- Albuquerque, P.H.M. ; de Moraes Souza, J.G. ; Kimura, H. Artificial intelligence in portfolio formation and forecast: Using different variance-covariance matrices. 2021 Communications in Statistics. Theory and Methods. 1-18
Paper not yet in RePEc: Add citation now
Alhashel, B.S. ; Almudhaf, F.W. ; Hansz, J.A. Can technical analysis generate superior returns in securitized property markets? Evidence from east Asia markets. 2018 Pacific-Basin Finance Journal. 47 92-108
- Arrow, K.J. ; Debreu, G. Existence of an equilibrium for a competitive economy. 1954 Econometrica. 22 265-290
Paper not yet in RePEc: Add citation now
Bazán-Palomino, W. ; Svogun, D. On the drivers of technical analysis profits in cryptocurrency markets: A distributed lag approach. 2023 International Review of Financial Analysis. -
Black, F. ; Scholes, M. The pricing of options and corporate liabilities. 1973 Journal of Political Economy. -
- Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on computational learning theory (pp. 144–152).
Paper not yet in RePEc: Add citation now
Bussmann, N. ; Giudici, P. ; Marinelli, D. ; Papenbrock, J. Explainable machine learning in credit risk management. 2021 Computational Economics. 57 203-216
- Chen, H. ; Xiao, K. ; Sun, J. ; Wu, S. A double-layer neural network framework for high-frequency forecasting. 2017 ACM Transactions on Management Information Systems (TMIS). 7 11-
Paper not yet in RePEc: Add citation now
- Chen, Y. ; Hao, Y. A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. 2017 Expert Systems with Applications. 80 340-355
Paper not yet in RePEc: Add citation now
- Claesen, M. ; De Moor, B. Hyperparameter search in machine learning. 2015 :
Paper not yet in RePEc: Add citation now
Coakley, J. ; Marzano, M. ; Nankervis, J. How profitable are FX technical trading rules?. 2016 International Review of Financial Analysis. 45 273-282
Cont, R. Empirical properties of asset returns: stylized facts and statistical issues. 2001 Quantitative Finance. 1 223-
- Cortes, C. ; Vapnik, V. Support-vector networks. 1995 Machine Learning. 20 273-297
Paper not yet in RePEc: Add citation now
Creamer, G. Model calibration and automated trading agent for euro futures. 2012 Quantitative Finance. 12 531-545
- Dastile, X. ; Celik, T. ; Potsane, M. Statistical and machine learning models in credit scoring: A systematic literature survey. 2020 Applied Soft Computing. 91 -
Paper not yet in RePEc: Add citation now
- de Oliveira, F.A. ; Nobre, C.N. ; Zárate, L.E. Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index–case study of PETR4, Petrobras, Brazil. 2013 Expert Systems with Applications. 40 7596-7606
Paper not yet in RePEc: Add citation now
De Spiegeleer, J. ; Madan, D.B. ; Reyners, S. ; Schoutens, W. Machine learning for quantitative finance: Fast derivative pricing, hedging and fitting. 2018 Quantitative Finance. 18 1635-1643
- Devaney, R. ; Nitecki, Z. Shift automorphisms in the Hénon mapping. 1979 Communications in Mathematical Physics. 67 137-146
Paper not yet in RePEc: Add citation now
- Dixon, M.F. ; Halperin, I. ; Bilokon, P. Machine learning in finance. 2020 Springer:
Paper not yet in RePEc: Add citation now
- Emerson, S., Kennedy, R., O’Shea, L., & O’Brien, J. (2019). Trends and applications of machine learning in quantitative finance. In 8th international conference on economics and finance research (pp. 1–9).
