create a website

Univariate Forecasting for REITs with Deep Learning: A Comparative Analysis with an ARIMA Model. (2023). Song, Han-Suck ; Axelsson, Birger.
In: Working Paper Series.
RePEc:hhs:kthrec:2023_010.

Full description at Econpapers || Download paper

Cited: 0

Citations received by this document

Cites: 55

References cited by this document

Cocites: 43

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

    This document has not been cited yet.

References

References cited by this document

  1. Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, 2014.

  2. Aguilar, M., Boudry, W., & Connolly, R. (2015). Cross-sectional dynamics of REIT market efficiency.
    Paper not yet in RePEc: Add citation now
  3. Almudhaf, F., & Hansz, A. J. (2018). Random walks and market efficiency: evidence from real estate investment trusts (REIT) subsectors. International Journal of Strategic Property Management, 22(2).
    Paper not yet in RePEc: Add citation now
  4. Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one, 12(7), e0180944.

  5. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828.
    Paper not yet in RePEc: Add citation now
  6. Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
    Paper not yet in RePEc: Add citation now
  7. Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of machine learning research, 13(2).
    Paper not yet in RePEc: Add citation now
  8. Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p.
    Paper not yet in RePEc: Add citation now
  9. Block, R. (2012). Investing in REITs (4th ed.). New Jersey: Wiley.
    Paper not yet in RePEc: Add citation now
  10. Brooks, C. (2019). Introductory Econometrics for Finance. Cambridge: Cambridge University Press, Fourth edition.

  11. Cao, J., Li, Z., & Li, J. (2019). Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical mechanics and its applications, 519, 127-139.

  12. Cavallie Mester, J. W. (2021). Using LSTM Neural Networks to Predict Daily Stock Returns. Degree Project.
    Paper not yet in RePEc: Add citation now
  13. Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), 1247-1250.
    Paper not yet in RePEc: Add citation now
  14. Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205.
    Paper not yet in RePEc: Add citation now
  15. Chou, J. S., Fleshman, D. B., & Truong, D. N. (2022). Comparison of machine learning models to provide preliminary forecasts of real estate prices. Journal of Housing and the Built Environment, 1-36.
    Paper not yet in RePEc: Add citation now
  16. Dreyfus, G. (2005). Neural Networks: Methodology and Applications. Springer, Berlin Heidelberg, 1st edition.
    Paper not yet in RePEc: Add citation now
  17. El-Amir, H., & Hamdy, M. (2019). Deep learning pipeline: building a deep learning model with TensorFlow. Apress.
    Paper not yet in RePEc: Add citation now
  18. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.

  19. Fjellström, C. (2022). Long Short-Term Memory Neural Network for Financial Time Series. arXiv preprint arXiv:2201.08218.
    Paper not yet in RePEc: Add citation now
  20. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
    Paper not yet in RePEc: Add citation now
  21. Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R. and Schmidhuber, J. (2017). LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10):2222-2232.
    Paper not yet in RePEc: Add citation now
  22. Grobys, K., Kolari, J. W., & Niang, J. (2022). Man versus machine: on artificial intelligence and hedge funds performance. Applied Economics, 1-15.

  23. Heaton, J. B., Polson, N. G., & Witte, J. H. (2017). Deep learning for finance: deep portfolios. Applied Stochastic Models in Business and Industry, 33(1), 3-12.

  24. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 17351780.
    Paper not yet in RePEc: Add citation now
  25. Hui, E. C. M., Wright, J. A., & Yam, S. C. P. (2014). Calendar effects and real estate securities. The Journal of Real Estate Finance and Economics, 49(1), 91-115.

