ISSN: 2056-3736 (Online Version) | 2056-3728 (Print Version)
Vasilios Plakandaras, Periklis Gogas and Theophilos Papadimitriou
Correspondence: Vasilios Plakandaras, vplakand@econ.duth.gr
Democritus University of Thrace, Department of Economics, Greece
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In this paper we re-evaluate the capital immobility hypothesis of Feldstein and Horioka (1980) for the case of the European Union and the Eurozone, based on long-run regressions. We employ the Long Run Derivative proposed by Fischer and Seater (1993) in order to examine capital mobility as a long-run phenomenon. In order to enhance the robustness of our results we also perform panel causality tests on our data as it is a common approach in this setting. Our empirical findings provide no evidence in favor of the capital immobility hypothesis. In fact, we reject capital immobility even before the creation of the European Union, the introduction of the Eurozone or the 2008 global financial crisis.
Feldstein -Horioka puzzle, Investment, Savings, International Economics
Andrews, D. W. K. (1991) Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation. Econometrica, 59, 817–858.
Andrews, D. W. K., and J. C. Monohan (1992) An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator. Econometrica, 60, 953–966.
Apergis N. and Tsoumas Chris (2009) A Survey of the Feldstein-Horioka Puzzle: What Has Been Done and Where We Stand. Research in Economics, 63 (2), 64-76.
Blanchard, O. and Giavazzi, G. (2001) Current account deficits in the Euro area: The end of the Feldstein-Horioka puzzle?. Brookings Papers on Economic Activity, 2, 147-209.
Choudhry T., Kling G. and Jayasekera R. (2014) The Global Financial Crisis and the European Single Market: The end of integration?. Journal of International Money and Finance 49, 191–196.
Dickey D. and Fuller W. (1979) Distribution of the Estimators for Autoregressive Time Series With a Unit Root. Journal of the American Statistical Association, 74 (366), 427-431.
Dumitrescu E.I and C. Hurlin (2012), Testing Granger Causality in Heterogeneous Panel Data Models. Economic Modelling, 29, 1450-1460.
Feldstein, M., Horioka, C. (1980) Domestic saving and international capital flows. Economic Journal, 90 (358), 314-329.
Fisher M. and Seater J. (1993) Long-Run Neutrality and Superneutrality in an ARIMA Framework. American Economic Review, 83, 402-415.
Johnson M. and Lamdin D. (2014) Investment and saving and the euro crisis: A new look at Feldstein–Horioka. Journal of Economics and Business, 76, 101-114.
Katsimi M. and Zoega G. (2016) European Integration and the Feldstein–Horioka Puzzle. Oxford Bulletin of Economics and Statistics, 78 (6), 834–852.
Krugman P., (1991) Has the adjustment process worked? In: Policy Analyses in International Economics 34. Institute for International Economics, Washington.
Kumar, S. and Bhaskara Rao, B. (2011). A time series approach to the Feldstein- Horioka puzzle with panel data from the OECD countries". The World Economy, 34(3), 473–485.
Kwiatkowski, D.; Phillips, P. C. B.; Schmidt, P.; Shin, Y. (1992). "Testing the null hypothesis of stationarity against the alternative of a unit root". Journal of Econometrics. 54 (1–3), 159–178.
Obstfeld M. and Rogoff K. (2001) The Six Major Puzzles in International Macroeconomics : Is There a Common Cause? , In : NBER Macroeconomics Annual 2000, vol. 15. MIT Press, pp.339–3.
Papadimitriou T., P. Gogas and GA Sarantitis (2014) Convergence of European business cycles: A complex networks approach. Computational Economics, 47 (2), 97-119.
Phillips, P. C. B. and Perron, P. (1988) Testing for a Unit Root in Time Series Regression. Biometrika, 75 (2), 335–346.
Schmitz, B. and Von Hagen, J. (2011). "Current account imbalances and financial integration in the euro area“, Journal of International Money and Finance, Vol. 30, 1676-1695.
Schwarz Gideon E. (1978)Estimating the dimension of a model", Annals of Statistics, 6 (2): 461–464,
Serletis A. and Gogas P. (2007) The Feldstein-Horioka Puzzle in an ARIMA Framework. Journal of Economic Studies, 34 (3), 194-210.
Telatar E., Telatar F. and Bolatoglou N. (2007) A regime switching approach to the Feldstein–Horioka puzzle: Evidence from some European countries. Journal of Policy Modeling, 29, 523–533.