Paper not yet in RePEc: Add citation now
Fama, E.F. Efficient capital markets: A review of theory and empirical work. 1970 The Journal of Finance. 25 383-417
Gerritsen, D.F. Are chartists artists? The determinants and profitability of recommendations based on technical analysis. 2016 International Review of Financial Analysis. 47 179-196
- Ghosh, S. ; Dasgupta, A. ; Swetapadma, A. A study on support vector machine based linear and non-linear pattern classification. 2019 En : 2019 international conference on intelligent sustainable systems. IEEE:
Paper not yet in RePEc: Add citation now
Gorenc Novak, M. ; Velušček, D. Prediction of stock price movement based on daily high prices. 2016 Quantitative Finance. 16 793-826
Gu, S. ; Kelly, B. ; Xiu, D. Empirical asset pricing via machine learning. 2020 The Review of Financial Studies. 33 2223-2273
- Gunduz, H. ; Yaslan, Y. ; Cataltepe, Z. Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations. 2017 Knowledge-Based Systems. 137 138-148
Paper not yet in RePEc: Add citation now
- Hilbert, M. ; Darmon, D. How complexity and uncertainty grew with algorithmic trading. 2020 Entropy. 22 499-
Paper not yet in RePEc: Add citation now
- Hoeffding, W. Probability inequalities for sums of bounded random variables. 1963 Journal of the American Statistical Association. 58 13-30
Paper not yet in RePEc: Add citation now
- Hsu, M.-W. ; Lessmann, S. ; Sung, M.-C. ; Ma, T. ; Johnson, J.E. Bridging the divide in financial market forecasting: Machine learners vs. financial economists. 2016 Expert Systems with Applications. 61 215-234
Paper not yet in RePEc: Add citation now
Jackson, A. ; Ladley, D. Market ecologies: The effect of information on the interaction and profitability of technical trading strategies. 2016 International Review of Financial Analysis. 47 270-280
Kozak, S. ; Nagel, S. ; Santosh, S. Shrinking the cross-section. 2020 Journal of Financial Economics. 135 271-292
- Kristian, T. ; Kristanti, F.T. Stock market prediction using multivariate neural network backpropagation. 2020 En : Understanding digital industry. Routledge:
Paper not yet in RePEc: Add citation now
- Kumar, D. ; Meghwani, S.S. ; Thakur, M. Proximal support vector machine based hybrid prediction models for trend forecasting in financial markets. 2016 Journal of Computer Science. 17 1-13
Paper not yet in RePEc: Add citation now
Kurani, A. ; Doshi, P. ; Vakharia, A. ; Shah, M. A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. 2023 Annals of Data Science. 10 183-208
- Kute, D.V. ; Pradhan, B. ; Shukla, N. ; Alamri, A. Deep learning and explainable artificial intelligence techniques applied for detecting money laundering–a critical review. 2021 IEEE Access. -
Paper not yet in RePEc: Add citation now
- Li, Z. ; Tam, V. Combining the real-time wavelet denoising and long-short-term-memory neural network for predicting stock indexes. 2017 En : 2017 IEEE symposium series on computational intelligence. IEEE:
Paper not yet in RePEc: Add citation now
Markowitz, H. Portfolio selection. 1952 The Journal of Finance. 7 77-91
- Menezes, M.V. ; Torres, L.C. ; Braga, A.P. Width optimization of RBF kernels for binary classification of support vector machines: A density estimation-based approach. 2019 Pattern Recognition Letters. 128 1-7
Paper not yet in RePEc: Add citation now
- Merello, S. ; Ratto, A.P. ; Oneto, L. ; Cambria, E. Ensemble application of transfer learning and sample weighting for stock market prediction. 2019 En : 2019 international joint conference on neural networks. IEEE:
Paper not yet in RePEc: Add citation now
- Min, B.H. ; Borch, C. Systemic failures and organizational risk management in algorithmic trading: Normal accidents and high reliability in financial markets. 2021 Social Studies of Science. -
Paper not yet in RePEc: Add citation now
Nakano, M. ; Takahashi, A. ; Takahashi, S. Bitcoin technical trading with artificial neural network. 2018 Physica A. Statistical Mechanics and its Applications. 510 587-609
- Naser, M. ; Alavi, A. Insights into performance fitness and error metrics for machine learning. 2020 :
Paper not yet in RePEc: Add citation now