  26. Hyndman, R. J., & Athanasopoulos, G. (2014). Forecasting: Principles and practice. otexts; 2014. Online at http://otexts. org/fpp.
    Paper not yet in RePEc: Add citation now
  27. Jirasakuldech, B., & Knight, J. (2005). Efficiency in the market for REITs: Further evidence. Journal of Real Estate Portfolio Management, 11(2), 123-132.
    Paper not yet in RePEc: Add citation now
  28. Kim, H. Y., & Won, C. H. (2018). Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models. Expert Systems with Applications, 103, 25-37.
    Paper not yet in RePEc: Add citation now
  29. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
    Paper not yet in RePEc: Add citation now
  30. Kleiman, R., Payne, J., & Sahu, A. (2002). Random walks and market efficiency: evidence from international real estate markets. Journal of Real Estate Research, 24(3), 279-298.

  31. Kuhle, J. L., & Alvayay, J. R. (2000). The efficiency of equity REIT prices. The Journal of Real Estate Portfolio Management, 6(4), 349-354.
    Paper not yet in RePEc: Add citation now
  32. Lazzeri, F. (2020). Machine learning for time series forecasting with Python. John Wiley & Sons.
    Paper not yet in RePEc: Add citation now
  33. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
    Paper not yet in RePEc: Add citation now
  34. Lee, C. K., Sehwan, Y., & Jongdae, J. (2007). Neural network model versus SARIMA model in forecasting Korean stock price index (KOSPI). Issues in Information System, 8(2), 372-378.
    Paper not yet in RePEc: Add citation now
  35. Lee, M. L., & Chiang, K. (2004). Substitutability between equity REITs and mortgage REITs. Journal of Real Estate Research, 26(1), 95-114.

  36. Lütkepohl, H., & Xu, F. (2012). The role of the log transformation in forecasting economic variables. Empirical Economics, 42(3), 619-638.

  37. Malkiel, B. G. (2019). A random walk down Wall Street: the time-tested strategy for successful investing.
    Paper not yet in RePEc: Add citation now
  38. Mei, J., & Gao, B. (1995). Price reversal, transaction costs, and arbitrage profits in the real estate securities market. Journal of Real Estate Finance and Economics, 11, 153-165.

  39. Michelucci, U. (2018). Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks. Apress, Berkeley, 1st edition.
    Paper not yet in RePEc: Add citation now
  40. Nareit (2022). FTSE NAREIT All Equity REITs (FNER).
    Paper not yet in RePEc: Add citation now
  41. Nguyen, T. H., Shirai, K., & Velcin, J. (2015). Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications, 42(24), 9603-9611.
    Paper not yet in RePEc: Add citation now
  42. Rather, A. M., Agarwal, A., & Sastry, V. N. (2015). Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 42(6), 3234-3241.
    Paper not yet in RePEc: Add citation now
  43. Schindler, F. (2011). Market efficiency and return predictability in the emerging securitized real estate markets. Journal of Real Estate Literature, 19(1), 111-150.
    Paper not yet in RePEc: Add citation now
  44. Schindler, F., Rottke, N., & Füss, R. (2010). Testing the predictability and efficiency of securitized real estate markets. Journal of Real Estate Portfolio Management, 16(2), 171-191.
    Paper not yet in RePEc: Add citation now
  45. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
    Paper not yet in RePEc: Add citation now
  46. Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (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). IEEE.
    Paper not yet in RePEc: Add citation now
  47. Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018). A comparison of ARIMA and LSTM in forecasting time series. In 2018 17th IEEE international conference on machine learning and applications (ICMLA) (pp. 1394-1401). IEEE.
    Paper not yet in RePEc: Add citation now
  48. Smith, L. N. (2017). Cyclical learning rates for training neural networks. In 2017 IEEE winter conference on applications of computer vision (WACV) (pp. 464-472). IEEE.
    Paper not yet in RePEc: Add citation now
  49. Stevenson, S. (2002). Momentum effects and mean reversion in real estate securities. Journal of Real Estate Research, 23, 47-64.

  50. Su, J. J., & Roca, E. (2012). Are securitised real estate markets efficient?: New international evidence based on an improved automatic Portmanteau test. Economic Modelling, 29(3), 684-690.

