that can represent such knowledge and correctly capture tone would improve predictive modelling that seeks to guage future company performance.

7 In sum, it can be said that voluntary disclosure literature appears to offer an opportunity to increase understanding of the role of accounting information in firm valuation and corporate finance. This knowledge could be represented within biologically inspired systems through the use of an ontology or expert system, which with the semantics extracted from the actual linguistic data could lead to improve predictive capabilities of financial models. 3. Artificial Intelligence (AI) - Biologically Inspired Approaches Ekbia (2010) outlined some of the main approaches in AI and their basic understanding of intelligence. They are outlined below with brief reviews on their applicability to the financial domain:- Knowledge-Intensive AI is motivated by the idea that a vast amount of knowledge is the key to intelligence, in particular machines need to be given common sense knowledge for it to understand the meaning of concepts. SenticNet would fall under this umbrella, as would expert systems. These are programs that use information in a knowledge base, using a set of inference procedures, to solve problems that require significant human expertise for their solution. Expert systems (ES) lend themselves to dealing with qualitative data, as the knowledge base can be updated and inference mechanism adjusted. The information is used to mimic the decision making of experts. Expert Sytems have been used in forecasting for example FINEVA (FINancian AVAaluation) is a multicriteria knowledge-based ES to assess firm performance and viability. The output of this system demonstrates the ranking of analyzed firms based on class of risk. Within narratives the determinant of disclosure could be added to the knowledge base eg poor readability could be resultant from poor performance, fraud or that prevalence of good news and self references are indicative of good firm prospects or higher number of forward looking statements given to counteract stock prices which poorly reflect future earnings. In financial prediction and planning domain, several ES have been compared to statistical methods. Results given by Bahrammirzaee (2010) indicate that Experts systems outperform. However ES provide a prescription and not a prediction. That means that if a goal is given then, a knowledge based ES suggests a course of action, while a simulation model predicts the consequences of a selected course of action under some experimental conditions (Bahrammirzaee, 2010). Case-Based AI. The premise of this approach is that people reason and learn from experience and that the knowledge acquired from experience, captured in cases, is central to their understanding of the world. This would translate into AI as indexing cases as they occur so that, much later, they can be remembered and efficiently retrieved in similar situations. The algorithm would use such experience to reason about the task at hand. Sheng-Li et al (2009) argue that Case Based Reasoning (CBR) has good predictive capabilities, it is non parametric method which does not require any data distribution assumptions, an incremental learning technique that can retain new cases without reprocessing. However the determination of similar cases can be problematic. The authors propose a hybrid model for the financial prediction task:

8 CBR augmented with Genetic Algorithm (GA) and the fuzzy K nearest neighbour (KNN) method. GAs are used to compute the optimal weight vectors of financial variables and KNN are used to compute distances between cases. The authors in their conclusion contend that financial ratios extracted for the knowledge elicitation task can be extended to pick up more features of interest with which to compare cases. This is again where the technique could be augmented with narrative content that would aid the prediction task. Both CBR and Artificial Neural Networks (ANN) have been intensively used in the finance domain. The evidence indicates that CBR can be better in some areas for example (Behbood, 2011) conclude that it gives better results when markets deviate from a stable equilibrium. Other researchers have also found it to have superior performance that ANN (Slonim, 2001; Shen 2012). This approach utilises one of the fundamentals of brain like activity, learning. It learns from experience and uses that knowledge to make new deductions Biologically Inspired or Artificial Intelligence. These are models and techniques inspired by natural mechanisms, such as those studied in Biology. Examples would include Artificial Neural Networks (ANN), Machine Learning (ML), and Genetic Algorithms (GA). In Finance, they have been used in areas such as, business failure prediction, debt risk assessment, security market applications, financial forecasting, portfolio management, fraud detection and insurance. For example ANNs have been designed using financial data of banking customers as the input vector and the actual decisions of the credit analyst as the desired output vector. The objective of the system would be to imitate the human expert in granting credit and setting credit limits. Bahrammirzaee (2010) in his comprehensive review of ANN, finds that they outperform traditional statistical approaches but questions their accuracy. This he concludes is due to the difficulty of determining the proper size and structure of a neural net for a given problem, poor pattern matching capabilities and prediction based on past events which may not be repeated, especially within a financial setting. Within forecasting, typically financial ratios are used to feed into these models, accuracy could be significantly enhanced by using data captured from narratives. For example sentiment polarity extracted by SenticNet or building up features from grammar rules, context analysis at the sentence level to capture company strategic intentions or likelihood of bankruptcy. This could be quantified and fed to the ANN. Furthermore, within the realm of machine learning Chi et al (2011) use both financial and non-financial features with SVM model for financial distress prediction and get improved results as compared with approaches that use only financial features. SVM overcome the weakness ANN which tend to converge on local optimum with respect to the training data and generalise better with respect to the test data. SVM are known as maximal margin classifier in which the classification problem can be represented as an optimization process. Genetic Algorithms have been used to find parameters for an SVM for diagnosing business crises (Chih-Hung W and Gwo-Hshiung Tzeng, 2007). Genetic algorithms find the optimal set of features which are then added to the feature set, boosting performance of the SVM classifiers.

