[ieee 2012 international conference on advances in social networks analysis and mining (asonam 2012)...

Download [IEEE 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) - Istanbul (2012.08.26-2012.08.29)] 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - Stock Market Investment Advice: A Social Network Approach

Post on 28-Mar-2017




2 download

Embed Size (px)


  • Stock Market Investment Advice: A Social Network Approach

    Negar Koochakzadeh Computer Science

    Department University of Calgary

    Calgary, Canada nkoochak@ucalgary.ca

    Keivan Kianmehr Electrical and Computer Engineering Department

    Western University London, Canada


    Atieh Sarraf Computer Science

    Department University of Calgary

    Calgary, Canada sarrafsa@ucalgary.ca

    Reda Alhajj Computer Science

    Department University of Calgary

    Calgary, Canada alhajj@ucalgary.ca

    Abstract Making investment decision on various available stocks in the market is a challenging task. Econometric and statistical models, as well as machine learning and data mining techniques, have proposed heuristic based solutions with limited long-range success. In practice, the capabilities and intelligence of financial experts is required to build a managed portfolio of stocks. However, for non-professional investors, it is too complicated to make subjective judgments on available stocks and thus they might be interested to follow an expert's investment decision. For this purpose, it is critical to find an expert with similar investment preferences. In this work, we propose to benefit from the power of Social Network Analysis in this domain. We first build a social network of financial experts based on their publicly available portfolios. This social network is then used for further analysis to recommend an appropriate managed portfolio to non-professional investors based on their behavioral similarities to the expert investors. This approach is evaluated through a case study on real portfolios. The result shows that the proposed portfolio recommendation approach works well in terms of Sharpe ratio as the portfolio performance metric.

    Keywords-component; Stock Market Investment Decision, Social Network Analysis, Clustering, Classification, Sharpe Ratio.

    I. INTRODUCTION Stock market investment is a very complex and multi-

    faceted decision problem. This decision process involves stock selection and weighting, such that the collection of stocks satisfies an investors objectives. It involves forecasting the performance and the volatility of available stocks as well as models for using these predictions in order to obtain a portfolio of stocks that suits the investors preference profile. Financial theorists and investors have been dealing with this issue for many years.

    Econometric and statistical models as well as machine learning and data mining techniques have been used by many researchers and analysts to propose heuristic solutions. In 1950s Markowitz proposed the modern portfolio theory, and since then researchers have considered the correlation between stocks and the trade-off between return and risk of the investment [1]. In addition, several researchers concentrate on the development of realistic models which, in

    addition to the two basic criteria of return and risk, also consider other equally important criteria derived from fundamental analysis [2, 3]. Fundamental analysis is the study of the sector and company financial indicators to determine the value of a stock. It aims to determine the financial health of the company, by useful ratios, based on the companys financial statements [2]. In general multiple criteria have to be considered for making investment decision [3]. Each stock instance can be described by a set of features which represent important financial information regarding the company that the stock represents.

    Although a great deal of effort has been devoted to developing systems for stock investment decision, limited success has been achieved and reported. Quantitative models for stock selection and portfolio management face the challenge of determining the most efficacious factors. In addition, it is believed that the main reason for the slow progress is the abrupt changes of the structural relationship between the stock price and its determinants over time. This phenomenon of unstable structural parameters in asset price models is a special case of a general fundamental critique of econometric and statistical models [4].

    In practice, financial experts apply their personal capabilities and intelligence (human-thinking and skills) to solve the problem based on their knowledge of existing theories and strategies. In this case, investment decision results constitute what is called a managed portfolio. However, integrating domain expert knowledge into the process of making investment decisions is very costly and experts need to concentrate on a limited number of available assets as studying a huge number of them through this process is not feasible.

    Although professional analysts and fund managers make subjective judgments based on objective technical indicators, it is too complicated for non-professionals to do so. This is the motivation of our proposed approach to study the available portfolios of expert investors and recommend the most appropriate portfolio to the non-professional investor. Since in this approach behavioral similarity between investors is an important aspect, Social Network Analysis (SNA) is applied as a solution strategy. SNA was emphasized starting in 1934 as a subarea of sociology and

    2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

    978-0-7695-4799-2/12 $26.00 2012 IEEEDOI 10.1109/ASONAM.2012.22


    2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

    978-0-7695-4799-2/12 $26.00 2012 IEEEDOI 10.1109/ASONAM.2012.22


  • anthropology to study the connectedness of people in groups. A social network consists of a set of actors and their relationships; by considering graph theory concepts this is represented as a graph where actors and their relationships correspond to nodes and links, respectively.

    In this work, we first construct a social network of expert investors based on their public available portfolios. Each expert is a node in this network and the weighted link between two nodes shows how similar two experts are based on their portfolios. In the next step, various communities of experts are detected from this social network. On the other hand, a non-professional investor is assigned to an appropriate community of expert based on the similarity that he/she has to the experts of each of the existing communities. One expert is then selected as the representative of each community whose portfolio is suggested to the non-professional investor.

    Our paper proceeds as follows. Background and related studies are presented in Section II. In Section III, experts social network construction is explained in detail. Next in Section IV, we discuss the recommendation system to suggest a managed portfolio built by an expert to a non-professional investor. The proposed approach is then evaluated in a case study described in Section V. Finally, Section VI presents the conclusions drawn from our research and directions for future work.

    II. BACKGROUND AND RELATED WORKS In traditional investment decision approaches, investors

    focus only on maximizing the expected return without considering the concept of investment risk [1, 5]. In financial investments, it is important for investors to control and manage the risk to which they subject themselves while searching for high returns [6]. In general, investment opportunities that offer higher returns also entail higher risks [6]. Therefore, there is always a trade-off between risk and return in the investment decision process.

    Markowitz was the first to quantify the link that exists between portfolio risk and return through which he founded the modern portfolio theory [1]. He demonstrated that the portfolio risk comes from the covariance of the assets making up the portfolio. Following this theory, other financial models (such as CAPM and APT) considered two criteria of risk and return as well as other fundamental criteria in the valuation of assets.

    During 1950s and 1960s, Markowitz and Sharpe introduced a model called Capital Asset Pricing Model (CAPM) [3]. It evaluates the asset return in relation to the market return and the sensitivity of the asset to the market [3]. In other words, by measuring the market risk of each asset, a risk-adjusted expected return is measured by CAPM. This model is based on the assumption of Efficient Market Hypothesis (EMH). According to EMH, any useful patterns should have been reflected in the current price [2].

    In the next era, the Arbitrage Pricing Theory (APT) is widely used in portfolio management as an alternative approach to CAPM. APT has the benefit of being a more

    powerful theory by requiring less stringent assumptions than CAPM while producing similar results. The difficulty with APT is that it shows that there is a way to forecast expected asset returns but it does not specify how the process works. The key idea of APT is that there exists a set of factors based on which expected returns can be described as a linear combination of each assets exposure to these factors [2].

    Unrealistic assumptions and time complexity of the required calculation in financial theories are issues that make them not applicable in real world problems [5]. Therefore, in practice, a more comprehensive solution is needed. Professional analysts and fund managers make subjective judgment, based on objective technical indicators. Subsequently, computer scientists have tried to apply AI with the purpose of replacing financial professional intelligence by AI. Currently, soft computing techniques are widely accepted in studying investment management and evaluating market behavior [7]. Various techniques have been proposed for this purpose such as Neural Networks (NN), Genetic Algorithms (GA) and Support Vector Mac


View more >