  51. Working paper presented at NAREIT-AREUEA Real Estate Research Conference, New York. Akita, R., Yoshihara, A., Matsubara, T., & Uehara, K. (2016, June). Deep learning for stock prediction using numerical and textual information. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) (pp. 1-6). IEEE.
    Paper not yet in RePEc: Add citation now
  52. WW Norton & Company. McNelis, P. D. (2005). Neural networks in finance: gaining predictive edge in the market. Academic Press.
    Paper not yet in RePEc: Add citation now
  53. Yu, Q., Wang, K., Strandhagen, J. O., & Wang, Y. (2017). Application of long short-term memory neural network to sales forecasting in retail—a case study. In International Workshop of Advanced Manufacturing and Automation (pp. 11-17). Springer, Singapore.
    Paper not yet in RePEc: Add citation now
  54. Zhang, R., Chen, Z., Chen, S., Zheng, J., Büyüköztürk, O., & Sun, H. (2019). Deep long short-term memory networks for nonlinear structural seismic response prediction. Computers & Structures, 220, 55-68.
    Paper not yet in RePEc: Add citation now
  55. Zou, Z., & Qu, Z. (2020). Using LSTM in Stock prediction and Quantitative Trading. CS230: Deep Learning, Winter, 1-6.
    Paper not yet in RePEc: Add citation now

Cocites

Documents in RePEc which have cited the same bibliography

  1. Modelling with Neural Networks and Time-Series Forecasting Inventory Control and Cost Reduction in Supply Chain Process. (2025). Kuppulakshmi, V ; Sugapriya, C ; Kanchana, A ; Nagarajan, D.
    In: SN Operations Research Forum.
    RePEc:spr:snopef:v:6:y:2025:i:2:d:10.1007_s43069-025-00466-5.

    Full description at Econpapers || Download paper

  2. Multi-Modal Temporal Dynamic Graph Construction for Stock Rank Prediction. (2025). Chen, Long ; Guan, Ziyu ; Xu, Cai ; Wei, Zengyu ; Liu, Ying.
    In: Mathematics.
    RePEc:gam:jmathe:v:13:y:2025:i:5:p:845-:d:1604527.

    Full description at Econpapers || Download paper

  3. A Framework for Gold Price Prediction Combining Classical and Intelligent Methods with Financial, Economic, and Sentiment Data Fusion. (2025). Taneva-Angelova, Gergana ; Raychev, Stefan ; Ilieva, Galina.
    In: IJFS.
    RePEc:gam:jijfss:v:13:y:2025:i:2:p:102-:d:1671850.

    Full description at Econpapers || Download paper

  4. Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China. (2024). Shu, Jun ; Liu, Bin ; Pan, KE ; He, Zuli ; Han, Suyue ; Xia, Xinyu.
    In: PLOS ONE.
    RePEc:plo:pone00:0302558.

    Full description at Econpapers || Download paper

  5. Modeling inflation rate factors on present consumption price index in Ethiopia: threshold autoregressive models approach. (2023). Kebede, Belete ; Abebe, Alebachew ; Temesgen, Aboma.
    In: Future Business Journal.
    RePEc:spr:futbus:v:9:y:2023:i:1:d:10.1186_s43093-023-00241-0.

    Full description at Econpapers || Download paper

  6. Forecasting returns volatility of cryptocurrency by applying various deep learning algorithms. (2023). Shaikh, Parvez Ahmed ; Khan, Farman Ullah.
    In: Future Business Journal.
    RePEc:spr:futbus:v:9:y:2023:i:1:d:10.1186_s43093-023-00200-9.

    Full description at Econpapers || Download paper

  7. Dynamic and context-dependent stock price prediction using attention modules and news sentiment. (2023). Knigstein, Nicole.
    In: Digital Finance.
    RePEc:spr:digfin:v:5:y:2023:i:3:d:10.1007_s42521-023-00089-7.