9 Outwith these 3 main approaches, hybrid systems are worth mentioning because hybridization (e.g., mixing different functions in order to perform a complex task) would also fall into the realm of Artificial Intelligence (AI). For example, Kuo et al (1996) designed a system for stock market forecasting that concerned qualitative and quantitative factors simultaneously. This system was composed of integrating NN (Neural Net) for quantitative factors and fuzzy Delphi model for qualitative factors and got acceptable results. Hybrid systems typically combine the power of statistical models with neural nets and get superior result (Bahrammirzaee, 2010). Neural Nets are merged with Fuzzy Logic mechanisms or probabilistic methods. These tools can also be used on their own and fall under the soft computing category. They are widely used in financial applications as are tolerant to imprecision and uncertainty, which a human expert would deal with regularly. For example, Tan et al (2005) propose Genetic complementary learning system (a combination of the Genetic Algorithm and Neural Nets). It is based on hippocampus-inspired learning that is believed to be a mechanism underlying pattern recognition in human. This coupled with GA the authors contend offer good solutions to stock price and bank failure prediction task. The authors themselves in the conclusion advocate that its predictive capabilities would be enhanced with more narrative content inclusion. To date the success of any prediction and modelling techniques employed using narratives have rested on the correct identification of features that capture semantics of the data in question. Within financial narratives for example a number or researchers have tried to pick up tone/sentiments expressed to link it to performance, strategic intentions, potential bankruptcy [see Appendix One]. However keyword searches are limited to syntactic analysis and fail to capture meaning which can be dispersed within natural language. 4. Preliminary Results Sentic Net To correct this deficiency, Cambria et al (2012) propose a concept based technology (SenticNet) using an ontology ConceptNet (a common sense knowledge base) tuned to picking up emotive content, using biologically-inspired affective categorisation model that can potentially describe the full range of emotional experiences. Dimensionality reduction techniques are employed to cluster similar concepts together. This when deployed within a financial narrative setting could pick up tone with polarity rating, more accurately. Loughran and Macdonald, 2012 (will be referred to as L&M)) build up a dictionary of words that denote sentiment in Finance, these could be used to build up the concepts within ConceptNet. For example L&M argue that words such as (tax, costs, loss, capital, cost, expense, and expenses) denoted as negative words generally. Within a financial setting, firm costs, sources of capital, or the amount of tax paid are neutral in nature; managers using this language are merely describing their operations. Once such relationships are captured within ConceptNet, SenticNet with its polarity detection and clustering techniques can classify sentences by tone. Thereafter once a sentence has been correctly tagged for its polarity, it can be used as training data to aid classification (this was done by Cambria et al, 2013 using Artificial Neural Network, which was fed affectively annotated text using SenticNet [43][44]). The above approach was followed (L&M s

10 financial wordlist was incorporated into SenticNet) and the algorithms were run over extracts (the management discussion and analysis section) from ten company annual reports within the UK Food and Beverage sector. Some of the sentences passed to SenticNet with results are shown in appendix two. SenticNet was able to pick up the correct tone in all sentences that were put through it. This would be highly beneficial as tone within a financial context is taken as indictor of company performance, likely stock markets returns, used in fraud detection, early warning of company distress and impression management. Plethora of researchers have used tone to guage firm behaviour [see Appendix One]. Diction (the words that the author uses) are one of the main ways to determine tone (Nordquist, 2012) and this is what SenticNet is performing, examining words and the concepts that are enveloped within these words, demystified using an affective ConceptNet ontology.

11 5. Discussion and Conclusion It is generally accepted that outbreak of corporate financial crises have been preceded by symptoms and alarms. Predictive modelling within finance has missed out on forecasting some big blunders. A significant contributing factor is that it has failed to take into account the abounding narrative content which many have touted to be key to knowledge discovery. The focus solely on non-text derived quantitative data is limiting as it is mostly backward-looking, point-in-time measures and therefore leads to inaccurate prediction. Research done on narrative disclosure strongly indicate that value relevant information is contained in documents such as annual reports, business news, analyst reviews. The question boils down to how to extract the relevant semantic information contained in textual format for use in analysis and prediction tasks? SenticNet for opinion mining was mentioned as a way forward to capture semantics in a robust and complete manner. SenticNet correctly picked up tone from extracts of company annual reports. This would greatly aid predictive capabilities of financial models as tone from extensive research in narratives has been shown to be an accurate indicator of firm intention and prospects. To build Biologically Inspired or Cognitive Systems a vital component is learning and using it to improve its performance. This is where SenticNet can be developed further to completely build computational model that mimic the human brain. Within finance analysis and prediction could be done using the captured semantics through cognitive systems like SenticNet and then results fed into Machine Learning algorithms, as shown by Cambria et al (2013). Similarly, semantics of non-opinionated data could be captured through ontologies, probabilities, lexical analysis as part of the building blocks for cognitive systems. What techniques are chosen depends on the task at hand. The question that needs to be asked is What data structures are useful for representing knowledge and what algorithms operate on those knowledge structures to produce intelligent behaviour? (Lee and Ho, 2009). Bahrammirzaee, (2010) concludes after reviewing previous research that the accuracy of these artificial intelligent methods is superior to that of statistical methods in dealing with financial problems, especially with regard to nonlinear patterns. References 1. Ahmed, K., & Courtis, J. K. (1999) Associations between corporate characteristics and disclosure levels in annual reports: a meta-analysis. British Accounting Review, 31(1), Bahrammirzaee, A. (2010) A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems Neural Computing and Applications, Vol 19, Issue 8.

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