Andrey Kudryavtsev
Correspondence: Andrey Kudryavtsev, andreyk@yvc.ac.il
Department of Economics and Management, The Max Stern Yezreel Valley College, Israel
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Present study explores the effect of the availability heuristic (representing people's tendency to determine the likelihood of an event according to the easiness of recalling similar instances, and, thus, to overweight current information, as opposed to processing all relevant information) on stock price dynamics following days of extremely high trading volumes. I hypothesize that if the sign of a stock's return on the day when it registers an extremely high trading volume corresponds to the sign of the same day's stock market index return, then because of the effect of the availability heuristic, investors may consider the underlying important news to have a greater subjective probability of leading to stock returns of the respective sign, amplifying the latter and creating overreaction, which results in subsequent price reversal. Defining high-volume days according to a number of alternative proxies, I document that, in line with my hypothesis, both positive and negative high-volume day stock returns accompanied by the same-sign contemporaneous daily market returns are followed by significant reversals on the next trading day and over five- and twenty-day intervals following the event, the magnitude of the reversals increasing over longer post-event windows, while high-volume day stock price changes taking place on the days when the market index moves in the opposite direction are followed by non-significant price drifts. The results remain robust after accounting for additional company-specific (size, beta, historical volatility) and event-specific (event-day stock's return) factors, and are stronger for low capitalization and high volatility stocks.
Availability Heuristic; Behavioral Finance; High Trading Volumes; Overreaction; Stock Price Reversals.
Baker, M., and J. Stein, 2004, Market Liquidity as a Sentiment Indicator, Journal of Financial Markets, 7(3), 271-299.
Baker, M., and J. Wurgler, 2006, Investor Sentiment and the Cross-Section of Stock Returns, Journal of Finance, 61(4), 1645-1680.
Bamber, L.S., O.E. Barron, and T.L. Stober, 1997, Trading Volume and Different Aspects of Disagreement Coincident with Earnings Announcements, The Accounting Review, 72(4), 575-597.
Bamber, L.S., O.E. Barron, and T.L. Stober, 1999, Differential Interpretations and Trading Volume, The Journal of Financial and Quantitative Analysis, 34(3), 369-386.
Bamber, L.S., O.E. Barron, and D.E. Stevens, 2011, Trading Volume around Earnings Announcements and Other Financial Reports: Theory, Research Design, Empirical Evidence, and Directions for Future Research, Contemporary Accounting Research, 28(2), 431-471.
Barber, B.M., and T. Odean, 2008, All that Glitters: The Effect of Attention and News on the Buying behavior of Individual and Institutional Investors, Review of Financial Studies, 21(2), 785-818.
Barron, O.E., Harris, D.G., and M. Stanford, 2005, Evidence That Investors Trade on Private Event-Period Information around Earnings Announcements, The Accounting Review, 80(2), 403-421.
Beaver, W.H., 1968, The Information Content of Annual Earnings Announcements, Empirical Research in Accounting: Selected Studies, Supplement to Journal of Accounting Research, 6, 67–92.
Caginalpa, G., and M. Desantisa, 2011, Stock Price Dynamics: Nonlinear Trend, Volume, Volatility, Resistance and Money Supply, Quantitative Finance, 11(6), 849-861.
Campbell, J.Y., S.J. Grossman, and J. Wang, 1993, Trading Volume and Serial Correlation in Stock Returns, The Quarterly Journal of Economics, 108(4), 905–939.
Chen, G., M. Firth, and O.M. Rui, 2001, The Dynamic Relation between Stock Returns, Trading Volume and Volatility, The Financial Review, 36(2), 153-174.
Chordia, T., S.W. Huh, and A. Subrahmanyam, 2007, The Cross-Section of Expected Trading Activity, Review of Financial Studies, 30(2), 709-740.
Crouch, R.L., 1970, A Nonlinear Test of the Random-Walk Hypothesis, American Economic Review, 60(1), 199-202.
Daniel, K., D. Hirshleifer, and S. H. Teoh, 2002, Investor Psychology in Capital Markets: Evidence and Policy Implications, Journal of Monetary Economics, 49(1), 139-209.
De Long, B., A. Shleifer, L. Summers, and R. Waldmann, 1990, Positive Feedback Investment Strategies and Destabilizing Rational Speculation, Journal of Finance, 45(2), 379-386.
