پورحیدری، امید، اعظمی، زینب. (1389). شناسایی نوع اظهارنظر حسابرسان با استفاده از شبکه‌های عصبی. دانش حسابداری، 1(3)، 97-77.
دموری، داریوش، فرید، داریوش و اشهر، مرتضی. (1390). پیش‌بینی شاخص کل بورس اوراق بهادار تهران با استفاده از الگوریتم پرواز پرندگان و مقایسۀ آن با الگوهای سنتی. دانش حسابداری، 2(5). 30-7.
میر فخرالدینی، حیدر. میبدی، حمید. مروتی، علی. (1392). پیش‌بینی مصرف انرژی ایران با استفاده از الگوی ترکیبی الگوریتم ژنتیک- شبکۀ عصبی مصنوعی و مقایسه آن با الگوهای سنتی، پژوهش‌های مدیریت در ایران، 17(2)، 222-197.
نقدی، سجاد. (1393). پیش‌بینی سود هر سهم شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران: مقایسۀ الگوی‌های سری زمانی، شبکۀ عصبی و الگوریتم ژنتیک، پایان‌نامۀ کارشناسی ارشد حسابداری، دانشگاه شهید بهشتی.
Arabmazaryazdi, M., Naghdi, S. (2013). Debt policy prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization, International Journal of Management Perspective, 2(5), 43-55.
Gaertner, F.B., Kausar, A., Steele, L.B. (2016). The usefulness of negative aggregate earnings changes in predicting future gross domestic product growth, FARS mid-year meeting, Nanyang Technological University.
Demori, D., Darioush, F., Ashar, M., (2011). Predicting Tehran Stock Market aggregete index with particle swarm optimization and comparsion with traditional models, Journal of Accounting Knowledge. 2 (5), 7-30 [In Persian].
Gallo, L., Hann, R., Li, C. (2013). Aggregate earnings surprises, monetary policy, and stock returns. The 2013 JCAE Symposium, University of Maryland.
Hann, R., Lee, H., Li, C. (2015). Do large firms tell us more about the macro economy? Evidence from managers’ financing decisions, American Accounting Association Annual Meeting. Conference on Teaching and Learning in Accounting. New York.
Haung, M. (2015). Predictive power of aggregate accounting earnings growth for growth of future GDP. Master Thesis, Eastern Illinois University.
Kennedy, J., Eberhart, R.C. (1995). A new optimizer using particle swarm theory. 6th International Symposium on Micro Machine and Human Science. Nagoya, Japan, 39–43.
Konchitchki, Y., Patatoukas, P.N. )2014a(. Accounting earnings and gross domestic product, Journal of Accounting and Economics, 57(1), 76–88.
Konchitchki, Y., Patatoukas, P.N. (2014b). Taking the pulse of the realeconomy using financial statement analysis: Implications for macro forecasting and stock valuation. The Accounting Review, 89(2), 669–694.
Kothari, K. (2001). Capital market research in accounting. Journal of Accounting and Economics, 31, 105–231.
Kothari, S.P., Shivacumar, L., Urcan, O. (2013). Aggregate earnings surprises and inflation forecasts. Working Paper. MIT.
Lev, B., Thiagarajan, S.R. (1993). Fundamental information analysis, Journal of Accounting Research, 31(2), 190-215.
Mirfakhraddiny, H.,
Babaei Meybodi, H. and
Morovati, A. (2013). Forecast consumption energy of Iran using hybrid model of artificial neural networks and genetic algorithms and Compared with traditional methodes,
Management Research in Iran. 17(2), 197-222 [In Persian].
Naghdi, S. (2014). Forecasting EPS of Iranian listed companies: A comparison of Time series, neural network and genetic algorithms models. Master Thesis, Shahid Beheshti University [In Persian].
Nallareddy, S., Ogneva, M. (2017). Predicting restatements in macroeconomic indicators using accounting information, The Accounting Review, 92 (2), 151-182.
Pourheidari, O., Azami, Z. (2008). Identifying auditors’ opinions with neural networks. Journal of Accounting Knowledge. 1(3), 77-97 [In Persian].
Shivakumar, L., and Oktay, O. (2014). Why do aggregate earnings shocks predict future infation shocks? 11th London Business School Accounting Symposium. London.
Sumiyana, S. (2014). Could the Aggregate of Accounting Earnings Predict Gross Domestic Products, Economics and Business seminar, University Gadjah Mada.
Teräsvirta, T. (2005). Forecasting economic variables with nonlinear models, SSE/EFI Working Paper, Series in Economics and Finance 598, Stockholm School of Economics.
)