    Full description at Econpapers || Download paper

  8. A comparative study on effect of news sentiment on stock price prediction with deep learning architecture. (2023). Pokhrel, Nawa Raj ; Dahal, Keshab Raj ; Joshi, Jeorge ; Banjade, Huta R ; Gupta, Ankrit ; Mahatara, Sharad ; Gaire, Santosh.
    In: PLOS ONE.
    RePEc:plo:pone00:0284695.

    Full description at Econpapers || Download paper

  9. Predict Stock Prices Using Supervised Learning Algorithms and Particle Swarm Optimization Algorithm. (2023). Hosseini, Soodeh ; Bazrkar, Mohammad Javad.
    In: Computational Economics.
    RePEc:kap:compec:v:62:y:2023:i:1:d:10.1007_s10614-022-10273-3.

    Full description at Econpapers || Download paper

  10. Univariate Forecasting for REITs with Deep Learning: A Comparative Analysis with an ARIMA Model. (2023). Song, Han-Suck ; Axelsson, Birger.
    In: Working Paper Series.
    RePEc:hhs:kthrec:2023_010.

    Full description at Econpapers || Download paper

  11. Forecasting oil, coal, and natural gas prices in the pre-and post-COVID scenarios: Contextual evidence from India using time series forecasting tools. (2023). Murshed, Muntasir ; Abduvaxitovna, Shamansurova Zilola ; Manigandan, Palanisamy ; Alam, Md Shabbir ; Pachiyappan, Duraisamy.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:81:y:2023:i:c:s0301420723000508.

    Full description at Econpapers || Download paper

  12. Data vs. information: Using clustering techniques to enhance stock returns forecasting. (2023). Fernandez Bariviera, Aurelio ; Saenz, Javier Vasquez ; Quiroga, Facundo Manuel.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:88:y:2023:i:c:s1057521923001734.

    Full description at Econpapers || Download paper

  13. The optimal forecast model for consumer price index of Puntland State, Somalia. (2022). Mohamed, Jama ; Ali, Abdullahi Osman.
    In: Quality & Quantity: International Journal of Methodology.
    RePEc:spr:qualqt:v:56:y:2022:i:6:d:10.1007_s11135-022-01328-6.

    Full description at Econpapers || Download paper

  14. Artificial Neural Network for Modeling the Economic Performance: A New Perspective. (2022). Mohamed, Ahmed.
    In: Journal of Quantitative Economics.
    RePEc:spr:jqecon:v:20:y:2022:i:3:d:10.1007_s40953-022-00297-9.

    Full description at Econpapers || Download paper

  15. Swarm Intelligence Based Hybrid Neural Network Approach for Stock Price Forecasting. (2022). Singh, Uday Pratap ; Jain, Sanjeev ; Kumar, Gourav.
    In: Computational Economics.
    RePEc:kap:compec:v:60:y:2022:i:3:d:10.1007_s10614-021-10176-9.

    Full description at Econpapers || Download paper

  16. Towards Crafting Optimal Functional Link Artificial Neural Networks with Rao Algorithms for Stock Closing Prices Prediction. (2022). Das, Subhranginee ; Nayak, Sarat Chandra ; Sahoo, Biswajit.
    In: Computational Economics.
    RePEc:kap:compec:v:60:y:2022:i:1:d:10.1007_s10614-021-10130-9.

    Full description at Econpapers || Download paper

  17. Development of Intelligent Stock Trading System Using Pattern Independent Predictor and Turning Point Matrix. (2022). Song, Yoojeong ; Lee, Jong Woo.
    In: Computational Economics.
    RePEc:kap:compec:v:59:y:2022:i:1:d:10.1007_s10614-020-10066-6.

    Full description at Econpapers || Download paper

  18. Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning. (2022). Han, Chulwoo.
    In: Management Science.
    RePEc:inm:ormnsc:v:68:y:2022:i:10:p:7701-7741.

