54,052 research outputs found
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Nowadays, with the availability of massive amount of trade data collected,
the dynamics of the financial markets pose both a challenge and an opportunity
for high frequency traders. In order to take advantage of the rapid, subtle
movement of assets in High Frequency Trading (HFT), an automatic algorithm to
analyze and detect patterns of price change based on transaction records must
be available. The multichannel, time-series representation of financial data
naturally suggests tensor-based learning algorithms. In this work, we
investigate the effectiveness of two multilinear methods for the mid-price
prediction problem against other existing methods. The experiments in a large
scale dataset which contains more than 4 millions limit orders show that by
utilizing tensor representation, multilinear models outperform vector-based
approaches and other competing ones.Comment: accepted in SSCI 2017, typos fixe
Forecasting Financial Time Series using Linear Predictive Filters
Forecasting financial time series is regarded as one of the most challenging applications
of time series prediction due to their dynamic nature. However, it is the fundamental
element of most investment activities thus attracting the attention of practitioners and
researchers for many decades.
The purpose of this research is to investigate and develop novel methods for the prediction
of financial time series considering their dynamic nature. The predictive performance
of asset prices time series themselves is exploited by applying digital signal
processing methods to their historical observations. The novelty of the research lies in
the design of predictive filters by maximising their spectrum flatness of forecast errors.
The filters are then applied to forecast linear combinations of daily open, high, low
and close prices of financial time series.
Given the assumption that there are no structural breaks or switching regimes in a
time series, the sufficient and necessary conditions that a time series can be predicted
with zero errors by linear filters are examined. It is concluded that a band-limited
time series can be predicted with zero errors by a predictive filter that has a constant
magnitude response and constant group delay over the bandwidth of the time series.
Because real world time series are not band-limited thus cannot be forecasted without
errors, statistical tests of spectrum flatness which evaluate the departure of the spectral
density from a constant value are introduced as measures of the predictability of
time series. Properties of a time series are then investigated in the frequency domain using its spectrum flatness. A predictive filter is designed by maximising the error
spectrum flatness that is equivalent to maximise the “whiteness” of forecast errors in
the frequency domain.
The focus is then placed on forecasting real world financial time series. By applying
spectrum flatness tests, it is found that the property of the spectrum of a linear
combination of daily open, high, low and close prices, which is called target prices, is
different from that of a random walk process as there are much more low frequency
components than high frequency ones in its spectrum. Therefore, an objective function
is proposed to derive the target price time series from the historical observations of
daily open, high, low and close prices. A predictive filter is then applied to obtain
the one-step ahead forecast of the target prices, while profitable trading strategies
are designed based on the forecast of target prices series. As a result, more than
70% success ratio could be achieved in terms of one-step ahead out-of-sample forecast
of direction changes of the target price time series by taking the S&P500 index for
example
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
We develop a large-scale deep learning model to predict price movements from
limit order book (LOB) data of cash equities. The architecture utilises
convolutional filters to capture the spatial structure of the limit order books
as well as LSTM modules to capture longer time dependencies. The proposed
network outperforms all existing state-of-the-art algorithms on the benchmark
LOB dataset [1]. In a more realistic setting, we test our model by using one
year market quotes from the London Stock Exchange and the model delivers a
remarkably stable out-of-sample prediction accuracy for a variety of
instruments. Importantly, our model translates well to instruments which were
not part of the training set, indicating the model's ability to extract
universal features. In order to better understand these features and to go
beyond a "black box" model, we perform a sensitivity analysis to understand the
rationale behind the model predictions and reveal the components of LOBs that
are most relevant. The ability to extract robust features which translate well
to other instruments is an important property of our model which has many other
applications.Comment: 12 pages, 9 figure
Relationship between degree of efficiency and prediction in stock price changes
This study investigates empirically whether the degree of stock market
efficiency is related to the prediction power of future price change using the
indices of twenty seven stock markets. Efficiency refers to weak-form efficient
market hypothesis (EMH) in terms of the information of past price changes. The
prediction power corresponds to the hit-rate, which is the rate of the
consistency between the direction of actual price change and that of predicted
one, calculated by the nearest neighbor prediction method (NN method) using the
out-of-sample. In this manuscript, the Hurst exponent and the approximate
entropy (ApEn) are used as the quantitative measurements of the degree of
efficiency. The relationship between the Hurst exponent, reflecting the various
time correlation property, and the ApEn value, reflecting the randomness in the
time series, shows negative correlation. However, the average prediction power
on the direction of future price change has the strongly positive correlation
with the Hurst exponent, and the negative correlation with the ApEn. Therefore,
the market index with less market efficiency has higher prediction power for
future price change than one with higher market efficiency when we analyze the
market using the past price change pattern. Furthermore, we show that the Hurst
exponent, a measurement of the long-term memory property, provides more
significant information in terms of prediction of future price changes than the
ApEn and the NN method.Comment: 10 page
Predicting stock market movements using network science: An information theoretic approach
A stock market is considered as one of the highly complex systems, which
consists of many components whose prices move up and down without having a
clear pattern. The complex nature of a stock market challenges us on making a
reliable prediction of its future movements. In this paper, we aim at building
a new method to forecast the future movements of Standard & Poor's 500 Index
(S&P 500) by constructing time-series complex networks of S&P 500 underlying
companies by connecting them with links whose weights are given by the mutual
information of 60-minute price movements of the pairs of the companies with the
consecutive 5,340 minutes price records. We showed that the changes in the
strength distributions of the networks provide an important information on the
network's future movements. We built several metrics using the strength
distributions and network measurements such as centrality, and we combined the
best two predictors by performing a linear combination. We found that the
combined predictor and the changes in S&P 500 show a quadratic relationship,
and it allows us to predict the amplitude of the one step future change in S&P
500. The result showed significant fluctuations in S&P 500 Index when the
combined predictor was high. In terms of making the actual index predictions,
we built ARIMA models. We found that adding the network measurements into the
ARIMA models improves the model accuracy. These findings are useful for
financial market policy makers as an indicator based on which they can
interfere with the markets before the markets make a drastic change, and for
quantitative investors to improve their forecasting models.Comment: 13 pages, 7 figures, 3 table
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