38,508 research outputs found
Exploiting Errors for Efficiency: A Survey from Circuits to Algorithms
When a computational task tolerates a relaxation of its specification or when
an algorithm tolerates the effects of noise in its execution, hardware,
programming languages, and system software can trade deviations from correct
behavior for lower resource usage. We present, for the first time, a synthesis
of research results on computing systems that only make as many errors as their
users can tolerate, from across the disciplines of computer aided design of
circuits, digital system design, computer architecture, programming languages,
operating systems, and information theory.
Rather than over-provisioning resources at each layer to avoid errors, it can
be more efficient to exploit the masking of errors occurring at one layer which
can prevent them from propagating to a higher layer. We survey tradeoffs for
individual layers of computing systems from the circuit level to the operating
system level and illustrate the potential benefits of end-to-end approaches
using two illustrative examples. To tie together the survey, we present a
consistent formalization of terminology, across the layers, which does not
significantly deviate from the terminology traditionally used by research
communities in their layer of focus.Comment: 35 page
Decision support from financial disclosures with deep neural networks and transfer learning
Company disclosures greatly aid in the process of financial decision-making;
therefore, they are consulted by financial investors and automated traders
before exercising ownership in stocks. While humans are usually able to
correctly interpret the content, the same is rarely true of computerized
decision support systems, which struggle with the complexity and ambiguity of
natural language. A possible remedy is represented by deep learning, which
overcomes several shortcomings of traditional methods of text mining. For
instance, recurrent neural networks, such as long short-term memories, employ
hierarchical structures, together with a large number of hidden layers, to
automatically extract features from ordered sequences of words and capture
highly non-linear relationships such as context-dependent meanings. However,
deep learning has only recently started to receive traction, possibly because
its performance is largely untested. Hence, this paper studies the use of deep
neural networks for financial decision support. We additionally experiment with
transfer learning, in which we pre-train the network on a different corpus with
a length of 139.1 million words. Our results reveal a higher directional
accuracy as compared to traditional machine learning when predicting stock
price movements in response to financial disclosures. Our work thereby helps to
highlight the business value of deep learning and provides recommendations to
practitioners and executives
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
Ensemble Binary Segmentation for irregularly spaced data with change-points
We propose a new technique for consistent estimation of the number and
locations of the change-points in the structure of an irregularly spaced time
series. The core of the segmentation procedure is the Ensemble Binary
Segmentation method (EBS), a technique in which a large number of multiple
change-point detection tasks using the Binary Segmentation (BS) method are
applied on sub-samples of the data of differing lengths, and then the results
are combined to create an overall answer. We do not restrict the total number
of change-points a time series can have, therefore, our proposed method works
well when the spacings between change-points are short. Our main change-point
detection statistic is the time-varying Autoregressive Conditional Duration
model on which we apply a transformation process in order to decorrelate it. To
examine the performance of EBS we provide a simulation study for various types
of scenarios. A proof of consistency is also provided. Our methodology is
implemented in the R package eNchange, available to download from CRAN
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
BinarEye: An Always-On Energy-Accuracy-Scalable Binary CNN Processor With All Memory On Chip in 28nm CMOS
This paper introduces BinarEye: a digital processor for always-on Binary
Convolutional Neural Networks. The chip maximizes data reuse through a Neuron
Array exploiting local weight Flip-Flops. It stores full network models and
feature maps and hence requires no off-chip bandwidth, which leads to a 230
1b-TOPS/W peak efficiency. Its 3 levels of flexibility - (a) weight
reconfiguration, (b) a programmable network depth and (c) a programmable
network width - allow trading energy for accuracy depending on the task's
requirements. BinarEye's full system input-to-label energy consumption ranges
from 14.4uJ/f for 86% CIFAR-10 and 98% owner recognition down to 0.92uJ/f for
94% face detection at up to 1700 frames per second. This is 3-12-70x more
efficient than the state-of-the-art at on-par accuracy.Comment: Presented at the 2018 IEEE Custom Integrated Circuits Conference
(CICC). Presentation is available here:
https://www.researchgate.net/publication/324452819_Presentation_on_Binareye_at_CIC