Ozbayoglu, A.M. ; Gudelek, M.U. ; Sezer, O.B. Deep learning for financial applications: A survey. 2020 Applied Soft Computing. -
- Patel, J. ; Shah, S. ; Thakkar, P. ; Kotecha, K. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. 2015 Expert Systems with Applications. 42 259-268
Paper not yet in RePEc: Add citation now
- Patel, J. ; Shah, S. ; Thakkar, P. ; Kotecha, K. Predicting stock market index using fusion of machine learning techniques. 2015 Expert Systems with Applications. 42 2162-2172
Paper not yet in RePEc: Add citation now
- Peng, Y. ; Albuquerque, P.H.M. ; de Sá, J.M.C. ; Padula, A.J.A. ; Montenegro, M.R. The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with support vector regression. 2018 Expert Systems with Applications. 97 177-192
Paper not yet in RePEc: Add citation now
- Peng, Y. ; Albuquerque, P.H.M. ; do Nascimento, I.F. ; Machado, J.V.F. Between nonlinearities, complexity, and noises: An application on portfolio selection using kernel principal component analysis. 2019 Entropy. 21 376-
Paper not yet in RePEc: Add citation now
- Peng, Y. ; Albuquerque, P.H.M. ; Kimura, H. ; Saavedra, C.A.P.B. Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators. 2021 Machine Learning with Applications. -
Paper not yet in RePEc: Add citation now
Peng, Y. ; Nagata, M.H. An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data. 2020 Chaos, Solitons & Fractals. 139 -
- Probst, P. ; Boulesteix, A.-L. ; Bischl, B. Tunability: Importance of hyperparameters of machine learning algorithms. 2019 Journal of Machine Learning Research. 20 1934-1965
Paper not yet in RePEc: Add citation now
Renault, T. Sentiment analysis and machine learning in finance: A comparison of methods and models on one million messages. 2020 Digital Finance. 2 1-13
- Robinson, C. What is a chaotic attractor?. 2008 Qualitative Theory of Dynamical Systems. 7 227-236
Paper not yet in RePEc: Add citation now
- Rundo, F. ; Trenta, F. ; di Stallo, A.L. ; Battiato, S. Machine learning for quantitative finance applications: A survey. 2019 Applied Sciences. 9 5574-
Paper not yet in RePEc: Add citation now
- Severino, M.K. ; Peng, Y. Machine learning algorithms for fraud prediction in property insurance: Empirical evidence using real-world microdata. 2021 Machine Learning with Applications. -
Paper not yet in RePEc: Add citation now
- Sezer, O.B. ; Ozbayoglu, A.M. Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. 2018 Applied Soft Computing. 70 525-538
Paper not yet in RePEc: Add citation now
- Shynkevich, Y. ; McGinnity, T.M. ; Coleman, S.A. ; Belatreche, A. ; Li, Y. Forecasting price movements using technical indicators: Investigating the impact of varying input window length. 2017 Neurocomputing. 264 71-88
Paper not yet in RePEc: Add citation now
Tao, R. ; Su, C.-W. ; Xiao, Y. ; Dai, K. ; Khalid, F. Robo advisors, algorithmic trading and investment management: Wonders of fourth industrial revolution in financial markets. 2021 Technological Forecasting and Social Change. 163 -
- Thurnhofer-Hemsi, K. ; López-Rubio, E. ; Molina-Cabello, M.A. ; Najarian, K. Radial basis function kernel optimization for support vector machine classifiers. 2020 :
Paper not yet in RePEc: Add citation now
Vecchi, E. ; Berra, G. ; Albrecht, S. ; Gagliardini, P. ; Horenko, I. Entropic approximate learning for financial decision-making in the small data regime. 2023 Research in International Business and Finance. -
- Weerts, H.J. ; Mueller, A.C. ; Vanschoren, J. Importance of tuning hyperparameters of machine learning algorithms. 2020 :
Paper not yet in RePEc: Add citation now
- Weng, B. ; Ahmed, M.A. ; Megahed, F.M. Stock market one-day ahead movement prediction using disparate data sources. 2017 Expert Systems with Applications. 79 153-163
Paper not yet in RePEc: Add citation now
Yaohao, P. ; Albuquerque, P.H.M. Non-linear interactions and exchange rate prediction: Empirical evidence using support vector regression. 2019 Applied Mathematical Finance. 26 69-100
Zarrabi, N. ; Snaith, S. ; Coakley, J. FX technical trading rules can be profitable sometimes!. 2017 International Review of Financial Analysis. 49 113-127