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Exchange Rate Prediction Using Time Series Approach

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Proceedings of Data Analytics and Management (ICDAM 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 786))

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Abstract

Many countries suffer from high inflation due to global issues, and when a developed country has severe inflation, its currency rate falls. The study focuses on predicting the exchange rate with the modeling technique of time series using the Akaike Information Criterion (AIC). The seasonal ARIMA model of (0,1,0)(2,1,1)12 has been selected for prediction purposes based on the ACF, PACF, and seasonality components. Based on this chosen model, exchange rates have been predicted and compared with the historical dataset to validate the model. The model can be helpful for an investor who wants to invest in the currency/forex market.

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References

  1. Jabeen M, Rashid A (2021) Macroeconomic news and exchange rates: exploring the role of order flow. Glob J Emerg Mark Econ 14:222–245. https://doi.org/10.1177/09749101211040146

    Article  Google Scholar 

  2. Hillebrand E, Mikkelsen JG, Spreng L, Urga G (2023) Exchange rates and macroeconomic fundamentals: evidence of instabilities from time-varying factor loadings. J Appl Econ 1–21. https://doi.org/10.1002/jae.2984

  3. Pant K, Rawat R, Pant A (2023) Forecasting of nifty 50 index volatility using seasonal ARIMA model. In: International conference on advancement in computation & computer technologies (InCACCT), Gharuan, India, pp 271–274. https://doi.org/10.1109/incacct57535.2023.10141726

  4. Avramov D (2002) Stock return predictability and model uncertainty. J Financ Econ 64:423–458. https://doi.org/10.1016/s0304-405x(02)00131-9

    Article  Google Scholar 

  5. Pant K, Srivastava B (2021) Investigation of factors influencing risk-averse investor’s perception: fixed deposit vs. mutual funds (debt-based). J Trans Stud Rev 28(2):131–148

    Google Scholar 

  6. Kamruzzaman M, Khudri MM, Rahman MM (2017) Modeling and predicting stock market returns: a case study on Dhaka stock exchange of Bangladesh. Dhaka Univ J Sci 65:97–101. https://doi.org/10.3329/dujs.v65i2.54515

  7. Mehtab S, Sen J (2019) A robust predictive model for stock price prediction using deep learning and natural language processing. In: 7th International conference on business analytics and intelligence (BAICONF), IIM Bangalore. https://doi.org/10.2139/ssrn.3502624

  8. Hao J, Feng QQ, Li J, Sun X (2023) A bi-level ensemble learning approach to complex time series forecasting: taking exchange rates as an example. J Forecast. https://doi.org/10.1002/for.2971

  9. Selvin S, Vinayakumar R, Gopalakrishnan EA, Menon VK, Soman KP (2017) Stock price prediction using LSTM, RNN and CNN-sliding window model. In: 2017 International conference on advances in computing, communications and informatics (ICACCI), pp 1643–1647. https://doi.org/10.1109/icacci.2017.8126078

  10. Babu CN, Reddy BE (2014) Selected Indian stock predictions using a hybrid ARIMA-GARCH model. In: 2014 International conference on advances in electronics computers and communications, pp 1–6. https://doi.org/10.1109/icaecc.2014.7002382

  11. Majumder MdMR, Hossain MdI, Hasan MK (2019) Indices prediction of Bangladeshi stock by using time series forecasting and performance analysis. In: 2019 International conference on electrical, computer and communication engineering (ECCE), pp 1–5. https://doi.org/10.1109/ecace.2019.8679480

  12. Wang Y, Guo Y (2020) Forecasting method of stock market volatility in time series data based on mixed model of ARIMA and XGBoost. China Commun 17:205–221. https://doi.org/10.23919/jcc.2020.03.017

  13. Kumar M, Thenmozhi M (2014) Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models. Int J Bank Acc Fin 5:284. https://doi.org/10.1504/ijbaaf.2014.064307

  14. Devi BU, Sundar D, Alli P (2013) An effective time series analysis for stock trend prediction using ARIMA model for nifty midcap-50. Int J Data Min Knowl Manage Process 3(1):65–78. https://doi.org/10.5121/ijdkp.2013.3106

  15. Ariyo AA, Adewumi AO, Ayo CK (2014) Stock price prediction using the ARIMA model. In: 2014 UKSim-AMSS 16th international conference on computer modelling and simulation, pp 106–112. https://doi.org/10.1109/uksim.2014.67

  16. Kumar D (2014) Correlations, return and volatility spillovers in Indian exchange rates. Glob Bus Rev 15:77–91. https://doi.org/10.1177/0972150913515577

    Article  Google Scholar 

  17. Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74:427–431. https://doi.org/10.2307/2286348

    Article  MathSciNet  Google Scholar 

  18. Montgomery DC, Jennings CL, Kulahci M (2015) Introduction to time series analysis and forecasting. Wiley

    Google Scholar 

  19. Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice. Otexts, Heathmont, Vic.

    Google Scholar 

  20. Akaike H (1969) Fitting autoregressive models for prediction. Ann Inst Stat Math 21:243–247. https://doi.org/10.1007/bf02532251

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Vocational Studies, Graphic Era Hill University, Dehradun, Uttarakhand, India

    Kamal Pant

  2. School of Computing, Graphic Era Hill University, Dehradun, Uttarakhand, India

    Alka Pant

Authors
  1. Kamal Pant
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  2. Alka Pant
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Corresponding author

Correspondence to Alka Pant .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Bhagwan Parshuram Institute of Technology, New Delhi, Delhi, India

    Abhishek Swaroop

  2. Jan Wyżykowski University, Polkowice, Poland

    Zdzislaw Polkowski

  3. Polytechnic Institute of Portalegre, Portalegre, Portugal

    Sérgio Duarte Correia

  4. Centre for Communications Technology, London Metropolitan University, London, UK

    Bal Virdee

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Pant, K., Pant, A. (2024). Exchange Rate Prediction Using Time Series Approach. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 786. Springer, Singapore. https://doi.org/10.1007/978-981-99-6547-2_35

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