    Full description at Econpapers || Download paper

  19. A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models. (2022). Ser, Yee Chee ; Gerogiannis, Vassilis C ; Thong, Pham Huy ; Tuan, Nguyen Trung ; Li, Madeline Hui ; Son, Le Hoang ; Cuong, LE ; Selvachandran, Ganeshsree.
    In: Mathematics.
    RePEc:gam:jmathe:v:10:y:2022:i:8:p:1329-:d:795708.

    Full description at Econpapers || Download paper

  20. Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment. (2022). Koenigstein, Nicole.
    In: Papers.
    RePEc:arx:papers:2205.01639.

    Full description at Econpapers || Download paper

  21. Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction. (2022). Yin, Yilong ; Nie, Xiushan ; Li, Xiaojie ; Wang, Meng ; Du, Juan ; Zhang, Chunyun ; Cui, Chaoran.
    In: Papers.
    RePEc:arx:papers:2107.14033.

    Full description at Econpapers || Download paper

  22. Analysis of Romanian Air Quality using Machine Learning Techniques. (2022). Niculae, Andreea-Mihaela.
    In: Database Systems Journal.
    RePEc:aes:dbjour:v:13:y:2022:i:1:p:1-10.

    Full description at Econpapers || Download paper

  23. The Prediction of Gold Futures Prices at the Shanghai Futures Exchange Based on the MEEMD-CS-Elman Model. (2021). Ma, Ying ; Wang, Xiaowen ; Li, Wen.
    In: SAGE Open.
    RePEc:sae:sagope:v:11:y:2021:i:1:p:21582440211001866.

    Full description at Econpapers || Download paper

  24. Accuracies of Model Risks in Finance using Machine Learning. (2021). SADEFO KAMDEM, Jules ; Osei, Salomey ; Fadugba, Jeremiah ; Mpinda, Berthine Nyunga.
    In: Working Papers.
    RePEc:hal:wpaper:hal-03191437.

    Full description at Econpapers || Download paper

  25. Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market. (2021). Jeong, Seunghwan ; Oh, Kyong Joo ; Nam, Hyun ; Lee, Hee Soo.
    In: Sustainability.
    RePEc:gam:jsusta:v:13:y:2021:i:3:p:1011-:d:483338.

    Full description at Econpapers || Download paper

  26. Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda. (2021). Sharma, Gagan ; Chopra, Ritika.
    In: JRFM.
    RePEc:gam:jjrfmx:v:14:y:2021:i:11:p:526-:d:672223.

    Full description at Econpapers || Download paper

  27. A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed. (2021). Elsaraiti, Meftah ; Merabet, Adel.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:20:p:6782-:d:658694.

    Full description at Econpapers || Download paper

  28. Climate Finance: Mapping Air Pollution and Finance Market in Time Series. (2021). Fang, Zheng ; Wang, Sheng ; Peng, Ruiming ; Xie, Jian Ying.
    In: Econometrics.
    RePEc:gam:jecnmx:v:9:y:2021:i:4:p:43-:d:694969.

    Full description at Econpapers || Download paper

  29. Study on early warnings of strategic risk during the process of firms’ sustainable innovation based on an optimized genetic BP neural networks model: Evidence from Chinese manufacturing firms. (2021). Duan, Yunlong ; Chin, Tachia ; Fang, Qifeng ; Yang, Meng ; Zhou, LI ; Mu, Chang ; Deng, Zhiqing.
    In: International Journal of Production Economics.
    RePEc:eee:proeco:v:242:y:2021:i:c:s0925527321002693.

    Full description at Econpapers || Download paper

  30. Forecasting stock index price using the CEEMDAN-LSTM model. (2021). Lin, YU ; Liao, Ying ; Yan, Yan ; Xu, Jiali ; Ma, Feng.
    In: The North American Journal of Economics and Finance.
    RePEc:eee:ecofin:v:57:y:2021:i:c:s1062940821000553.

    Full description at Econpapers || Download paper

  31. Design and Analysis of Robust Deep Learning Models for Stock Price Prediction. (2021). Sen, Jaydip ; Mehtab, Sidra.
    In: Papers.
    RePEc:arx:papers:2106.09664.