Diamond, D., and R.E. Verrecchia, 1987, Constraints on Short-Selling and Asset Price Adjustment to Private Information, Journal of Financial Economics, 18(2), 277-311.
Epps, T.W., 1975, Security Price Changes and Transaction Volumes: Theory and Evidence, American Economic Review, 65(4), 586–597.
Epps, T.W., 1977, Security Price Changes and Transaction Volumes: Some Additional Evidence, Journal of Financial and Quantitative Analysis, 12(1), 141–146.
Frieder, L., 2003, Evidence on Behavioral Biases in Trading Activity, Working Paper, UCLA, The Anderson School.
Gallant, R., P. Rossi, and G. Tauchen, 1992, Stock Prices and Volume, Review of Financial Studies, 5(2), 199-242.
Ganzach, Y., 2001, Judging Risk and Return of Financial Assets, Organizational Behavior and Human Decision Processes, 83(2), 353-370.
Garfinkel, J.A., and J. Sokobin, 2006, Volume, Opinion Divergence, and Returns: A Study of Post-Earnings Announcement Drift, Journal of Accounting Research, 44(1), 85-112.
Glaser, M., and M. Weber, 2009, Which Past Returns Affect Trading Volume? Journal of Financial Markets, 12(1), 1-31.
Griffin, M., F. Nardari, and R.M. Stulz, 2007, Do Investors Trade More When Stocks Have Performed Well? Evidence from 46 Countries, Review of Financial Studies, 20(3), 905–951.
Harris, L., 1983, The Joint Distribution of Speculative Prices and of Daily Trading Volume, Working Paper, University of Southern CA.
Harris, M., and A. Raviv, 1993, Differences of Opinion Make a Horse Race, Review of Financial Studies, 6(3), 473-506.
Hirshleifer, D., A. Subrahmanyam, and S. Titman, 1994, Security Analysis and Trading Patterns When Some Investors Receive Information before Others, Journal of Finance, 49 (5), 1665-698.
Hirshleifer, D., A. Subrahmanyam, and S. Titman, 2006, Feedback and the Success of Irrational Investors, Journal of Financial Economics, 81(2), 311-338.
Holthausen, R.W., and R.E. Verrecchia, 1990, The Effect of Informedness and Consensus on Price and Volume Behavior, The Accounting Review, 65(1), 191-208.
Hong, H., and J.C. Stein, 1999, A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets, Journal of Finance, 54(6), 2143-2184.
Hong, H., and J.C. Stein, 2007, Disagreement and the Stock Market, Journal of Economic Perspectives, 21(2), 109-128.
Hong, H., and J. Yu, 2009, Gone Fishin’: Seasonality in Trading Activity and Asset Prices, Journal of Financial Markets, 12(4), 672-702.
Israeli, D., 2015, Trading Volume Reactions to Earnings Announcements and Future Stock Returns, Working Paper, Interdisciplinary Center Herzliya.
Kandel, E., and N. Pearson, 1995, Differential Interpretation of Public Signals and Trade in Speculative Markets, Journal of Political Economy, 103(4), 831-872.
Karpoff, J.M., 1986, A Theory of Trading Volume, Journal of Finance, 41(5), 1069-1087.
Karpoff, J.M., 1987, The Relation between Price Changes and Trading Volume: A Survey, Journal of Financial and Quantitative Analysis, 22(1), 109-126.
Khan, S.U., and F. Rizwan, 2008, Trading Volume and Stock Returns: Evidence from Pakistan’s Stock Market, International Review of Business Research Papers, 4(2), 151-162.
Kim, O., and R.E. Verrecchia, 1991, Market Reaction to Anticipated Announcements, Journal of Financial Economics, 30(2), 273-309.
Kim, O., and R.E. Verrecchia, 1994, Market Liquidity and Volume around Earnings Announcements, Journal of Accounting and Economics, 17(1), 41–67.
Kim, O., and R.E. Verrecchia, 1997, Pre-Announcement and Event-Period Private Information, Journal of Accounting and Economics, 24(3), 395-419.
Kliger, D, and A. Kudryavtsev, 2010, The Availability Heuristic and Investors’ Reaction to Company-Specific Events, Journal of Behavioral Finance, 11(1), 50-65.