Evolving intraday foreign exchange trading strategies utilizing multiple instruments price series
We propose a Genetic Programming architecture for the generation of foreign
exchange trading strategies. The system's principal features are the evolution
of free-form strategies which do not rely on any prior models and the
utilization of price series from multiple instruments as input data. This
latter feature constitutes an innovation with respect to previous works
documented in literature. In this article we utilize Open, High, Low, Close bar
data at a 5 minutes frequency for the AUD.USD, EUR.USD, GBP.USD and USD.JPY
currency pairs. We will test the implementation analyzing the in-sample and
out-of-sample performance of strategies for trading the USD.JPY obtained across
multiple algorithm runs. We will also evaluate the differences between
strategies selected according to two different criteria: one relies on the
fitness obtained on the training set only, the second one makes use of an
additional validation dataset. Strategy activity and trade accuracy are
remarkably stable between in and out of sample results. From a profitability
aspect, the two criteria both result in strategies successful on out-of-sample
data but exhibiting different characteristics. The overall best performing
out-of-sample strategy achieves a yearly return of 19%.Comment: 15 pages, 10 figures, 9 table
Long-term stock index forecasting based on text mining of regulatory disclosures
Share valuations are known to adjust to new information entering the market,
such as regulatory disclosures. We study whether the language of such news
items can improve short-term and especially long-term (24 months) forecasts of
stock indices. For this purpose, this work utilizes predictive models suited to
high-dimensional data and specifically compares techniques for data-driven and
knowledge-driven dimensionality reduction in order to avoid overfitting. Our
experiments, based on 75,927 ad hoc announcements from 1996-2016, reveal the
following results: in the long run, text-based models succeed in reducing
forecast errors below baseline predictions from historic lags at a
statistically significant level. Our research provides implications to business
applications of decision-support in financial markets, especially given the
growing prevalence of index ETFs (exchange traded funds).Comment: Accepted at Decision Support Systems journa
The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts
We propose a multivariate elastic net regression forecast model for German
quarter-hourly electricity spot markets. While the literature is diverse on
day-ahead prediction approaches, both the intraday continuous and intraday
call-auction prices have not been studied intensively with a clear focus on
predictive power. Besides electricity price forecasting, we check for the
impact of early day-ahead (DA) EXAA prices on intraday forecasts. Another
novelty of this paper is the complementary discussion of economic benefits. A
precise estimation is worthless if it cannot be utilized. We elaborate possible
trading decisions based upon our forecasting scheme and analyze their monetary
effects. We find that even simple electricity trading strategies can lead to
substantial economic impact if combined with a decent forecasting technique
Threshold-Based Portfolio: The Role of the Threshold and Its Applications
This paper aims at developing a new method by which to build a data-driven
portfolio featuring a target risk-return. We first present a comparative study
of recurrent neural network models (RNNs), including a simple RNN, long
short-term memory (LSTM), and gated recurrent unit (GRU) for selecting the best
predictor to use in portfolio construction. The models are applied to the
investment universe consisted of ten stocks in the S&P500. The experimental
results shows that LSTM outperforms the others in terms of hit ratio of
one-month-ahead forecasts. We then build predictive threshold-based portfolios
(TBPs) that are subsets of the universe satisfying given threshold criteria for
the predicted returns. The TBPs are rebalanced monthly to restore equal weights
to each security within the TBPs. We find that the risk and return profile of
the realized TBP represents a monotonically increasing frontier on the
risk-return plane, where the equally weighted portfolio (EWP) of all ten stocks
plays a role in their lower bound. This shows the availability of TBPs in
targeting specific risk-return levels, and an EWP based on all the assets plays
a role in the reference portfolio of TBPs. In the process, thresholds play
dominant roles in characterizing risk, return, and the prediction accuracy of
the subset. The TBP is more data-driven in designing portfolio target risk and
return than existing ones, in the sense that it requires no prior knowledge of
finance such as financial assumptions, financial mathematics, or expert
insights. In a practical application, we present the TBP management procedure
for a time horizon extending over multiple time periods; we also discuss their
application to mean-variance portfolios to reduce estimation risk.Comment: 20 pages, 7 figure
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