    Full description at Econpapers || Download paper

  32. The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR. (2020). Liao, Zhewen ; Jin, Zhenni ; Sun, YI ; Lai, Lin ; Guo, Kun.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:39:y:2020:i:7:p:1166-1178.

    Full description at Econpapers || Download paper

  33. Forecasting Monthly Prices of Gold Using Artificial Neural Network. (2020). Okezie, Uche-Ikonne ; Bright, Oiorha ; Maxwell, Obubu ; Chukwudike, Nwokike ; Henry, Ukomah ; Ala, Ugo.
    In: Journal of Statistical and Econometric Methods.
    RePEc:spt:stecon:v:9:y:2020:i:3:f:9_3_2.

    Full description at Econpapers || Download paper

  34. Predictive power of ARIMA models in forecasting equity returns: a sliding window method. (2020). Guo, Xiaomin ; Hu, Ruizhi ; Dong, Huijian ; Reichgelt, Han.
    In: Journal of Asset Management.
    RePEc:pal:assmgt:v:21:y:2020:i:6:d:10.1057_s41260-020-00184-z.

    Full description at Econpapers || Download paper

  35. Comparing Weighted Markov Chain and Auto-Regressive Integrated Moving Average in the Prediction of Under-5 Mortality Annual Closing Rates in Nigeria. (2020). Obasohan, Phillips Edomwonyi.
    In: International Journal of Statistics and Probability.
    RePEc:ibn:ijspjl:v:9:y:2020:i:3:p:13.

    Full description at Econpapers || Download paper

  36. An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids. (2019). .
    In: Energy & Environment.
    RePEc:sae:engenv:v:30:y:2019:i:1:p:63-80.

    Full description at Econpapers || Download paper

  37. HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction. (2019). Lee, Sang Hoon ; Kim, Raehyun ; Jeong, Minbyul ; Kang, Jaewoo ; Ho, Chan.
    In: Papers.
    RePEc:arx:papers:1908.07999.

    Full description at Econpapers || Download paper

  38. Enhancing Stock Movement Prediction with Adversarial Training. (2019). He, Xiangnan ; Chua, Tat-Seng ; Sun, Maosong ; Ding, JI ; Chen, Huimin ; Feng, Fuli.
    In: Papers.
    RePEc:arx:papers:1810.09936.

    Full description at Econpapers || Download paper

  39. Temporal Relational Ranking for Stock Prediction. (2019). Liu, Yiqun ; Wang, Xiang ; He, Xiangnan ; Chua, Tat-Seng ; Feng, Fuli ; Luo, Cheng.
    In: Papers.
    RePEc:arx:papers:1809.09441.

    Full description at Econpapers || Download paper

  40. Adaptive Market Hypothesis and Artificial Neural Networks: Evidence from Pakistan. (2019). Ayub, Usman ; Kayani, Sehrish ; Jadoon, Imran Abbas.
    In: Global Regional Review.
    RePEc:aaw:grrjrn:v:4:y:2019:i:2:p:190-203.

    Full description at Econpapers || Download paper

  41. A Thick ANN Model for Forecasting Inflation. (2018). Iqbal, Javed ; Hanif, Muhammad ; Mughal, Khurrum S.
    In: SBP Working Paper Series.
    RePEc:sbp:wpaper:99.

    Full description at Econpapers || Download paper

  42. Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network. (2018). Liu, Huicheng.
    In: Papers.
    RePEc:arx:papers:1811.06173.

    Full description at Econpapers || Download paper

  43. Evaluating the Performance of Inflation Forecasting Models of Pakistan. (2015). Malik, Muhammad Jahanzeb ; Hanif, Muhammad.
    In: SBP Research Bulletin.
    RePEc:sbp:journl:66.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2025-09-21 00:06:33 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Last updated August, 3 2024. Contact: Jose Manuel Barrueco.