Kudryavtsev, A., 2018, The Availability Heuristic and Reversals Following Large Stock Price Changes, Journal of Behavioral Finance, 19(2), 159-176.
Lee, B, J. O’Brien, and K. Sivaramakrishnan, 2007, An Analysis of Financial Analysts’ Optimism in Long-term Growth Forecasts, Journal of Behavioral Finance, 9(3), 171-184.
Lee, S.B., and O.M. Rui, 2002, The Dynamic Relationship between Stock Return and Trading Volume: Domestic and Cross-Country Evidence, Journal of Banking and Finance, 26(1), 51-78.
Llorente, G., R. Michaely, G. Saar, and J. Wang, 2002, Dynamic Volume-Return Relation of Individual Stocks, Review of Financial Studies, 15(4), 1005-1047.
Pathirawasam, C., 2011, The Relationship between Trading Volume and Stock Returns, Journal of Competitiveness, 3(3), 41-49.
Pisedtasalasai, A., and Gunasekarage, 2007, Causal and Dynamic Relationships among Stock Returns, Return Volatility and Trading Volume: Evidence from Emerging Markets in South-East Asia, Asia-Pacific Financial Markets, 14(4), 277-297.
Remorov, R., 2014, Stock Price and Trading Volume during Market Crashes, International Journal of Marketing Studies, 6(1), 21-30.
Rutledge, D.J.S., 1984, Trading Volume and Price Variability: New Evidence on the Price Effects of Speculation, In Peck A. E. (ed.), Selected Writings on Futures Markets: Research Directions in Commodity Markets, 237-251, Chicago: Chicago Board of Trade.
Saatccioglu, K., and L.T. Starks, 1998, The Stock Price–Volume Relationship in Emerging Stock Markets: The Case of Latin America, International Journal of Forecasting, 14(2), 215–225.
Schwert, W., 1989, Why Does Stock Market Volatility Change Over Time? Journal of Finance, 44(5), 1115-1155.
Shiller, R.J., 1998, Human Behavior and the Efficiency of the Financial System, NBER Working Paper.
Statman, M., S. Thorley, and K. Vorkink, 2006, Investor Overconfidence and Trading Volume, Review of Financial Studies, 19(4), 1531-1565.
Tversky, A., and D. Kahneman, 1973, Availability: A Heuristic for Judging Frequency and Probability, Cognitive Psychology, 4, 207-232.
Tversky, A., and D. Kahneman, 1974, Judgment under Uncertainty: Heuristics and Biases, Science, 185, 1124-1131.
Varian, H.R., 1989, Differences of Opinion in Financial Markets, In Financial Risk: Theory, Evidence and Implications: Proceedings of the 11th Annual Economic Policy Conference of the Federal Reserve Bank of St. Louis, 3–37.
Verrecchia, R.E., 1981, On the Relationship between Volume Reaction and Consensus of Investors: Implications for Interpreting Tests of Information Content, Journal of Accounting Research, 19(1), 271-283.
Westerfield, R., 1977, The Distribution of Common Stock Price Changes: An Application of Transactions Time and Subordinated Stochastic Models, Journal of Financial and Quantitative Analysis, 12(5), 743-765.
Ying, C.C., 1966, Stock Market Prices and Volumes of Sales, Econometrica, 34(3), 676-686.
Ziebart, D.A., 1990, The Association between Consensus of Beliefs and Trading Activity Surrounding Earnings Announcements, The Accounting Review, 65(2), 477-488.
Apostolos G. Christopoulos, Ioannis G. Dokas, Iraklis Kollias and John Leventides
Correspondence: Ioannis G. Dokas, intokas@econ.duth.gr
Department of Economics, Democritus University of Thrace, Greece
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The foremost aim of this paper is to propose a reliable methodology regarding the selection process of financial ratios as input variables in the construction of corporate failure prediction models. In this paper soft set theory is introduced. In the first stage, emphasis is given on the state of liquidity as a measure for the classification of a group of NASDAQ listed firms in two a priori groups (failed and non-failed) using four liquidity criteria as follows: current ratio 1, current liabilities to total liabilities 70%, Equity to Liabilities 0 and Total Debt to Total Assets 70%. In the second stage, a parameter reduction algorithm is applied in order to determine, from a group of ratios, those which provide significant predictive power and optimize the classification accuracy of the model. A tabular representation of a soft set is constructed in order to select the input variables in the model based on the importance degree of each financial ratio. The findings show that the primary assumptions relevant to the definition of failure based on the soft set theory approach are confirmed, though the majority of the significant ratios in the applied sample of listed firms are related to the analysis of profitability.
