17,091 research outputs found
Realtime market microstructure analysis: online Transaction Cost Analysis
Motivated by the practical challenge in monitoring the performance of a large
number of algorithmic trading orders, this paper provides a methodology that
leads to automatic discovery of the causes that lie behind a poor trading
performance. It also gives theoretical foundations to a generic framework for
real-time trading analysis. Academic literature provides different ways to
formalize these algorithms and show how optimal they can be from a
mean-variance, a stochastic control, an impulse control or a statistical
learning viewpoint. This paper is agnostic about the way the algorithm has been
built and provides a theoretical formalism to identify in real-time the market
conditions that influenced its efficiency or inefficiency. For a given set of
characteristics describing the market context, selected by a practitioner, we
first show how a set of additional derived explanatory factors, called anomaly
detectors, can be created for each market order. We then will present an online
methodology to quantify how this extended set of factors, at any given time,
predicts which of the orders are underperforming while calculating the
predictive power of this explanatory factor set. Armed with this information,
which we call influence analysis, we intend to empower the order monitoring
user to take appropriate action on any affected orders by re-calibrating the
trading algorithms working the order through new parameters, pausing their
execution or taking over more direct trading control. Also we intend that use
of this method in the post trade analysis of algorithms can be taken advantage
of to automatically adjust their trading action.Comment: 33 pages, 12 figure
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Lessons Learned and Next Steps in Energy Efficiency Measurement and Attribution: Energy Savings, Net to Gross, Non-Energy Benefits, and Persistence of Energy Efficiency Behavior
This white paper examines four topics addressing evaluation, measurement, and attribution of direct and indirect effects to energy efficiency and behavioral programs: Estimates of program savings (gross); Net savings derivation through free ridership / net to gross analyses; Indirect non-energy benefits / impacts (e.g., comfort, convenience, emissions, jobs); and, Persistence of savings
Cloud Index Tracking: Enabling Predictable Costs in Cloud Spot Markets
Cloud spot markets rent VMs for a variable price that is typically much lower
than the price of on-demand VMs, which makes them attractive for a wide range
of large-scale applications. However, applications that run on spot VMs suffer
from cost uncertainty, since spot prices fluctuate, in part, based on supply,
demand, or both. The difficulty in predicting spot prices affects users and
applications: the former cannot effectively plan their IT expenditures, while
the latter cannot infer the availability and performance of spot VMs, which are
a function of their variable price. To address the problem, we use properties
of cloud infrastructure and workloads to show that prices become more stable
and predictable as they are aggregated together. We leverage this observation
to define an aggregate index price for spot VMs that serves as a reference for
what users should expect to pay. We show that, even when the spot prices for
individual VMs are volatile, the index price remains stable and predictable. We
then introduce cloud index tracking: a migration policy that tracks the index
price to ensure applications running on spot VMs incur a predictable cost by
migrating to a new spot VM if the current VM's price significantly deviates
from the index price.Comment: ACM Symposium on Cloud Computing 201
Can Deep Learning Techniques Improve the Risk Adjusted Returns from Enhanced Indexing Investment Strategies
Deep learning techniques have been widely applied in the field of stock market prediction particularly with respect to the implementation of active trading strategies. However, the area of portfolio management and passive portfolio management in particular has been much less well served by research to date. This research project conducts an investigation into the science underlying the implementation of portfolio management strategies in practice focusing on enhanced indexing strategies. Enhanced indexing is a passive management approach which introduces an element of active management with the aim of achieving a level of active return through small adjustments to the portfolio weights. It then proceeds to investigate current applications of deep learning techniques in the field of financial market predictions and also in the specific area of portfolio management. A series of successively deeper neural network models were then developed and assessed in terms of their ability to accurately predict whether a sample of stocks would either outperform or underperform the selected benchmark index. The predictions generated by these models were then used to guide the adjustment of portfolio weightings to implement and forward test an enhanced indexing strategy on a hypothetical stock portfolio
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Selection-Based Learning: The Coevolution Of Internal And External Selection In High-Velocity Environments
To understand the effects of selection on firm-level learning, this study synthesizes two contrasting views of evolution. Internal selection theorists view managers in multiproduct firms as the primary agents of evolutionary change because they decide whether individual products and technologies are retained or eliminated. In contrast, external selection theorists contend that the environment drives evolution because it determines whether entire firms live or die. Though these theories differ, they describe tightly interwoven processes. In assessing the coevolution of internal and external selection among personal computer manufacturers across a 20-year period, we found that (1) firms learned cumulatively and adaptively from internal and partial external selection, the latter occurring when the environment killed part but not all of a firm; (2) internal and partial external selection coevolved, as each affected the other's future rate and the odds of firm failure; (3) partial external selection had a greater effect on future outcomes than internal selection; and (4) the lessons gleaned from prior selection were reflected in a firm's ability to develop new products, making that an important mediator between past and future selection events.Managemen
Bayesian forecasting and scalable multivariate volatility analysis using simultaneous graphical dynamic models
The recently introduced class of simultaneous graphical dynamic linear models
(SGDLMs) defines an ability to scale on-line Bayesian analysis and forecasting
to higher-dimensional time series. This paper advances the methodology of
SGDLMs, developing and embedding a novel, adaptive method of simultaneous
predictor selection in forward filtering for on-line learning and forecasting.
The advances include developments in Bayesian computation for scalability, and
a case study in exploring the resulting potential for improved short-term
forecasting of large-scale volatility matrices. A case study concerns financial
forecasting and portfolio optimization with a 400-dimensional series of daily
stock prices. Analysis shows that the SGDLM forecasts volatilities and
co-volatilities well, making it ideally suited to contributing to quantitative
investment strategies to improve portfolio returns. We also identify
performance metrics linked to the sequential Bayesian filtering analysis that
turn out to define a leading indicator of increased financial market stresses,
comparable to but leading the standard St. Louis Fed Financial Stress Index
(STLFSI) measure. Parallel computation using GPU implementations substantially
advance the ability to fit and use these models.Comment: 28 pages, 9 figures, 7 table
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