تعمیم مدل تحلیل پوششی داده ها از تحلیل بنیادی و کاربرد آن در بهینه سازی سبد سرمایه گذاری
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|23681||2007||25 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Banking & Finance, Volume 31, Issue 11, November 2007, Pages 3311–3335
Fundamental analysis is used in asset selection for equity portfolio management. In this paper, a generalized data envelopment analysis (DEA) model is developed to analyze a firm’s financial statements over time in order to determine a relative financial strength indicator (RFSI) that is predictive of firm’s stock price returns. RFSI is based on maximizing the correlation between the DEA-based score of financial strength and the stock market performance. This maximization involves a difficult binary nonlinear program that requires iterative re-configuration of parameters of financial statements as inputs and outputs. We utilize a two-step heuristic algorithm that combines random sampling and local search optimization. The proposed approach is tested with 230 firms from various US technology-industries to determine optimized RFSI indicators for stock selection. Then, those selected stocks are used within portfolio optimization models to demonstrate the usefulness of the scheme for portfolio risk management.
Fundamental analysis (FA) is the process of evaluating a public firm for its investment-worthiness by looking at its business at the basic or fundamental financial level, see for example, Thomsett (1998). It involves examining a firm’s financials and operations, especially sales, earnings, growth potential, assets, debt, management, products, and competition. FA may also include analyzing market behavior that stresses the study of underlying factors of supply and demand, see Doyle et al., 2003 and Piotroski, 2000. The main goal is to enhance the ability to predict future security price movement and then use such predictions to design equity portfolios. On the other hand, technical analysis (TA) operates on the theory that market prices at any given point in time reflect all known factors affecting supply and demand, as well as a firm’s relative financial strength. Thus, TA focuses on analyzing market prices themselves, rather than directly evaluating factors of fundamental strength or factors of supply and demand. Strategies based on TA generally utilize a series of calculations designed to detect when a price change is likely to occur so that an investor can manage market positions in the short-term, such as the case in highly leveraged derivative markets. In contrast, FA takes on a more long-term perspective in determining which firms are most likely to perform well in the future, based on their fundamental business strengths. The work in this paper complements the approach of fundamental analysis. The objective of our research is to focus only on the publicly-available financial statements of a given firm and to use them to determine a measure of underlying business strength for the firm. In determining the underlying financial health of a company, the raw financial numbers of a firm do not provide the perspective required to differentiate between healthy and unhealthy stocks for investment. In other words, the context provided by a comparison of a given firm to its industry and to the market as a whole is essential. Therefore, the focus in this paper is not to evaluate a firm’s business strength in isolation. Instead, a relative strength indicator is computed by comparing a given firm to many other firms which are in a similar business segment of the market, such as the industry to which the firm belongs. The central premise of this research is that market prices have factored in publicly-available information about the firm, but the future expectations of price performance are determined by the perceived business strength of the firm. Thus, this notion is consistent with the “efficient market theory”, where the price of a stock is assumed to reflect the knowledge and expectations of all investors since everyone has the same information about the stock. The aim of this paper is to provide a measurable (objective) metric of that knowledge that is highly correlated with stock price performance. Then, such a metric can be used as a proxy for gauging a firm’s expected financial performance, and hence the firm’s future stock price performance. In this sense, a company’s financial statements (income statement, balance sheet and cash flow statement) become indispensable resources for investment decision making. It must also be stated that this research is not focused on determining if a stock is undervalued, overvalued, or trading at fair market value, nor does it focus on qualitative market factors that are internal or external to the firm. Many quantitative models have been proposed in the literature for stock price prediction using financial statements – regression models and artificial neural network (ANN) models have been applied, see for instance, Kanas, 2001 and Quah and Srinivasan, 1999, and Thanassoulis (1993). In Kanas (2001), historical financial data is used as inputs and stock price is used as output in an ANN model. There are also other approaches based on applying regression-based techniques using data from financial statements as explanatory variables to predict future cash flow or stock performance. Ou (1989) used logistic regression to estimate the probability of an earnings increase in a subsequent year. Graham et al. (2002) applied ordinary least squares regression to determine a firm’s market value as a linear function of its earnings and book value. The work in this paper is motivated by the basic approach of Edirisinghe and Zhang (2007), where financial statement data was used in a data envelopment analysis (DEA) model. However, that work assumed that the analyst is able to categorize financial data into separate inputs and outputs in a DEA model specification to compute a financial strength metric. With such an “a priori” model specification, although measures of high financial strength may result, they could display low correlations with market returns. Then, the analyst runs the risk of reaching the inevitable (false) conclusion that underlying financial strength is not factored into market returns, contrary to the “efficient market hypothesis”. In this paper, we generalize the basic DEA methodology where inputs and outputs parameters are not fixed “a priori”, instead they are determined via an optimization process formulated to maximize correlations between financial strength and market returns. This process results in a relative financial strength indicator (RFSI) that is highly predictive of stock returns. To the best of our knowledge, a DEA-based financial strength metric for a firm has not been directly incorporated within fundamental analysis for stock investments. Data Envelopment Analysis (DEA) is commonly used to evaluate the relative efficiency of a number of Decision Making Units (DMUs). The basic DEA model in Charnes et al. (1978), called the CCR model, has lead to several extensions, most notably the BCC model of Banker et al. (1984) and the additive model of Charnes et al. (1985). DEA models have been extensively used in performance appraisal in a wide range of applications including financial performance as well as non-financial performance measurement. In the financial applications of DEA methodology, one particularly appealing idea is to measure managerial efficiency of a company by using its financial statements. For example, using certain financial ratios as inputs and outputs, DEA is used to evaluate performance of banks (Yeh, 1996), CRAF participants (Bowlin, 2004), defense business segments (Bowlin, 1999), and credit unions (Pille and Paradi, 2002). DEA-based efficiencies and Sharpe ratios are compared to evaluate performance of different hedge funds, see Gregoriou et al. (2005). Alam and Robin (1998) compute relative technical efficiencies for firms in the airline industry and analyze their association with corresponding stock price returns. However, their work is based upon input and output variables that are generally non-financial in nature and they are typically not found in the publicly-available financial statements. In all of the above approaches, the underlying DEA model is specified with a fixed (exogenous) set of input and output parameters to compute an efficiency score. Our work contrasts with the traditional DEA approach in that an input/output categorization is endogenously determined by a model that seeks the highest correlation between stock returns and efficiency metric. Consequently, our approach leads to identifying the “best-performing” companies from the “poor-performing”, i.e., stock screening, for consideration in equity portfolio management. Given that distribution parameters of stock returns are often subject to estimation error, screening mechanisms such as the proposed RFSI-based stock selection can yield better risk-reward characteristics in portfolio optimization. This is demonstrated by applying the RFSI-based approach to identify favorable stocks as candidates for stock portfolio optimization. The resulting portfolios are shown in this paper to have superior risk-reward performance. The remainder of the paper is organized as follows. In Section 2, the mathematical formulation of a standard DEA model is first provided, along with various financial parameters from income statements and balance sheets to be used as inputs and outputs. The generalized DEA approach is then discussed. In Section 3, the RFSI determination problem is formulated as a correlation maximization model, and a two-stage (heuristic) solution scheme is developed for its solution. Portfolio selection criteria based on statistical tests of RFSI are covered in Section 4. Section 5 applies the methodology in a case study involving 230 firms from six US industries. Using actual data from 1996 to 2002, the RFSI approach is applied to identify firms to include within a mean-variance quadratic portfolio optimization model. The paper concludes with some remarks in Section 6.