Failure prediction, definition of failure, liquidity and profitability, financial ratios, soft set theory, Logistic regression.
Ali, M. I., Feng, F., Liu, X., Min, W. K., & Shabir, M. (2009). “On some new operations in soft set theory”. Computers & Mathematics with Applications, 57(9), 1547-1553.
Almamy, J., Aston, J., Ngwa, L N (2016) “An evaluation of Altman's Z-score using cash flow ratio to predict corporate failure amid the recent financial crisis: Evidence from the UK”. Journal of Corporate Finance, 36, 278–285.
Altman E.I., (1977). “ZETA analysis A new model to identify bankruptcy risk of corporations”, Journal of Banking & Finance, 1(1), 2 –54.
Altman, E. (1968). “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy”. The Journal of Finance, 23 (4), 589 - 609.
Altman, E., Hotchkiss, E. (2006). “Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt”, 3rd Ed. Wiley Finance.
Atanassov, K.T. (1994). “Operators over Interval Valued Intuitionistic Fuzzy Sets”. Fuzzy Sets and Systems, 64(2), 159-174.
Balcaen, S., & Ooghe, H. (2004). “35 years of studies on business failure: an overview of the classical statistical methodologies and their related problems”. Journal of Vlerick Leuven Gent Working Paper, 15, 6.
Beaver, W. (1966). “Financial ratios as predictors of failure”. Journal of Accounting Research, 71-111.
Beaver, W. H., McNichols, M. F., & Rhie, J.-W. (2005). “Have financial statements become less Iinformative? Evidence from the ability of financial ratios to predict bankruptcy”, Review of Accounting Studies, 10, 93-122.
Beynon, M J.,Peel, MJ (2001) “Variable precision rough set theory and data discretisation: an application to corporate failure prediction” Omega, 29 561–576.
Charitou, A., Evi Neophytou,E., Charalambous, C (2004) “Predicting corporate failure: empirical evidence for the UK”. European Accounting Review, 13(3), 465-497.
Chen, D., Tsang, E. C. C., Yeung, D. S., & Wang, X. (2005). “The parameterization reduction of soft sets and its applications”. Computers & Mathematics with Applications, 49(5), 757-763.
Christopoulos, A G., Dokas, I G., Dimitrios H. Mantzaris, D H(2013) “The estimation of corporate liquidity management using artificial neural networks”. International Journal of Financial Engineering and Risk Management, 1(2), 193 -210.
Cielen, A., Peeters, L., & Vanhoof, K. (2004). “Bankruptcy prediction using a data envelopment analysis”. European Journal of Operational Research, 154(2), 526-532.
Courtis, J K (1978) “Modeling a financial ratios categoric framework”. Journal of Business Finance and Accounting, 5(4), 371 – 386.
Deakin, E. (1972) “A Discriminant Analysis of Predictors of Business Failure”. Journal of Accounting Research, 10, 167-179.
Dimitras, A I., Slowinski, R. Susmaga C. Zopounidis, C (1999) “Business failure prediction using rough sets”. European Journal of Operational Research, 114 263-280
Dirickx Y., Van Landeghem G (1994) “Statistical failure prevision problems”. Tijdschrift voor Economie en Management, 39(4), 429 - 462.
Doumpos, M.,.Zopounidis, C (1999) “A multinational discrimination method for the prediction of financial distress: the case of Greece” Multinational Finance Journal, 3(2), 71–101.
Feng, F., Cagman, N., Leoreanu-Fotea, V., & Akram, M. (2015). Emerging Trends in Soft Set Theory and Related Topics. The Scientific World Journal, 2015.
Frydman, H., Altman, E. I., & KAO, D. L. (1985). “Introducing recursive partitioning for financial classification: the case of financial distress”. The Journal of Finance, 40(1), 269-291.
Harrell, F. E. (2013). Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer Science & Business Media.
Hauser, R P., Booth, D(2011) “Predicting Bankruptcy with Robust Logistic Regression”. Journal of Data Science 9,565-584.
Iturriaga, F J L., Sanz, I P (2015) “Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks”. Expert Systems with Applications, 42, 2857–2869
Jardin, P (2010) “Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy”. Neurocomputing ,73, 2047–2060.
