Academic Research on Lookback Period 

• Length of comorbidity lookback period affected regression model performance of administrative health data, Preen, D. B., CD'Arcy, J. H., Spilsbury, K., Semmens, J. B., & Brameld, K. J. (2006). Journal of clinical epidemiology, 59(9), 940-946. The index cases included n = 349686 (procedural) and n = 326456 (medical) patients during the course of 1990 to 1996 with admission to the hospital. Admin record of the hospital was extracted for one hundred and two comorbidities, determined at the index admission. It was for lookback Periods of one, two, three and five years. The re-admissions and deaths were noticed within twelve months and thirty days. Hierarchy of regressions (nested and non-nested) was used. Likewise, the predictive models of the lookback period were used under the ROC - AUC curve, i.e. Receiver Operator Characteristic Area. 
• Prior Bankruptcy Automatically Tolls Lookback Period for Dischargeability of Tax Debt, Stuart, J. B. (2002). Banking LJ, 119, 682. This paper explains the advanced bankruptcy which automatically costs the Lookback Period for tax debt discharge-ability.
• The impact of the lookback period and definition of confirmatory events on the identification of incident cancer cases in administrative data, Czwikla, J., Jobski, K., & Schink, T. (2017). BMC medical research methodology, 17(1), 122. This paper evaluates the length effect of lookback period as well as the confirmation period. Also, the confirmatory events definition on the no. of incidents, the cancer cases noticed and ACI (Age-Standardized Cumulative Incidences calculated in admin record using the registry information of German cancer being a benchmark.
• Lookback options with discrete and partial monitoring of the underlying price, Heynen, R. C., & Kat, H. M. (1995). Applied Mathematical Finance, 2(4), 273-284. In this paper, the author proves that the price of BSL options (Black & Scholes Lookback 1973) can be taken in semi-closed form. Using these options, the economists monitor the price on a discrete basis, not continuous. The author derives formulas for pricing a number of partial and complete lookback options. Here, the price is monitored at not essentially equally spaced elements in time. As a result, the discrete price monitoring in place of continuous price has a fairly large influence on the lookback prices. But it does not take new hedging issues into consideration.
• A study of the parameters of a backpropagation stock price prediction model, Tan, C. N., & Wittig, G. E. (1993, November). In 1993 First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems(pp. 288-291). IEEE. A statistical analysis reports of ANN (Artificial Neural Network) apply an experiment on the BSPP model (Backpropagation Stock Price Prediction). This model was designed to evaluate its prediction ability. There were different parameters, including activation function, momentum, input noise, learning rate and no. of neurons (in hidden layer). The relevant predictive outcomes were recorded. This ANN model is considered as a black box since it takes inputs to get the expected result. This study is about black box behaviour in case of a change in parameters.
• Early exercise policies of American floating strike and fixed strike lookback options, Yu, H., Kuen Kwok, Y., & Wu, L. (2001). Nonlinear Analysis-Theory Methods and Applications, 47(7), 4591-4602. With the help of similarity transform, the authors show two types of US lookback models, i.e. fixed and floating strike using a partial differential equation. They test the early policies of these options. The realized extremum may be supervised in a discrete or continuous way for the price of assets. For its numerical value, many approaches are discussed to derive exactly correct and efficient results.
• A closed-form solution for perpetual American floating strike lookback options, Dai, M. (2000). For the perpetual model of US floating strike lookback period, a closed form outcome is extracted. It uses PDE (Partial Differential Equation) approach with continuous payment of dividend. To check the validity of this solution, the author performs numerical experiments.
• An analytic pricing formula for lookback options under stochastic volatility, Leung, K. S. (2013). Applied Mathematics Letters, 26(1), 145-149. In this research, the author derives a formula of analytical pricing using Hestons SV model (Stochastic Validity) for American FSL (floating strike lookback) periods. Then, he gives prices to the fixed options based on the relation of put-call parity and floating strike results.
• Pricing lookback and barrier options under the CEV process, Boyle, P. P. (1999). Journal of financial and quantitative analysis, 34(2), 241-264.This study is an examination of lookback period prices and barrier options in case the assets follow the process of CEV (Constant Elasticity of Variance). The author introduces the trinomial method for this purpose. Babbs technique can be useful for the lognormal situation. It is modified to get the value of the lookback period. As a result, the prices of look back and barrier options deviate to a greater extent. The models of Black Scholes and CEV have minor differences for the standard options. It is vital to have accurate model specs for the options which are more dependent on extreme as compared to standard options.
• Lookback options under the CEV process: a correction, Boyle, P. P., Tian, Y., & Imai, J. (1999). Journal of Financial and Quantitative Analysis Unpublished Appendixes, Notes, Comments, and Corrections http://depts. washington. edu/jfqa. This paper is an extension to the previous research by the same author on Lookback options in the process of Constant Elasticity of Variance (CEV). Using the Trinomial method, he approximates this process. The writer presents an improved analysis with a few corrections, comments, appendixes and important notes.