Daily Volume Forecasting using High Frequency Predictors

Alvim, Leandro G.M.; Santos, Clarice; Milidiú, Ruy Luiz

doi:10.2316/p.2010.674-047

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…Zheng, Moulines, and Abergel (2012) utilize logistic regression in order to predict the inter-trade price jump. Alvim, dos Santos, and Milidiu (2010) use support vector regression (SVR) and partial least squares (PLS) for trading volume forecasting for 10 Bovespa stocks. Pai and Lin (2005) use a hybrid model for stock price prediction.…”

Section: Regression Analysismentioning

confidence: 99%

Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods

Ntakaris

Magris

Kanniainen

et al. 2018

Journal of Forecasting

110

106

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Managing the prediction of metrics in high‐frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high‐frequency limit order markets for mid‐price prediction. We extracted normalized data representations of time series data for five stocks from the Nasdaq Nordic stock market for a time period of 10 consecutive days, leading to a dataset of ∼4,000,000 time series samples in total. A day‐based anchored cross‐validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state‐of‐the‐art methodologies. Performance of baseline approaches are also provided to facilitate experimental comparisons. We expect that such a large‐scale dataset can serve as a testbed for devising novel solutions of expert systems for high‐frequency limit order book data analysis.

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Section: Regression Analysismentioning

confidence: 99%

Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods

Ntakaris

Magris

Kanniainen

et al. 2018

Journal of Forecasting

110

106

View full text Add to dashboard Cite

show abstract

“…Trading volume prediction plays an important role in algorithmic trading strategies [4,5,7,14,18,27,43]. Many efforts are paid to volume prediction [1,2,9,16,17,23,24,34,37]. Machine learning or deep learning methods have many applications in volume prediction.…”

Section: Time Series Analysis and Volume Predictionmentioning

confidence: 99%

ASAT: Adaptively Scaled Adversarial Training in Time Series

Zhang¹,

Li²,

Bao³

et al. 2021

Preprint

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Adversarial training is a method for enhancing neural networks to improve the robustness against adversarial examples. Besides the security concerns of potential adversarial examples, adversarial training can also improve the performance of the neural networks, train robust neural networks, and provide interpretability for neural networks. In this work, we take the first step to introduce adversarial training in time series analysis by taking the finance field as an example. Rethinking existing researches of adversarial training, we propose the adaptively scaled adversarial training (ASAT) in time series analysis, by treating data at different time slots with timedependent importance weights. Experimental results show that the proposed ASAT can improve both the accuracy and the adversarial robustness of neural networks. Besides enhancing neural networks, we also propose the dimension-wise adversarial sensitivity indicator to probe the sensitivities and importance of input dimensions. With the proposed indicator, we can explain the decision bases of black box neural networks. CCS CONCEPTS• Computing methodologies → Neural networks.

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“…Several recent examples of attempts to predict volume behavior include Alvim et al ( 2010 ) and Chen et al ( 2016 ). In Alvim et al ( 2010 ), the authors tried to predict volume using Partial Least Squares (PLS) and Support Vector Regression (SVR). Both methods outperformed the benchmark, an approach based on the trading volume of the previous time intervals.…”

Section: Literature Reviewmentioning

confidence: 99%

Volume Prediction With Neural Networks

Libman

Haber

Schaps

2019

Front. Artif. Intell.

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Changes in intraday trading volume are integral to any algorithmic trading strategy. Accordingly, forecasting the change in trading volume is paramount to better understanding the financial markets. This paper introduces a new method to forecast the log change in trading volume, leveraging the power of Long Short Term Memory (LSTM) networks in conjunction with Support Vector Regression (SVR) and Autoregressive (AR) models. We show that LSTM contributes to a more accurate forecast, particularly when constructed as part of a hybrid model with AR. The algorithm is extended to include data about the time of day, helping the model associate the log change in trading volume with the current hour, which yields the best performance of all trials.

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Daily Volume Forecasting using High Frequency Predictors

Cited by 9 publications

References 10 publications

Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods

Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods

ASAT: Adaptively Scaled Adversarial Training in Time Series

Volume Prediction With Neural Networks

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