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http://dx.doi.org/10.7465/jkdi.2017.28.6.1481

Portfolio optimization strategy based on financial ratios  

Choi, Jung Yong (Division of Investment Information Engineering, Yonsei University)
Kim, Jiwoo (Department of Industrial Engineering, Yonsei University)
Oh, Kyong Joo (Department of Industrial Engineering, Yonsei University)
Publication Information
Journal of the Korean Data and Information Science Society / v.28, no.6, 2017 , pp. 1481-1500 More about this Journal
Abstract
This study examines the stability and excellence of portfolio investment strategies based on the accounting information of the Korean stock market. In the process of constructing the portfolio, various combinations of financial ratios are used to select the stocks with high expected return and to measure their performance. We also tried to improve our investment performance by using genetic algorithm optimization. The results of this study show that portfolio strategies using accounting information are effective for investment decision making and can achieve high investment performance. We also verify that portfolio strategy using genetic algorithms can be effective for investment decision making.
Keywords
financial ratio; genetic algorithm; portfolio decision making; portfolio strategies;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 Abarbanell, J. and Bushee, B. (1997). Fundamental analysis, future earnings, and stock price. Journal of Accounting Research, 35, 467-511.
2 Abarbanell, J. and Bushee, B. (1998). Abnormal returns to a fundamental analysis strategy. The Accounting Review, 73, 19-45.
3 Arbel, A. and Strebel, P. (1983). Pay attention to neglected firms. Journal of Portfolio Management, 9, 37-42.   DOI
4 Ball, R. and Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research, 6, 159-178.   DOI
5 Banz, R. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9, 3-18.   DOI
6 Cheong, D. J. and Oh, K. J. (2014). Using cluster analysis and genetic algorithm to develop portfolio investment strategy based on investor information. Journal of the Korean Data & Information Science Society, 25, 1-11.   DOI
7 Basu, S. (1977). The investment performance of common stocks in relation to their price-earnings ratio: a test of the efficient markets hypothesis. Journal of Finance, 32, 663-682.   DOI
8 Basu, S. (1983). The relationship between earnings yield, market value and return for NYSE common stocks: further evidence. Journal of Financial Economics, 12, 129-256.   DOI
9 Byun, H. W., Song, C. W., Han, S. K., Lee, T. K. and Oh, K. J. (2009). Using genetic algorithms to develop volatility index-assisted hierarchical portfolio optimization. Journal of the Korean Data & Information Science Society, 20, 1049-1060.
10 Chopra, N., Lakonishok, J. and Ritter, J. (1992). Measuring abnormal performance: does the market overreact? Journal of Financial Economics, 31, 235-268.   DOI
11 Chung, S. H. and Oh, K. J. (2014). Using genetic algorithm to optimize rough set strategy in KOSPI200 futures market. Journal of the Korean Data & Information Science Society, 25, 281-292.   DOI
12 DeBondt, W. and Thaler, R. (1985). Does the stock market overreact? Journal of Finance, 40, 793-805.   DOI
13 Fama, E. and French, K. (1995). Size and book to market factors in earnings and returns. Journal of Finance, 50, 131-155.   DOI
14 Dechow, P., Hutton, A. and Sloan, R. (1999). An empirical assessment of the residual income valuation model. Journal of Accounting and Economics, 26, 1-34.   DOI
15 Fama, E. and French, K. (1988). Permanent and temporary components of stock prices. Journal of Political Economy, 96, 246-273.   DOI
16 Fama, E. and French, K. (1992). The cross-section of expected stock return. Journal of Finance, 47, 427-465.   DOI
17 Frankel, R. and Lee, C. (1998). Accounting valuation, market expectation, and cross-sectional stock returns. Journal of Accounting and Economics, 25, 283-319.   DOI
18 Haugen, R. A. and Baker, N. L. (1996). Commonality in the determinants of expected stock returns. Journal of Financial Economics, 41, 401-439.   DOI
19 Hong, C. S. and Kwon, T. W. (2010). Distribution fitting for the rate of return and value at risk. Journal of the Korean Data & Information Science Society, 21, 219-229.
20 Jegadeesh, N. (1990). Evidence of predictable behavior of security returns. Journal of Finance, 45, 881-898.   DOI
21 Kim, K. J. and Han, I. G. (2000). Genetic algorithm approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Application, 19, 125-132.   DOI
22 Lakonishok, J., Shleifer, A. and Vishny, R. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49, 1541-1578.   DOI
23 Ohlson, J. A. (1995). Earnings, book values, and dividends in equity valuation. Contemporary accounting research, 11, 661-687.   DOI
24 Lee, C., Myers, J. and Swaminathan, B. (1999). What is the intrinsic value of the dow? Journal of Finance, 54, 1693-1741.   DOI
25 Lee, K. J., Lee, H. J. and Oh, K. J. (2015). Using fuzzy-neural network to predict hedge fund survival. Journal of the Korean Data & Information Science Society, 26, 1189-1198.   DOI
26 Lehmann, B. (1990). Fads, martingales, and market efficiency. Quarterly Journal of Economics, 105, 1-28.   DOI
27 Lev, B. and Thiagarajan, R. (1993). Fundamental information analysis. Journal of Accounting Research, 31, 190-215.   DOI
28 Nathan S., Sivakumar, K. and Vijayakumar, J. (2001) Returns to trading strategies based on price-toearnings and price-to-sales ratios. Journal of Investing, 10, 17-28.
29 Ou, J. and Penman, S. (1989a). Financial statement analysis and the prediction of stock returns. Journal of Accounting and Economics, 11, 295-329.   DOI
30 Penman, S. H. (1996). The articulation of price-earnings ratios and market-to-book ratios and the evaluation of growth. Journal of accounting research, 34, 235-259.   DOI