decompose_volume: Decompose Intraday Volume into Several Components

In intradayModel: Modeling and Forecasting Financial Intraday Signals

Decompose Intraday Volume into Several Components

Description

This function decomposes the intraday volume into daily, seasonal, and intraday dynamic components according to (Chen et al., 2016). If purpose = “analysis” (aka Kalman smoothing), the optimal components are conditioned on both the past and future observations. Its mathematical expression is \hat{x}_{\tau} = E[x_{\tau}|\{y_{j}\}_{j=1}^{M}], where M is the total number of bins in the dataset.

If purpose = “forecast” (aka Kalman forecasting), the optimal components are conditioned on only the past observations. Its mathematical expression is \hat{x}_{\tau+1} = E[x_{\tau+1}|\{y_{j}\}_{j=1}^{\tau}].

Three measures are used to evaluate the model performance:

• Mean absolute error (MAE): \frac{1}{M}\sum_{\tau=1}^M|\hat{y}_{\tau} - y_{\tau}| ;

• Mean absolute percent error (MAPE): \frac{1}{M}\sum_{\tau=1}^M\frac{|\hat{y}_{\tau} - y_{\tau}|}{y_{\tau}} ;

• Root mean square error (RMSE): \sqrt{\sum_{\tau=1}^M\frac{\left(\hat{y}_{\tau} - y_{\tau}\right)^2}{M}} .

Usage

decompose_volume(purpose, model, data, burn_in_days = 0)

Arguments

purpose	String "analysis"/"forecast". Indicates the purpose of using the provided model.
model	A model object of class "volume_model" from fit_volume().
data	An n_bin * n_day matrix or an xts object storing intraday volume.
burn_in_days	Number of initial days in the burn-in period for forecast. Samples from the first burn_in_days are used to warm up the model and then are discarded.

Value

A list containing the following elements:

• original_signal: A vector of original intraday volume;

• smooth_signal / forecast_signal: A vector of smooth/forecast intraday volume;

• smooth_components /forecast_components: A list of smooth/forecast components: daily, seasonal, intraday dynamic, and residual components.

• error: A list of three error measures: mae, mape, and rmse.

Author(s)

Shengjie Xiu, Yifan Yu and Daniel P. Palomar

References

Chen, R., Feng, Y., and Palomar, D. (2016). Forecasting intraday trading volume: A Kalman filter approach. Available at SSRN 3101695.

Examples

library(intradayModel)
data(volume_aapl)
volume_aapl_training <- volume_aapl[, 1:20]
volume_aapl_testing <- volume_aapl[, 21:50]
model_fit <- fit_volume(volume_aapl_training, fixed_pars = list(a_mu = 0.5, var_mu = 0.05),
                        init_pars = list(a_eta = 0.5))

# analyze training volume
analysis_result <- decompose_volume(purpose = "analysis", model_fit, volume_aapl_training)

# forecast testing volume
forecast_result <- decompose_volume(purpose = "forecast", model_fit, volume_aapl_testing)

# forecast testing volume with burn-in
forecast_result <- decompose_volume(purpose = "forecast", model_fit, volume_aapl[, 1:50],
                             burn_in_days = 20)

intradayModel documentation built on May 31, 2023, 8:49 p.m.