Kharal, A., & Ahmad, B. (2011). “Mappings on soft classes”. New Mathematics and Natural Computation, 7(03), 471-481.
Kong, Z., Gao, L., Wang, L., & Li, S. (2008). “The Normal Parameter Reduction of Soft Sets and its Algorithm”. Computers & Mathematics with Applications, 56(12), 3029-3037.
Kong, Z., Jia, W., Zhang, G., & Wang, L. (2015). “Normal Parameter Reduction in Soft Set Based on Particle Swarm Optimization Algorithm”. Applied Mathematical Modelling, 39(16), 4808 – 4820.
Lee, K., Booth, D., & Alam, P. (2005). “A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms”. Expert Systems with Applications, 29(1), 1-16.
Liang, D., Lu, C C., Tsai, C F., Shih, G A (2016) “Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study”. European Journal of Operational Research, 252, 561–572.
Ma, X., Sulaiman, N., Qin, H., Herawan, T., & Zain, J.M. (2011). “A new Efficient Normal Parameter Reduction Algorithm of Soft Sets”. Computers & Mathematics with Applications, 62(2), 588-598.
Maji, P. K., Biswas, R., & Roy, A. (2003). “Soft set theory”. Computers & Mathematics with Applications, 45(4), 555-562.
Majumdar, P., & Samanta, S. K. (2010). “On soft mappings”. Computers & Mathematics with Applications, 60(9), 2666-2672.
Majumdar, P., & Samanta, S. K. (2011). “On similarity measures of fuzzy soft sets”. Int. J. Adv. Soft. Comput. Appl, 2, 1-8.
Mcleay, S. and Omar, A. (2000). “The sensitivity of prediction models to the non-normality of bounded and unbounded financial ratios”. The British Accounting Review, 32 (2), 213-230.
Molodtsov, D. (1999). “Soft set theory—first results”. Computers & Mathematics with Applications, 37(4), 19-31.
Ohlson, J A (1980) “Financial Ratios and the probabilistic prediction of bankruptcy Management”. Journal of Accounting Research, 18 (1), 109-131.
Premachandra, I. M., Chen, Y., & Watson, J. (2011). “DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment”. Omega, 39(6), 620-626.
Scherger, V., Vigier, H. P., & Barberà-Mariné, M. G. (2014). “Finding business failure reasons through a fuzzy model of diagnosis”. Fuzzy economic review, 19(1), 45.
Tang, T C., Chi, L C (2005) “Neural networks analysis in business failure prediction of Chinese importers: A between-countries approach”. Expert Systems with Applications, 29, 244 – 255.
Vigier, H. P., & Terceño, A. (2008). “A model for the prediction of “diseases” of firms by means of fuzzy relations”. Fuzzy Sets and Systems, 159(17), 2299-2316.
Vranas, A S (1991) “The significance of financial characteristics in predicting business failure: an analysis in the Greek context”. Foundations of Computing and Decision Sciences, 17(4), 257 – 275.
Wilcox, J. N(1971) “A simple theory of financial ratios as predictors of failure”. Journal of Accounting Research, 389 – 395.
Xu, W., Xiao, Z., Dang, Z., Yang, D., Yang, X. (2014). “Financial Ratio Selection for Business Failure Prediction using Soft Set Theory”. Knowledge-Based Systems, 63, 59 -67.
Yang, X., Yu, D., Yang, J., & Wu, C. (2007). Generalization of soft set theory: from crisp to fuzzy case. In Fuzzy Information and Engineering (pp. 345-354). Springer Berlin Heidelberg.
Yeh, C C, Chi, D J, Hsu, M F (2010) “A hybrid approach of DEA, rough set and support vector machines for business failure prediction” Expert Systems with Applications, 37, 1535–1541.
Youn, H., Gu , Z (2010) “Predicting Korean lodging firm failures: An artificial neural network model along with a logistic regression model” International Journal of Hospitality Management 29 (2010) 120–127.
Zadeh, L.A. (1965). “Fuzzy Sets”. Information and Control, 8(3), 338-353.
Zmijewski, M E (1984) “Methodological Issues Related to the Estimation of Financial Distress Prediction Models”. Journal of Accounting Research, 22, 59 -82.
Zopounidis, C., Doumpos M(2001)“A preference disaggregation decision support system for financial classification problems” European Journal of Operational Research, 130( 2), 402–413.